Medical Care

Skip Navigation LinksHome > July 2008 - Volume 46 - Issue 7 > Risk Factors for Hospitalization Among Community-Dwelling Pr...
Medical Care:
doi: 10.1097/MLR.0b013e3181649426
Original Article

Risk Factors for Hospitalization Among Community-Dwelling Primary Care Older Patients: Development and Validation of a Predictive Model

Inouye, Sharon K. MD, MPH*†; Zhang, Ying MD, MPH†; Jones, Richard N. ScD*†; Shi, Peilin PhD†; Cupples, L Adrienne PhD‡; Calderon, Harold N. BBA§; Marcantonio, Edward R. MD, SM*†

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Background: Unplanned hospitalization often represents a costly and hazardous event for the older population.

Objectives: To develop and validate a predictive model for unplanned medical hospitalization from administrative data.

Research Design: Model development and validation.

Subjects: A total of 3919 patients aged ≥70 years who were followed for at least 1 year in primary care clinics of an academic medical center.

Measures: Risk factor data and the primary outcome of unplanned medical hospitalization were obtained from administrative data.

Results: Of 1932 patients in the development cohort, 299 (15%) were hospitalized during 1 year follow up. Five independent risk factors were identified in the preceding year: Deyo-Charlson comorbidity score ≥2 [adjusted relative risk (RR) = 1.8; 95% confidence interval (CI): 1.4–2.2], any prior hospitalization (RR = 1.8; 95% CI: 1.5–2.3), 6 or more primary care visits (RR = 1.6; 95% CI: 1.3–2.0), age ≥85 years (RR = 1.4; 95% CI: 1.1–1.7), and unmarried status (RR = 1.4; 95% CI: 1.1–1.7). A risk stratification system was created by adding 1 point for each factor present. Rates of hospitalization for the low- (0 factor), intermediate- (1–2 factors), and high-risk (≥3 factors) groups were 5%, 15%, and 34% (P < 0.0001). The corresponding rates in the validation cohort, where 328/1987 (17%) were hospitalized, were 6%, 16%, and 36% (P < 0.0001).

Conclusions: A predictive model based on administrative data has been successfully validated for prediction of unplanned hospitalization. This model will identify patients at high risk for hospitalization who may be candidates for preventive interventions.

© 2008 Lippincott Williams & Wilkins, Inc.


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