Predicting Readmission at Early Hospitalization Using Electronic Clinical Data An Early Readmission Risk Score
Predicting Readmission at Early Hospitalization Using Electronic Clinical Data An Early Readmission Risk Score
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DOI：
10.1097/mlr.0000000000000654
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发表时间：
2017-03-01
期刊：
影响因子：
3
通讯作者：
Johannes, Richard S.
中科院分区：
文献类型：
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作者：
Tabak, Ying P.;Sun, Xiaowu;Johannes, Richard S.
背景：在住院期间及早确定高危患者可能有助于减少再入院的努力。我们试图利用入院时获得的自动化临床数据来开发早期再入院风险预测模型。方法：我们使用派生队列开发了早期再入院风险模型，并使用验证队列对模型进行了验证。我们使用已发表的急性实验室死亡风险评分作为入院时临床严重程度的综合衡量标准，以及在过去90天内出院的数量作为疾病进展的衡量标准。然后，我们通过添加主次诊断和其他变量来评估管理数据增强型模型。结果：来自70家医院的1,195,640名成人出院，其中39.8%为男性，中位年龄为63岁(第一和第三四分位数：43，78)。30d再住院率为11.9%(n=142,211)。早期再入院模型得出了再入院与急性实验室死亡风险评分和90天内既往出院次数的分级关系。模型c-统计量为0.697，校正良好。当管理变量被添加到模型中时，c统计量增加到0.722。结论：自动化临床数据可以在住院早期生成公平区分的再入院风险评分。它可能在帮助早期护理过渡方面具有应用价值。添加管理数据可提高预测准确性。管理数据增强模型可用于医院比较和结果研究。
Background: Identifying patients at high risk for readmission early during hospitalization may aid efforts in reducing readmissions. We sought to develop an early readmission risk predictive model using automated clinical data available at hospital admission.Methods: We developed an early readmission risk model using a derivation cohort and validated the model with a validation cohort. We used a published Acute Laboratory Risk of Mortality Score as an aggregated measure of clinical severity at admission and the number of hospital discharges in the previous 90 days as a measure of disease progression. We then evaluated the administrative dataenhanced model by adding principal and secondary diagnoses and other variables. We examined the c-statistic change when additional variables were added to the model.Results: There were 1,195,640 adult discharges from 70 hospitals with 39.8% male and the median age of 63 years (first and third quartile: 43, 78). The 30-day readmission rate was 11.9% (n = 142,211). The early readmission model yielded a graded relationship of readmission and the Acute Laboratory Risk of Mortality Score and the number of previous discharges within 90 days. The model c-statistic was 0.697 with good calibration. When administrative variables were added to the model, the c-statistic increased to 0.722.Conclusions: Automated clinical data can generate a readmission risk score early at hospitalization with fair discrimination. It may have applied value to aid early care transition. Adding administrative data increases predictive accuracy. The administrative data-enhanced model may be used for hospital comparison and outcome research.