Abstract
BackgroundMost of readmission prediction models are implemented at the time of patient discharge. However, interventions which include an early in-hospital component are critical in reducing readmissions and improving patient outcomes. Thus, at-discharge high-risk identification may be too late for effective intervention. Nonetheless, the tradeoff between early versus at-discharge prediction and the optimal timing of the risk prediction model application remains to be determined. We examined a high-risk patient selection process with readmission prediction models using data available at two time points: at admission and at the time of hospital discharge.MethodsAn historical prospective study of hospitalized adults (≥65 years) discharged alive from internal medicine units in Clalit’s (the largest integrated payer-provider health fund in Israel) general hospitals in 2015. The outcome was all-cause 30-day emergency readmissions to any internal medicine ward at any hospital. We used the previously validated Preadmission Readmission Detection Model (PREADM) and developed a new model incorporating PREADM with hospital data (PREADM-H). We compared the percentage of overlap between the models and calculated the positive predictive value (PPV) for the subgroups identified by each model separately and by both models.ResultsThe final cohort included 35,156 index hospital admissions. The PREADM-H model included 17 variables with a C-statistic of 0.68 (95% CI: 0.67–0.70) and PPV of 43.0% in the highest-risk categories. Of patients categorized by the PREADM-H in the highest-risk decile, 78% were classified similarly by the PREADM. The 22% (n = 229) classified by the PREADM-H at the highest decile, but not by the PREADM, had a PPV of 37%. Conversely, those classified by the PREADM into the highest decile but not by the PREADM-H (n = 218) had a PPV of 31%.ConclusionsThe timing of readmission risk prediction makes a difference in terms of the population identified at each prediction time point – at-admission or at-discharge. Our findings suggest that readmission risk identification should incorporate a two time-point approach in which preadmission data is used to identify high-risk patients as early as possible during the index admission and an “all-hospital” model is applied at discharge to identify those that incur risk during the hospital stay.
Highlights
Most of readmission prediction models are implemented at the time of patient discharge
The remaining 22% who were classified by the PREADMH highest decile, but not by the Preadmission Readmission Detection Model (PREADM), had a positive predictive value (PPV) of 37%. Those classified by the PREADM into the highest decile but not by the PREADM-H (n = 218) had a PPV of 31%
Our findings suggest that readmission risk identification should incorporate a two-time-point approach in which preadmission data are used to identify high-risk patients as early as possible during the index admission and an at-discharge “all-hospital” model, which is applied to identify those who incur risk during the hospital stay
Summary
Most of readmission prediction models are implemented at the time of patient discharge. We examined a high-risk patient selection process with readmission prediction models using data available at two time points: at admission and at the time of hospital discharge. Interventions that are aimed at the prevention of hospital readmissions are increasingly guided by computerized risk prediction models, which identify high-risk patients [1]. With the advent of electronic health records (EHRs) [4], detailed data on key risk factors, including clinical and healthcare utilization, are available from the preadmission period [5]. A multi-condition electronic model, based on data available at admission, showed that meaningful patient-level risk stratification of readmission risk can occur early in the hospital stay without the need to wait for further information at time of discharge [7]. A recent review has demonstrated that preadmission prediction models performed comparably well to the at-discharge models [2]
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