Patient-Centered Care for the Older Adult With Hematologic Malignancy
Phase: NA
Conditions: Hematologic Malignancy
Sponsor: Dana-Farber Cancer Institute

OFFICIAL TITLE: Patient-Centered Care for the Older Adult with Hematologic Malignancy 
NCT NUMBER: NCT02359838 
DOCUMENT DATE: 08.26.2020 

Patient-Centered Care for the Older Adult with Hematologic Malignancy 
Gregory Abel and Jane Ann Driver 
Robert Soiffer, Tammy Hshieh, Richard Stone, Naeem Tahir, Andrew Hantel, and Clark 
DuMontier
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PROTOCOL SCHEMA:
CRC performs initial frailty assessment on all patients over 70 who 
present to DFCI for Hematologic Malignancy
Patients who are Robust
Patients who are Pre-Frail/Frail 
randomized (if consented)
Outcomes assessed at three, six, and twelve months for all; for those who have consented, samples 
provided to laboratories as needed
Half receive oncology plus 
geriatric MD care 
Half receive routine 
oncology care only
All receive routine 
oncology care
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Table of Contents
PROTOCOL SCHEMA…………………………………………………………………………...2
1.0 SUMMARY...............................................................................................................................4
2.0 BACKGROUND/RATIONALE………...................................................................................4
2.1 Overview…………………………………………………………………………..…..4
2.2 Frailty Assessment………….…………………………………………………………5
2.3 Potential Benefits……….……………………………………………………………..6
3.0 OBJECTIVES/STUDY AIMS..................................................................................................6
3.1 Specific Aim 1...............................................................................................................6
3.2 Specific Aim 2………………………………………………………………………...7
4.0 ELIGIBILITY……....................................................................................................................7
5.0 STUDY DESIGN AND PROCEDURES……………………………………………………..7
5.1 Patient Selection and Recruitment…………………………………………………….8
5.2 Registration Procedures……………………………………………………………….9
5.3 Frailty Measures……………………………………………………………………....9
5.4 Data Collection Procedures…………………………………………………………..12
5.5 Laboratory Correlates………………………………………………………………..13
6.0 ANALYTIC CONSIDERATIONS………………………………………………………….14
6.1 Specific Aim 1…………………………………………………………………….…14
6.2 Specific Aim 2……………………………………………………………………….14
6.3 Power Considerations………………………………………………………..………15
6.4 Randomization Procedures.....……………………………………………………….15
7.0 REGULATORY REQUIREMENTS………………………………………….…………….15
7.1 Informed Consent……………………………………………………………………15
7.2 Patient Confidentiality………………………………………….……………………16
7.3 Potential Benefits of the Proposed Research………………………………………...16
7.4 Importance of the Knowledge to be Gained…………………………………………16
8.0 PROJECT TIMELINE……………………………………………………………………….17
9.0 REFERENCES………………………………………………………………………………18
Appendix A: Frailty Scoring Values…………………………………………………………..…21
Appendix B: Patient Questionnaire……………………………………………………………...24
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1.0
SUMMARY
We are interested in measuring the frailty of older blood cancer patients with precision, 
determining how much it contributes to prognosis compared with clinical disease-specific 
measures, and determining if co-management by a geriatrician embedded in the oncology clinic 
can improve outcomes. We thus propose to characterize the prognostic value of frailty as 
compared to clinical prognostic models for older patients with blood cancers, and, using a 
randomized design, determine if co-management of frail older adults with blood cancers by an 
embedded geriatrician reduces frailty and/or improves outcomes such as survival and non-
scheduled hospital admissions.
2.0
BACKGROUND/RATIONALE
2.1  Overview 
The Institute of Medicine (IOM) defines patient-centered treatment as “care that is 
respectful of and responsive to individual patient preferences, needs, and values.”1 Given the 
explosion in diagnostic and treatment options and the aging of the US population, this focus is 
arguably nowhere more important than in cancer care for the elderly. In their recent 
report, “Delivering High-Quality Cancer Care: Charting a New Course for a System in Crisis,” 
the IOM made recommendations for older cancer patients, whose population is poised to double 
in less than two decades.2 The report describes the complexity of caring for older adults with 
cancer, as they have multiple chronic conditions, decreased cognition, may need assistance in 
activities of daily living, and may be more vulnerable to treatment side effects. The biology of 
their cancers is also often different, as it may result from—or interact with—age-related somatic 
mutations not seen in younger patients. Recommendations for this coming “crisis” included 
using “evidence-based care parameters” as the basis for medical decisions. This seems a tall 
order, given that there are sparse data to inform care for this population.
Aging is the result of the accumulation of molecular and cellular damage over time, 
eventually leading to a loss of this physiological reserve across many organ systems. In turn, 
frailty describes a state in which physiologic reserve is depleted to the point that even small 
stressors can result in poor outcomes, including delirium, falls, disability and death.3 Markers of 
frailty are very common in older patients with hematologic malignancy—over half have 
evidence of malnutrition, and over a third have impaired physical function.4 Increased frailty in 
this population has been associated with increased chemotherapy-related toxicity, poor response 
to therapy, inability to complete planned course of therapy, and mortality.5 Accordingly, the 
National Comprehensive Cancer Network recommends the incorporation of frailty assessment 
into the routine care of older cancer patients.6
Frailty is a dynamic process that can worsen or improve over time. For example, elderly 
cancer patients who undergo inpatient geriatric assessment and management have been shown to 
become less frail, more likely to return home, and less likely to have cognitive or functional 
decline.7 Inpatient geriatric co-management has also been shown to substantially improve mood, 
social function and pain management post-discharge.8 A systematic review of geriatric 
evaluation and management of older adults with blood cancers found that non- oncologic 
interventions are implemented by geriatricians in over 70% of patients, most often targeting 
nutrition, mood, physical function, polypharmacy, and social function.4 
While there are limited studies regarding geriatric co-management in the outpatient 
hematology setting, and no randomized controlled trials to determine the best model of 
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delivering such care, observational research is instructive. Elderly AML is an excellent example: 
our own work has shown that markers of frailty are independent prognostic factors for death in 
models that included cytogenetic risk group, even for those patients with good baseline 
performance status (ECOG 0-1).9 Given this context, we are interested in measuring the frailty of 
older adults with blood cancers with precision, determining how much it contributes to prognosis 
compared with clinical disease-specific measures and molecular markers, and determining if co-
management by a geriatrician embedded in the oncology clinic can decrease frailty and improve 
disease-related outcomes.
Indeed, we have begun to see the increased burden of caring for elderly patients in 
hematologic oncology at Dana-Farber. During the period March 2013 to February 2014, for 
patients 75 and older, we had 3429 clinical visits in lymphoma (674 unique patients), 1704 for 
leukemia/related disorders (318 unique patients), and 3,245 for multiple myeloma (516 unique 
patients). Our older patient volume reflects an outstanding opportunity to both improve our care 
delivery and research the impact of frailty on disease outcomes. We wish to launch a research 
program to engage these patients in formal geriatric assessment and management, with the aim 
of addressing this important clinical and data gap.
2.2  Frailty Assessment
There are many frailty models, but two of the most commonly used models are the 
phenotype model and the cumulative deficit model.10 The phenotype model is a categorical 
model that uses 5 separate variables to identify frailty. The variables are interrelated and are 
indicative of decreased physiologic reserve and resistance to stressors, two conditions that 
constitute the clinical definition of frailty.3 The phenotype model is also popular because of its 
clinical reproducibility.11 The cumulative deficit model is a continuous model of frailty that uses 
assessment of a large index of symptoms to determine frailty status,10,12 with a higher number of 
symptoms leading to a greater likelihood of being frail.10,13 The cumulative deficit model is 
popular because it can evaluate impairments in many biological systems, is graded, and is 
conceptually simple.13
Rockwood and Mitnitski created a standard cumulative deficit approach with their 
Comprehensive Geriatric Assessment (CGA), with approximately 40 variables, some of which 
are self-reported (e.g. “how would you rate your health?”), others are ascertained by tests such as 
the Montreal Cognitive Assessment (MoCA)14, and others are determined by clinical evaluation 
(e.g. high blood pressure).15 The efficacy of this model was examined by Searle et al. in a 2008 
study, which analyzed a group of 754 community dwelling elderly people who were assessed 
and then longitudinally followed for outcomes. 12 The study found that the CGA was a 
significant predictor of mortality. 
Sheppard et al. examined a cohort of 1,288 older women with breast cancer in order to 
validate use of this frailty index in a cancer population, aiming to assess its utility in predicting 
non-initiation or discontinuation of adjuvant hormonal therapy.16 The investigators used a 35-
item index adapted from the Searle approach. The index included self-reported items relating to 
limitations in basic and instrumental activities of daily living, sensory deficits, functioning, and 
the pre-diagnosis of comorbidity. They did not include measures of cognitive impairment, 
because women with cognitive impairment were excluded. 
After calculating frailty scores on the 0-1 scale, cut-points of 0-0.2 for “robust”, 0.2-0.35 
for “pre-frail”, and 0.35-1.0 for “Frail” were used. The study found that both frailty and pre-
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frailty were significantly related to non-initiation of therapy. Although a trend was cited for 
frailty or pre-frailty and discontinuation of therapy, after considering covariates, no significant 
relationship was demonstrated. These data suggest that the frailest women never initiated 
therapy, which may explain why a significant relationship between frailty and discontinuation 
was not observed.16 Sheppard concluded that although the results for frailty were suggestive, 
they should be confirmed in other studies of cancer populations. We will aim to apply this 
cumulative deficit assessment technique to DFCI’s older hematologic oncology population.
The phenotype approach takes into account 5 factors that indicate frailty, including 
unintentional weight loss, self-reported exhaustion, low energy expenditure, slow gait speed, and 
weak grip strength. After evaluation, people with 3 or more of the factors are categorized as 
“frail”, people with 1 or 2 factors are “pre-frail” and people with no factors are “robust”.10 In a 
longitudinal study of community dwelling men and women aged 65 and older, Fried and 
associates found that people who were initially categorized as frail based on the phenotype 
model had more adverse outcomes at 3 and 5 year follow ups compared to people who were 
categorized as robust. Those who were found to be pre-frail at baseline exhibited outcome 
measures that fell intermediate between the other two groups.3 Our study will look to expand 
upon these results and validate the phenotype approach, in addition to the cumulative deficit 
approach above.
2.3  Potential Benefits
The clinical and research program we propose will involve formal geriatric assessment 
and management of older patients with hematologic malignancies. This will likely be of direct 
benefit to patients as geriatric assessment has been shown to improve functional status, decrease 
hospitalization, and even lengthen life in non-cancer populations. In addition, by providing these 
services in the setting of a randomized controlled trial, we will be able to assess the efficacy of 
geriatric co-management and determine which components are most valuable to improving 
patient outcomes. Our study will help to further validate frailty assessment as a tool for the 
evaluation of geriatric cancer patients. Finally, this project will generate a comprehensive 
database of clinical and disease-specific factors that will allow us to build models to predict 
prognosis and toxicity and provide pathologic samples of older patients with hematologic 
cancers for study in the laboratory. These data in turn will ultimately be used to individualize 
cancer therapy for older patients with hematologic cancers.
3.0
OBJECTIVES/STUDY AIMS
The goal of this study is to establish a research program – the Older Adult Hematologic 
Malignancy (OHM) program – that will address important gaps in data about how best to care 
for older adults with hematologic malignancies. It is clear that markers of frailty are important 
predictors of tolerance of therapy and survival, but few studies have assessed their value in this 
population. Geriatric assessment and co-management are associated with improvements in 
quality of life, resource utilization and survival, but have not been tested scientifically in 
hematologic malignancies. We therefore propose the following specific aims:
3.1  Specific Aim 1
Characterize the prognostic value of frailty as compared to clinical prognostic 
models for older patients with blood cancers. We will perform a combined frailty 
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assessment in patients 70 and older who present to DFCI with a new diagnosis of MDS or 
a hematologic malignancy. Patients will be categorized as robust, pre-frail or frail based 
on their frailty score. We will then determine how the presence or absence of frailty 
predicts important outcomes such as treatment, resource utilization and mortality. In a 
subset of patient who specifically agree, frailty will be re-assessed at subsequent visits.
Hypothesis: Frailty will predict survival for those older patients with low-risk disease on 
molecular markers/disease-based risk systems, but be trumped by these for patients with 
high-risk disease.
3.2  Specific Aim 2
Determine if co-management of frail and pre-frail older adults with blood cancers 
by an embedded geriatrician improves outcomes. Patients who score as pre-frail or 
frail will be randomized to geriatric assessment and co-management with a geriatrician or 
usual care. They will be followed for disease-specific outcomes, with the primary 
outcome being overall survival at 1 year.
Hypothesis: For those assessed to be frail with rigorous measurement, co-management 
by an embedded geriatrician will improve overall survival at one year.
4.0
ELIGIBILITY
All patients aged 70 and older who present for an initial consultation at the DFCI for 
MDS or a hematologic malignancy (transplantation consultation excluded) are eligible. Note that 
until September 2020, before virtual assessments, only patients aged 75 and older were eligible 
due to resource limitations; this has been changed to include patients aged 70 and older because 
we can now accommodate more patient assessments virtually. For Specific Aim 2, insurance 
information will be reviewed to ensure geriatric referral is covered for all potential participants. 
This step will allow us to avoid a situation arising wherein a participant is randomized to 
geriatric co-management, yet their insurance does not cover geriatrician visits. As baseline 
geriatric assessment by our research assistant and later randomization to co-management by a 
geriatrician are currently precious resources, in practicality, to be eligible, patients will have to 
present on one of the three days per week that geriatric assessments are available, and be willing 
to be seen for geriatric co-management on one of the two geriatrician clinic days. 
5.0
STUDY DESIGN and PROCEDURES
Due to challenges presented by the ongoing COVID-19 pandemic and the specific 
vulnerability of the population we intend to study, we recognize the need to preserve the safety 
and comfort of the participating patients. For this reason, we propose an updated study design 
that allows the completion of remote, in-home frailty assessments that minimize exposure risk. 
This adapted workflow is illustrated below and described in the following sections.
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5.1  Patient Selection and Recruitment
All eligible patients will be given an in-person or virtual appointment with our clinical 
research coordinator before their first meeting with their hematologist to discuss the study; the 
type of appointment will depend on availability during the COVID-19 pandemic. This CRC will 
have been trained in frailty assessment by Dr. Driver and will be responsible for obtaining 
informed consent and completing the baseline frailty assessment for patients who consent. 
Importantly, patients will be able to consent to the baseline assessment, a potential follow-up 
assessment at a future visit, and/or allowing collection of extra samples for the laboratory. 
Categorical frailty status (robust versus frail/pre-frail) will be emailed to clinicians. 
For Specific Aim 2 (now closed to enrollment), patients will be allowed to see a 
geriatrician outside of the trial design if they ask for this or if their physician requests it. We 
acknowledge that this may somewhat affect our randomized design, however, we feel that it is 
important to offer this service. Frail or pre-frail patients who were not randomized to geriatric 
co-management but who see a geriatrician anyway will first be analyzed in an intent-to-treat 
manner and then excluded from a secondary analysis to assess if this event affects overall 
findings. 
As above, frail, pre-frail and robust patients will undergo additional prospective 
evaluation to assess for study outcomes: for Specific Aim 1 we will compare frail and pre-frail 
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patients who did not see a geriatrician to robust patients, and for Specific Aim 2, we will 
compare frail and pre-frail patients who saw a geriatrician to those who did not (see schema).
For all patients who go on to see a geriatrician, whether inside or outside of the trial 
design, continued follow-up (i.e. past the initial comprehensive geriatric assessment) will be 
determined by discussion between the geriatrician, the patient, and the patient’s DFCI 
oncologist. As long as this resource remains available, patients will be able to follow-up with our 
geriatricians as long as they continue to receive their care at DFCI.
5.2  Registration Procedures
5.2.1  General Guidelines for DF/HCC and DF/PCC Institutions
The 166 patients who participated in the randomized control trial (Aim 1) were all 
entered into the Clinical Trials Management System (CTMS) OnCore per DF/HCC guidance. As 
of September 1, 2020, all those who participate in the observational arm (Aim 2) will be also be 
prospectively registered in OnCore as per DF/HCC Policy REGIST-101.
5.3  Frailty Measures
We will use a combination of validated frailty assessment tools which are listed in 
Appendix A and summarized below in Tables 1 and 2. Using both the phenotype and cumulative 
deficit models (see above) is beneficial to provide more validity for a frailty assessment, and 
cross validity between the two models has been established.11,17 At current, there is still some 
debate as to the true clinical definition of frailty, and different studies have used different 
approaches to assess the frailty.11 Using both models will allow for a more rigorous evaluation.
Table 1: Summary of cumulative deficit domains
Domain
In-person and 
Virtual 
Administration 
Variable type
Cut-point
Functional Status in 
ADLs12,18
Patient questionnaire 
#1-1412,19,20
Binary
See Appendix A
Overall self 
assessment of health 
and 
performance 
Status12,21
Patient Questionnaire 
#15-20, verified by 
physician report
Continuous
See Appendix A
*Weight loss3,11,12
Patient questionnaire 
#21
Binary
See Appendix A
*Self-reported 
exhaustion3,11,12
Patient questionnaire 
#24-25
Continuous
See Appendix A
Psychological 
Status12,18
Patient questionnaire 
#26-28
Continuous
See Appendix A
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Comorbidity12,18-20
Medical Record 
Review
Continuous
See Appendix A
*Physical Function 
and BMI3,11,12
Grip strength, gait 
speed, and BMI 
recorded by research 
assistant
continuous
See Appendix A
Cognition18
Clock in the Box22 (in-
person) or Clock Draw 
(virtual) and MoCA14 
delayed recall 
administered by 
research assistant
continuous12
See Appendix A
*Also included in phenotype assessment
Table 2: Summary of phenotype questions 
Domain:
Measure:
In-person and 
Virtual 
Administration:
Results:
Weight Loss
In last year, weight has 
decreased by greater 
than or equal to 10 lbs 
or 5% of body 
weight3,11
Patient questionnaire 
#21 and medical 
record review by 
research assistant
Positive: Patient has lost 
10 lbs or more in last year 
3,11
Negative: Patient has not 
lost 10 lbs or more in last 
year3,11
Self-reported 
Exhaustion
Two questions from 
the CES-D Scale23 
about exhaustion
Patient questionnaire 
#24-25
Positive: Patient answers: 
“most of the time” for 
either question
Negative: Patient answers: 
“some of the time” or 
“rarely” for both 
questions3
Energy 
Expenditure
Ability to walk11
Patient questionnaire 
#4 and ability to 
complete gait speed 
test at time of 
assessment
Positive: Needing 
assistance with walking or 
being unable to walk
Negative: No assistance 
needed for walking11
Gait speed
NIH 4 meter gait 
speed test24,25
Administered by 
research assistant or 
trained caregiver
Positive: Rapid gait speed 
less than or equal to cut-
points provided by Gill et 
al.26
Negative: Rapid gait 
speed greater than cut-
points provided by Gill et 
al.26
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Grip strength
Jamar Hand 
Dynamometer 
(Sammons Preston 
Roylan, Bolingbrook, 
IL) grip strength test26
Administered by 
research assistant or 
estimated by the 
following question: 
Do you have 
difficulty gripping 
with your hands 
(e.g. opening a jar)? 
Yes or No.  
Positive: Average strength 
less than or equal to the 
cut-points provided by 
Fried et al.,3 or self-
reported difficulty gripping  
Negative: Average strength 
greater than the cut-points 
provided by Fried et al.,3 or 
no self-reported difficulty 
gripping  
We will calculate two scores for frailty; one based on the cumulative deficit model and the other 
on the phenotype model. If patient’s score as frail or pre-frail by either method they will be 
eligible for evaluation by a geriatrician. 
For the Cumulative deficit score (table 1), the cumulative total of deficits is determined 
by adding all of the deficits that are present (deficit present = 1, deficit absent = 0, and for some 
variables, partial deficit = 0.25, 0.5, or 0.75) and dividing the result by the total number of items 
in the index. This study will use a frailty index comprising of 43 items (see appendix A). Of 
note, up to 92 symptoms can be assessed, although as few as 30 items can be used without loss 
of predictive validity.18,20 Furthermore, the actual composition of the index is not crucial. As 
long as a sufficient number of variables are used, the variables can be selected at random and still 
yield comparable results.27 After calculating a total frailty score on the 0-1 scale, cut-points of 0-
0.2 for “robust”, 0.2-0.35 for “pre-frail”, and 0.35-1.0 for “frail” will be used to categorize 
patients, as was done in the Sheppard study.16 
For the Phenotype score (table 2), the 5 phenotype variables will be assessed by obtaining 
information both from the patient questionnaire and from physical tests administered by the 
research assistant or patient caregiver. After evaluation, people positive for 3 or more of the 
factors are categorized as “frail”, people with 1 or 2 factors are “pre-frail” and people with no 
factors are “robust”.10 Of note, all 5 of the phenotype measures are also included in the 
cumulative deficit assessment. 
 
5.4  Data Collection Procedures
Patients will be approached or contacted by the research assistant either at their scheduled 
appointment with the DFCI oncologist (in-person) or shortly after that appointment (virtual). The 
research assistant will then consent the patients and document consent with either the patient’s 
wet-ink signature (in-person) or the signature of the research assistant verifying that verbal 
consent was given (virtual). The assessment will be conducted in-person or remotely, by phone 
or video conference, to preserve the safety of our patient population during the ongoing COVID-
19 pandemic. 
In-person assessments will begin with a 28-item self-report questionnaire (see appendix 
B) filled out by the patient. Next, the research assistant will administer the delayed recall section 
of the Montreal Cognitive Assessment (MoCA), clock-in-the-box test, grip strength test, and gait 
speed test. That will end the in-person frailty assessment. Virtual assessments contain minor 
changes to accommodate remote completion. The 28-item questionnaire, MoCA recall, and 
modified Clock Draw are conducted by the research assistant during a telehealth appointment. A 
patient caregiver will be trained to administer a remote gait speed test with a 4-meter strip of 
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ribbon that will be mailed in advance. Grip strength will be estimated using the following 
question: “Do you have difficulty gripping with your hands (e.g. opening a jar)? Yes or No.”    
At baseline, the research assistant will perform a medical record review to obtain 
comorbidity information and body mass index. A follow up medical record review will be 
completed at one year from enrollment in order to obtain the outcome data listed in Table 3, 
which are all secondary outcomes of interest. There will also be general periodic assessments for 
overall survival.
Table 3: Summary of outcomes 
Outcome:
Timeline for 
assessment:
Operationalization
Obtained from:
Survival*
6 months, 1 year
Overall survival
Patient medical 
record and outside 
records
Disease severity
Baseline
Disease Severity
Patient medical 
record and outside 
records
Treatment 
assignment
6 months
Supportive care only vs. 
chemotherapy vs. low dose 
chemotherapy vs. 
investigational treatment; 
any disease-modifying 
treatment versus not
Patient medical 
record and  outside 
records
Hospitalizations
6 months
Number and length of 
hospitalizations since 
enrollment
Patient medical 
record  and outside 
records
Emergency 
department visits
6 months
Number of ED visits
Patient medical 
record and outside 
records
Code status 
documentation
6 months
Code status entered into the 
medical record
Patient medical 
record
Continuation of 
care at DFCI
6 months
Number of visits and length 
of follow-up at DFCI; 
receipt of treatment at 
DFCI
Patient medical 
record
Δ Number of 
medications
Baseline to 3 
months
Overall number of 
medications = number of 
cancer meds + number of 
non cancer meds
Patient medical 
record
Δ Blood pressure
Baseline,6 months
Measured at each visit
Patient medical 
record
Content Analysis of 
Notes
6 months
Only patients randomized 
to geriatrician
Patient medical 
record
*primary outcome
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5.5  Laboratory Correlates
We have engaged Dr. Ebert, Dr. Lane, Dr. Ritz and Dr. Wu as active members of the 
OHM team. We are hopeful that our new infrastructure for the rapid identification and provision 
of samples to their labs (one of the CRC duties, see below) will result in acceleration of current 
efforts at characterizing hematopoietic events specific to the elderly with blood cancers. Each 
week, we will provide them with de-identified list of patients -- gender, age and malignancy type 
-- who have consented to extra samples and are coming in for blood draw and/or marrow.  These 
samples will serve as a source of controls for ongoing studies focusing on how age-related 
changes in hematopoietic progenitor subsets, changes in the bone marrow, and in circulating 
levels of cytokines observed in healthy older individuals may evolve into frank cancer. They will 
also serve as a source of overt cancer samples to compare to candidate markers from the new 
pre-cursor clinic, which is expected to contain many elderly patients. 
More specifically, we have identified a potential biomarker of interest, p16INK4a, a tumor 
suppressor protein in the peripheral blood as an objective measure to elucidate the association 
between frailty and disease genetics.28 Patients who have consented to providing additional 
samples from blood tests and return to Dana-Farber will have their p16INK4a levels measured. The 
samples will be collected from DFCI Lab Services and then processed by personnel in the Lane 
lab. All forms of patient identification associated with the sample will be removed and coded 
only with the corresponding OHM patient identification number. If the patient’s frailty 
assessment is more than two weeks old, we will also approach the patient to conduct a second 
assessment. 
We will measure p16INK4a expression from peripheral blood T-cells isolated by magnetic 
bead separation, where CD3+ and CD3- fractions will be isolated, cell pellets flash frozen, and 
additional aliquots viably cryopreserved in 10% DMSO. Total RNA will be isolated from CD3+ 
T cell pellets and p16INK4a expression will be measured using a validated Nanostring assay 
developed by the Burd lab at Ohio State University. As the characterization of p16INK4a is only 
available at OSU, we will be procuring the services of Dr. Burd’s lab as an external resource to 
analyze p16INK4a levels in our samples. To facilitate preliminary analyses and to be able to 
combine with existing p16 INK4a normative data at OSU, Dr. Burd’s lab will be sent limited 
clinical data for each sample: age, gender, malignancy, frailty status, and ECOG score. Of note, 
these data will only be linked to participants via study ID; Dr. Burd’s lab will not be sent 
participants’ names, medical record numbers, dates of birth, or any other identifying data. Both 
the study data and the resulting analyses will be sent electronically as secure and encrypted files.  
Additionally, stored cell pellets and cryopreserved cells will be used for orthogonal 
validation experiments in the Lane lab, including additional sorting of viable cells (e.g., if other 
cell types, or subpopulations within CD3+ T cells are suggested to be relevant based on our pilot 
studies or others’ data). Individual patient samples in the Lane lab will be destroyed one year 
after enrollment of the last participant of the study. 
The integration of molecular diagnostics could lead to a new paradigm at the population 
level. These data may allow some (robust) older patients with blood cancers appropriately 
receive full-dose treatment despite their advanced age, and others (frail) are protected from 
unnecessary morbidity arising from the same. It also addresses issues relevant to the unequal 
burden of cancer and outcomes in diverse populations, as the frail elderly have both a higher 
incidence of cancer and also experience generally less favorable outcomes. 
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6.0
ANALYTIC CONSIDERATIONS
6.1  Specific Aim 1
We will first perform descriptive analyses of covariates of frailty/pre-frailty in our 
population, including age, gender, race-ethnicity, and disease type. Means and proportions will 
be used to describe patient characteristics, individual and summary frailty responses, baseline 
clinical data, and treatment assignment. We will identify variables that are associated with the 
outcomes of interest using Kaplan-Meier curves and log-rank tests. Given limited numbers of 
patients and other study resources, we will plan to combine the frail and pre-frail groups in our 
analyses for Aim 1 (frail/pre-frail versus robust), but if power allows, we will also compare 
outcomes for all three groups (frail versus pre-frail versus robust).We will use the tests above for 
univariate analyses to determine whether any of the relevant geriatric domains, socio-
demographic factors, or clinical data are associated with choice of treatment, length of time in 
the hospital and survival. We will also perform univariable and multivariable analyses to 
determine if frailty interacts with other patient characteristics to influence outcomes. We will 
also conduct analyses specific to disease clinics at DFCI (MDS/leukemia versus lymphoma 
versus myeloma).  We will consider a p value < 0.05 to be significant, and all analyses will be 
carried out using SAS v.9.
6.2  Specific Aim 2
For frail patients, pre-frail patients and the combined cohort, we will create multivariable models 
to assess the impact of geriatric co-management on the outcomes listed in Table 3, controlling 
for age, gender, race-ethnicity, and disease type. We will consider a p value < 0.05 to be 
significant, and all analyses will be carried out using SAS v.9. Of note, we again plan to combine 
the frail and pre-frail groups in our analysis given limited resources and lack of current data as to 
whether or not geriatric intervention is more helpful in one of these groups; if power allows (see 
below), we will also adjust for frail versus pre-frail status.
6.3  Power Considerations
Power analysis is based on Specific Aim 2, as the patients enrolled therein will be a 
subset of the patients from Specific Aim 1. The study is powered to detect a difference in the 
one-year overall survival rate (i.e., the primary endpoint) between the two groups in Specific 
Aim 2: those patients who see a geriatrician (Group 2) and those who do not (Group 1). Based on 
prior observational data, overall survival in Group 1 is expected to be 68% at one year (this is 
based on the one-year OS of a sample of 114 MDS patients followed at DFCI from 2006 to 
2012). We expect the one-year overall survival rate in Group 2 (the intervention group) to be 
85%, which corresponds a 25% of improvement over Group 1. A total sample size of 160 (107 
Group 1; 53 Group 2) would achieve 80% power to detect this difference, at 0.05 one-sided type 
I error level.
Indeed, the number of patients seen in the OHM program will be limited by the resources 
of the CRC and the geriatricians. In one year, given current numbers, we would expect 600 
unique myeloma, lymphoma and AML/MDS patients over aged 75 (see above) to present at 
DFCI. We will be aiming to undertake baseline assessments for all new patients appearing on 
four calendar days each week.  We originally hoped to have 540 potential patients for Specific 
Aim 1.  As of December 18, 2017, 494 patients out of the 570 patients approached (87%) have 
agreed to Specific Aim 1.  Given our line of success with enrolling patients since February 3, 
2015, and after discussions with our clinicians, we hope to recruit three times the number of 
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patients originally planned for this aim in order to conduct disease-specific analyses.  We hope to 
have 1,620 potential patients for Specific Aim 1 so we may stratify analyses by our three 
disease-specific clinics at DFCI (MDS/leukemia, lymphoma, and myeloma).
If 216 (60%) of the 540 patients, originally planned for accrual, are ultimately assessed 
by the CRC, and 25% found to be frail or pre-frail  continue to receive care at DFCI, and agree to 
the RCT, this leaves 54 to be randomized to geriatric co-management vs. routine oncology care 
(27 in each group) per year. Our hope is to reach a sample size of 160 patients (107 Group 1; 53 
Group 2) for Specific Aim 2 in three years, but we will extend the study until we achieve this 
enrollment. Of note, we have expanded our geriatrician clinics to two sessions per week to 
accommodate this increased patient load.
6.4  Randomization Procedures
Randomization will be stratified by general disease type (myeloma versus lymphoma 
versus MDS/AML). Specific procedures will include permutation block randomization in blocks 
of four and six to avoid predictability of intervention groups.  
7.0
REGULATORY REQUIREMENTS
7.1  Informed Consent
Informed consent will be required for all patients in the study. Patients will be able to 
consent to the baseline assessment, a potential follow-up assessment at a future visit, and/or 
allowing collection of extra samples for the laboratory. 
 
7.2  Patient Confidentiality
Subjects will be identified by their initials and study ID number only. All sensitive 
electronic information will be kept in a database on a secure server and will be accessed via a 
Partners desktop computer within DFCI or an encrypted and password protected Partners laptop. 
Data transfer will occur via encrypted thumb drive. In addition, the computers to be used in this 
project are stored in secure offices and are accessible only to authorized personnel. All sensitive 
paper information will be locked in a file cabinet in the principal investigator’s office.  All study 
personnel will undergo data security and confidentiality training. All data will be destroyed one 
year after relevant publications.
 
7.3  Potential Benefits of the Proposed Research
Patients may or may not benefit from this study. However, the benefit to society in this 
study is substantial. Discoveries made from this data will be useful to scientists, practicing 
physicians and individual patients. Specifically, as geriatric assessment and management have 
been shown to improve functional status, decrease hospitalization, and even lengthen life in non-
cancer settings, we hope that our work will eventually create a more accurate prognostic model 
and patient-centered approach for older patients with blood cancers. The data we generate from 
our analysis will serve as pilot data for larger and grantable projects. In addition, we are hopeful 
that the creation of an infrastructure for the rapid identification and dissemination of samples to 
our wet lab collaborators will result in improving ongoing efforts at defining the important 
molecular mechanisms of hematologic cancers in the elderly, a vital future source of patient-
centered care.
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7.4  Importance of the Knowledge to be Gained
This study will generate valuable information about the treatment and outcomes of older 
patients with hematologic malignancies. This in turn will lead to improvements in the assessment 
and clinical care of these patients. The risk-benefit ratio is highly favorable given the potentially 
large societal benefits and essentially negligible risk to the participants.
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8.0 
PROJECT TIMELINE
January 2015
Obtain OHRS approval
February 2015
Begin baseline geriatric assessments and randomization
August 2015
Interim analysis for Specific Aim 1
May 2018
Finish enrollment for Specific Aim 2 
June 2018 to August 2019
Continuing enrolling participants to Specific Aim 1; Interim 
analyses for Specific Aim 1; Analysis and abstract write-up for 
Specific Aim 2
September 2019 to 
January 2020
Final manuscript for Specific Aim 2
February 2020 to January 
2024
Continuing enrolling participants to Specific Aim 1; Interim 
analyses for Specific Aim 1; Continue follow-up for outcomes. 
January 2024 to January 
2025
Specific Aim 1 analysis; Final manuscript for Specific Aim 1
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9.0
REFERENCES
1.
Institute of Medicine Committee on Quality of Health Care in A. Crossing the Quality 
Chasm: A New Health System for the 21st Century. Washington (DC): National Academies 
Press (US)
Copyright 2001 by the National Academy of Sciences. All rights reserved.; 2001.
2.
Delivering High-Quality Cancer Care: Charting a New Course for a System in Crisis: 
The National Academies Press; 2013.
3.
Fried LP, Tangen CM, Walston J, et al. Frailty in older adults: evidence for a phenotype. 
The journals of gerontology Series A, Biological sciences and medical sciences 2001;56:M146-
56.
4.
Hamaker ME, Prins MC, Stauder R. The relevance of a geriatric assessment for elderly 
patients with a haematological malignancy - a systematic review. Leukemia research 
2014;38:275-83.
5.
Tucci A, Ferrari S, Bottelli C, Borlenghi E, Drera M, Rossi G. A comprehensive geriatric 
assessment is more effective than clinical judgment to identify elderly diffuse large cell 
lymphoma patients who benefit from aggressive therapy. Cancer 2009;115:4547-53.
6.
Hurria A, Browner IS, Cohen HJ, et al. Senior adult oncology. Journal of the National 
Comprehensive Cancer Network : JNCCN 2012;10:162-209.
7.
Ellis G, Whitehead MA, Robinson D, O'Neill D, Langhorne P. Comprehensive geriatric 
assessment for older adults admitted to hospital: meta-analysis of randomised controlled trials. 
BMJ 2011;343:d6553.
8.
Rao AV, Hsieh F, Feussner JR, Cohen HJ. Geriatric evaluation and management units in 
the care of the frail elderly cancer patient. The journals of gerontology Series A, Biological 
sciences and medical sciences 2005;60:798-803.
9.
Sherman AE, Motyckova G, Fega KR, et al. Geriatric assessment in older patients with 
acute myeloid leukemia: a retrospective study of associated treatment and outcomes. Leukemia 
research 2013;37:998-1003.
10.
Clegg A, Young J, Iliffe S, Rikkert MO, Rockwood K. Frailty in elderly people. Lancet 
2013;381:752-62.
11.
Rockwood K, Andrew M, Mitnitski A. A Comparison of Two Approaches to Measuring 
Frailty in Elderly People. The Journals of Gerontology Series A: Biological Sciences and 
Medical Sciences 2007;62:738-43.
12.
Searle SD, Mitnitski A, Gahbauer EA, Gill TM, Rockwood K. A standard procedure for 
creating a frailty index. BMC geriatrics 2008;8:24.
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13.
Rockwood K, Mitnitski A. Frailty in relation to the accumulation of deficits. The journals 
of gerontology Series A, Biological sciences and medical sciences 2007;62:722-7.
14.
Nasreddine ZS, Phillips NA, Bedirian V, et al. The Montreal Cognitive Assessment, 
MoCA: a brief screening tool for mild cognitive impairment. Journal of the American Geriatrics 
Society 2005;53:695-9.
15.
Rockwood K, Silvius JL, Fox RA. Comprehensive geriatric assessment. Helping your 
elderly patients maintain functional well-being. Postgraduate medicine 1998;103:247-9, 54-8, 
64.
16.
Sheppard VB, Faul LA, Luta G, et al. Frailty and Adherence to Adjuvant Hormonal 
Therapy in Older Women With Breast Cancer: CALGB Protocol 369901. Journal of clinical 
oncology : official journal of the American Society of Clinical Oncology 2014;32:2318-27.
17.
Cigolle CT, Ofstedal MB, Tian Z, Blaum CS. Comparing models of frailty: the Health 
and Retirement Study. Journal of the American Geriatrics Society 2009;57:830-9.
18.
Rockwood K, Song X, MacKnight C, et al. A global clinical measure of fitness and 
frailty in elderly people. CMAJ : Canadian Medical Association journal = journal de 
l'Association medicale canadienne 2005;173:489-95.
19.
Fillenbaum GG, Smyer MA. The development, validity, and reliability of the OARS 
multidimensional functional assessment questionnaire. Journal of gerontology 1981;36:428-34.
20.
Song X, Mitnitski A, Rockwood K. Prevalence and 10-year outcomes of frailty in older 
adults in relation to deficit accumulation. Journal of the American Geriatrics Society 
2010;58:681-7.
21.
Oken MM, Creech RH, Tormey DC, et al. Toxicity and response criteria of the Eastern 
Cooperative Oncology Group. American journal of clinical oncology 1982;5:649-55.
22.
Chester JG, Grande LJ, Milberg WP, McGlinchey RE, Lipsitz LA, Rudolph JL. 
Cognitive screening in community-dwelling elders: performance on the clock-in-the-box. The 
American journal of medicine 2011;124:662-9.
23.
Radloff LS. The CES-D Scale: A Self-Report Depression Scale for Research in the 
General Population. Applied Psychological Measurement 1977;1:385-401.
24.
Studenski S, Perera S, Patel K, et al. Gait speed and survival in older adults. JAMA : the 
journal of the American Medical Association 2011;305:50-8.
25.
Kallen M, Slotkin J, Griffith J, et al. Toolbox 4-Meter Walk Gait Speed Test Technical 
Manual.  NIH Toolbox Technical Manual: Northwestern University and the National Institutes 
of Health; 2012.
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26.
Gill TM, Gahbauer EA, Allore HG, Han L. Transitions between frailty states among 
community-living older persons. Archives of internal medicine 2006;166:418-23.
27.
Rockwood K, Mitnitski A, Song X, Steen B, Skoog I. Long-Term Risks of Death and 
Institutionalization of Elderly People in Relation to Deficit Accumulation at Age 70. Journal of 
the American Geriatrics Society 2006;54:975-9.
28.
Liu Y, Sanoff HK, Cho H, et al. Expression of p16(INK4a) in peripheral blood T-cells is 
a biomarker of human aging. Aging cell 2009;8:439-48.
29.
Flegal KM, Graubard BI, Williamson DF, Gail MH. Cause-specific excess deaths 
associated with underweight, overweight, and obesity. JAMA : the journal of the American 
Medical Association 2007;298:2028-37.
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Appendix A: Frailty Scoring Values
Cumulative Deficit Model (CDM) Variable Index and Scoring
Obtainment
Measured Variable
CDM Scoring (scaled 0-1)
1
Bathing 
2
Dressing 
3
Getting in/out of chair 
4
*Walking around house 
5
Eating 
6
Grooming 
7
Using Toilet 
8
Getting up/down Stairs 
9
Lifting 10 lbs 
10
Shopping 
11
Doing housework 
12
Meal preparations 
13
Taking medication 
14
Handling finances 
With some help or completely 
unable=1; Without help=0
 
15
Walk outside
<3 days=1; ≥3 days=0
16
Self-report ECOG PS 
3-4=1; 1-2=0.5; 0=0
17
Self rating of health 
Poor=1; Fair=0.75; Good=0.5; V. 
Good=0.25; Excellent=0
18
How health has changed in last year 
Worse=1; Better/Same=0
19
Stayed in bed at least half the day due to 
health (in last month) 
20
Cut down on usual activity (in last 
month) 
21
*Lost more than 10 lbs in last year
22
**Difficulty gripping with hands
Yes=1; No=0
23
**Self-reported walking speed
“Slow” = 1; “Steady Average” or 
“Fast” = 0
24
*Feel Everything is an Effort 
25
*Have Trouble getting going
26
Feel Depressed
27
Feel Lonely
Most of time=1; Some time=0.5; 
Rarely=0
Patient 
questionnaire
28
Feel Happy
Most of time=0, Some time=0.5, 
Rarely=1
29
High blood pressure 
30
Heart attack 
31
CHF
32
Stroke 
33
Cancer 
34
Diabetes 
35
Arthritis 
Patient 
medical 
record
36
Chronic Lung Disease
Yes=1; Suspect=0.5; No=0
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37
BMI 
38
*Usual pace walk speed
39
*Rapid pace walk speed
40
MoCA 5 word delayed recall
41
*** Clock-in-the-box or Clock Draw
See Tables below for cut points
42
Ability to explain presentation to DFCI   
Assessed by 
research staff
43
Ability to complete assessment without 
assistance of others
“Yes” = 0; “No” = 1
* Also used in calculation of Phenotype Frailty Score
** Used in calculation of Phenotype Frailty Score if respective physical measure is not available
*** Clock-in-the-box used during in-person assessments; Clock Draw used during virtual 
assessments
Physical variable cut-points12 
Variable
Deficit for Men
Deficit for Women
Source of Cut point
Body Mass Index 
(BMI)
<18.5, ≥ 30 as a 
deficit.
25-<30 as a 'half 
deficit'
<18.5, ≥ 30 as a deficit.
25-<30 as a 'half 
deficit'
Published 29
Grip Strength (GS 
in kg)
For BMI ≤ 24, GS ≤ 
29
For BMI 24.1–28, GS 
≤ 30
For BMI >28, GS ≤ 
32
For BMI ≤ 23, GS ≤ 17
For BMI 23.1–26, GS 
≤ 17.3
For BMI 26.1–29, GS 
≤ 18
For BMI>29, GS ≤ 21
Published 3,26
Rapid pace Walk
<0.61 m/s (6.56 sec)
<0.61 m/s (6.56 sec)
Published 26
Usual pace Walk
<0.38 m/s (10.50 sec)
<0.38 m/s (10.50 sec)
Published 12
MoCA 5 word delayed recall normative data and cut-points14
Normal Control
Mild Cognitive 
Impairment
Alzheimer’s Disease
AVG
SD
AVG
SD
AVG
SD
Memory
3.73
1.27
1.17
1.47
0.52
1.03
Words Recalled 
Successfully
5
4
3
2
1
0
Corresponding CDM 
Score
0
0.25
0.5
0.75
1
1
Clock-in-the-box (CIB) normative data for age and education and cut-points22
Education
Age (y)
Less than High 
School (SD)
High School 
(SD)
College (SD)
Graduate School 
(SD)
75-79
5.1 (1.9)
6.3 (1.6)
6.4 (1.3)
6.6 (1.4)
80-84
4.6 (1.9)
5.8 (1.5)
5.9 (1.6)
6.7 (1.2)
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≥85
4.9 (1.3)
5.4 (1.3)
5.8 (1.7)
6.5 (1.5)
CIB Score
8
7
6
5
0-4
Clock Draw Score
7
6
5
4
0-3
Corresponding CDM Score 
0
0.25
0.5
0.75
1
Phenotype Model Variable Index
Index 
Item*
Measured Variable
Scoring
1
21
Weight loss
Yes=1, No=0
2
24,25
Self-Reported Exhaustion
Most of the time (for either) =1, 
some or rarely (for both) =0
3
4
Energy Expenditure
Some assistance or completely 
unable=1, without assistance=0
4
38,39
or 23
Gait Speed (usual and rapid pace)
IF 38, 39:
Slower than cut-point=1, faster 
than cut-point=0
IF 23: “Slow” = 1; “Steady 
Average” or “Fast” = 0
5
36 or 
22
Grip Strength 
IF 36: Weaker than cut-point=1 
(for strongest measurement), 
stronger than cut-point=0
IF 22: Yes = 1; No = 0
*Numbers correspond to item number in Cumulative Deficit Variable Index (above).
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Appendix B: Patient Questionnaire
The following items (1-14) are about activities you might do during a typical day. Are you able 
to do these activities- Without help? With Some Help? or Completely Unable?
(Please Circle One Number on Each Line)
Without help With Some Help 
Completely Unable 
1. Taking a bath or shower
[1] 
[2] 
[3] 
2. Getting Dressed
[1] 
[2] 
[3]
3. Getting in/out of a chair
[1] 
[2] 
[3]
4. Walking around the house
[1] 
[2] 
[3]
5. Eating
[1] 
[2] 
[3]
6. Grooming (personal hygiene)
[1] 
[2] 
[3]
7. Using the toilet
[1] 
[2] 
[3] 
8. Going up/down stairs
[1] 
[2] 
[3]
9. Lifting 10 lbs 
[1] 
[2] 
[3]
10. Shopping
[1] 
[2] 
[3]
11. Doing housework
[1] 
[2] 
[3]
12. Preparing meals
[1] 
[2] 
[3]
13. Taking medication
[1] 
[2] 
[3]
14. Handling your own money
[1] 
[2] 
[3]
15. During the last week, on how many days did you walk outside? (circle one)
0 
1 
2 
3 
4 
5 
6 
7
16. Which option below describes your level of physical activity over the past week? (mark 
one)  
Fully active, able to carry on all usual activities without restriction
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Restricted in strenuous activity; can walk; able to carry out light housework
Can walk and care for self; up more than 1/2 day
Need some help taking care of self; spend more than 1/2 day in bed or chair
Cannot take care of self at all and spend all of my time in bed or chair
17. Overall, how would you rate your health? (circle one)
Excellent
Very good
Good
Fair
Poor
18. How would you say your health has changed in the last year? (mark one)
Worse
 Same
 Better
19. In the last month, have you ever stayed in bed at least half of the day due to your health?
Yes
No
20. In the last month, have you cut down on your usual activity due to your health?
Yes
No
21. Have you lost more than 10 lbs unintentionally in the last year?
Yes
No
22. Do you have difficulty gripping with your hands (e.g. opening a jar)?
Yes
No
23. Which of the following best describes your usual walking pace?
Slow
             Steady Average
 Fast
24. How often do you feel that everything is an effort?
Most of the time
Some of the time
 Rarely
25. How often do you feel that you have trouble getting going?
Most of the time
 Some of the time
 Rarely
26. How often do you feel depressed?
Most of the time
 Some of the time
 Rarely
27. How often do you feel lonely?
Most of the time
 Some of the time
 Rarely
28. How often do you feel happy?
Most of the time
 Some of the time
 Rarely
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