Predicting Inpatient Readmission and Outpatient Admission in Elderly: A Population-Based Cohort Study.

Recognizing potentially avoidable hospital readmission and admissions are important health care quality issues. We develop prediction models for inpatient readmission and outpatient admission to hospitals for older adults In the retrospective cohort study with 2 million sampling file of the National Health Insurance Research Database in Taiwan, older adults (aged ≥65 y/o) with a first admission in 2008 were enrolled in the inpatient cohort (N = 39,156). The outpatient cohort included subjects who had ≥1 outpatient visit in 2008 (N = 178,286). Each cohort was split into derivation (3/4) and validation (1/4) data set. Primary outcome of the inpatient cohort: 30-day readmission from the date of discharge. The outpatient cohort included hospital admissions within the 1-year follow-up period. Candidate risk factors include demographics, comorbidities, and previous health care utilizations. Series of logistic regression models were applied with area under the receiver operating curves (AUCs) to identify the best model. Roughly 1 of 7 (14.6%) of the inpatients was readmitted within 30 days, and 1 of 5 (19.1%) of the outpatient cohort was admitted within 1 year. Age, education, use of home health care, and selected comorbidities (e.g., cancer with metastasis) were included in the final model. The AUC of the inpatient readmission model was 0.655 (95% confidence interval [CI] 0.646-0.664) and outpatient admission model was 0.642 (95% CI 0.639-0.646). Predictive performance was maintained in both validation data sets. The goodness-to-fit model demonstrated good calibration in both groups. We developed and validated practical clinical prediction models for inpatient readmission and outpatient admissions for general older adults with indicators easily obtained from an administrative data set.