Predicting Future Functional Status for Seriously Ill Hospitalized Adults: The SUPPORT Prognostic Model

Patients hospitalized with serious illnesses and their physicians and families need estimates of likely survival time and functional status to identify the most desirable plan of care. Although several models have been developed to predict prognosis for survival [1-7], few have forecast the patient's ability to do important activities of daily living [8-12]. Information about functional status would help people weigh the merits of life-sustaining therapy and plan for supportive care. Large computerized databases with accurate clinical data are now available that could be used to generate such prognostic information if accurate models were developed [13, 14]. Thus, we developed and validated a model to estimate the probability of a patient having severe functional limitations 2 months after hospitalization for serious illness. Methods Our participants were selected from the Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatments (SUPPORT), a multicenter study of outcomes and decision making for seriously ill hospitalized adults. Phase I of the study described the process of decision making and developed models to predict outcomes. Phase II was an intervention trial to evaluate the effect of prognostic information and enhanced communication. A description of the study design has been published [15]. Patients enrolled in SUPPORT had one of nine illnesses: acute respiratory failure, multisystem organ failure with sepsis, cancer with multiorgan system failure, chronic obstructive pulmonary disease, congestive heart failure, chronic liver failure, nontraumatic coma, colon cancer, or lung cancer. These categories were chosen to identify a cohort of patients with an anticipated 6-month mortality rate of 50% [15]. Patients with these illnesses were eligible for study participation if they met defined severity criteria at hospital admission or at any time during their stay in an intensive care unit. Patients were excluded if they did not speak English; had the acquired immunodeficiency syndrome (AIDS), were pregnant, or had multiple trauma; died within 48 hours of hospitalization; or were scheduled for discharge within 72 hours of admission. Phase I data collection took place between June 1989 and June 1991 at the Beth Israel Hospital, Boston, Massachusetts; MetroHealth Medical Center, Cleveland, Ohio; Duke University Medical Center, Durham, North Carolina; Marshfield Clinic/St. Joseph's Hospital, Marshfield, Wisconsin; and the University of California, Los Angeles, Medical Center. Phase II of the study took place from January 1992 to January 1994 at the same institutions. Data Collection Baseline data were abstracted from medical records of the patients and were obtained from interviews with patients and surrogates between days 2 and 6 after study entry. Follow-up data were collected 2 months later by telephone interview. The surrogate was defined as the person who would make decisions about the patient's care if the patient was unable to do so. Independent variables collected from the medical record included diagnostic category, other medical conditions, age, race, sex, education level, income, insurance status, enrolling hospital, and duration of hospitalization before study entry. Acute physiologic and neurologic status were determined using the SUPPORT physiology score [14] and a modified Glasgow coma score [16] measured on the third day after study entry. The patient and surrogate hospital interviews (Appendix 1) included questions on reported performance of activities of daily living 2 weeks before study admission, using a modified version of the Katz Index of activities of daily living [17]; exercise tolerance 2 weeks before admission, using a modification of the Duke Activity Status Index [18]; and quality of life at the time of interview using a 5-point rating scale [19]. During a telephone interview at month 2, the patient was asked about his or her functional status using the Sickness Impact Profile [20] and the patient and surrogate were asked about the patient's activities of daily living and overall quality of life. The Katz Index of activities of daily living [17] was modified so that it could be obtained during an interview with the patient. Walking was added to the six basic activities included in the original index. Questions were worded to obtain a report of actual performance rather than perceived capacity. This modified index was scored on a 7-point scale, with each point indicating dependence on assistance for one of the following basic functions: eating, continence, toileting, transferring, bathing, dressing, and walking. Patients and their surrogates were asked to report on functioning for 2 weeks before study entry in order to approximate a baseline level of functioning. Appendix 1.Table of Measures* The Duke Activity Status Index is a patient-reported measure of ability to do several personal, household, and recreational activities, each of which was calibrated to its metabolic requirements to assess cardiovascular capacity [18]. In our study, the item on sexual function was deleted and three items about yard work, moderate recreation, and strenuous recreation were combined into a single item. Questions were asked in hierarchical order so that patients unable to do less strenuous activities were not asked about more demanding activities. Patients and their surrogates were asked about the patient's ability to do as many as 11 activities 2 weeks before study admission. The Duke Activity Status Index was scored so that a higher score indicated greater metabolic capacity. Quality of life at the time of the interview was assessed using a 5-point scale ranging from excellent to poor. The Sickness Impact Profile is a generic health status measure that assesses sickness-related dysfunction [20]. It has 136 items grouped in 12 areas of activity: sleep and rest, emotional behavior, body care and movement (for example, bathing or transferring from bed to chair), eating, home management, mobility (for example, staying at home), social interaction, ambulation, alertness behavior, communication, recreation, and work. Sickness Impact Profile scores range from 0 to 100, with a higher score indicating greater dysfunction. The Sickness Impact Profile has been tested and used extensively in clinical and health services research studies [21, 22] and has shown consistently high reliability coefficients and excellent convergent and discriminant validity [23]. The clinical validity and responsiveness to change of the Sickness Impact Profile have also been shown [22]. Management of Missing Data for Independent Variables Because interview data were not available for every patient and surrogate and because analyses done on subsets of a database may be biased (for example, persons not responding may be sicker than respondents), we developed a substitution and imputation strategy for missing independent variables. We gave priority to patient rather than surrogate responses about functioning and quality of life. When the patient response was missing, a calibrated surrogate response was used. On the basis of the subset of cases for which patient and surrogate responses were available, surrogate responses were calibrated to achieve a distribution similar to that of patient scores. When neither patient nor surrogate response was available, an imputation strategy was used. Ordinal logistic or linear regression models containing age, SUPPORT physiology score at day 3, diagnosis, number of additional diagnoses, cancer diagnosis, site, interview status, and length of time in the hospital before study entry were used to predict surrogate-reported activities of daily living, quality of life, and Duke Activity Status Index. Surrogate responses were estimated instead of patient responses because surrogate responses were available for a broader range of situations. The estimated surrogate responses were then calibrated to the patient distribution in the same way that actual surrogate responses were calibrated (Appendix 2). Appendix 2Summary of Substitution of Imputation Strategies for Predictor Variables Development of Predictive Model To estimate the probability that survivors had severe functional limitation after 2 months, we used a previously established definition of severe functional limitation indicating problems that would require nearly constant personal assistance (that is, Sickness Impact Profile scores 30 or patient-reported activities of daily living scores 4). If the patient could not be interviewed, a surrogate report of an activities of daily living score of 5 or more was used because for cases in which patient and surrogate responded, a surrogate-reported score of 5 corresponded to a patient-reported score of 4. One hundred eleven patients were classified as severely limited on the basis of both Sickness Impact Profile scores and activities of daily living scores, 121 were classified as severely limited on the basis of Sickness Impact Profile scores alone, and 349 were classified as severely limited on the basis of activities of daily living scores alone. Patients who were comatose or intubated at month 2 were also classified as severely limited (n = 11). Analysis On the basis of published reports [8, 10, 24-28] and clinical experience, we hypothesized that disease type and patient demographic characteristics, severity of illness, previous functional status, and self-rated quality of life would be important determinants of future functional status. Because lead time may be important in prognostic models [3, 29], we included a variable for time spent in the hospital before study enrollment. Candidate variables were entered into a backward stepwise logistic regression model. A variable was defined as important and was retained if its chi-square statistic was greater than twice its degrees of freedom [30]. Five potential interactions between prognostic variables were prespecified using a publ