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Department of Veterans Affairs

Health Services Research & Development Service Evidence-based Synthesis Program

Life Expectancy Calculators

June 2016

Prepared for:

Department of Veterans Affairs

Veterans Health Administration

Quality Enhancement Research Initiative

Health Services Research & Development Service

Washington, DC 20420 

Prepared by:

Evidence-based Synthesis Program (ESP) Center

Minneapolis VA Medical Center

Minneapolis, MN 

Tim Wilt, MD, MPH, Director

Nancy Greer, PhD, Program Manager 

Investigators:

Principal Investigator: 

Thomas Rector, PhD, PharmD

Co-investigators: 

Brent Taylor, PhD

Shahnaz Sultan, MD, MHSc

Aasma Shaukat, MD, MPH

Selcuk Adabag, MD, MS

David Nelson, PhD

Timothy Capecchi, MD

 Research Associate:

Roderick MacDonald, MS

NOTE: This publication is for internal use of the Department of 

Veterans Affairs and should not be distributed outside the agency.
Life Expectancy Calculators Evidence-based Synthesis Program

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PREFACE 

The VA Evidence-based Synthesis Program (ESP) was established in 2007 to provide timely and 

accurate syntheses of targeted healthcare topics of particular importance to clinicians, managers, and 

policymakers as they work to improve the health and healthcare of Veterans. QUERI provides funding 

for four ESP Centers, and each Center has an active University affiliation. Center Directors are 

recognized leaders in the field of evidence synthesis with close ties to the AHRQ Evidence-based 

Practice Centers. The ESP is governed by a Steering Committee comprised of participants from VHA 

Policy, Program, and Operations Offices, VISN leadership, field-based investigators, and others as 

designated appropriate by QUERI/HSR&D.

The ESP Centers generate evidence syntheses on important clinical practice topics. These reports help: 

Develop clinical policies informed by evidence;

Implement effective services to improve patient outcomes and to support VA clinical practice 

guidelines and performance measures; and 

Set the direction for future research to address gaps in clinical knowledge. 

The ESP disseminates these reports throughout VA and in the published literature; some evidence 

syntheses have informed the clinical guidelines of large professional organizations. 

The ESP Coordinating Center (ESP CC), located in Portland, Oregon, was created in 2009 to expand the 

capacity of QUERI/HSR&D and is charged with oversight of national ESP program operations, program 

development and evaluation, and dissemination efforts. The ESP CC establishes standard operating 

procedures for the production of evidence synthesis reports; facilitates a national topic nomination, 

prioritization, and selection process; manages the research portfolio of each Center; facilitates editorial 

review processes; ensures methodological consistency and quality of products; produces “rapid response 

evidence briefs” at the request of VHA senior leadership; collaborates with HSR&D Center for 

Information Dissemination and Education Resources (CIDER) to develop a national dissemination 

strategy for all ESP products; and interfaces with stakeholders to effectively engage the program. 

Comments on this evidence report are welcome and can be sent to Nicole Floyd, ESP CC Program 

Manager, at Nicole.Floyd@va.gov. 

Recommended citation: Rector T, Taylor BC, Sultan S, Shaukat A, Adabag S, Nelson D, Capecchi T, 

MacDonald R, Greer, N, Wilt TJ. Life Expectancy Calculators, VA ESP Project #09-009; 2016. 

This report is based on research conducted by the Evidence-based Synthesis Program (ESP) Center located at 

the Minneapolis VA Medical Center, Minneapolis, MN, funded by the Department of Veterans Affairs, Veterans 

Health Administration, Office of Research and Development, Quality Enhancement Research Initiative. The 

findings and conclusions in this document are those of the author(s) who are responsible for its contents; the 

findings and conclusions do not necessarily represent the views of the Department of Veterans Affairs or the 

United States government. Therefore, no statement in this article should be construed as an official position of the 

Department of Veterans Affairs. No investigators have any affiliations or financial involvement (eg, employment, 

consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or 

royalties) that conflict with material presented in the report.
Life Expectancy Calculators Evidence-based Synthesis Program

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TABLE OF CONTENTS 

Definitions ...................................................................................................................................... 1

Executive Summary ...................................................................................................................... 2

Introduction ................................................................................................................................. 2

Methods ...................................................................................................................................... 2

Data Sources and Searches ..................................................................................................... 2

Study Selection ....................................................................................................................... 3

Data Abstraction, Quality Assessment, Synthesis, and Strength of Evidence ........................ 3

Results .........................................................................................................................................3

Key Question 1 ....................................................................................................................... 3

Key Question 2 ....................................................................................................................... 4

Key Question 3 ....................................................................................................................... 4

Discussion ................................................................................................................................... 4

Key Findings ........................................................................................................................... 4

Applicability ........................................................................................................................... 5

Research Gaps/Future Research ............................................................................................. 5

Conclusions ............................................................................................................................. 5

Introduction ................................................................................................................................... 6

Methods ........................................................................................................................................ 10

Topic Development ................................................................................................................... 10

Search Strategy ......................................................................................................................... 10

Study Selection ......................................................................................................................... 10

Data Abstraction ....................................................................................................................... 11

Quality Assessment ................................................................................................................... 11

Data Synthesis ........................................................................................................................... 11

Rating the Strength of Evidence ............................................................................................... 11

Peer Review .............................................................................................................................. 12

Results .......................................................................................................................................... 13

Literature Flow ......................................................................................................................... 13

Reviewed Studies .................................................................................................................. 14
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Study Quality ........................................................................................................................ 16

Key Question 1: Between 2011 and 2016, have there been any additional reports of life 

expectancy calculators that may have sufficient predictive accuracy for use in adult 

primary care practice? ................................................................................................... 18

Summary of Findings ............................................................................................................ 18

Conclusion for Key Question #1........................................................................................... 21

Rating for the Strength of Evidence for KQ1: High ............................................................. 21

Key Question 2: Of the life expectancy calculators being reviewed, have any external 

validation studies been published between 2011 and 2016? If yes, what population was 

studied and what was the predictive accuracy therein? ................................................ 22

Summary of Findings ............................................................................................................ 22

Conclusion for Key Question #2........................................................................................... 22

Rating for the Strength of Evidence for KQ2: Insufficient ................................................... 23

Key Question 3: What is the clinical use of the mortality prediction models (aka life 

expectancy calculators), and was there improvement in patient survival times, healthrelated quality of life, provider-patient communication, patient satisfaction and 

participation in clinical decisions, or lower healthcare utilization and costs? .............. 24

Summary of Findings ............................................................................................................ 24

Conclusion for Key Question #3........................................................................................... 24

Rating for the Strength of Evidence for KQ 3: Insufficient .................................................. 24

Summary and Discussion ........................................................................................................... 25

Key Findings ............................................................................................................................. 25

Limitations ................................................................................................................................ 25

Applicability of Findings to the VA Population ....................................................................... 26

Research Gaps/Future Research ............................................................................................... 26

Conclusions ............................................................................................................................... 27

References .................................................................................................................................... 28

Tables

Table 1. Study Population Characteristics ................................................................................ 14

Table 2. Description of Prediction Models ............................................................................... 16

Table 3. Quality of Included Studies ........................................................................................ 17

Table 4. Predictive Performance of Models under Review ...................................................... 19

Table 5. Validation of Mortality Prediction Models ................................................................. 22
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Figures

Figure 1. Conceptual Framework ............................................................................................. 10

Figure 2. Literature Flow Chart ................................................................................................ 13

Appendix A. Search Strategy ..................................................................................................... 30

Appendix B. Peer Review Comments/Author Responses ....................................................... 31

Appendix C. Evidence Tables .................................................................................................... 35

Table 1. Study Characteristics .................................................................................................. 35

Table 2. Model Characteristics and Performance ..................................................................... 46
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DEFINITIONS 

Term Definition

Life expectancy The estimated (calculated) average number of years a group of people is expected 

to live. Most individuals in the group will live longer or shorter than the average life 

expectancy.

Mortality 

Prediction Model

A statistical model that uses predictor variables to the estimate the probability (risk 

score) an individual will be alive or deceased at a specified future time. 

Risk Groups Groups formed by categorizing individual estimated probabilities of dying. 

Survival (or 

mortality) curve

Graphical plot of the estimated cumulative probability of surviving (or dying) versus 

time. Cumulative probabilities are often reported as a percentage. 

Median survival 

time

The time when the cumulative probability of survival (or death) reaches 0.50 (50%). 

May be used as a proxy for life expectancy because 50% of the people in a group 

are expected to live longer and 50% shorter than the estimated median survival time.

Validation Testing a prediction model in a new sample of patients that was not used to develop 

the model. Often validation is done by randomly splitting a sample of patients into 

one or more subsamples and using one subsample to develop the model and the 

other subsample to validate the model. However, this approach may be overly 

optimistic in regards to future predictive performance because the distributions of 

predictor variables and mortality tend to be similar in randomly split samples.

Calibration The difference in the predicted number of deaths as compared to the observed 

number in each risk group. If the differences are small, the model is well-calibrated 

to the studied sample. 

C-statistic A measure of how well the prediction model’s risk scores discriminate individuals 

who did or did not die within a specified period of time. C-statistics indicate the ability 

of a prediction model to rank individuals in concordance with their observed survival 

times. A model with a C-statistic that’s not much better than 0.5 will not predict who 

will live or die much better than flipping a coin. On the other hand, the closer the Cstatistic is to 1.0, the more likely it is that the prediction model can be used to make

accurate survival predictions with an acceptably low number of prediction errors. 

Sensitivity The proportion of all decedents during a period of time that had risk scores 

exceeding a threshold being used to predict death. 

Specificity The proportion of survivors during a period of time that had risk scores less than a 

threshold being used to predict survival. 

Positive Predictive 

Value

Given a proposed risk score threshold for making prognostic predictions, the 

proportion of patients above the threshold that would be predicted to die within a 

specified period of time and do. 

Negative 

Predictive Value

Given a proposed risk score threshold for making prognostic predictions, the 

proportion of patients below the threshold that would be predicted to survive for a 

specified period of time and do. 
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EXECUTIVE SUMMARY

INTRODUCTION 

Estimation of life expectancy serves an important role in clinical decisions about screening for 

disease and treatment in primary care practices. Increasingly, clinical guidelines recommend a 

course of action in context of the patient’s life expectancy. While many tools have been 

developed to estimate life expectancy or the likelihood of surviving for a period of time, there 

are no well-established decision support tools that are broadly applicable to primary care. 

For a life expectancy calculator to gain widespread use in primary care, it needs to have 

acceptable and validated predictive accuracy at decision-relevant times such as 6 months or 5 

years, proven usefulness, and be readily available. In 2012, a systematic review by Yourman et 

al summarized existing life expectancy calculators that may be useful for older patients in 

primary care. However, none of the reviewed calculators met our criteria for widespread use. 

The purpose of this systematic review was to update the search on life expectancy calculators, 

with focus on calculators that may be valuable for use in the Veterans Health Administration

population receiving primary care. 

With input from Operational Partners and Technical Expert Panel members, we developed the 

following Key Questions for the review: 

Key Question 1: Between 2011 and 2016, have there been any additional reports of life 

expectancy calculators that may have sufficient predictive accuracy for use in adult primary care 

practice? 

Key Question 2: Of the life expectancy calculators being reviewed have any external validation 

studies been published between 2011 and 2016? If yes, what population was studied and what 

was the predictive accuracy therein?

Key Question 3: What is the clinical use of the mortality prediction models (aka life expectancy 

calculators), and was there improvement in patient survival times, health-related quality of life, 

provider-patient communication, patient satisfaction and participation in clinical decisions, or 

lower healthcare utilization and costs? 

METHODS 

Data Sources and Searches 

We searched MEDLINE (Ovid) from 2011 to May 2016 using title words for life expectancy, 

calculators or models, survival, mortality, death, and validation or calibration. The search was 

limited to English language and studies of humans middle-aged (45 plus years) and older. We 

also limited the search to relevant study designs. 
Life Expectancy Calculators Evidence-based Synthesis Program

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Study Selection

The conceptual basis for this review was that a decision support tool would be electronically 

implemented to provide quantitative survival estimates to primary care providers who along with 

their patients would use the estimates when making healthcare decisions and thereby improve 

patient outcomes and healthcare value. We included studies of methods to estimate probabilities 

of survival or dying within a specified period of time. In addition, we were interested in studies 

of the use and effects of the resulting estimates. 

We required that studies offer a method of estimation based on variables that would be generally 

available for primary care outpatients. Thus studies of estimates that require inpatient variables

or patient surveys, studies of novel biomarkers, or studies of individual predictors, including 

those based on only age, sex, and race, were excluded. Studies that were limited to tests for 

associations such as relative risks, odds ratios, and hazard ratios were considered preliminary 

work and excluded. 

Data Abstraction, Quality Assessment, Synthesis, and Strength of Evidence

Information on study sample and prediction model characteristics and performance were 

abstracted into evidence tables by one trained investigator and verified by the principal

investigator. 

We assessed quality of each study using selected items from the CHecklist for critical Appraisal 

and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS). Five 

criteria were selected: 1) predictor definition/measurement, 2) outcome definition/measurement, 

3) independence of outcome and predictor assessments, 4) completeness of follow-up/predictor 

data, and 5) validation.

The evidence is narratively described without any formal meta-analysis. The strength of 

evidence for each key question is rated as high, low, or insufficient based on the number of 

quality studies and the consistency of the results. 

RESULTS

Our literature search yielded 8,120 potentially eligible studies. Titles were reviewed by trained 

investigators and 509 studies were selected for abstract review. Each abstract was reviewed by 2 

trained investigators and 51 studies were identified for full-text review. Full-text articles were 

reviewed by an investigator and the principal investigator. We included 10 studies from the 

literature search and an additional study identified from hand-searching studies included in the 

full-text review phase.

Key Question 1

We found 8 different mortality prediction models (in 11 reports). All had encouraging predictive 

accuracy. All but one model used patient’s age and sex along with diagnostic codes extracted 

from electronic databases with or without additional variables. Models developed for mortality 

risk adjustment focused on mortality during the first year of follow-up while models intended for 

decisions about cancer screening had longer time horizons of 5 to 10 years. The bestcharacterized model based on VHA data, known as the Care Assessment Needs (CAN) score, 
Life Expectancy Calculators Evidence-based Synthesis Program

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was not strictly a life-expectancy model as it focused on deaths without a preceding 

hospitalization rather than all deaths. 

Overall, the 8 models were reasonably well-calibrated, reporting relatively small differences 

between observed and predicted mortality in arbitrary mortality risk groups. Thus, if a clinician 

used one of the models to place a patient in a mortality risk group, the average or median 

predicted mortality for the risk group could be used as an estimate. However, some individual 

predicted probabilities of survival may be substantially different from the average of the risk 

group. Furthermore, the differences in estimates between risk groups may not be sufficient to 

prompt clinicians or patients to make different decisions about screening or treatment.

The strength of evidence of the body of evidence for Key Question 1 was rated as high based on 

finding several studies of acceptable quality that reported a mortality prediction model based on 

similar predictor variables that had potentially useful predictive accuracy. 

Key Question 2

One of the life expectancy models identified in Key Question 1 was in a sense validated in the 

older patients from the population used to generate the model and a sub-population of individuals 

with schizophrenia. Another model was in a sense validated in the older patients and a subpopulation of heart failure patients. Calibration was not always as good in the sub-populations. 

Not finding any true external validation studies of the prediction models in primary care 

practices, we rated the strength of evidence for Key Question 2 as insufficient. 

Key Question 3

We found no studies that examined the effects of using the reviewed mortality prediction models

in clinical care. 

The strength of evidence for Key Question 3 was rated as insufficient. 

DISCUSSION 

Key Findings

Between 2011 and 2016, 11 studies reported on 8 mortality prediction models; all but one 

of the included studies (a study from Japan) utilized data from large electronic databases. 

Models were developed for different purposes including development of individual risk 

scores to adjust for possible difference in mortality risk when comparing healthcare

outcomes, to help primary care teams assesses short-term risk of hospitalization or death 

without hospitalization, or to help healthcare providers judge whether patient will or 

won’t die within a specified time pertinent to decisions about screening for cancer. 

C-statistics ranged from 0.77 to 0.90, indicating the models provide good discrimination 

of those who survived or died during the varying periods of follow-up. Few studies 

reported the sensitivity, specificity, or positive or negative predictive values for a 

proposed risk score threshold that would be used to determine which patients should or 

shouldn’t be screened or treated. 
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The prediction models were generally well-calibrated to the test samples with seemingly 

insubstantial differences between observed and predicted mortality across a range of risk 

groups. 

We found no true external validation studies of the reviewed mortality prediction models. 

None of the models have been externally validated for general primary care use.

No studies meeting eligibility criteria for the review examined the impact of using one of 

the life expectancy calculators to improve healthcare decisions or outcomes. 

Applicability

Several of the prediction models identified for this review could potentially be adapted for use in 

VA primary care practices. However, the discrimination and calibration of the models may vary 

in VA primary care practices that may have substantially different distributions of the predictors 

and/or mortality, and therefore would need to be evaluated. To adapt a mortality prediction 

model, the sources of data and definitions of predictor variables will need to be consistent across 

VA healthcare systems and over time. Centralized prediction models that are made widely 

available should be calibrated to and validated in several VA primary care patient populations 

and periodically checked and recalibrated if necessary. Although feasible, we found no studies to 

indicate whether making a reasonably accurate life expectancy calculator available to VA 

primary care providers or patients would influence their healthcare decisions or outcomes. 

Research Gaps/Future Research 

Although healthcare providers and guidelines make recommendations based, in part, on 

assessments of life expectancy, there is no widely accepted statistical tool for estimating patients’ 

life expectancy or probability of survival, particularly for prolonged periods, for example, 10 

years. Research on the clinical usefulness and impact of mortality prediction models on clinical 

decisions and outcomes is needed to guide further development and engender widespread 

acceptance. Analytical life expectancy predictions have to be demonstrated to be more accurate 

than clinicians’ intuitive prognostic assessments, preferably using statistics that allow clinicians 

to compare prognostic errors. Additionally, they should use patient information that is readily 

and reliably available. Strong comparative evidence that using a quantitative prediction model 

can reduce practice variation and improve healthcare decisions and outcomes most likely will be 

needed to change current practices.

Conclusions 

Life expectancy calculators based on readily available electronic data that have acceptable 

performance for estimating one-, 5-, and 10-year life expectancy in middle age to older adults are 

feasible. These calculators need to be validated for use in primary care practices. There are no 

data on the effect of using these life expectancy calculators on patient or provider decisions or 

outcomes. If a life expectancy calculator is made available, it remains to be determined whether 

primary care providers would use it or whether it would improve healthcare delivery, resource 

use, patient experiences, or outcomes. 
Life Expectancy Calculators Evidence-based Synthesis Program

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EVIDENCE REPORT

INTRODUCTION 

Life expectancy, an estimate of the number of remaining years of life a person has, is an 

important consideration for making clinical decisions in primary care. For example, colorectal 

cancer screening guidelines state that clinicians should only screen patients with an estimated life 

expectancy of at least 10 years because otherwise benefits of cancer detection are unlikely to 

outweigh the harms and costs. Referral to hospice care is often based on a life expectancy of less 

than 6 months. Implantable cardiac defibrillators are not indicated if the patient is not expected to 

live longer than one year. 

Most currently available life expectancy calculators or life tables are based on a person’s age, 

gender, and race. These calculators may not be widely used in clinical practice because clinicians 

usually consider other key factors such as the presence and severity of life-threatening diseases 

and functional status. Given the uncertainty inherent in formulating a prognosis and desire to 

avoid prognostic errors, clinical prognostic assessments are often qualitative, such as thinking a 

patient has a ‘higher’ risk of dying, and often are not shared with patients.1

 In contrast, survival 

prediction models typically incorporate a number of variables to calculate a quantitative estimate

of the patient’s probability of surviving or dying during a specified period of time.

This systematic review focused on identifying and evaluating reports of multivariable 

quantitative prediction models (aka calculators) of all-cause mortality published in 2011 and 

thereafter. Others have reviewed reports of predictive models for older patients from before 

2011.2,3

 These previous reviews listed a large number of prediction models that are available for 

primary care or population-based settings. However, the reviewers stated the evidence was 

insufficient to support their widespread clinical use. Of interest for this review were prediction 

models of all-cause mortality that would generally be applicable to most patients seen in primary 

care practices without off-putting effort by clinicians to ascertain the predictor variables and 

calculate the estimates. In addition, we were interested in reports that provided assessments of a 

proposed model’s predictive accuracy, external validity, and ideally impact on clinical decisionmaking and patient outcomes. The ultimate goal is to identify and evaluate life expectancy 

calculators that primary care providers would be willing and able to use and share with their 

patients to improve participation and satisfaction with clinical decisions that are based, in part, 

on life expectancy. Ultimately, efforts to make a validated and accurate life expectancy 

calculator readily available to clinicians will need to demonstrate benefits in terms of improving 

healthcare outcomes and efficiency as well as patient experiences. 

The Key Questions for this review and our approach to evaluating the pertinent evidence were as 

follows. 

KQ1: Between 2011 and 2016, have there been any additional reports of life expectancy 

calculators that may have sufficient predictive accuracy for use in adult primary care 

practice? 

Most mortality prediction models are not used to calculate a patient’s life expectancy (number of 

years of life remaining on average) per se. Rather they estimate probabilities of surviving or 
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dying within a specified period of time. If all patients being studied were followed to the end of a 

time period of interest, for example, 5 years, then multivariable logistic regression models are

typically used to estimate a patient’s probability of dying before the end of the period. As a 

proxy for life expectancy, one might say if one’s estimated probability of dying within 5 years is 

greater than 0.5 (50%), one’s life expectancy is less than 5 years. 

During development of mortality prediction models, the estimated probabilities of dying are 

often arbitrarily categorized into risk groups, and the predicted number of deaths in each risk 

group is compared to the observed number. If the differences are small, the prediction model was

well-calibrated to the studied sample. Primary care providers may be more willing to use a wellcalibrated model, especially if patients placed in particular well-calibrated risk groups would be 

treated differently (presumably because the intervention has a different likelihood of benefiting 

or harming patients in a particular risk group). Ideally the range of individual risk estimates 

within a risk group would be narrow, making it easier to apply the risk group’s average 

probability of dying to individuals within the group. 

If a study couldn’t follow all subjects for the entire time period of interest, the varying follow-up 

times can still be used to estimate survival (or mortality) curves. Survival curves have time on 

the x-axis and the estimated cumulative proportion surviving on the y-axis. The median survival 

time is the time when a survival curve reaches 0.5 on the y-axis. The median survival time can 

also be used as a proxy for life expectancy (an average) because half of the group lived longer 

and half shorter than the median survival time. To estimate life expectancy, one would have to fit 

a parametric equation to the survival curve and extrapolate it to cover the period of interest or 

until the curve reaches zero (all people in the cohort are deceased).4,5 Cox proportional hazards 

regression models are often used to relate multiple predictor variables to the survival times. One 

can use a fitted Cox regression model to estimate the probability of surviving (or dying) at a 

specified time or to estimate a survival (or mortality) curve using a patient’s values of the 

predictor variables. 

A C-statistic is commonly reported to help evaluate the ability of a mortality prediction model to 

identify (discriminate) the patients who did or didn’t die within the period of follow-up. It is a 

measure of concordance or correlation (hence, the name “C-statistic”) between observed and 

estimated of survival probabilities or times. Thus, C-statistics measure the ability of a model to 

rank patients according to their risk but do not assess the ability of a model to assign accurate 

probabilities of surviving or the model’s calibration. A C-statistic equal to 0.5 indicates the 

model did not discriminate those who survived or died during the period of follow-up any better 

than flipping a coin. 

If a model’s calculated risks are categorized using a particular cut-off to predict whether a patient 

will or will not die within a specified period of time, then the sensitivity (the proportion of all 

deaths that had risk scores exceeding the cut-off), and the specificity (the proportion of survivors 

that had risk scores less than the cut-off) can be estimated for the cut-off. Whether any cut-offs 

have a sufficient sensitivity and/or specificity for clinicians to use needs to be determined. The 

likelihood of finding a cut-off that has both a high sensitivity and specificity increases as the 

value of the C-statistic approaches 1.0. Given a proposed cut-off for making prognostic 

predictions, the positive predictive value (the proportion of patients above the cut-off that would 

be predicted to die and do) and the negative predictive value (the proportion of patients below 

the cut-off that would be predicted to survive that do) can be estimated. Primary care providers 
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may require a very high positive and/or negative predictive value – that is, few prediction errors

– before they’re willing to use a quantitative model to predict whether a patient will survive. 

KQ2: Of the life expectancy calculators being reviewed, have any external validation 

studies been published between 2011 and 2016? If yes, what population was studied and 

what was the predictive accuracy therein?

Validation refers to testing a prediction model in a new sample of patients that was not used to 

develop the model. Ideally, a mortality prediction model should be validated in the patient 

population in which it will be used or a very similar patient population. Often validation is done 

by randomly splitting a sample of patients into one or more subsamples and using one subsample 

to develop the model and another subsample to validate the model. However, this approach may 

be overly optimistic in regards to future predictive performance because the distributions of 

predictor variables and mortality tend to be similar in randomly split samples. Validation studies 

that used different patients and time periods were considered to be more informative. 

Before validating the predictive accuracy in a new sample of patients, it is appropriate to check 

the calibration of the prediction model in the new sample and perhaps recalibrate it before 

examining the predictive performance. Some validation studies may simply re-estimate the 

regression coefficients for the predictor variables although this practice is discouraged because 

the new estimates may be less precise if the external validation sample is small and less 

generalizable than re-calibrated estimates. 

KQ3: What is the clinical use of the mortality prediction models (aka life expectancy 

calculators), and was there improvement in patient survival times, health-related quality of 

life, provider-patient communication, patient satisfaction and participation in clinical 

decisions, or lower healthcare utilization and costs? 

To address this key question, we were particularly interested in finding randomized controlled 

trials that compared clinical use of a mortality prediction model to usual care without the 

prediction model. 

PICOTS

Population: Middle- to older-aged adults (age 45 years and older) that were not in a hospital at

the start of follow-up. If the study included a younger adult age range, it was included especially 

if predictive accuracy was evaluated in a more elderly subgroup. 

Intervention: Calculation of life expectancy (or a proxy) using predictor variables that are 

generally available for outpatients being seen by primary care providers. Less generalizable 

disease-specific mortality prediction models or those that incorporated disease-specific or novel 

predictor variables were excluded. Models that predicted inpatient mortality or used a number of 

inpatient-specific predictor variables that would only be available for hospitalized patients were 

also excluded. Models that incorporated inpatient diagnosis codes as well as outpatient diagnosis 

codes were included. 

Comparator: None
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Outcomes

KQ1, KQ2: Predictive accuracy in a validation sample. See text under KQ1 and KQ2 for a 

detailed explication.

KQ3: Clinical use of a prediction model, improvement in patients’ survival times, health-related 

quality of life, provider-patient communication, patient satisfaction and participation in clinical 

decisions, and healthcare utilization and costs when prediction model was made available to 

primary care providers. 

Timing: Any follow-up time period that may be pertinent to a clinical decision. 

Setting: Outpatient primary care. 
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METHODS 

TOPIC DEVELOPMENT

The topic was nominated by Dr. David MacPherson (Chief Medical Officer, VISN4). Drs. 

Stephan Fihn (Office of Analytics and Business Intelligence) and Joe Francis (Clinical Analytics 

and Reporting) were consulted and agreed to be operational partners for the review. The key 

questions and scope of the review were developed with input from the operational partners and a 

Technical Expert Panel.

SEARCH STRATEGY 

We searched MEDLINE (Ovid) from 2011 to May 2016 using title words for life expectancy, 

calculators or models, survival, mortality, death, and validation or calibration. The search was 

limited to English language and studies of humans middle-aged (45 plus years) and older. We 

also limited the search to relevant study designs. The full search strategy is presented in 

Appendix A. 

STUDY SELECTION

The following conceptual framework (Figure 1) guided study selection. 

Figure 1. Conceptual Framework

 KQ1 & 2 KQ3

The conceptual basis for this review was that a decision support tool would be electronically 

implemented to provide quantitative survival estimates to primary care providers who along with 

their patients would use the estimates when making healthcare decisions and hopefully improve 

patient outcomes and the efficiency of healthcare. For example, healthcare providers might place 

patients into risk groups that would be treated differently or they might somehow share the 

estimates with patients. Either individual or risk group estimates of life expectancy or 

probabilities of survival or dying within a specified period of time were of interest. 

The methods of estimation had to be generally applicable to primary care patient populations. 

Hence a large numbers of disease- or cause-specific mortality prediction models were excluded. 

Included studies had to offer a method of estimation based on variables that would be generally 

Outpatient 

Primary 

Care 

Accurately 

Estimate 

Patients’ Life 

Expectancy or 

Probability of 

Survival

Apply 

Estimates to 

Clinical 

Decision

Outcomes

Survival Time

Health Related Quality 

of Life

Health Care Utilization 

& Costs

Patient Participation in 

& Satisfaction with 

Decisions 
Life Expectancy Calculators Evidence-based Synthesis Program

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available for outpatients. Estimates that require inpatient variables that would only be available 

for a minority of primary care patients were excluded. Likewise studies of novel biomarkers or 

individual predictors, including those based on only age, sex, and race, were excluded. Reports 

of predictive values (percentage of predictions that were correct), including group survival 

curves, observed versus predicted survival/mortality (aka calibration), and sensitivity or 

specificity of predictions were considered to be most pertinent to clinical decision-makers and 

thus this review. Studies that were limited to tests for associations such as relative risks, odds 

ratios, and hazard ratios were considered to be too preliminary for inclusion in this review. 

Studies of the clinical use of survival estimates were of particular interest. For example, 

estimates might be used to help providers and patients make a specified decision about 

prevention or treatment. Estimates might be used to help healthcare providers prioritize their 

patients for medical evaluation or services according to the expected net benefit based, in part,

on life expectancy. Clinical trials might have used the prediction model to help select or identify 

patients for whom a treatment is most likely to be of benefit or least likely to harm.

The effects of applying prognostic estimates on patients’ survival and/or health-related quality of 

life are the ultimate outcomes of interest. In addition, we sought studies of the effects of using 

survival estimates on healthcare utilization, costs, and patient satisfaction or participation in 

healthcare decisions.

DATA ABSTRACTION

Data were abstracted into evidence tables by one trained investigator and verified by the 

principal investigator. We abstracted information on study characteristics (country, study dates, 

source of data, participants, mortality) and model characteristics and performance (intended use 

of model, predictors, timing of predictor and survival assessments, modelling method, methods 

used to select predictors, mortality risk groups, and predictive performance). 

QUALITY ASSESSMENT

Quality of individual studies was assessed using selected items from the CHecklist for critical 

Appraisal and data extraction for systematic Reviews of prediction Modelling Studies 

(CHARMS).6 Five criteria were selected: 1) predictor definition/measurement, 2) outcome 

definition/measurement, 3) independence of outcome and predictor assessments, 4) completeness 

of follow-up/predictor data, and 5) validation.

DATA SYNTHESIS

The evidence is narratively described without any formal meta-analysis.

RATING THE STRENGTH OF EVIDENCE

The strength of evidence for each key question is rated as high, low, or insufficient based on the 

number of quality studies and the consistency of the results.
Life Expectancy Calculators Evidence-based Synthesis Program

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PEER REVIEW

A draft version of this report was reviewed by content experts as well as clinical leadership. 

Reviewer’s comments and our responses are presented in Appendix B and the report was

modified accordingly.
Life Expectancy Calculators Evidence-based Synthesis Program

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RESULTS

LITERATURE FLOW

Our literature search yielded 8,120 titles (Figure 2). Titles were reviewed by trained investigators 

and 509 studies were selected for abstract review. Each abstract was reviewed by 2 trained

investigators and 51 studies were identified for full-text review. Full-text articles were reviewed

by an investigator and the Principal Investigator. We included 10 studies from the literature 

search and an additional study identified from hand-searching studies included in the full-text

review phase.

Figure 2. Literature Flow Chart 

MEDLINE Search

8,120 titles

Abstracts Reviewed: 

509

Excluded: 7,611 

titles

Excluded: 458 

abstracts

Full-text Review: 51 

articles

Excluded: 41 articles

-Not method-suitable for 

primary care decision 

support: 16

-Variables not available in 

primary care: 18

-Hospital setting: 3

-Disease specific mortality: 4

-Individual risk factors/

predictors: 0

Hand-searching 

reference lists: 1 article 

Included: 11articles
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Reviewed Studies

Table 1 summarizes the 11 eligible studies of 8 different mortality prediction models (see 

Appendix C, Tables 1 & 2 for more details). Six studies were done in the US.7-12

 Three were 

based on large Veterans Health Administration (VHA) electronic databases.

8,11,12 Others utilized 

Medicare data7,9 or local electronic health records.

10 Four studies were from Canada using 

electronic administrative data representing the entire provinces of Ontario13-15 or 

Saskatchewan.

16 One study from Japan used data collected by other studies combined with an

extensive search for deaths.

17 The median age of the Canadian provincial cohorts was in the midforties range, and over 60 in the other studies. All but the VA studies had approximately equal or 

greater inclusion of women and men. Only 2 studies examined race as a potential predictor.8,10

Table 1. Study Population Characteristicsa

Study Location

Year(s) Population Primary Data Source Subjects

Austin 

201115

Ontario, Canada 

2007-08

general adults provincial health 

plan

n=10,498,413

median age 46

female 51%

Austin 

201114

Ontario, Canada 

2007-08

general adults provincial health 

plan

n=10,498,413

median age 46

female 51%

Austin 

201213

Ontario, Canada 

2007-08

adults with 

schizophrenia

provincial health 

plan

n= 94,466

median age 47

female 54%

Gagne 

20117 PA, NJ, USA2004-05

low income 

Medicare

Medicare & 

Pharmacy 

Assistance 

Programs

n=120,679

mean age 80

female 83%

Quail 

201116

Saskatchewan, 

Canada 2001-02

general adults provincial health 

plan

n=662,423

median age 48

female 51%

Stefos 

20128

USA

2007-08

VHA healthcare VHA electronic 

records

n=4,774,000

mean age 62

female 6%

Mathias 

201310 Chicago, USA2003-05

multispecialty 

group practice

electronic health 

records (EPIC)

n=7,463

mean age 62

female 60%

Tan 20139 USA

1999-09

2000 Medicare 

enrollees

Medicare n=1,137,311

mean age 76

female 60%

Ogata 

201317 Kyushu Island, Japan 1999-09

representative 

cohorts from 

other studies

study data plus 

extensive search 

for death records

n=2021

mean age 63

female 59%

Wang 

201312b

USA 2010-12 VHA healthcare VHA electronic 

records

n=4,598,408

mean age 64

female 6%

Wang 

201211b

USA 2009-10 VHA healthcare

with heart failure

VHA electronic 

records

n=198,640

mean age 73

female 2% a Shaded rows are VHA studies b VA Care Assessment Needs (CAN) model
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As summarized in Table 2, 4 of the mortality prediction models presented in 6 articles were 

developed to calculate individual risk scores to adjust for possible differences in mortality risk 

when comparing healthcare outcomes in different groups of patients.

7,8,13-16 One VHA model was

developed to help primary care teams assess the short-term risk of hospitalization or death 

without hospitalization.

11,12 Three prediction models were developed to help healthcare providers 

judge whether patients would or would not die within a specified time.

9,10,17 All of the prediction 

models incorporated the patients’ age and sex and all except one17 included selected diagnoses.

All of the predicted models that incorporated diagnosis codes used a method to group ICD-9 or 

ICD-10 diagnosis codes into predictor variables, except Mathias 2013.10 A varying number 

included other demographic and clinical assessments such as vital signs, laboratory results, and 

prescription medications as predictor variables. 

Models developed for mortality risk adjustment focused on mortality during the first year of 

follow-up, when the overall mortality ranged from < 1% to 7.5% depending on the types of 

patients selected for the study. Other models focused on whether patients would be expected 

(predicted) to live for time horizons of 5 or 10 years.

9,10,17 These longer time horizons had higher

mortality. To assess care needs, Wang et al modeled deaths that occurred without a 

hospitalization within one year rather than total all-cause mortality, but did so using VHA 

data.11,12 Presumably the same data could be utilized to model total mortality. 
Life Expectancy Calculators Evidence-based Synthesis Program

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Table 2. Description of Prediction Modelsa 

Study Intended Use Types of Predictors Mortality being Predicted

(mortality%)

Austin 

201115

Statistical risk 

adjustment

Patients’ age, sex, and diagnosesb

recorded in previous 2 years

Death within 1 year (0.8%)

Subgroup age > 65 (NR)

Austin 

201114

Statistical risk 

adjustment

Patients’ age, sex, and diagnosesb 

recorded in previous 2 years

Death within 1 year (0.8%)

Austin 

201213

Statistical risk 

adjustment

Patients’ age, sex, and diagnosesb

recorded in previous 2 years

Death within 1 year (2.0%)

Gagne 

20117 Statistical risk adjustment

Patients’ age, sex, and diagnosesc

recorded in previous year

Death within 1 year (7.5%)

Quail 

201116

Statistical risk 

adjustment

Patients’ age, sex, income, region, and

diagnosesc recorded in previous year

Death within 1 year (1.3%)

Subgroup age > 65 (5.1)

Stefos 

20128

Statistical risk 

adjustment

Selected patient demographics and

diagnoses,

d including chronic disease 

registries, in previous year

Death within 1 year (5.5%)

Mathias 

201310

Health care decision Selected patient demographics, 

diagnoses, vital signs, laboratory test 

results, medications, and prior 

healthcare utilization in prior 1-2 years

Death within 5 years (11%)

Tan 20139 Health care decision Patients’ age, sex, and diagnoses,

c

and healthcare utilization recorded in 

previous year

Death within 10 years 

(women 51%; men 57%)

Ogata 

201317

Health care decision Cardiovascular risk: factors age, sex, 

smoking, blood pressure, cholesterol, 

glycated hemoglobin

Death within 10 years 

(site 1 13%; site 2 28%)

Wang 

201312e

Identify patients at 

higher risk of 

hospitalization taking 

competing risk of 

death into account

Selected patient demographics, 

diagnoses, vital signs, laboratory test 

results, medications, and prior 

healthcare utilization in previous year

Death without 

hospitalization within 90 

days (0.7%) & 1-year 

2.8%)

Wang 

201211e

Identify patients at 

higher risk of 

hospitalization taking 

competing risk of 

death into account

Selected patient demographics, 

diagnoses,

b,d vital signs, laboratory test 

results, medications, and prior 

healthcare utilization in previous year

Death without 

hospitalization within 30 

days (0.9%) & 1-year 

(7.1%)

a Shaded rows are VHA studies b Diagnosis codes grouped using Aggregated Diagnosis Group software c Diagnosis codes grouped into Charlson & Elixhauser categories d Diagnosis codes grouped using Hierarchical Coexisting Conditions e VA Care Assessment Needs (CAN) model

NR = not reported 

Study Quality

Most studies were judged to have acceptable quality based on meeting at least 4 of the 5 criteria 

(Table 3). The extent of missing predictor variables often was not reported, presumably because 

a relatively small proportion of subjects with missing predictor values were excluded from model 

development and validation. The Ogata study that utilized data from 2 previous cohort studies 

had extensive amounts of missing data predictor values for one study site and the completeness 

of the post hoc searches for dates of death in the community wasn’t clear.

17
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Table 3. Quality of Included Studiesa 

Study 

author, 

year

Predictor 

definition or 

measurement 

same for 

deceased/

survivors

Outcome definition 

or measurement 

same for deceased/ 

survivors

Outcome 

assessed 

independent of 

predictors (eg,

blinded)

Incomplete follow-up or 

missing predictor data 

(%, handling of)

Method of validation Overall quality

Austin, 

201115

Yes Yes Yes NR Assessed over fitting by 

bootstrap methods

Acceptable

Austin, 

201114

Yes Yes Yes NR Assessed over fitting by 

bootstrap methods

Acceptable

Austin, 

201213

Yes Yes Yes NR Application of previously 

developed model for general 

adult population

Acceptable

Gagne, 

20117

Yes Yes NR NR External sample Acceptable

Quail, 

201116

Yes Yes Yes Missing income imputed External sample & time Acceptable

Stefos, 

20128

Yes Yes Yes NR Copas test for overfitting 

(repeated, split sample, 

cross-validation design)

Acceptable

Mathias, 

201310

Yes Yes Yes NR 10-fold cross validation Acceptable

Tan, 

20139

Yes Yes Yes NR Random split sample Acceptable

Ogata, 

201317

Yes Yes NR 3.6% missing data/lost at 

site 1; 80% excluded at 

site 2 due to missing 

data/lost or < 10 years 

follow-up

1) Split sample (random) 

from first site and 2) second 

site only served as validation 

site

Unacceptable

Wang, 

201312b 

Yes Yes Yes Yes Randomly split sample Acceptable

Wang 

201211b 

Yes Yes Yes Yes Randomly split sample Acceptable

a Shaded rows are VHA studies b VA Care Assessment Needs (CAN) model

NR = not clearly reported
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KEY QUESTION 1: Between 2011 and 2016, have there been any 

additional reports of life expectancy calculators that may have 

sufficient predictive accuracy for use in adult primary care practice?

Summary of Findings

Most studies reported a range of estimated probabilities of dying that included some values 

greater than 0.5 (50%) that could be interpreted as having a life expectancy less than the model’s

time horizon (Table 4). Wang et al modeled death without a hospitalization, thus their model 

cannot be used as is to estimate life expectancy.11,12 However, as noted previously, most likely 

their prediction model could be adapted to do so. 

The reported C-statistics ranged from 0.779 to over 0.9015,16 indicating that the models

discriminated those who survived or died by rank ordering subjects according to their risk of 

dying, but not perfectly. The latter 2 studies achieved these high levels of discrimination using 

the patient’s age and gender in combination with diagnostic codes extracted from large 

provincial health plan databases to predict 1-year mortality. These very high levels of 

discrimination most likely were related to inclusion of a large proportion of younger adults that 

would be less likely to die than older adults. In contrast, Gagne et al, who used similar predictors 

extracted from state health plan databases, only had a C-statistic of 0.79 in a more elderly sample 

of patients with higher mortality at one year.7 Tan et al also used similar predictors extracted 

from Medicare data; however their lower C-statistic is for deaths that occurred over a much 

longer 10-year time span (these are inherently more difficult to estimate than a shorter time span

due to interim changes in predictors and health).

9 Using Tan et al’s 10-year mortality prediction 

model, over 80% of the subjects that had a predicted 10-year probability of dying that was 

greater than 0.75 (75%) died within this time interval whereas approximately 65% with a 10-year 

probability of dying between 0.50 to 0.74 died. The positive and negative predictive values were 

63% and 92% using a cut-off for Mathias et al’s predicted probability of dying within 5 years

equal to 0.5 (50%).

10 Other reports did not focus on any particular risk score threshold to predict 

survival and therefore did not estimate sensitivity, specificity, or predictive values.

The differences between observed and predicted mortality in subjects that were grouped 

according to their estimated probabilities of dying were mostly small, that is, the prediction

models were well-calibrated to the test sample. Thus, if a clinician used the prediction model to 

place a patient in a mortality risk group, the observed mortality in the group most likely would be 

similar to the risk group’s average or median predicted mortality. If a risk group’s individual 

estimated probabilities of dying within the specified follow-up time are all either greater than or 

less than 0.5, as a proxy one might say their life expectancy is either less than or greater than the 

specified follow-up time, respectively.
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Table 4. Predictive Performance of Models under Reviewa 

Study

Estimated

Probabilities of 

Dying

Discrimination Calibration† 

Austin 

201115

0.00 to 0.90 C-statistic: 0.92 Differences between observed and 

predicted mortality <1% in all 100 risk 

categories except top 3 where 

differences < 3%

Austin 

2011bb14

0.00 to 0.90 C-statistic: 0.92 Differences between observed and 

predicted mortality ~1% in in all 20 risk 

categories 

Gagne 

20117

Mean of 1st decile

0.03 to 10th decile

0.55

C-statistic: 0.79 Under predicted by ~ 3% in 5th & 6th 

deciles with 20 -25% mortality, over 

predicted by ~ 10% in 10th decile with ~ 

45% mortality, otherwise observed and 

predicted mortality % within ~1%

Quail 

201116

NR C-statistic with Charlson 

comorbidities: 0.90

C-statistic with Elixhauser 

comorbidities: 0.91

NR

Stefos 

20128

Mean of 1st decile 

0.025 to 10th

decile 0.94

C-statistic: 0.86 Ratio of observed to predicted

number of deaths by predicted 

risk decile

Predicted O/P ratio

2.5% 0.94

13% 1.12

24% 1.12

34% 1.06

45% 0.98

55% 0.95

65% 0.91

75% 0.87

85% 0.87

94% 0.86

Mathias 

201310

Mean of 1st decile 

0.036 to 10th

decile 0.55

C-statistic: 0.86

Sensitivity/specificity using 

cut-off for predicted 

probability of dying within 5 

years = 0.5 (50%): 

31%/98%

Positive/negative predictive 

value using cut-off for 

predicted probability of 

dying within 5 years = 0.5 

(50%): 63%/92%

Differences between observed and 

predicted mortality <3% across all 

deciles of predicted risk

Tan 20139 1st quartile < 0.25 

to 4th quartile > 

0.75

C-statistic at 10 years: 

Women: 0.79

Men: 0.7

Positive predictive value for

LE < 10 years for 

women/men: ~75%/75%

Observed mortality within quartiles of 

predicted probabilities for women/men: 

1st quartile: 17%/20%

2nd quartile: 35%/35%

3rd quartile: 65%/64%

4th quartile: 88%/90% 
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Ogata 

201317

Predicted to die 

in 10 years: 

Site 1: 36/365 

(9.9%)

Site 2: 35/170 

(20.6%)

C-statistic 

Site 1: 0.83

Site 2: 0.85

Difference between observed and 

predicted mortality: 

 Site 1: 3.5% 

 Site 2: 7.6%

Wang 

201312c,d

1-year mean of 

1st decile < 0.01 

to 10th decile 

~0.14

C-statistic: 0.85 Differences between observed and 

predicted in each decile of predicted 

probabilities: negligible 

Observed deaths if in upper 5% of 1-year

predicted probabilities of death without 

hospitalization: 19.4% a Shaded rows are VHA studies 

b Same prediction model as Austin 201115 except regression coefficients converted to a single weighted Mortality 

Risk Score

c Only predicting deaths without hospitalization, not total deaths d VA Care Assessment Needs (CAN) model

NR = not reported
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Conclusion for Key Question #1

Since 2011 several life expectancy calculators have been reported that may have sufficient 

predictive accuracy for general use in adult primary care practice. Accurate proxy life 

expectancy calculators can be developed using age, gender, and diagnosis codes and perhaps a 

few other select variables obtained from administrative or electronic medical records. However, 

all of the models under review would require some adaptation before being made available to 

VA primary care providers. Few studies reported the sensitivity, specificity, or positive or 

negative predictive values for a risk threshold proposed for a clinical decision such as whether a

patient should or shouldn’t be screened or treated.

Rating for the Strength of Evidence for KQ1: High 

Several prediction models utilizing variables that are generally available in electronic medical 

records had consistently good predictive accuracy. 
Life Expectancy Calculators Evidence-based Synthesis Program

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KEY QUESTION 2: Of the life expectancy calculators being reviewed, 

have any external validation studies been published between 2011 

and 2016? If yes, what population was studied and what was the 

predictive accuracy therein?

Summary of Findings

Our review didn’t find any true validation studies. We found 2 separate quasi-validation 

reports11,13 and 2 elderly subgroup analyses.

12,15 The predictive performance in these patient 

subgroups is summarized in Table 5. Regression coefficients for the predictor variables were reestimated for the 2 elderly subgroup analyses12,15 and the Wang et al study of patients with heart 

failure prior to examining the predictive accuracy in the subpopulations.11 Compared to the 

mortality prediction models for general adult patient populations, both the Austin and Wang 

models had poorer discrimination of survivors and decedents in the elderly and schizophrenic or 

heart failure subpopulations. Calibration of the Austin model was poorer in the schizophrenic 

subpopulation whereas after recalibration the calibration of the Wang model was maintained in

the elderly and heart failure subpopulations. 

Table 5. Validation of Mortality Prediction Modelsa 

Study Population 

Subgroup

Number

Mortality Discrimination Calibration

Austin 

201115

Age > 65 years n=NR

1–year NR

C-statistic: 0.81 Differences between 

observed and predicted 

mortality NR

Austin 

201213

Schizophrenia n=94,466

1–year 2.0%

C-statistic: 0.84 Differences between 

observed and predicted 

mortality NR, however 

calibration plots indicated 

significantly worse 

calibration 

Wang 

201312b,c Age > 65 years n=2,129,0631–year ~4.7%

C-statistic: 0.80 Small differences 

between observed and 

predicted in each decile 

of predicted probabilities

Wang 

201211b,c

Heart failure n=198,640

1–year 7.1%

C-statistic: 0.76 Differences between 

observed and predicted 

mortality % in each decile 

of predicted probabilities 

were negligible a Shaded rows are VHA studies b Only predicting deaths without hospitalization, not total deaths c VA Care Assessment Needs (CAN) model 

NR = not reported

Conclusion for Key Question #2

Discrimination of the deceased versus surviving patients was reduced in these more homogenous 

subpopulations with higher mortality than the overall general populations. Calibration of the 

prediction models was not always as good in the subpopulations despite recalibration. The 

calibration of a mortality prediction model should be examined in a representative sample of the 
Life Expectancy Calculators Evidence-based Synthesis Program

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primary care practice in which it will be used, and the predictive accuracy should be validated 

using time horizons that are commensurate with the clinical decisions it will support.

Rating for the Strength of Evidence for KQ2: Insufficient

None of the 4 studies were true validation studies that examined the predictive performance in 

external samples of primary care practices.
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KEY QUESTION 3: What is the clinical use of the mortality prediction 

models (aka life expectancy calculators), and was there improvement 

in patient survival times, health-related quality of life, provider-patient 

communication, patient satisfaction and participation in clinical 

decisions, or lower healthcare utilization and costs?

Summary of Findings

We found no studies that examined the effects of using the reviewed mortality prediction models

in clinical care.

We found 2 reports that alluded to use of other life expectancy calculators. One study 

retrospectively evaluated the potential impact of a previously developed 4-year life expectancy 

calculator (Health and Retirement Study Mortality Risk Index18).

19 A retrospective patient cohort 

of 8,090 that was age 65 and older at the time of their last visit to a primary care practice in

2003-04 was studied. The prediction model identified 1,241 of the 8,090 (15%) as having a 40 to 

70% probability of dying within 4 years. Their observed mortality was 670/1,241 (54%); thus,

the authors concluded it would have been reasonable for these patients to forego a screening 

colonoscopy due to their limited life expectancy. Whether the primary care providers and 

patients would have come to the same conclusion with or without the life expectancy calculator 

was not determined.

Clinicians in the United Kingdom developed a 3-year mortality risk calculator for elderly 

patients with breast cancer to help determine whether they should undergo surgical (if expected 

to live at least 3 years) or medical therapy.

20 Their disease-specific prediction model (not 

included in this review) incorporated the American Society of Anesthesiologists (ASA) grade, 

age, ethnicity, bilateral or previous contralateral breast cancer, and nurse administered 

questionnaires to obtain the Charlson comorbidity score, Mini Mental State Examination score, 

Geriatric Depression Scale score, and Barthel and Instrumental Indexes of Activities of Daily 

Living. The prediction model was used in a Co-morbidity Clinic for patients who were 

potentially unfit or declined standard treatment for breast cancer, and was reportedly useful for

shared decision-making about treatment. The report did not state exactly how the risk model was 

employed or communicated to patients or actually study its impact on the treatment decision.

Conclusion for Key Question #3

We found no studies of the effects of the reviewed mortality prediction models on clinical 

decisions and outcomes. Clinical outcome studies of evaluating life expectancy calculator for 

clinical decision-making are needed to support their implementation by healthcare organizations

and use by clinicians and patients. Studies should focus on a well-defined clinical decision in the 

applicable population, for example, decisions about cancer screening based on 10 year life 

expectancy or palliative care based on life expectancy less than 6-12 months. 

Rating for the Strength of Evidence for KQ 3: Insufficient
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SUMMARY AND DISCUSSION

KEY FINDINGS

Between 2011 and 2016, 11 studies reported on 8 mortality prediction models; all but one 

of the included studies (a study from Japan) utilized data from large electronic databases. 

Models were developed for different purposes, including development of individual risk 

scores to adjust for possible difference in mortality risk when comparing healthcare

outcomes, to help primary care teams assess short-term risk of hospitalization or death 

without hospitalization, or help healthcare providers judge whether a patient will or won’t 

die within a specified time pertinent to decisions about screening for cancer.

C-statistics ranged from 0.77 to 0.90, indicating the models provide good discrimination 

of those who survived or died during the varying periods of follow-up. Few studies 

reported the sensitivity, specificity, or positive or negative predictive values for a 

proposed risk score threshold that would be used to determine which patients should or 

shouldn’t be screened or treated.

The prediction models were generally well-calibrated to the test samples with seemingly 

insubstantial differences between observed and predicted mortality across a range of risk 

groups.

We found no true external validation studies of the reviewed mortality prediction models.

None of the models have been externally validated for general primary care use.

No studies meeting eligibility criteria for the review examined the impact of using one of 

the life expectancy calculators to improve on healthcare decisions or outcomes.

LIMITATIONS

None of the life expectancy calculators under review were developed or validated specifically to 

estimate life expectancy (average survival times). The mortality prediction models can provide 

individual or average estimates of the probability of surviving for a specific period for a risk 

group that may be used as proxies for life expectancy. The range of individual estimates of 

survival probabilities within risk groups may be an important consideration when using the 

group average to estimate life expectancy, but often was not reported. Furthermore, the 

uncertainty inherent in personalized estimates may preclude their use, especially if a prediction 

model isn’t based on a large number of patients that were followed for periods of clinical interest 

such as 5 or 10 years. The best way to communicate model predictions to providers and patients 

was not addressed. A number of disease-specific prediction models that were excluded from this 

review may be better-suited to disease-specific clinical decisions.
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APPLICABILITY OF FINDINGS TO THE VA POPULATION

Several of the prediction models listed in the KQ1 section seemingly could be adapted for use in 

VA primary care practices. The VA model of Wang et al (aka the Care Assessment Needs or 

CAN score) most likely could be modified to estimate the total mortality risk for time periods

that are most relevant for specified clinical decisions.

12 The work by Stefos et al supports the 

notion that the VA electronic records can be used to produce well-calibrated proxy estimates of

life expectancy.

8 Several other studies support the impression that models based on patient 

demographics, the presence of life-threatening conditions, and perhaps history of healthcare

utilization and other readily available variables can provide mortality estimates that have good, 

but less than perfect, predictive accuracy. The small numbers of quasi-validation studies suggest

that the discrimination and calibration may vary when externally validated in VA primary care 

practices that may have substantially different distributions of the predictors and/or mortality. To 

adapt a mortality prediction model, the sources of data and definitions of predictor variables will 

need to be consistent across VA healthcare systems and over time. Centralized prediction models 

that are made widely available should be calibrated to and validated in several VA primary care 

patient populations and periodically checked for accuracy and recalibrated if necessary. The VA 

should consider models of survival curves that would provide estimates of life expectancy in 

addition to probabilities of survival.4

Although feasible, we found no studies to indicate whether making a reasonably accurate life 

expectancy calculator available to VA primary care providers or patients would influence their 

healthcare decisions or outcomes. A recent VA study by Tang et al suggested that having limited 

life expectancy as estimated by the investigators based on patients’ age and Charlson comorbidity count was associated with an appropriately reduced rate of prostate-specific antigen

screening for prostate cancer (39% when the estimated life expectancy was less than 5 years

versus 77% with an estimated life expectancy of 10 or more years).

21 Unfortunately, this study 

did not ascertain whether or how the clinicians estimated life expectancy or whether a VA effort 

to provide a validated quantitative calculator would influence their prognostic assessment or 

screening decisions.

RESEARCH GAPS/FUTURE RESEARCH

Although healthcare providers and guidelines make recommendations based, in part, on 

assessments of life expectancy, there is no widely accepted statistical tool for estimating patients’ 

life expectancy particularly for prolonged periods of 10 years. Research on the clinical 

usefulness and impact of life expectancy calculators on clinical decisions and outcomes is 

needed to guide further development and engender widespread acceptance. Hundreds of 

statistical prognostic tools have been developed, but are not being used in practice.

22,23 Readily 

available statistical life expectancy estimates require both clinical face validity and proven 

accuracy for diverse decision-relevant risk groups and periods of time.

24 Analytical life 

expectancy predictions have to be demonstrated to be more accurate than clinicians’ intuitive 

prognostic assessments preferably using statistics that allow clinicians to compare prognostic

errors. Additionally, they should use patient information that is readily and reliably available. 

Whether use of a standardized life expectancy calculator would reduce undesirable practice 

variation in regards to decisions involving estimates of life expectancy needs to be determined. 

Strong comparative evidence that using a quantitative prediction model can improve healthcare

decisions and outcomes most likely will be needed to change current practices. 
Life Expectancy Calculators Evidence-based Synthesis Program

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CONCLUSIONS

Life expectancy calculators based on readily available electronic data that have acceptable 

performance for estimating one-, 5-, and 10-year life expectancy in middle age to older adults are 

feasible. These calculators need to be validated for use in primary care practices. There are no 

data on the effect of using these life expectancy calculators on patient or provider decisions or 

outcomes. If a life expectancy calculator is made available, it remains to be determined whether 

primary care providers would use it or whether it would improve healthcare delivery, resource 

use, patient experiences, or outcomes.
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REFERENCES

1. Schoenborn NL, Bowman TL, Cayea D, Pollack CE, Feeser S, Boyd C. Primary care 

prectitioners' views on incorporating long-term prognosis in the care of older adults. 

JAMA Intern Med. 2016;176(5):671-678.

2. Yourman L, Lee S, Schonberg M, Widera E, Smith A. Prognostic indices for older 

adults: a systematic review. JAMA. 2012;307(2):182-192.

3. Minne L, Ludikhuize J, de Rooij SE, Abu-Hanna A. Characterizing predictive models of 

mortality for older adults and their validation for use in clinical practice. J Am Geriatr 

Soc. 2011;59(6):1110-1115.

4. Lee SJ, Boscardin J, Kirby KA, Covinsky KE. Individualizing life expectancy estimates 

for older adults using the Gompertz Law of Human Mortality. PLOS One. 

2014;9(9):e108540.

5. Baade PD, Youlden DR, Andersson TML, et al. Estimating the change in life expectancy 

after a diagnosis of cancer among the Australian population. BMJ Open. 2015;5:e006740.

6. Moons K, de Groot J, Bouwmeester W, et al. Critical appraisal and data extraction for 

systematic reviews of prediction modelling studies: the CHARMS checklist. PLOS Med. 

2014;11(10):e1001744.

7. Gagne JJ, Glynn RJ, Avorn J, Levin R, Schneeweiss S. A combined comorbidity score 

predicted mortality in elderly patients better than existing scores. J Clin Epidemiol. 

2011;64(7):749-759.

8. Stefos T, Lehner L, Render M, Moran E, Almenoff P. Determining population based 

mortality risk in the Department of Veterans Affairs. Health Care Manag Sci. 

2012;15(2):121-137.

9. Tan A, Kuo YF, Goodwin JS. Predicting life expectancy for community-dwelling older 

adults from Medicare claims data. Am J Epidemiol. 2013;178(6):974-983.

10. Mathias JS, Agrawal A, Feinglass J, Cooper AJ, Baker DW, Choudhary A. Development 

of a 5 year life expectancy index in older adults using predictive mining of electronic 

health record data. J Am Med Inform Assoc. 2013;20(e1):e118-124.

11. Wang L, Porter B, Maynard C, et al. Predicting risk of hospitalization or death among 

patients with heart failure in the veterans health administration. Am J Cardiol. 

2012;110(9):1342-1349.

12. Wang L, Porter B, Maynard C, et al. Predicting risk of hospitalization or death among 

patients receiving primary care in the Veterans Health Administration. Med Care. 

2013;51(4):368-373.

13. Austin PC, Newman A, Kurdyak PA. Using the Johns Hopkins Aggregated Diagnosis 

Groups (ADGs) to predict mortality in a population-based cohort of adults with 

schizophrenia in Ontario, Canada. Psychiatry Res. 2012;196(1):32-37.

14. Austin PC, van Walraven C. The mortality risk score and the ADG score: two pointsbased scoring systems for the Johns Hopkins aggregated diagnosis groups to predict 
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mortality in a general adult population cohort in Ontario, Canada. Med Care. 

2011;49(10):940-947.

15. Austin PC, van Walraven C, Wodchis WP, Newman A, Anderson GM. Using the Johns 

Hopkins Aggregated Diagnosis Groups (ADGs) to predict mortality in a general adult 

population cohort in Ontario, Canada. Med Care. 2011;49(10):932-939.

16. Quail JM, Lix LM, Osman BA, Teare GF. Comparing comorbidity measures for 

predicting mortality and hospitalization in three population-based cohorts. BMC Health 

Serv Res. 2011;11:146.

17. Ogata K, Miyamoto T, Adachi H, et al. New computer model for prediction of individual 

10-year mortality on the basis of conventional atherosclerotic risk factors. 

Atherosclerosis. 2013;227(1):159-164.

18. Lee S, Lindquist K, Segal M, Covinsky K. Development and validation of a prognostic 

index for 4-year mortality in older adults. JAMA. 2006;295(7):801-808.

19. Synnestvedt MB, Weiner MG. Evaluation of the use of electronic health data to classify 

four-year mortality risk for older adults undergoing screening colonoscopies. Proc AMIA 

Symp. 2011;2011:1361-1365.

20. Stotter A, Reed MW, Gray LJ, Moore N, Robinson TG. Comprehensive Geriatric 

Assessment and predicted 3-year survival in treatment planning for frail patients with 

early breast cancer. Br J Surg. 2015;102(5):525-533.

21. Tang V, Shi Y, Fung K, et al. Clinician factors associated with prostate-specific antigen 

screening in older veterans with limited life expectancy. JAMA Intern Med. 2016.

22. Wyatt J, Altman D. Commentary: prognostic models: clinically useful or quickly 

forgotten? BMJ. 1995;311:1539-1541.

23. Siontis G, Tzoulaki I, Ioannidis J. Predicting death: an empirical evaluation of predictive 

tools for mortality. Arch Intern Med. 2011;171(19):1721-1726.

24. Moons K, Altman D, Vergouwe Y, Royston P. Prognosis and prognostic research: 

application and impact of prognostic models in clinical practice. BMJ. 2009;338:b606.
Life Expectancy Calculators Evidence-based Synthesis Program

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APPENDIX A. SEARCH STRATEGY 

MEDLINE (Ovid) through May 2016 

1 (life adj expectancy).m_titl.

2 (survival or mortality or death).m_titl.

3 1 or 2 

4 (calculat$ or instrument$ or index or indice$ or model$ or tool$ or prognosis or risk or 

predict$ or estimat$).m_titl.

5 3 and 4 

6 (valid$ or calibrat$ or compar$).mp. [mp=title, abstract, original title, name of substance 

word, subject heading word, keyword heading word, protocol supplementary concept word, rare 

disease supplementary concept word, unique identifier] 

7 5 and 6 

8 limit 5 to (english language and humans and yr="2011 -Current" and ("middle-aged (45 

plus years)" or "all aged (65 and over)" or "aged (80 and over)")) 

9 limit 7 to (english language and humans and yr="2011 -Current" and ("middle-aged (45 

plus years)" or "all aged (65 and over)" or "aged (80 and over)")) 

10 limit 8 to (clinical trial or comparative study or controlled clinical trial or evaluation 

studies or meta analysis or observational study or pragmatic clinical trial or randomized 

controlled trial or systematic reviews or validation studies)

11 limit 9 to (clinical trial or comparative study or controlled clinical trial or evaluation 

studies or meta analysis or observational study or pragmatic clinical trial or randomized 

controlled trial or systematic reviews or validation studies)

12 8 NOT 9
Life Expectancy Calculators Evidence-based Synthesis Program

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APPENDIX B. PEER REVIEW COMMENTS/AUTHOR RESPONSES

Question Reviewer Comment Author Response

Are the objectives, 

scope, and methods 

for this review clearly 

described?

Yes Thank you

Yes

Yes

Is there any indication 

of bias in our synthesis 

of the evidence?

No Thank you

No

No

Are there any 

published or 

unpublished studies 

that we may have 

overlooked?

No No response needed

Yes - 1. Cruz et al JAMA research letter 2012 Predicting 10 year 

mortality in older adults. Granted, a research letter not a full research 

publication, but a high impact journal and an important update to the 

prior 4 year mortality index.

2. Lee et al PLOS one 2014 Individualizing life expectancy estimates 

using Gompertz... The conclusion of the report discuss lack of 

prognostic models that estimate life expectancy (time to death) rather 

than mortality risk (risk of death over a given time frame). This study 

estimates life expectancy, using the Lee index mentioned in 1 above.

3. Schonberg M JAGS 2011 External Validation of an index to predict 

9 year...Granted, included in our review by Yourman, published in 

2011 so I believe should have been included.

We have reviewed the suggested studies. 

Although the Cruz and Schoenborn 

prognostic models are potentially useful, 

these reports were not included in this 

review because the mortality prediction 

models are based on self-reported 

national survey data and would require 

similar patient questionnaires be 

administered by primary care clinics. We 

have included the Lee et al 2014 

reference so readers can see how one 

could model survival curves to estimate 

life expectancy. 

No No response needed

Additional suggestions 

or comments can be 

provided below. If 

applicable, please 

p. 5 line 5 - should be "insubstantial" rather than "unsubstantial"

p. 5 line 10 - "quasi-validation" should probably be defined in the table 

We have made the suggested change on 

pg 5, line 5. 

‘Quasi’ simply means the studies were 
Life Expectancy Calculators Evidence-based Synthesis Program

32

indicate the page and 

line numbers from the 

draft report.

on p 1 (or avoided altogether as an imprecise term). not true external validation studies, 

however they were in a sense validation 

studies. We have pointed that none of the 

studies reviewed for KQ2 were true 

validation studies, and dropped ‘quasi’ 

from the Table title. We did not feel a 

need to define quasi in the table on page 

1. 

Major points:

1. As senior author of the Yourman systematic review of prognostic 

indices for older adults, I'm pleased that this report has been 

commissioned and well executed. Many of the conclusions remain the 

same - people just are not studying the use/usefulness of prognostic 

indices in clinical practice. As a key member of the eprognosis team, a 

free website dedicated to making prognostic models available to 

clinicians, I can testify that prognostic models are being used in 

everyday practice. Clinicians come up to me all the time, from all over 

the country, and say "I use eprognosis." It seems to be more the 

specialists that use the models rather than the primary care clinicians, 

as a recent study by Nancy Shoenborn in JAMA Internal Medicine 

2016 suggests. 

2. The conclusions are based on the time limited update between 

2011 and 2016. Yet the underlying question "are there clinically useful 

prognostic models for VA primary care patients" need not be restricted 

to this time frame. Certainly, risk may change with time. But have 

things changed that much between, say 2000, and 2016? My guess is 

no, the same factors that put persons at risk in 2000 are likely to put 

persons at risk in 2016. Age, gender, functional limitations. We haven't 

cured cancer. To really address the underlying question, the VA will 

need to consider the accuracy and pragmatic usefulness of prior 

studies not included in this report.

3. Why no mention of the CAN score? This is the giant elephant in the 

room that VA researchers are talking about. Apparently it's calculated 

for veterans and hard but possible to access in the medical record. 

What's the evidence? How useful is it? Omission is a major limitation 

as it will be the first thing on many VA clinician/researcher's minds 

when they think VA data and prognostic index.

4. Minor points

-Explain what a "quasi-validation" study means.

The Schoenborn article is now mentioned 

in the Introduction of the Evidence Report 

to support an important statement that 

providers often don’t share long-term 

prognostic assessments with their 

patients in part due to their uncertainty.

We agree. The VA can refer to these 

previous reviews, thus we focused on 

more recent studies.

We had not pointed out that the 2 Wang 

articles cited for KQ1 and KQ2 represent 

the VA Care Assessment Needs (CAN) 

model. We have now repeatedly made 

this connection in the text and table 

footnotes. 
Life Expectancy Calculators Evidence-based Synthesis Program

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-The two studies by Austin seem to really be one study. This question 

comes up immediately as all data in the tables about them are 

essentially identical. One shouldn't have to wait for a footnote to 

discover one study is simply developing a point score system using 

the same model. Not clear if this requires a separate citation and line 

in the table, as it's the same index. Might be better organized if by 

index rather than by publication.

‘Quasi’ is used to indicate the studies 

were not true external validation studies, 

however they were in a sense validation 

studies. We have pointed that none of the 

studies reviewed for KQ2 were true 

validation studies, and dropped ‘quasi’ 

from the Table title. We did not feel a 

need to define quasi in the table on page 

1. 

Thank you for the opportunity to review this excellent ESP. 

The authors sought to review recent life expectancy calculators for use 

in primary care practice. In particular, they looked for new calculators 

published since 2011, external validations of those new calculators, 

and clinical applications of the calculators. 

Their research questions were specific, their methods were clear, and 

the document is well written. Their primary findings are also clear. 

There have been multiple calculators created, including one in VA. 

These calculators have had quite good testing characteristics 

generally, but have not been validated in external samples and have 

not had any study in clinical practice. 

The tables and figures were clear and will be a valuable summary for 

anyone doing this type of work. 

Their findings drive home what I think will be a central issue in health 

services and implementation science for the next decade or more. 

This is that Big Data will make risk prediction reasonably easy and 

accurate, but having prediction tools does not tell us what predictions 

are useful, how do we use them, and how do we integrate tools into 

the system to be useful to patients, providers, and healthcare leaders. 

These questions will likely be very important, but they’ve shown few

people seem to be asking them. 

At the broadest level, I feel like the questions were very well 

formulated, but the underlying problems the questions were trying to 

solve were somewhat less so. Exactly how is this information going to 

be used inside or outside VA? If this question were answered more 

explicitly, some of their other choices may have been slightly different. 

Thank you.

As pointed out in the text, the current VA 

model known as the CAN score doesn’t 

predict all-cause model and wasn’t 

developed to estimate life expectancy or 

evaluated as such, but could be adapted 
Life Expectancy Calculators Evidence-based Synthesis Program

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For example, they found a good study by Wang (the senior author of 

which is an operational partner on this project) that used VA data. If 

VA wants to implement a life expectancy calculator, shouldn’t they just 

use this tool? Are there things other tools did that a modified VAbased tool would want to do? 

For KQ2 and KQ3, they were unlikely to find validation studies since 

the studies included were already recent. Here they could have also 

used any study in Yourman’s 2011 JAMA paper, which was one of the 

inspirations for this study. The team that wrote the JAMA paper has a 

website, eprognosis.org, that does some of the things that KQ3 

wanted to address, though I don’t know if they have validated it in any 

way. While adding older studies may have been reasonably out of 

scope for this project, it could have found some interesting 

information. 

Their search strategy was reasonable. They only used Medline, which 

can miss citations. Because this is not a meta-analysis, missing 

citations is not a terribly big deal, so I find this reasonable. 

While I know this isn’t the primary point of an ESP, I do wish the 

authors expanded their interpretive Research Gaps section a little. I 

was curious what they felt about each of the different scores, how they 

differ, and what future developers or users could learn from them. 

A few small typos

P8 “appropriate to to” 

P12 Moons2014 should be inside the period.

to do so. 

If an effort to guide the VA, we tried to 

focus on what the good performing 

models had in common, and what further 

evaluation would be needed to support 

their use. 

The P8 typo has been corrected and all 

references have been converted to 

superscript format.
Life Expectancy Calculators Evidence-based Synthesis Program

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APPENDIX C. EVIDENCE TABLES 

Table 1. Study Characteristics 

Author, Year

Country, Region

Study Dates

Source of Dataa Participants Participant Description Mortality Outcome Risk of Bias

Aggregated Diagnosis Groups ( not VHA)

Austin, 201115

(General Adult 

Population)

Canada, Ontario

2007-2008

Administrative 

healthcare 

databases in 

Ontario, Canada

-Registered 

Persons Database 

(RPDB)

-Discharge 

Abstract Database

-Ontario Health 

Insurance Plan 

physician billing 

database

-Ontario Mental 

Health Reporting 

System

Inclusion criteria: all persons 

in RPDB alive on their birthday 

in 2007

Exclusion criteria: age <20, 

age >100

Recruitment method: N/A

N=10,498,413

Age (years): 46 (median)

Gender (% male): 49

Race: NR

Predictors:

Aggregated Diagnosis 

Groups [ADGs] with 

largest adjusted odds 

ratios; all OR>1.5

a) Psychosocial: recurrent 

or persistent, unstable 

(23.4%) 

b) Malignancy (5.8%)

c) Chronic medical: 

unstable (17.1%)

d) Time limited: major – 

primary infections (7.4%)

e) Time limited: major 

(4.5%)

f) Likely to recur: 

progressive (2.4%)

Definition: mortality 

within 365 days of index 

date (birthday in 2007)

Measurement method: 

from RPDB (linked by 

encrypted health 

number)

Duration of follow-up: 

365 days

Number of deaths: 

85,007 (0.8%)

Predictor 

definition/measurement 

same for deceased/

survivors: Yes

Outcome 

definition/measurement 

same for deceased/

survivors: Yes

Outcome assessed 

independent of predictors 

(eg, blinded): Yes

Incomplete follow-up or 

missing predictor data 

(%, handling of): NR

Method of validation:

Assessed over fitting by 

bootstrap methods
Life Expectancy Calculators Evidence-based Synthesis Program

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Author, Year

Country, Region

Study Dates

Source of Dataa Participants Participant Description Mortality Outcome Risk of Bias

Austin, 201114

(General Adult 

Population)

Canada, Ontario

2007-2008

Administrative 

healthcare 

databases in 

Ontario, Canada

-Registered 

Persons Database 

(RPDB)

-Discharge 

Abstract Database

-Ontario Health 

Insurance Plan 

physician billing 

database

-Ontario Mental 

Health Reporting 

System

Inclusion criteria: all persons 

in RPDB alive on their birthday 

in 2007

Exclusion criteria: age <20, 

age >100

Recruitment method: N/A

N=10,498,413

Age (years): 46 (median)

Gender (% male): 49

Race: NR

Subgroup of 395,009 

residing in rural areas

Predictors:

Aggregated Diagnosis 

Groups [ADGs] with 

largest adjusted odds 

ratios; all OR>1.5

a) Psychosocial: recurrent 

or persistent, unstable 

(23.4%) 

b) Malignancy (5.8%)

c) Chronic medical: 

unstable (17.1%)

d) Time limited: major – 

primary infections (7.4%)

e) Time limited: major 

(4.5%)

f) Likely to recur: 

progressive (2.4%)

Definition: mortality 

within 365 days of index 

date (birthday in 2007)

Measurement method: 

from RPDB (linked by 

encrypted health 

number)

Duration of follow-up: 

365 days

Number of deaths: 

85,007 (0.8%)

Rural subgroup:

4,464 (1.1%)

Predictor 

definition/measurement 

same for deceased/

survivors: Yes

Outcome 

definition/measurement 

same for deceased/

survivors: Yes

Outcome assessed 

independent of predictors 

(eg, blinded): Yes

Incomplete follow-up or 

missing predictor data 

(%, handling of): NR

Method of validation:

Assessed over fitting by 

bootstrap methods
Life Expectancy Calculators Evidence-based Synthesis Program

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Author, Year

Country, Region

Study Dates

Source of Dataa Participants Participant Description Mortality Outcome Risk of Bias

Austin, 201213

(Adult Population 

with 

Schizophrenia)

Canada, Ontario

2007-2008

Administrative 

healthcare 

databases in 

Ontario, Canada

-Registered 

Persons Database 

(RPDB)

-Discharge 

Abstract Database

-Ontario Health 

Insurance Plan 

physician billing 

database

-Ontario Mental 

Health Reporting 

System

Inclusion criteria: all persons 

in RPDB alive on 1/1/ 2007 

that had previous diagnosis of 

schizophrenia (295.x in ICD-9 

or F20/F25 in ICD-10)

Exclusion criteria: age <20, 

age >100

Recruitment method: N/A

N=94,466

Age (years): 47 (median)

Gender (% male): 46

Race: NR

Predictors:

Aggregated Diagnosis 

Groups [ADGs] with 

largest adjusted odds 

ratios; all OR>1.5

a) Psychosocial: recurrent 

or persistent, unstable 

(71%) 

b) Time limited: major – 

primary infections (12%)

c) Malignancy (4.9%)

d) Likely to recur 

progressive (3.9%)

Definition: mortality 

within 365 days of index 

date (1/1/ 2007)

Measurement method: 

from RPDB (linked by 

encrypted health 

number)

Duration of follow-up: 

365 days

Number of deaths: 

1915 (2.0%)

Predictor 

definition/measurement 

same for deceased/

survivors: Yes

Outcome 

definition/measurement 

same for deceased/

survivors: Yes

Outcome assessed 

independent of predictors 

(eg, blinded): Yes

Incomplete follow-up or 

missing predictor data 

(%, handling of): NR

Method of validation:

Application of previously 

developed model for 

general adult population

Charlson and/or Elixhauser Comorbidities
Life Expectancy Calculators Evidence-based Synthesis Program

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Author, Year

Country, Region

Study Dates

Source of Dataa Participants Participant Description Mortality Outcome Risk of Bias

Gagne, 20117

(Medicare 

enrollees, 

age > 65)

Pennsylvania, 

New Jersey,

United States

2004-2005

Inpatient & 

outpatient 

Medicare claims 

data & pharmacy 

databases in 

Pennsylvania and 

New Jersey for 

low-income 

Medicare enrollees 

who don’t qualify 

for Medicaid

Development 

cohort:

Pharmacy 

Assistance 

Contract for Elderly

(PACE) 

Pennsylvania

Validation cohort:

Pharmacy 

Assistance for the 

Aged and 

Disabled) PAAD) 

New Jersey

Inclusion criteria: Medicare 

enrollees with coninuous drug 

coverage through PACE 

(development cohort) or PAAD 

(validation cohort) and at least 

one pharmacy claim during 

the 4 months before baseline 

year and survived the baseline 

year

NPA=120,679

Age 80

Gender (% female): 83 

Race: NR

NNJ=123,855

Age 79

Gender (% female): 77

Race: NR

Predictors: Weighted 

comorbidity score 

calculated for 37 

Romano/Charlson or 

vanWalraven/Elixhauser 

ICD-9 comorbidity 

classifications, age and 

gender; key predictors

Heart failure:23%

Dementia: 9.0%

Renal failure: 6.9%

Metastatic cancer 1.8%

Weight loss: 1.5%

Definition: 1 year 

mortality 

(also had 30,90,180 day 

mortality)

Measurement method: 

NR

Duration of follow-up: up 

to 1 year

Number of deaths: 

Development cohort 

N=10,769 (8.9%)

Validation cohort

N=9,230 (7.5%)

Predictor 

definition/measurement 

same for deceased/

survivors: Yes

Outcome 

definition/measurement 

same for deceased/

survivors: NR

Outcome assessed 

independent of predictors 

(eg, blinded): NR

Incomplete follow-up or 

missing predictor data 

(%, handling of): NR

Method of validation:

External sample
Life Expectancy Calculators Evidence-based Synthesis Program

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Author, Year

Country, Region

Study Dates

Source of Dataa Participants Participant Description Mortality Outcome Risk of Bias

Quail, 201116

Canada, 

Saskatchewan

(provincial health 

plan)

Fiscal years 

2001-2002

Provincial 

Discharge Abstract 

Database (ICD-9 & 

10 codes), Medical 

Services Database 

(ICD-9 codes), 

Population 

Registry, & Vital 

Statistics Registry

Income from 

residence in 

census region

Inclusion criteria: residents 

age 20 and older with 

uninterrupted coverage in year 

(comorbidities) were assessed 

(FY2001)

Exclusion criteria: federal 

employees, inmates, & First

Nation people 

General Population/Age > 

65 years 

N= 662,423/137,700

Age: 48/75

Female 51%/57%

Race: NR

Charlson score 0.3/0.7

Elixhauser 

Heart failure: 2.0/8.1

Metastatic cancer:0.8/2.4

Renal failure: 0.6/1.8

Weight loss: 0.1/0.2

Pulmonary disease: 

8.4/12.7

 

1-year mortality

General Population/Age 

> 65 years 

1.3%/ 5.1%

Predictor 

definition/measurement 

same for deceased/

survivors: Yes

Outcome 

definition/measurement 

same for deceased/

survivors: Yes

Outcome assessed 

independent of predictors 

(eg, blinded): Yes

Incomplete follow-up or 

missing predictor data 

(%, handling of): missing 

income imputed

Method of validation: 

external sample & time
Life Expectancy Calculators Evidence-based Synthesis Program

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Author, Year

Country, Region

Study Dates

Source of Dataa Participants Participant Description Mortality Outcome Risk of Bias

Tan 20139

United States

1999-2009

5% random sample 

of Medicare 

enrollees in 2000

Medicare 

enrollment files, 

carrier files, 

outpatient 

statistical analysis 

files (outpatient 

visits), Medicare 

provider analysis 

and review files 

(hospital stays)

Inclusion criteria:

66-90 years in 2000, full 

coverage in Medicare Parts A 

(hospital care) and B 

(physician and outpatient 

services) in 1999, and not in 

Medicare Advantage HMO 

coverage at any time in 1999

N=1,137,311 Medicare 

beneficiaries 

Women: 60%

Age (SD) 76 (6.5)

Men: 40%

Age (SD) 75 (6.1)

Prevalence of 

comorbidities with > 80% 

10-year mortality rates in

women/men (%)

Heart failure: 8.2/8.8

Pulmonary circulation 

disease: 0.9/0.8 

Metastatic cancer: 0.8/0.9

Renal failure: 1.3/2.0

Weight loss:1.5/1.2

Neurological

disorders:2.9/3.3

Substance abuse: 0.2/0.5

Dementia: 2.6/1.7

Psychoses: 1.3/0.9

1-, 5-, 7-, 10-year 

mortality stratified by 

sex

 

Measurement method: 

Medicare enrollment

10-year mortality

Women: 51%

Men: 57%

Predictor 

definition/measurement 

same for deceased/

survivors: Yes

Outcome 

definition/measurement 

same for deceased/

survivors: Yes

Outcome assessed 

independent of predictors 

(eg, blinded): Yes

Incomplete follow-up or 

missing predictor data 

(%, handling of): NR

Method of validation: 

random split sample 

Electronic Medical Records
Life Expectancy Calculators Evidence-based Synthesis Program

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Author, Year

Country, Region

Study Dates

Source of Dataa Participants Participant Description Mortality Outcome Risk of Bias

Mathias, 201310

US, Chicago

2003-2008

EHRs (EpicCare) 

from a large, 

academic, 

multispecialty 

group practice and 

affiliated hospitals

National Center for 

Health Statistics 

National Death 

Index (NDI) for 

2003-2008

Inclusion criteria: outpatients, 

age 50 and older, at least 1 

visit to the group practice 

during 2003

Exclusion criteria: none 

reported

Recruitment method: N/A

N=7,463

Age (years): 62 (mean)

Gender (% male): 40

Race: 

White 51%; Black 24%; 

Hispanic 5%; Asian 3%; 

Other/Unknown 8%

-Predictors: 

Count (mean[SD]) 2.5 

(2.0)

Present in >10%

a) Hypertension 52%

b) Any vascular disease 

17%

c) Diabetes 17%

d) Any cancer 15%

e) Tobacco use 11%

-Vital signs:

BP 131/79

-Lab results:

Albumin (g/dl) 3.7 (0.4)

Creatinine (mg/dl) 1.1 (0.8)

-Outpatient medications

Digoxin 3%

Loop diuretic 8%

-Utilization

PCP visits 1.2 (1.8)

Hospitalizations in past 

year 0.3 (1.6); 1-2 years 

prior 0.2 (0.7)

Definition: death within 5 

years of last outpatient 

encounter in 2003

Duration of follow-up: 5 

years

Number of deaths: 838 

(11.2%) 

Predictor 

definition/measurement 

same for deceased/

survivors: Yes

Outcome 

definition/measurement 

same for deceased/

survivors: Yes

Outcome assessed 

independent of predictors 

(eg, blinded): Yes

Incomplete follow-up or 

missing predictor data 

(%, handling of): NR

Method of validation: 

10 fold cross validation

Data from Other Studies
Life Expectancy Calculators Evidence-based Synthesis Program

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Author, Year

Country, Region

Study Dates

Source of Dataa Participants Participant Description Mortality Outcome Risk of Bias

Ogata, 201317

Japan; 

Tanushimaru, Uka

1999-2009

Baseline data 

collected for other 

study (one site was 

part of Seven 

Countries Study in 

Japan); reportedly 

similar to general 

population of Japan

Survival or death 

from review of 

obituaries, medical 

records, death 

certificates, 

hospital charts, and 

interviews with 

primary care 

physicians, 

families, and other 

witnesses

Inclusion criteria: residents 

age >40 years, gave informed 

consent

Exclusion criteria: missing 

data or lost to follow-up

Recruitment method: “invited”

N=2,021

Age (years): 63 (mean)

Gender (% male): 41

Race: NR (Japanese 

study)

Predictors: 6 established 

cardiovascular risk factors

Age (see above)

Sex (see above)

Systolic blood pressure: 

134 mmHg

HbA1c: 5.6%

Total cholesterol: 200 

mg/dl

Current smoker 16%

Definition: survival or 

death within 10 years of 

baseline testing 

Duration of follow-up: 10 

years

Number of deaths: 

-Training sample: 

204/1486 (14%)

-Test sample 1:

49/365 (13%)

-Test sample 2:

48/170 (28%)

Predictor 

definition/measurement 

same for deceased/

survivors: Yes

Outcome 

definition/measurement 

same for deceased/

survivors: Yes

Outcome assessed 

independent of predictors 

(eg, blinded): Unclear

Incomplete follow-up or 

missing predictor data: 

3.6% missing data/lost at 

site 1; 80% excluded at 

site 2 due to missing 

data/lost or < 10 years 

follow-up

Method of validation: a) 

split sample (random) 

from first site and b) 

second site only served 

as validation site

Veterans Health Administration
Life Expectancy Calculators Evidence-based Synthesis Program

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Author, Year

Country, Region

Study Dates

Source of Dataa Participants Participant Description Mortality Outcome Risk of Bias

Stefos, 20128

US (VHA) 

Fiscal year 2007-

2008

VA electronic 

administrative data 

files

Inclusion criteria: all patients 

who received care at a VA 

hospital in fiscal year 2008 

that were assigned to a 

primary care provider 

Exclusion criteria: none 

reported

Recruitment methods: N/A

N=4,774,000

Age: 62

Gender: 94% male

Race: 55% white

Key Predictors

Categorizations of ICD-9 

diagnosis codes from any 

type of encounter into 

Hierarchical Coexisting 

Conditions (HCC) using 

Diagnostic Cost Group 

(DCG) software (eg

metastatic cancer, endstage liver disease, 

respiratory arrest, coma of 

brain compression/

anoxia) 

-Cancer: 17% 

-Chronic obstructive 

pulmonary disease: 10%

-VA chronic disease 

registry: 4%

-VA priority status (eg,

catastrophically disabled 

(4%), low income or 

Medicaid (29%))

Definition: death from 

any cause within 12 

months of last FY2008 

VA clinical contact

Measurement method: 

not clear

Duration of follow-up: 1 

year

Number of deaths: 

262,260 (5.5%)

Predictor 

definition/measurement 

same for deceased/

survivors: Yes

Outcome 

definition/measurement 

same for deceased/

survivors: Yes

Outcome assessed 

independent of predictors 

(eg, blinded): Yes 

(database)

Incomplete follow-up or 

missing predictor data: 

NR 

Method of validation: 

Copas test for overfitting 

(repeated, split sample, 

cross validation design)
Life Expectancy Calculators Evidence-based Synthesis Program

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Author, Year

Country, Region

Study Dates

Source of Dataa Participants Participant Description Mortality Outcome Risk of Bias

Wang, 201312

(VHA primary care 

population for FY 

2011)

Predictors from 

VHA electronic 

National Patient 

Care Database & 

Corporate Data 

Warehouse 

including 

demographics, 

diagnoses from 

inpatient & 

outpatient records, 

medications, vital 

signs, laboratory 

tests & healthcare

utilization

VHA Databases

Death: VHA’s vital 

status file

Inclusion criteria: 

All patients enrolled and 

assigned to a primary care 

provider within VHA on 

October 1, 2010 (the index 

date) 

Exclusion criteria: 

Patients with no recorded use 

of any health service during 

the prior year (5%); patients 

who were hospitalized, 

admitted to a hospital, or died 

on October 1, 2010

N=4,598,408

Age: mean 64 years range 

18-110 

Gender (male): 94%

Race: NR

Predictors (select):

Charlson comorbidities & 

hierarchial condition 

categories

Heart failure: 5.1%

Renal failure: 6.3%

Chronic pulmonary

disease: 11%

Metastatic cancer:1.3%

Substance abuse: 6.1%

Heart rate > 100: 2.9%

Respiration rate > 20: 

2.4%

Albumin < 3.5: 4.1%

Definition: death without 

hospitalization within 90 

days or 1 year 

Measurement method: 

VHA Vital Status File

Duration of follow-up: 1 

year

Number of deaths: 

32,147 (0.7%) in 90 

days

120,192 (2.8%) in 1 

year

Predictor 

definition/measurement 

same for deceased/

survivors: Yes

Outcome 

definition/measurement 

same for deceased/

survivors: Yes

Outcome assessed 

independent of predictors 

(eg, blinded): Yes

Incomplete follow-up or 

missing predictor data 

(%, handling of): Yes

Method of validation:

randomly split sample 
Life Expectancy Calculators Evidence-based Synthesis Program

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Author, Year

Country, Region

Study Dates

Source of Dataa Participants Participant Description Mortality Outcome Risk of Bias

Wang, 201211

(VHA heart failure 

population from

2009)

-Predictors from 

VHA electronic 

National Patient 

Care Database & 

Corporate Data 

Warehouse 

including 

demographics, 

diagnoses from 

inpatient & 

outpatient records, 

medications, vital 

signs, laboratory 

tests & healthcare

utilization

VHA Databases

-Death: VHA’s vital 

status file

Inclusion criteria: 

Heart failure diagnosis within 

VHA in year prior to June 1, 

2009, the index date

Exclusion criteria: NR

N=198,640

Age: mean 73 years 

Gender (male): 98%

Race: NR

Predictors (select):

Charlson comorbidities & 

hierarchial condition 

categories

Heart failure: 100%

Renal failure: 25%

Chronic pulmonary 

disease: 31%

Metastatic cancer: 2.7%

Dementia: 8.3%

Respiration rate > 20: 

6.7%

Albumin < 2.5: 1.3%

Heart rate > 85: 16%

Definition: death without 

hospitalization within 30 

days or 1 year 

Measurement method: 

VHA Vital Status File

Duration of follow-up: 1 

year

Deaths: 1,788 (0.9%) in 

30 days, 14,103 (7.1%) 

in 1 year

Predictor 

definition/measurement 

same for deceased/

survivors: Yes

Outcome 

definition/measurement 

same for deceased/

survivors: Yes

Outcome assessed 

independent of predictors 

(eg, blinded): Yes

Incomplete follow-up or 

missing predictor data 

(%, handling of): Yes

Method of validation:

randomly split sample a eg, cohort, clinical trial participants, registry b Lee SJ et al. Development and validation of a prognostic index for 4-year mortality in older adults. JAMA. 2006;295(7):801-808.

BP = blood pressure; EHR = electronic health record; N/A = not applicable; NR = not reported; PCP = primary care provider; VA = Veterans Affairs; VHA = Veterans Health 

Administration
Life Expectancy Calculators Evidence-based Synthesis Program

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Table 2. Model Characteristics and Performance

Author, Year Intended Use 

Predictorsa

Timing of Predictor Assessmentb 

Modelling Method

Method for Selection of 

Predictors for Inclusion

Mortality Risk 

Groups Predictive Performancec

Aggregated Diagnosis Groups ( not VHA)

Austin 201115

(General Adult 

Population)

Risk 

adjustment

Demographic: age, sex

Patient History: diagnoses 

associated with hospital admissions 

(ICD-10) and physician billing claims 

(ICD-9) from past 2 years matched 

with 32 Aggregated Diagnosis 

Groups (ADGs) (requires proprietary 

software license)

Timing: Previous 2 years including 

index date

Logistic regression

Backwards elimination- final 

model age, sex and 28 

ADG’s

None pre-specified Predicted probabilities 

Range 0.00 to 0.90

C-statistic

Validation cohort: 0.92 Age 

> 65: 0.81

Age < 65: 0.82

Calibration

Differences between 

observed and predicted 

mortality < 1% in all 100 

centiles of predicted risk 

except top 3; biggest 

difference 3% 

Calibration plot

Intercept 0.007

Slope 0.996 
Life Expectancy Calculators Evidence-based Synthesis Program

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Author, Year Intended Use 

Predictorsa

Timing of Predictor Assessmentb 

Modelling Method

Method for Selection of 

Predictors for Inclusion

Mortality Risk 

Groups Predictive Performancec

Austin 201114

(General Adult 

Population)

Risk 

adjustment

Demographic: age, sex

Patient History: diagnoses 

associated with hospital admissions 

(ICD-10) and physician billing claims 

(ICD-9) from past 2 years matched 

with 32 ADGs (requires proprietary 

software license)

Timing: Previous 2 years including 

index date

Mortality Risk Score (MRS) 

derived using regression 

coefficients of above final 

model including age and 

sex.

Weighted ADG Score plus 

age and sex

One- rather than 2-year 

look-back 

None pre-specified Predicted probabilities 

Range 0.00 to 0.90

C-statistics

Validation cohort: 

MRS: 0.92 for 1- and 2-year 

look-back period

Rural subgroup: 0.90

ADG: 0.91 for 1- and 2-year 

look-back period

Calibration

Differences between 

observed and predicted 

mortality ~ 1% in 20 

groups of predicted risk 

for both MRS and ADG

Individual predicted 

probability of dying within 

1 year increasingly under 

estimates observed 

mortality as predicted 

probabilities exceed ~0.2

Calibration plots

MRS: Intercept 0.007

Slope 0.996

Rural subgroup: 

Intercept 0.142

Slope 0.960

ADG: Intercept 0.006

Slope 0.996
Life Expectancy Calculators Evidence-based Synthesis Program

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Author, Year Intended Use 

Predictorsa

Timing of Predictor Assessmentb 

Modelling Method

Method for Selection of 

Predictors for Inclusion

Mortality Risk 

Groups Predictive Performancec

Austin, 201213

(Adult 

population 

with schizophrenia)

Canada, 

Ontario

2007-2008

Risk 

adjustment

Application of previously developed 

model for adult general population 

(See Austin 2011a)

Application of previously 

developed model for adult 

general population (see 

Austin 2011a) to 

subpopulation with 

schizophrenia

None pre-specified Predicted probabilities NR 

for adult general 

population model 

C-statistic: 0.84

Calibration plot

Intercept 0.356

Slope 0.0.805

Charlson and/or Elixhauser Comorbidities

Gagne, 20117

(Medicare 

enrollees, 

age > 65)

Pennsylvania, 

New Jersey,

United States

2004-2005

Provide 

comorbidity 

score for risk 

adjustment

Demographic: age, sex

Comorbidities: ICD-9 diagnosis 

codes

Recorded during a baseline year

Jan 1, 2004 to Dec 31, 2004

Logistic regression to 

assign weights for 37 

unique comorbidities; no 

variable selection

Final weighted model 

20 comorbidities with 

nonzero weights plus age, 

sex

Comorbidity scores in 

validation cohort

median: 1 interquartile 

range: 2

range: -2 to 18

zero: 27%

None pre-specified Predicted probabilities; 

individual range NR; 

lower decile ~3% upper 

decile ~55% 

C-statistic

Validation cohort: NJ 

1 year 0.79

30 day 0.86

90 day 0.82

180 day 0.81

Calibration curve under 

predicted by ~ 3% in 5 % 

6th deciles with 20 -25% 

mortality, over predicted 

by ~ 10% in 10th decile 

with ~ 45% mortality, 

otherwise observed and 

predicted mortality % 

within ~1% 
Life Expectancy Calculators Evidence-based Synthesis Program

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Author, Year Intended Use 

Predictorsa

Timing of Predictor Assessmentb 

Modelling Method

Method for Selection of 

Predictors for Inclusion

Mortality Risk 

Groups Predictive Performancec

Quail, 201116

Canada, 

Saskatchewan

(provincial 

health plan)

Fiscal years 

2001-2002

Population riskadjustment 

Demographic: patient: age, gender, 

income, & region

Comorbidities: 17 in weighted 

Charlson score or 31 separate 

Elixhauser comorbidities based on 

diagnoses from baseline year 

(FY2001) from inpatient & outpatient 

records

Logistic regression model 

including age, sex, income, 

region plus Charlson 

comorbidity score or 

individual Elixhauser 

comorbidities fit to the data;

no statistical variable 

selection 

None pre-specified Predicted probabilities: NR

C-statisitc general 

population/age > 65 

years: 

Carlson model: 0.90/0.78

Elixhauser model: 0.91/0.80

Calibration: NR

Prediction error (Brier score 

– mean squared 

difference between 

individual predicted 

probabilities and death 

=1 or survival=0)

Carlson model: 0.01/0.04

Elixhauser model: 0.01/0.04
Life Expectancy Calculators Evidence-based Synthesis Program

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Author, Year Intended Use 

Predictorsa

Timing of Predictor Assessmentb 

Modelling Method

Method for Selection of 

Predictors for Inclusion

Mortality Risk 

Groups Predictive Performancec

Tan 20139

United States

1999-2009

Healthcare 

decisionmaking

(expected to 

live long 

enough to 

benefit from a 

service such as 

cancer 

screening)

Demographic: age, sex

Comorbidities: 31 from Elixhauser, 

17 from Charlson based on Quan 

coding algorithm of ICD-9 codes; 

appearing on 2 or more claims at 

least 30 days apart

Utilization: number of hospital 

admissions, number of outpatient 

visits in previous 12 months (1999)

Variable selection: Series of 

logistic regression models 

with varying combinations 

of predictors; final model 

chosen based on best Cstatistic, Akaike information 

criterion, and percent 

correctly classified

Final model: Age + 31 

individual Elixhauser 

comorbidities stratified by 

sex

Sex-specific Cox 

proportional hazards 

models to generate K-M 

curves using median 

survival time as proxy of life 

expectancy

None prespecified; 

Predicted risk of death 

within 10 years 

categorized as <25%, 

25-49%, 50-74%, and 

>75% by sex

5- and 10-year life 

expectancy often used 

for decisions about 

cancer screening

Predicted 10-year mortality: 

NR 

C-statistic Women/Men

10 years: 0.79/0.77

5 years: 0.78/0.76

1 year: 0.79/0.77

1-, 5-, and 10-year

observed mortality fell 

within quartiles of 

predicted probabilities 

except 1-year observed 

mortality was less than 

predicted for 50% to 

75% & > 75% quartiles 

of risk for both women & 

men 

Positive predictive value 

(observed mortality by 

predicted life expectancy 

(LE using median 

survival time as proxy) 

was similar for women & 

men) 

LE < 10 years: 75% 

LE < 5 years: 69%

LE < 1 year: 48%

Electronic Medical Records
Life Expectancy Calculators Evidence-based Synthesis Program

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Author, Year Intended Use 

Predictorsa

Timing of Predictor Assessmentb 

Modelling Method

Method for Selection of 

Predictors for Inclusion

Mortality Risk 

Groups Predictive Performancec

Mathias, 

201310

Healthcare 

decisionmaking (eg,

expected to live 

long enough to 

benefit from a 

service such as 

cancer 

screening)

Demographic: 11 attributes; age, 

sex, marital status, race/ethnicity, 

socioeconomic status

Comorbidities: 117 attributes based 

on ICD-9 codes, current procedural 

terminology codes or substance use 

statuses; codes extracted from 

encounter diagnoses, past medical 

history, past surgical history, social 

history, and problem list; additional 

26 attributes for counts of 

encounters related to frequent 

exacerbations of conditions or active 

diagnoses

Vital signs: 24 attributes of heart 

rate, SBP, DBP, pulse pressure 

Medications: 664 medication 

attributes classified using VA codes; 

used medication list at index visit or 

medications ordered in year prior; 

added focus on some classes of 

medications

Laboratory: 120 laboratory attributes 

based on 24 tests

Utilization: 50 attributes; discharge 

status, hospital admissions, ED 

visits, home health referrals, 

provider visits

Timing: year prior to index visit 

except home health referrals and 

provider visits included 1-2 years 

prior 

Rotation forest ensembling 

technique with alternating 

decision tree

Correlation Feature 

Selection (CFS) and 

manual review/reduction 

used to reduce number of 

attributes (eliminate low 

face validity, redundant, 

problematic reliability); 

information gain metric

Final model: 24 predictors 

Predicted risk of death 

within 5 years < 50% 

or > 50% 

5-year life expectancy 

often used for 

decisions about 

cancer screening

Predicted risk of 50% 

equivalent to median 

life expectancy of 5 

years

Predicted Mortality

Lowest risk decile 3.6%

Highest risk decile 

92.5%

C-statistic 0.86 (0.85, 0.87)

Sensitivity: 31%

Specificity: 98%

Positive predictive value: 

63%

Negative predictive value: 

92%

Correct predictions: 90%

Calibration

Difference between 

observed and predicted 

mortality <3% across all 

deciles of predicted risk

Data from Other Studies
Life Expectancy Calculators Evidence-based Synthesis Program

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Author, Year Intended Use 

Predictorsa

Timing of Predictor Assessmentb 

Modelling Method

Method for Selection of 

Predictors for Inclusion

Mortality Risk 

Groups Predictive Performancec

Ogata, 201317

Japan; 

Tanushimaru, 

Uka

1999-2009

Predict if 

individual will 

live or die 

within 10 years 

using only 

cardiovascular 

risk factors

Demographic: Age, gender, smoking 

status

Vital signs: SBP

Laboratory: HbA1c, total cholesterol

Supervised statistical 

pattern recognition with a 

minimum distance classifier 

to derive regression 

coefficients for

6 predictors preselected by 

authors

Coefficient for total 

cholesterol is negative; 

opposite of Western cohorts

Live or die within 10 

years 

Predicted to die in 10 years 

in validation samples

Site 1: 36/365 (9.9%)

Site 2: 35/170 (20.6%)

C-statistic for predicting 

survival and death

Site 1: 0.83

Site 2: 0.85

Calibration: difference (in % 

dead) between observed 

and expected 

 Site 1: 3.5%

Site 2: 7.6% 

Veterans Health Administration

Stefos, 20128

US (VHA) 

Fiscal year 

2007-2008

Estimate 

adjusted

mortality 

statistics for VA 

hospital-based 

patient 

populations 

Age, gender, VA priority status, 

marital status, race, insurance (ie,

not insured by public or private 

insurance plan)

Average driving time to 3 VA 

institutions (closest providing 

primary care, closest providing 

secondary/intermediate care, 

closest providing tertiary/specialty 

care)

139 Hierarchical Coexisting 

Conditions (HCCs) 

Membership in a VA Registry 

Program (special emphasis 

programs for specific chronic 

condictions)

Timing of Assessment: VA 

administrative data from FY 2008

Hierarchical generalized 

linear mixed model with 

random effect for hospital 

population

2-stage estimation with 

insignificant (P>.10) 

covariates eliminated after 

first stage; final model 

included 14 demographic 

and 139 morbidity HCC 

measures

None pre-specified Range of predictive 

probabilities 2.5% in 

lowest decile to 94% in 

highest decile 

C-statistic: 0.86

Calibration: 

Observed:Predicted (O/P) - 

number of deaths by risk 

decile

Predicted O/P ratio

2.5% 0.94

13% 1.12

24% 1.12

34% 1.06

45% 0.98

55% 0.95

65% 0.91

75% 0.87

85% 0.87

94% 0.86
Life Expectancy Calculators Evidence-based Synthesis Program

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Author, Year Intended Use 

Predictorsa

Timing of Predictor Assessmentb 

Modelling Method

Method for Selection of 

Predictors for Inclusion

Mortality Risk 

Groups Predictive Performancec

Wang, 201312

(VHA primary 

care 

population

from FY 2011)

Identify highrisk (of 

hospitalization 

or death 

without 

hospitalization) 

primary care 

patients who 

might benefit 

from care 

coordination 

and special 

management 

programs such 

as intensive 

case 

management, 

telehealth, 

home care, 

specialized 

clinics, and 

palliative care

Demographic: age, sex, marital 

status, VHA enrollment priority

Medical conditions: Deyo-Charlson 

index (ICD-9) & hierarchical 

condition classification of diagnoses

Vital signs: blood pressure, heart 

rate, respiratory rate, BMI

Prior year use of VHA health 

services: indicators for categories of 

numbers & types of outpatient visits, 

ER visits, and hospitalizations over 

the past year and past month

Medications dispensed: number of 

refills, 31 types of medications

Laboratory results: Albumin, blood 

urea nitrogen, creatinine, potassium, 

white blood cell count 

Multinomial logistic 

regression with 3 mutually 

exclusive categories: 

hospitalization, death 

without hospitalization, and 

neither event. Separate 

models for 90-day and 1-

year endpoints.

Backwards elimination 

followed by forward 

selection including select 2-

way interactions. 

Final models contained up 

to 190 coefficients 

(numerous categorical 

variables had multiple 

coefficients)

None pre-specified Predicted probabilities 

90-day: lower decile <0.1% 

to upper decile ~4%

1-year: lower decile <0.1% 

to upper decile ~14%

1-year: age > 65 years:

lower 5% <0.1%, upper

5% ~27%

C-statistic

90-day death: 0.86

1 year death: 0.85

1 year death >65 years: 

0.80

Calibration plots (Cox)

90-day death: 

Intercept: -0.016 

Slope: 0.999 

1 year death: 

Intercept: 0.001 

Slope: 1.002 

Small differences between 

observed and predicted in 

each decile of predicted 

probabilities; same if age > 

65 years 

Observed deaths if in upper 

5% of predicted 

probabilities of death 

without hospitalization

90-day: 6.2%

1-year: 19.4%

1-year age >65 years: 

24.6%
Life Expectancy Calculators Evidence-based Synthesis Program

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Author, Year Intended Use 

Predictorsa

Timing of Predictor Assessmentb 

Modelling Method

Method for Selection of 

Predictors for Inclusion

Mortality Risk 

Groups Predictive Performancec

Wang, 201211

(VHA heart 

failure 

population 

from 2009)

Identify highrisk (of 

hospitalization 

or death 

without 

hospitalization) 

patients with 

heart failure

who might 

benefit from 

care 

coordination 

and special 

management 

programs such 

as intensive 

case 

management, 

telehealth, 

home care, 

specialized 

clinics, and 

palliative care

Demographics: age, sex, marital 

status, VHA enrollment priority

Medical conditions: Deyo-Charlson 

index (ICD-9) & hierarchical 

condition classification of diagnoses

Vital signs: blood pressure, heart 

rate, respiratory rate, BMI

Prior year use of VHA health 

services: indicators for categories of 

numbers & types of outpatient visits, 

ER visits, and hospitalizations over 

the past year and past month

Medications dispensed: number of 

refills, 31 types of medications

Laboratory results: Albumin, blood 

urea nitrogen, creatinine, potassium, 

white blood cell count 

Multinomial logistic 

regression with 3 mutually 

exclusive categories: 

hospitalization, death 

without hospitalization, and 

neither event. Separate 

models for 30-day and 1-

year endpoints.

Backwards elimination 

followed by forward 

selection including select 2-

way interactions such as 

age > 65 with medical 

conditions, medications & 

hospitalizations. 

Final models contained up 

to 190 coefficients 

(numerous categorical 

variables had multiple 

coefficients); same 

variables as other Wang 

report but regression 

coefficients and variable 

selection specific to this 

sample

None pre-specified Predicted probabilities 

30-day: lower decile <0.1% 

to upper decile ~4%

1-year: lower decile <0.8% 

to upper decile ~23%

C-statistic

30-day death: 0.80

1 year death: 0.76

 

Calibration plots (Cox)

30-day death: 

Intercept: -044% 

Slope: 1.000 

1 year death: 

Intercept: -0.094 

Slope: 0.96 

Differences between 

observed and predicted 

mortality % in each decile of 

predicted probabilities were 

not substantial

Observed mortality rates if 

in upper 5% of predicted 

probabilities of death 

without hospitalization

30-day: 0.9

1-year: 0.34 a Definition and method of measurement b eg, at patient presentation, at event (retrospective) c Distribution of predicted probabilities, C-statistic, sensitivity/specificity for select cutpoints/risk groups, predicted/observed mortality in different risk groups, calibration slope, 

positive and negative predictive values

ADG = Aggregated Diagnosis Groups (Johns Hopkins); DBP = diastolic blood pressure; SBP = systolic blood pressure