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A Prognostic Model for 1-Year Mortality in Older Adults after Hospital Discharge

      Abstract

      Purpose

      To develop and validate a prognostic index for 1-year mortality of hospitalized older adults using standard administrative data readily available after discharge.

      Subjects and methods

      The prognostic index was developed and validated retrospectively in 6382 older adults discharged from general medicine services at an urban teaching hospital over a 4-year period. Potential risk factors for 1-year mortality were obtained from administrative data and examined using logistic regression models. Each risk factor associated independently with mortality was assigned a weight based on the odds ratios, and risk scores were calculated for each patient by adding the points of each independent risk factor present. Patients in the development cohort were divided into quartiles of risk based on their final risk score. A similar analysis was performed on the validation cohort to confirm the original results.

      Results

      Risk factors independently associated with 1-year mortality included: aged 70 to 74 years (1 point); aged 75 years and greater (2 points); length of stay at least 5 days (1 point); discharge to nursing home (1 point); metastatic cancer (2 points); and other comorbidities (congestive heart failure, peripheral vascular disease, renal disease, hematologic or solid, nonmetastatic malignancy, and dementia, each 1 point). In the derivation cohort, 1-year mortality was 11% in the lowest-risk group (0 or 1 point) and 48% in the highest-risk group (4 or greater points). Similarly, in the validation cohort, 1-year mortality was 11% in the lowest risk group and 45% in the highest-risk group. The area under the receiver operating characteristic curve was 0.70 for the derivation cohort and 0.68 for the validation cohort.

      Conclusion

      Reasonable prognostic information for 1-year mortality in older patients discharged from general medicine services can be derived from administrative data to identify high-risk groups of persons.

      Keywords

      Modern medicine and public health advancements have led to greater numbers of older persons living with chronic, debilitating illnesses for which there are limited or no curative therapies. Health care systems are facing increasing responsibility for addressing the need for supportive services throughout the continuum of illness and during end-of-life care.
      • Morrison R.S.
      • Meier D.E.
      Palliative care.
      • Morrison R.S.
      • Siu A.L.
      • Leipzig R.M.
      • et al.
      The hard task of improving quality of care at the end of life.
      Unfortunately, many clinicians do not discuss palliative care and hospice options until very late in the disease course when patients and families are unable to benefit fully from these resources.
      • Lynn J.
      • Goldstein N.E.
      Advance care planning for fatal chronic illness: avoiding commonplace errors and unwarranted suffering.
      An important barrier to palliative care is the ability to identify patients at higher risk for mortality earlier in the illness trajectory.
      • Christakis N.A.
      Identifying persons at greatest risk for death could prompt health care providers to reassess goals of care, redefine medically necessary therapies, focus on symptom control, assess other physical, psychosocial, and spiritual problems, and consider earlier palliative care consultation and hospice referral.
      • Lynn J.
      Serving patients who may die soon and their families: the role of hospice and other services.
      • More than a quarter of patients aged 65 years and older were deceased within a year after hospital discharge.
      • A point system created from administrative data can provide reasonable prognostic information to sort patients into groups of risk for mortality.
      • Mortality risk stratification can help physicians and family members plan for the care of patients who may be at increased risk of death in the coming year.
      Prognostication has been recognized as an important but often neglected part of patient management, especially for discussions of goals of care, treatment preferences, advance planning, and clinical therapeutic options.
      • Christakis N.A.
      • Lynn J.
      • Teno J.M.
      • Harrell F.E.
      Accurate prognostications of death: opportunities and challenges for clinicians.
      There are a few prognostic indices available for predicting mortality in hospitalized older adults, but their use has not been incorporated into routine medical practice. Some existing models are applicable only to specific patient populations and disease states
      • Knaus W.A.
      • Wagner D.P.
      • Draper E.A.
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      The APACHE III prognostic system: risk prediction of hospital mortality for critically ill hospitalized adults.
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      Prognosis in lung cancer: physicians’ opinions compared with outcome and a predictive model.
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      • Tsunoda J.
      • Inoue S.
      • Chihara S.
      Survival prediction of terminally ill cancer patients by clinical symptoms: development of a simple indicator.
      or require subjective assessments of risk by clinicians.
      • Evans C.
      • McCarthy M.
      Prognostic uncertainty in terminal care: can the Karnovsky index help?.
      Others require use of lengthy formulas
      • Knaus W.A.
      • Harrell F.E.
      • Lynn J.
      • et al.
      The SUPPORT prognostic model: objective estimates of survival for seriously ill hospitalized older adults.
      or knowledge of certain laboratory data and functional status,
      • Walter L.C.
      • Brand R.J.
      • Counsell S.R.
      • et al.
      Development and validation of a prognostic index for 1-year mortality in older adults after hospitalization.
      • Teno J.M.
      • Harrell F.E.
      • Knaus W.A.
      • et al.
      Prediction of survival for older hospitalized patients; the HELP survival model.
      which are not always available in a patient’s chart.
      • Bogardus S.T.
      • Towle V.
      • Williams C.S.
      • et al.
      What does the medical record reveal about functional status? A comparison of medical record and interview data.
      Persons with chronic, progressive, and disabling illnesses often require hospitalization in the last year of life, leading to further functional decline and morbidity.
      • Covinsky K.E.
      • Palmer R.M.
      • Fortinsky R.H.
      • et al.
      Loss of independence in activities of daily living in older adults hospitalized with medical illnesses: increased vulnerability with age.
      • Lunney J.R.
      • Lynn J.
      • Foley D.J.
      • et al.
      Patterns of functional decline at the end of life.
      Hospitalization therefore may present a key trigger point for identifying persons at greatest risk for mortality in the ensuing year.
      The goal of this study was to develop an easy-to-use prognostic index for older patients on general medical wards using information readily available from standard administrative data shortly after hospital discharge.

      Methods

      Participants

      The data used in these analyses were collected on individuals enrolled in a prospective cohort study comparing costs and outcomes of care by hospitalist and non-hospitalist physicians. It was conducted on an academic general medicine service at the University of Chicago Hospitals in Chicago, Ill from July 1, 1997 through June 30, 2001. The study included 14,661 patients admitted to the general medicine service either directly or transferred from nonmedical services or intensive care. Details of the study are published elsewhere.
      • Meltzer D.
      • Manning W.B.
      • Morrison J.
      • et al.
      Effects of physician experience on costs and outcomes on an academic general medicine service: results of a trial of hospitalists.
      This study involved an analysis of the subset of patients who were aged 65 years and older. Because it focused on survival of patients after discharge, those who died in the hospital were excluded. Patients admitted July 1997 through June 1999 were used as the derivation cohort, and those admitted from July 1999 through June 2001 were used to validate the model. Of the 6534 patients in the derivation cohort, 2839 (44%) were aged 65 years and older, and of these, 100 (4%) died in the hospital and were excluded. Of the 8127 patients in the validation cohort, 3780 (47%) were aged 65 years and older, of which 137 (4%) died in the hospital and were excluded. This study was approved by the Institutional Review Board at the University of Chicago.

      Data Collection and Measurements

      Administrative data were accessed using software from Transitions Systems, Inc., which contains all of the ICD-9 codes required for billing for Medicare and other payers. All primary and secondary code diagnoses in ICD-9 codes of all outpatient and inpatient encounters in the year before the index hospital admission were extracted. These ICD-9 codes were then used to create indicator variables for 14 co-morbid conditions defined by Romano et al that extended earlier work by Deyo et al constructing a claims-based measure of comorbidity based on the Charlson Index.
      • Romano R.S.
      • Roos L.S.
      • Jollis J.G.
      • et al.
      Adapting clinical comorbidity index for use with ICD-9-CM administrative data: Differing perspectives.
      • Deyo R.A.
      • Cherkin D.C.
      • Ciol M.A.
      Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases.
      Comorbid conditions were coded as present or absent with the exception of cancer, which was coded as either absent, solitary malignancy, or metastatic solid tumor. Hematologic malignancies were included with solitary tumors.
      Other risk factors for 1-year mortality were chosen based on clinical relevance, prevalence in the sample, and ease of abstraction from data (Table 1). Age was divided into 5-year intervals. Length of hospital stay was categorized as <5 days or ≥5 days, based on the mean length of stay in the derivation cohort.
      Table 1Characteristics of Patients in Derivation and Validation Cohorts
      CharacteristicDerivation (n=2739) n (%)Validation (n=3643) n (%)P Value
      Age, years
       Mean (SD)78 (8.3)78 (8.1).15
       65-69533 (19)624 (17).05
       70-74566 (21)723 (20)
       75-79581 (21)825 (23)
       80-84451 (16)676 (19)
       85-89336 (12)456 (12)
       ≥90272 (10)339 (9)
      Women1733 (63)2372 (65).13
      Discharge to nursing home or skilled nursing facility415 (15)602 (17).16
      Length of stay ≥5 days967 (35)1116 (31)<.001
      Dead at 1 year722 (26)950 (26)
      Comorbid conditions
       Myocardial infarction344 (13)512 (14).08
       Congestive heart failure635 (23)879 (24).38
       PVD441 (16)611 (17).47
       Cerebrovascular disease367 (13)501 (14).68
       Dementia322 (12)422 (12).83
       COPD743 (27)881 (24).008
       Rheumatologic disease162 (6)189 (5).21
       Peptic ulcer disease107 (4)75 (2)<.001
       Diabetes916 (31)1382 (38)<.001
       Renal disease323 (13)452 (12).46
       Liver disease50 (2)84 (2).19
       Hematologic and solid malignancy468 (17)504 (14)<.001
      Metastatic cancer206 (8)156 (4)<.001
      Acquired immune deficiency syndrome9 (0.3)3 (0.08).03
      PVD=peripheral vascular disease; COPD=chronic obstructive pulmonary disease.

      Outcome

      The outcome of interest was death within 1 year after hospital discharge. Mortality was assessed 365 days from the day of discharge by linking patients with the Social Security Death Index.

      Social Security Death Index Interactive Search. Available at: http://ssdi.genealogy.rootsweb.com/cgi-bin/ssdi.cgi/. Accessed July 10, 2002.

      Survival status and date of death were matched to the patient according to social security number, name, and date of birth. Data on survival status were obtained on 99.8% of the subjects.

      Prognostic Index Derivation

      Bivariate relationships between potential risk factors and mortality were assessed in the derivation cohort using logistic regression models. Significant variables (P <.05) were entered into a multiple logistic regression model. Risk factors that remained significant after adjustment (P <.05) were used to create the predictive model.
      The accuracy of this model was evaluated using methods similar to those employed by Walter et al.
      • Walter L.C.
      • Brand R.J.
      • Counsell S.R.
      • et al.
      Development and validation of a prognostic index for 1-year mortality in older adults after hospitalization.
      Risk of death for each subject was estimated based on the final logistic regression model in the development cohort. The subjects were divided into quartiles of risk. The predictive accuracy of the logistic regression model was determined by comparing predicted and observed mortality in the validation cohort.
      A 1-year mortality risk scoring system was created by assigning points to each risk factor by dividing each beta coefficient in the model by the lowest beta coefficient (congestive heart failure) and rounding to the nearest integer.
      • Walter L.C.
      • Brand R.J.
      • Counsell S.R.
      • et al.
      Development and validation of a prognostic index for 1-year mortality in older adults after hospitalization.
      • Concato J.
      • Feinstein A.R.
      • Holford T.R.
      The risk of determining risk with multivariable models.
      A risk score was determined for each subject by adding up the points for each risk factor present. Subjects in the derivation and validation cohorts were divided into quartiles based on their risk scores. The prognostic accuracy of the predicted mortality was determined by comparing predicted and observed mortality across these quartiles of predicted mortality in both cohorts.
      • Concato J.
      • Feinstein A.R.
      • Holford T.R.
      The risk of determining risk with multivariable models.
      Kaplan-Meier curves were used to examine the performance of the prognostic index.
      Finally, the area under the receiver operating characteristic (ROC) curves was calculated for the logistic regression model for the development and validation cohorts.

      Results

      Study Populations

      Characteristics of the derivation and validation cohorts are listed in Table 1. The mean (SD) age of the patients in the derivation cohort was 78 (8.3) years. Sixty-three percent were female, 81% were African American, and 15% were discharged to a nursing home or skilled nursing facility. Thirty-five percent had a length of stay of at least 5 days in the hospital before discharge. During 1-year of follow-up, 722 patients (26%) died.
      The mean (SD) age of the patients in the validation cohort was 78 (8.1) years. Sixty-five percent were female, 80% were African American, and 17% were discharged to a nursing home or skilled nursing facility. Thirty-one percent had a length of stay at least 5 days in the hospital before discharge. During 1-year of follow-up, 950 patients (26%) died.

      Derivation of the Prognostic Index

      Risk factors with significant bivariate associations with 1-year mortality are listed in Table 2. Of these, age >70 years, length of stay at least 5 days, discharge to nursing home, congestive heart failure, peripheral vascular disease, dementia, hematologic and solid malignancy, and metastatic cancer remained significantly associated after adjustment (Table 3). By quartiles of risk, 1-year mortality ranged from 11% in the lowest-risk quartile to 48% in the highest risk quartile in the derivation cohort and from 11% to 45% in the validation cohort (Table 4, top panel). There was good calibration of the model, with close agreement between observed and predicted mortality across the risk quartiles. The ROC curve area was also similar between the derivation and validation cohorts, 0.70 and 0.68, respectively.
      Table 2Bivariate Associations of Risk Factors and 1-Year Mortality in Derivation Cohort
      CharacteristicOdds Ratio (CI)P Value
      Age, years
       70-741.6 (1.2-2.2)<.001
       75-792.1 (1.6-2.9)
       80-841.9 (1.4-2.6)
       85-892.8 (2.0-3.9)
       ≥903.0 (2.1-4.2)
      Women0.9 (0.7-1.0).06
      Discharge to nursing home or skilled nursing facility2.0 (1.6-2.5)<.001
      Length of stay ≥5 days1.7 (1.5-2.1)<.001
      Comorbid conditions
       Myocardial infarction1.0 (0.8-1.3).86
       Congestive heart failure1.4 (1.2-1.7)<.001
       Peripheral vascular disease1.9 (1.5-2.3)<.001
       Cerebrovascular disease1.4 (1.1-1.8).002
       Dementia2.0 (1.6-2.6)<.001
       Chronic obstructive pulmonary disease1.0 (0.8-1.2).71
       Rheumatologic disease0.9 (0.6-1.2).39
       Peptic ulcer disease1.8 (1.2-2.7).005
       Diabetes0.8 (0.7-0.9).02
       Renal disease1.8 (1.4-2.3)<.001
       Liver disease1.2 (0.7-2.2).56
       Hematologic and solid malignancy2.3 (1.9-2.8)<.001
       Metastatic cancer3.8 (2.9-5.1)<.001
       Acquired immune deficiency syndrome2.2 (0.6-8.4).23
      CI=95% confidence interval.
      Table 3Risk Factors Associated with 1-Year Mortality in Derivation Cohort in Multivariate Analyses
      CharacteristicOdds Ratio (CI)P ValuePoints
      Age, years
       70-741.6 (1.2-2.2).0031
       75-792.2 (1.6-3)<.0012
       80-842 (1.4-2.8)<.0022
       85-892.9 (2.1-4.1)<.0032
       ≥903 (2.1-4.4)<.0042
      Discharge to nursing home or skilled nursing facility1.7 (1.4-2.2)<.0051
      Length of stay ≥5 days1.5 (1.3-1.8)<.0061
      Comorbid conditions
       Congestive heart failure1.3 (1.1-1.7).0051
       PVD1.8 (1.4-2.3)<.0011
       Dementia1.6 (1.2-2.1)<.0011
       Renal disease1.7 (1.3-2.2)<.0011
       Hematologic and solid malignancy1.7 (1.3-2.1)<.0011
       Metastatic cancer3.1 (2.2-4.4)<.0012
      CI=95% confidence interval; PVD=peripheral vascular disease.
      Table 4Validation of Prognostic Index: 1-Year Mortality in Derivation and Validation Cohorts by Risk Strata
      Risk StrataDerivation CohortValidation Cohort
      n=Died/At Risk
      Data on mortality missing on 11 subjects.
      % (CI)n=Died/At Risk% (CI)
      Quartile of risk
       168/63611 (8-13)90/85311 (8-13)
       2119/69817 (14-20)199/95921 (18-23)
       3207/71129 (26-32)249/91027 (24-31)
       4325/68348 (44-51)412/92145 (42-48)
       ROC curve area0.70.68
      Risk group by points
       0-1110/79914 (11-16)155/110414 (12-16)
       2130/71918 (15-21)233/95324 (22-27)
       3180/56332 (28-36)242/81830 (26-33)
       ≥4299/64746 (42-50)320/76842 (38-45)
       ROC curve area0.670.65
      ROC=receiver operating characteristic; CI=95% confidence interval.
      low asterisk Data on mortality missing on 11 subjects.

      Prognostic Risk Scoring System

      The points assigned to each of the 9 final risk factors in the prognostic scoring system are listed in Table 3. A final risk score was calculated by adding the points designated for each risk factor. For example, a 90-year-old patient (2 points) with congestive heart failure (1 point) and who is discharged to a nursing home (1 point) will have a final risk score of 4 points.
      Patients were divided into risk groups of roughly equal size based on their risk scores (Table 4, bottom panel). In the derivation cohort, mortality ranged from 14% in the lowest-risk group (0 to 1 point) to 46% in the highest risk group (at least 4 points). Similar results were found in the validation cohort with the low-risk group and highest risk groups having a 1-year mortality of 14% and 42%, respectively. The ROC curve areas for the derivation and validation cohorts were similar, 0.67 and 0.65, respectively.
      Kaplan-Meier survival curves for both the derivation and validation cohorts were nearly identical (data on all subjects not shown). The survival curve for the validation cohort demonstrates that the 4 risk groups have survival trajectories that diverge early after hospital discharge (Figure 1). For example, 3 months after discharge, approximately 25% of the high risk patients (group 4) were already deceased, compared with <5% in the lowest quartile (group 1).
      Figure thumbnail gr1
      FigureKaplan-Meier survival curve for the validation cohort, stratified by risk groups.

      Discussion

      We have created a relatively simple prognostic index for older adults discharged from a general medicine service that stratifies patients at risk for 1-year mortality using information readily available from standard administrative data. Because many Medicare beneficiaries are hospitalized at least once in the year before death,

      Dartmouth Atlas of Healthcare Interactive Search. Available at: http://www.dartmouthatlas.org/. Accessed May 21, 2004.

      the hospital admission becomes an important window of opportunity for recognizing persons at risk for further decline and mortality. As medical records are becoming more electronically based, this prognostic data can be generated from a computerized support system using standard Medicare billing forms shortly after a patient is discharged from the hospital and made available to primary care physicians. Indeed, ICD-9 code data are currently readily available in hospitals and other health care systems and can be easily accessed by trained personnel. Future validation work may lead to development of a pocket-sized card or program for personalized digital assistants that clinicians can apply routinely using ICD-9 code data that are readily available from discharge summaries or online records once a patient is discharged. This information can, in turn, prompt clinicians to initiate discussions on advance care planning and goals of care in their patients who may be at an increased risk of dying in the ensuing year.
      As the number of older persons living with serious chronic diseases grows, more persons will be in need of palliative care support. Optimal care involves advance planning and evaluating goals of care over the course of the illness with attention to symptom management, caregiver support, and thoughtful use of resources. One might argue that all older adults admitted to the hospital are at risk for further decline, and this should prompt discussions of goals of care and advance care planning in everyone. Although physicians commonly encounter situations requiring the use of prognostication, they often feel poorly prepared for it.
      • Christakis N.A.
      • Iwashyna T.J.
      Attitude and self-reported practice regarding prognostication in a national sample of internists.
      They find it stressful, and often avoid delivering prognostic information even if the patient requests it.
      • Christakis N.A.
      • Lamont E.B.
      Extent and determinants of error in doctors’ prognoses in terminally ill patients: Prospective cohort study.
      • Lamont E.B.
      • Christakis N.A.
      Prognostic disclosure to patients with cancer near the end of life.
      They may refrain from hospice discussions because of the constraints of hospice eligibility requirements and the difficulty in predicting death within 6 months.
      • McGorty E.K.
      • Bornstein B.H.
      Barriers to physicians’ decisions to discuss hospice: insights gained from the United States hospice model.
      This prognostic tool may help overcome these barriers by providing objective information that distinguishes high-risk groups of patients who may benefit from palliative care resources. Lynn has suggested that “comprehensive end-of-life services are best triggered by recognition that the patient is sick enough that ‘dying this year would not be a surprise’. Rather than targeting patients who ‘will die’… programs for end-of-life care should target those who ‘reasonably might die’.”
      • Lynn J.
      Serving patients who may die soon and their families: the role of hospice and other services.
      Indeed, identifying patients further “upstream” from hospice eligibility and imminent death fits with the broader movement within palliative care to offer aggressive symptom management and supportive care to anyone suffering from advanced illness, even if they are pursuing active disease-specific treatment or cure of the underlying condition.
      • Meier D.E.
      • Morrison R.S.
      • Cassel C.K.
      Improving palliative care.
      For example, recently hospitalized patients found to be at increased risk of death in 1 year may be referred to an outpatient palliative care program that provides services to patients facing serious illnesses.
      • Rabow M.W.
      • Dibble S.L.
      • Pantilat S.Z.
      • McPhee S.J.
      The comprehensive care team: a controlled trial of outpatient palliative medicine consultation.
      • Casarett D.J.
      • Hirschman K.B.
      • Coffey J.F.
      • Pierre L.
      Does a palliative care clinic have a role in improving end-of-life care? Results of a pilot program.
      This model’s performance is modest compared with other sophisticated tools such as SUPPORT (ROC=0.79) and APACHE (ROC=0.90), but this model does not require additional data collection and complex calculations of these other tools.
      • Knaus W.A.
      • Wagner D.P.
      • Draper E.A.
      • et al.
      The APACHE III prognostic system: risk prediction of hospital mortality for critically ill hospitalized adults.
      • Knaus W.A.
      • Harrell F.E.
      • Lynn J.
      • et al.
      The SUPPORT prognostic model: objective estimates of survival for seriously ill hospitalized older adults.
      It is most useful in sorting patients into groups of risk; therefore, clinical decisions on individual patients should not be based on the risk strata alone.
      There are several additional reasons to suspect that the ready delivery of prognostic information may be of value for patients and the health care system. First, widespread media attention to end-of-life care has increased public awareness of the importance of these issues.
      • Goodnough A.
      Feeding tube is removed in Florida right-to-die case: governor says he will try to stop death.
      Second, efforts to control rising health care costs have provided hospitals, managed care organizations, and payers increasing incentives to more efficiently allocate expenditures for patients likely to be close to the end of life. Third, independent of cost considerations, better identification of patients at high risk of death may lead to earlier hospice referral, redefining the usefulness of medical therapies, and enhanced provision of comfort measures. Finally, prognostic estimates allow comparison of outcomes between health care providers or health care delivery systems in order to improve future medical care.
      • Christakis N.A.
      Another potential use for this prognostic model may be in risk-adjusting postdischarge outcomes (eg, comparing home care providers) when only administrative data are available.
      This study has several limitations. First, it was developed and validated at a single site in a university hospital setting. Second, the demographics of our population include predominantly African American persons, which may not represent hospital settings with other ethnic and racial populations. Third, length of stay and nursing home placement vary regionally, which may alter the performance of the model in other sites. As with other prognostic indices, the validity and generalizability of this model needs to be tested in other locations with different groups of patients.
      • Justice A.
      • Covinsky K.E.
      • Berlin J.A.
      Assessing the generalizability of prognostic information.
      • McGinn T.G.
      • Guyatt G.H.
      • Wyer P.C.
      • et al.
      Evidenced-Based Medicine Working Group
      Users’ guides to the medical literature, XXII: how to use articles about clinical decision rules.
      • Laupacis A.
      • Wells G.
      • Richardson S.
      • Tugwell P.
      Users’ guides to the medical literature, V: How to use an article about prognosis.
      Also, with the exception of coding for malignancy, this index does not account for severity of comorbid conditions, which is often not available in administrative data sets. Lack of severity rating could potentially weaken the model’s predictability. In addition, this prognostic model does not include laboratory data or functional status that might have improved its performance. However, although the prognostic importance of functional decline has been well documented in the literature,
      • Teno J.M.
      • Harrell F.E.
      • Knaus W.A.
      • et al.
      Prediction of survival for older hospitalized patients; the HELP survival model.
      • Reuben D.B.
      • Rubenstein L.V.
      • Hirsch S.H.
      • Hays R.D.
      Value of functional status as a predictor of mortality: results of a prospective study.
      • Narain P.
      • Rubenstein L.Z.
      • Wieland G.D.
      • et al.
      Predictors of immediate and 6-month outcomes in hospitalized elderly patients The importance of functional status.
      the clinical reality is that accurate documentation of Activities of Daily Living is lacking in up to 98% of medical charts.
      • Bogardus S.T.
      • Towle V.
      • Williams C.S.
      • et al.
      What does the medical record reveal about functional status? A comparison of medical record and interview data.
      Additional variables might improve the model’s precision but at the cost of requiring other information often lacking in administrative data, thus making it less likely to be used in routine clinical practice. Lastly, because this model was derived from data in patients aged 65 years and older, it may not be applicable to a younger population.
      In summary, this prognostic index provides a reasonable method for identifying older persons at high risk for 1-year mortality after surviving a hospital stay. Hospital discharge may be the window of opportunity for identifying prognostic issues and initiating future discussions on advance directives, patient preferences, and future goals of care. Although many advocate discussing advance directives with all patients, use of this prognostic index may at least encourage clinicians who do not universally discuss advance directives to have discussions with individuals at greatest risk of death. This prognostic information may be most useful to the primary care provider once the patient has left the hospital and is recovering from an acute illness. In addition, hospitals and other health care systems may use this prognostic information for quality improvement efforts in increasing advance directives and hospice referrals in high-risk patients.

      Acknowledgments

      Supported by the University of Chicago Hospitals, Chicago, Illinois; the Charles E. Culpeper Foundation, New York, New York; the National Institute of Aging, Bethesda, Maryland; the Robert Wood Johnson Foundation, Princeton, New Jersey; the Hulda B. and Maurice L. Rothschild Foundation, Chicago, Illinois; the Academic and Managed Care Forum, Hartford, Connecticut; and the John A. Hartford Foundation/American Federation for Aging Research, New York, New York.

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