Clalit Research Institute, Clalit Health Services, Shoham 2, Ramat Gan, Israel.
Cheryl Spencer Department of Nursing, Faculty of Social Welfare and Health Sciences, University of Haifa, 31905, Haifa, Israel.
BMC Med Inform Decis Mak. 2019 Jun 26;19(1):118. doi: 10.1186/s12911-019-0836-6.
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.
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.
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%.
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.
大多数再入院预测模型都是在患者出院时实施的。然而，包括早期院内治疗的干预措施对于降低再入院率和改善患者预后至关重要。因此，出院时的高风险识别对于有效的干预可能为时已晚。尽管如此，早期与出院时预测的权衡以及风险预测模型应用的最佳时机仍有待确定。我们使用在两个时间点可用的数据，即入院时和出院时，检查了一个使用再入院预测模型的高危患者选择过程。
这是一项对 2015 年在克拉利特（以色列最大的综合支付方-服务提供方健康基金）综合医院内科单元出院存活的≥65 岁住院成年人进行的历史前瞻性研究。结局为任何内科病房的全因 30 天急诊再入院。我们使用了先前验证过的入院前再入院检测模型（PREADM），并开发了一个新的模型，该模型将 PREADM 与医院数据相结合（PREADM-H）。我们比较了模型之间的重叠百分比，并分别计算了每个模型和两个模型确定的亚组的阳性预测值（PPV）。
最终队列包括 35156 例指数住院患者。PREADM-H 模型包含 17 个变量，C 统计量为 0.68（95%置信区间：0.67-0.70），风险最高类别中的 PPV 为 43.0%。在 PREADM-H 模型中被归类为最高风险十分位数的患者中，有 78%的患者被 PREADM 归类为相似类别。在 PREADM-H 模型中被归类为最高风险十分位数的 22%（n=229）患者，而不是 PREADM，则其 PPV 为 37%。相反，在 PREADM 模型中被归类为最高风险十分位数但未被 PREADM-H 模型归类的患者（n=218）的 PPV 为 31%。
再入院风险预测的时间会影响每个预测时间点（入院时或出院时）确定的人群。我们的研究结果表明，再入院风险识别应采用两时间点方法，即使用入院前数据尽早识别高危患者，并在出院时应用“全院”模型来识别在住院期间产生风险的患者。
