Validation of case-mix measures derived from self-reports of diagnoses and health.

Self-reported chronic diseases and health status are associated with resource use. However, few data exist regarding their ability to predict mortality or hospitalizations. We sought to determine whether self-reported chronic medical conditions and the SF-36 could be used individually or in combination to assess co-morbidity in the outpatient setting. The study was designed as a prospective cohort study. Patients were enrolled in the primary care clinics at seven Veterans Affairs (VA) medical centers participating in the Ambulatory Care Quality Improvement Project (ACQUIP). 10,947 patients, > or = 50 years of age, enrolled in general internal medicine clinics who returned both a baseline health inventory checklist and the baseline SF-36 who were followed for a mean of 722.5 (+/-84.3) days. The primary outcome was all-cause mortality, with a secondary outcome of hospitalization within the VA system. Using a Cox proportional hazards model in a development set of 5,469 patients, a co-morbidity index [Seattle Index of Co-morbidity (SIC)] was constructed using information about age, smoking status and seven of 25 self-reported medical conditions that were associated with increased mortality. In the validation set of 5,478 patients, the SIC was predictive of both mortality and hospitalizations within the VA system. A separate model was constructed in which only age and the PCS and MCS scores of the SF-36 were entered to predict mortality. The SF-36 component scores and the SIC had comparable discriminatory ability (AUC for discrimination of death within 2 y 0.71 for both models). When combined, the SIC and SF-36 together had improved discrimination for mortality (AUC = 0.74, p-value for difference in AUC < 0.005). A new outpatient co-morbidity score developed using self-identified chronic medical conditions on a baseline health inventory checklist was predictive of 2-y mortality and hospitalization within the VA system in general internal medicine patients.

[1]  J. Gold,et al.  Validation of a combined comorbidity index. , 1994, Journal of clinical epidemiology.

[2]  M. Hornbrook,et al.  Assessing relative health plan risk with the RAND-36 health survey. , 1995, Inquiry : a journal of medical care organization, provision and financing.

[3]  W H Rogers,et al.  Comparison of methods for the scoring and statistical analysis of SF-36 health profile and summary measures: summary of results from the Medical Outcomes Study. , 1995, Medical care.

[4]  C. Mackenzie,et al.  A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. , 1987, Journal of chronic diseases.

[5]  M. Ingber,et al.  Risk Adjustment for the Medicare program: lessons learned from research and demonstrations. , 1998, Inquiry : a journal of medical care organization, provision and financing.

[6]  M. Charlson,et al.  Clinical assessments as predictors of one year survival after hospitalization: implications for prognostic stratification. , 1988, Journal of clinical epidemiology.

[7]  T. Lumley,et al.  Time‐Dependent ROC Curves for Censored Survival Data and a Diagnostic Marker , 2000, Biometrics.

[8]  J. Guralnik,et al.  Assessing the impact of comorbidity in the older population. , 1996, Annals of epidemiology.

[9]  L. Goldman,et al.  The SUPPORT Prognostic Model: Objective Estimates of Survival for Seriously Ill Hospitalized Adults , 1995, Annals of Internal Medicine.

[10]  L. Epstein,et al.  The validity of self-reports on chronic disease: characteristics of underreporters and implications for the planning of services. , 1996, Public health reviews.

[11]  L I Iezzoni,et al.  Judging hospitals by severity-adjusted mortality rates: the influence of the severity-adjustment method. , 1996, American journal of public health.

[12]  Gregory E. Simon,et al.  A Chronic Disease Score with Empirically Derived Weights , 1995, Medical care.

[13]  Michael J. Goodman,et al.  Chronic disease, functional health status, and demographics: a multi-dimensional approach to risk adjustment. , 1996, Health services research.

[14]  C Safran,et al.  Predicting In‐Hospital Mortality The Importance of Functional Status Information , 1995, Medical care.

[15]  T. Mayer,et al.  The association of the SF-36 health status survey with 1-year socioeconomic outcomes in a chronically disabled spinal disorder population. , 1999, Spine.

[16]  R. Elashoff,et al.  Patterns of care related to age of breast cancer patients. , 1987, JAMA.

[17]  William A. Knaus,et al.  The SUPPORT Prognostic Model , 1995 .

[18]  R. Deyo,et al.  Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. , 1992, Journal of clinical epidemiology.

[19]  T. Lash,et al.  Comparison of interview-based and medical-record based indices of comorbidity among breast cancer patients. , 1999, Medical care.

[20]  J E Ware,et al.  Overview of the SF-36 Health Survey and the International Quality of Life Assessment (IQOLA) Project. , 1998, Journal of clinical epidemiology.

[21]  J Concato,et al.  Importance of functional measures in predicting mortality among older hospitalized patients. , 1998, JAMA.

[22]  P. Grambsch,et al.  Proportional hazards tests and diagnostics based on weighted residuals , 1994 .

[23]  S Greenfield,et al.  The Importance of Co-existent Disease in the Occurrence of Postoperative Complications and One-Year Recovery in Patients Undergoing Total Hip Replacement: Comorbidity and Outcomes After Hip Replacement , 1993, Medical care.

[24]  Taking Health Status Into Account When Setting Capitation Rates: A Comparison of Risk-Adjustment Methods , 1996 .

[25]  L I Iezzoni,et al.  Predicting Who Dies Depends on How Severity Is Measured: Implications for Evaluating Patient Outcomes , 1995, Annals of Internal Medicine.

[26]  B D Kahan,et al.  The impact of comorbid and sociodemographic factors on access to renal transplantation. , 1993, JAMA.

[27]  M. Extermann,et al.  Measuring comorbidity in older cancer patients. , 2000, European journal of cancer.

[28]  D. Bates,et al.  Diagnosis-Based Risk Adjustment for Medicare Capitation Payments , 1996, Health care financing review.

[29]  L. Kazis,et al.  Comparing generic and disease-specific measures of physical and role functioning: results from the Veterans Health Study. , 1998, Medical care.

[30]  G. Lyman,et al.  Comorbidity and functional status are independent in older cancer patients. , 1998, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[31]  S. Kaplan.  Patient reports of health status as predictors of physiologic health measures in chronic disease. , 1987, Journal of chronic diseases.

[32]  Donald R. Miller,et al.  Health Status in VA Patients: Results from the Veterans Health Study , 1999, American journal of medical quality : the official journal of the American College of Medical Quality.

[33]  L I Iezzoni,et al.  The importance of comorbidities in explaining differences in patient costs. , 1996, Medical care.

[34]  K. E. Kilpatrick,et al.  Differences in patient characteristics between Veterans Administration and community hospitals. Implications for VA planning. , 1987, Medical care.

[35]  S. Greenfield,et al.  Outcomes of patients with hypertension and non-insulin dependent diabetes mellitus treated by different systems and specialties. Results from the medical outcomes study. , 1995, JAMA.

[36]  S H Kaplan,et al.  Development and testing of a new measure of case mix for use in office practice. , 1995, Medical care.

[37]  D W Bates,et al.  Can comorbidity be measured by questionnaire rather than medical record review? , 1996, Medical care.

[38]  E. Fisher,et al.  Studying Outcomes and Hospital Utilization in the Elderly: The Advantages of a Merged Data Base for Medicare and Veterans Affairs Hospitals , 1992, Medical care.