Developing and validating EHR-integrated readmission risk prediction models for hospitalized patients with diabetes

项目总结/摘要 再次入院是一种不受欢迎的、代价高昂的结局，但可能是可以预防的。住院 糖尿病患者在30天内再入院的风险高于 无糖尿病患者，美国糖尿病患者中发生> 100万例再入院 每年。某些干预措施可以降低再入院风险，但应用这些干预措施， 广泛地是成本过高的。提高干预措施效率的一个办法是， 再入院风险是针对高风险患者。我们以前发表了一个模型，糖尿病 早期再入院风险指标（DERRITM），预测全因30天再入院的风险 糖尿病患者的情况。然而，DERRI具有适度的预测准确性（C-统计量0.63- 0.65）。 0.69），并需要手动输入数据。最近，我们证明了将变量添加到 DERRI显著提高了预测准确性（DERRIplus，C-统计量0.82）。但使用 这种基于人工输入数据来预测再入院风险的更大模型将过于费力 用于临床环境。事实上，大多数再入院风险预测模型受到以下因素的限制： 准确性和易用性之间的权衡;缺乏与 临床工作流程;准确性中等;缺乏验证;仅依赖于可用数据 出院后。 目前建议的目标是：1）制定更准确的全因 使用电子健康记录（EHR）数据的计划外30-D再入院风险预测模型， 糖尿病患者（eDERRI）; 2）将模型转换为自动化的，基于EHR的工具 预测住院患者再入院风险的百分比; 3）前瞻性验证 eDERRI模型和工具。新的eDERRI模型将扩展 DERRIplus基于EHR数据的可用性（例如，社会人口统计学，遭遇史， 药物使用、实验室结果、合并症和住院时间）。为了开发模型，我们 将利用来自PaTH临床数据研究网络（CDRN）的数据，这是一个多中心，40多个 国家以患者为中心的临床研究网络（PCORnet）。我们将 应用最先进的深度学习方法来开发最佳预测模型。这个项目 将分析一个大型的，多中心的近340,000出院队列与尖端 技术来开发更好的模型，并将其转化为自动化工具， 糖尿病患者的再入院风险。拟议的工具将确定更高的风险 患者更有可能从干预中受益，从而改善护理并降低成本。

PROJECT SUMMARY/ABSTRACT Hospital readmission is an undesirable, costly outcome that may be preventable. Hospitalized patients with diabetes are at higher risk of readmission within 30 days (30-d readmission) than patients without diabetes, and >1 million readmissions occur among diabetes patients in the US annually. Certain interventions can reduce readmission risk, but applying these interventions widely is cost prohibitive. One approach for improving the efficiency of interventions that reduce readmission risk is to target high-risk patients. We previously published a model, the Diabetes Early Readmission Risk Indicator (DERRITM), that predicts the risk of all-cause 30-d readmission of patients with diabetes. The DERRI, however, has modest predictive accuracy (C-statistic 0.63- 0.69), and requires manual data input. Recently, we demonstrated that adding variables to the DERRI substantially improves predictive accuracy (DERRIplus, C-statistic 0.82). However, using this larger model to predict readmission risk based on manual input of data would be too labor intensive for clinical settings. Indeed, most readmission risk prediction models are limited by the trade-off between accuracy and ease of use; lack of translation to a tool that integrates with clinical workflow; modest accuracy; lack of validation; and dependence on data only available after hospital discharge. The objectives of the current proposal are: 1) To develop more accurate all-cause unplanned 30-d readmission risk prediction models using electronic health record (EHR) data of patients with diabetes (eDERRI); 2) To translate the models to an automated, EHR-based tool that predicts % readmission risk of hospitalized patients; and 3) To prospectively validate the eDERRI models and tool. The new eDERRI models will expand upon the variables in the DERRIplus based on availability in EHR data (e.g., sociodemographics, encounter history, medication use, laboratory results, comorbidities, and length of stay). To develop the models, we will leverage data from the PaTH Clinical Data Research Network (CDRN), a multi-center, 40-plus hospital member of the National Patient-Centered Clinical Research Network (PCORnet). We will apply state-of-the-art deep-learning methods to develop optimal predictive models. This project will analyze a large, multi-center cohort of nearly 340,000 discharges with cutting-edge techniques to develop better models and translate them to an automated tool that predicts readmission risk for individual patients with diabetes. The proposed tool would identify higher risk patients more likely to benefit from intervention, thus improving care and reducing costs.