Older Age, Aggressiveness of Care, and Survival for Seriously Ill, Hospitalized Adults

Compared with younger patients with illness of similar severity, seriously ill elderly patients receive fewer procedures, fewer life-sustaining treatments, and hospital care that is less costly (1, 2). Numerous investigations have found that older patients tend to have poorer outcomes of serious illness and invasive procedures (3-6). None of these studies, however, has considered age-related differences in treatment strategies as a potential contributor to better outcomes for younger patients. We hypothesized that less aggressive treatment of seriously ill elderly patients may contribute to higher mortality and that adjustment for treatment intensity and decisions about withholding life-sustaining treatments would diminish and potentially eliminate the short-term survival disadvantage associated with older age. We studied 9105 seriously ill patients enrolled in the Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatments (SUPPORT) to evaluate the independent effect of patient age on short-term survival and to examine whether the shorter survival associated with older age can be explained in part by lower treatment intensity. Methods Study Design This paper describes secondary analyses of data from SUPPORT, a prospective study of preferences for care, decision making, and outcomes in 9105 patients hospitalized at five geographically diverse academic medical centers (Beth Israel Hospital, Boston, Massachusetts; MetroHealth Medical Center, Cleveland, Ohio; Duke University Hospital, Durham, North Carolina; St. Joseph's Hospital, Marshfield, Wisconsin; and University of California at Los Angeles Hospital, Los Angeles, California). A description of the methods and objectives of this study has been published (7, 8). Briefly, patients were screened for eligibility at hospital admission and daily while they were in the intensive care unit. Patients were enrolled if they were 18 years of age or older and met illness severity criteria for at least one of nine diagnostic categories: acute respiratory failure, chronic obstructive lung disease, congestive heart failure, cirrhosis, nontraumatic coma, metastatic colon cancer, advanced lung cancer, multiple organ system failure with sepsis, or multiple organ system failure with malignant condition. Diagnostic criteria were designed so that on average, patients would have a 50% probability of surviving 6 months. Eligible patients who were discharged or who died within 48 hours were excluded. The study design was approved by the institutional review board at each medical center, and informed consent was obtained before interviews. On the basis of problems identified during the observational portion of the study (phase I; enrollment from June 1989 through June 1991), an intervention was developed and implemented during phase II (enrollment from January 1992 through January 1994). Clinicians of patients randomly assigned to the intervention group received information about their patients' preferences and prognoses and were assigned clinical nurse specialists to facilitate symptom control and communication with their patients. Because no differences in targeted patient outcomes were observed between the intervention group and the control group in phase II or between patients in phase I and phase II (8), we combined phase I and phase II patients for our analyses. Data Collection Data were collected from paper and electronic medical records and by interviews with patients and their surrogates (defined as persons who would make care decisions if patients were unable to do so). Medical record abstractors gathered information, including diagnoses, comorbid conditions, and acute physiology data included in the Acute Physiology and Chronic Health Evaluation III prognostic system (3). Abstractors used a revised version of the Therapeutic Intervention Scoring System to collect detailed data on resource utilization on study days 1, 3, 7, 14, and 25. This measure of resource intensity is a valid, reliable, additive scale that assigns 1 point for minor interventions, such as pulse oximetry, chest physical therapy, and peripheral intravenous therapy and 2 to 4 points for more substantial interventions, such as intubation, thrombolytic therapy, endoscopy, and surgery (9, 10). In addition, abstractors collected information on the presence and timing of decisions about use of mechanical ventilation, major surgery, dialysis, blood transfusion, vasopressors, organ transplantation, tube feeding, cardiopulmonary resuscitation, and treatment in the intensive care unit. Major surgery was defined as a procedure that took place in the operating room. The presence of a decision to withhold a life-sustaining treatment was defined as chart documentation of a decision to withhold the treatment if it were required to sustain life. Interviewers used a modified version of the Katz Index of Activities of Daily Living scale (11, 12) and the Duke Activity Status Index (13, 14) to question patients and their surrogates between study days 3 and 6 about patients' functional and activity status 2 weeks before hospital admission. Patients were excluded from interviews if they were unable to communicate for such reasons as intubation, coma, or cognitive impairment. Each patient was followed for 180 days, and complete mortality data through 180 days were collected. Statistical Analysis We used descriptive statistics to characterize the study sample. For bivariable and multivariable analyses, the outcome of interest was survival duration through 180 days of follow-up and the independent variable of interest was patient age. For bivariable analyses examining age as a categorical variable, we divided patients into five age groups based on decade of life and the age distribution of the sample (<50 years, 50 to 59 years, 60 to 69 years, 70 to 79 years, and 80 years). The 6-month mortality rate was computed for each age group, and chi-square tests for trend were used to evaluate the association between age and survival. In multivariable analyses, age was examined as a linear spline function to allow for nonlinear association with mortality across the age range. Five-year increments from 50 to 85 years of age were examined as potential knots, or inflection points, to describe the relation between age and survival and to avoid the assumption of a constant mortality effect across the age range. The final model contains age as a linear spline with a single knot at 70 years of age because this optimized the model's explanatory power (based on the maximum likelihood ratio chi-square value). A second knot was not included because adding an additional knot did not increase the likelihood ratio chi-square value by more than 2 (a criterion chosen a priori). We used Cox proportional-hazards modeling to evaluate the association of age with survival time. We used study day 3 as the time of origin for the survival analyses because study inclusion criteria required survival to study day 3. We adjusted for other sociodemographic variables (sex, ethnicity, and income), baseline functional status, and severity of illness. The Activities of Daily Living and Duke Activity Status Index scores were used to adjust for baseline functional status. Adjustment for severity of illness was based on the SUPPORT prognostic model. The SUPPORT model was developed from data on 4301 patients enrolled in phase I to provide accurate estimates of survival through 180 days. The model has excellent discrimination, is well calibrated, and was prospectively validated on the first 4028 patients enrolled in phase II of SUPPORT (15). To develop a model with optimal prognostic performance, investigators examined an exhaustive list of prespecified variables thought to be potentially correlated with survival (including diagnoses; physiologic variables, such as vital signs and common laboratory measurements; neurologic function measured by using the Glasgow Coma Scale [16]; and prognostic variables specific to each diagnostic category). To adjust for severity of illness in our models, we included all variables in the SUPPORT prognostic model except patient age: the SUPPORT Physiology Score from study day 3 (based on 11 physiologic measurements), the SUPPORT diagnostic categories, the number of days that patients were hospitalized before study admission, whether patients had cancer (metastatic or nonmetastatic), and a modified version of the Glasgow Coma Score from study day 3. All patients had data available for study days 1 and 3; we used data from day 3 to adjust for severity of illness because these data were more highly correlated with mortality than were day 1 data and because day 3 was the starting time for our survival analyses. We also adjusted for the number of comorbid illnesses. This model (referred to hereafter as the severity-adjusted model) provided our estimate of the association of patient age and survival, after adjusting for potential confounding baseline patient characteristics. To assess whether differences in aggressiveness of care contributed to worse survival for elderly patients and to estimate the association of older age and survival independent of both illness severity and aggressiveness of treatment, we developed a second model (the severity- and treatment-adjusted model) that included further adjustment for potential age-related differences in aggressiveness of treatment. To adjust for intensity of treatment, we used the Therapeutic Intervention Scoring System on study days 1, 3, 7, 14, and 25. Resource intensity was adjusted for as a time-varying covariate that accounted for changes in resource intensity throughout each patient's hospitalization. We also used time-varying covariates to adjust for the presence and timing of decisions to withhold major surgery, dialysis, tube feeding, transfer to the intensive care unit, blood transfusion, and vasopressors. In addition, we adjusted for the presence of a do-not-resuscitate