科研之友
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摘要
Purpose. A practical tool for predicting the risk of 30-day readmissions using data readily available to pharmacists before hospital discharge is described.
Methods. A retrospective cohort study to identify predictors of potentially avoidable 30-day readmissions was conducted using transitions-of-care pharmacy notes and electronic medical record data from a large health system. Through univariate and multivariable logistic regression analyses of factors associated with unplanned readmissions in the study cohort (n = 690) over a 22-month period, a risk prediction tool was developed. The tool's discriminative ability was assessed using the C statistic; its calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test.
Results. Three factors predictive of readmission risk were identified; these variables-medication count, comobidity count, and health insurance status at discharge-form the 3-predictor MEDCOINS score. Among patients identified as being at high risk for readmission using the MEDCOINS tool, the estimated readmission risk was 22.5%, as compared with an observed readmission rate of 21.9%. The discriminatory performance of MEDCOINS scoring was fair (C statistic = 0.65 [95% confidence interval, 0.60-0.70]), with good calibration (Hosmer-Lemeshow p = 0.99).
Conclusion. Among a cohort of patients who were seen by a transitions-of-care pharmacist during an inpatient hospitalization, comorbidity burden, number of medications, and health insurance coverage were most predictive of 30-day readmission. The MEDCOINS tool was found to have fair discriminative ability and good calibration.
- 出版日期2018-2-1
