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- Title
Identifying patients at highest-risk: the best timing to apply a readmission predictive model.
- Authors
Flaks-Manov, Natalie; Topaz, Maxim; Hoshen, Moshe; Balicer, Ran D.; Shadmi, Efrat
- Abstract
<bold>Background: </bold>Most 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.<bold>Methods: </bold>An 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.<bold>Results: </bold>The 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%.<bold>Conclusions: </bold>The 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.
- Subjects
ISRAEL; HOSPITAL admission &; discharge; PREDICTION models; HOSPITAL wards; PATIENT selection; PATIENT readmissions
- Publication
BMC Medical Informatics & Decision Making, 2019, Vol 19, Issue 1, pN.PAG
- ISSN
1472-6947
- Publication type
journal article
- DOI
10.1186/s12911-019-0836-6