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

项目摘要/摘要 再次住院是一种不受欢迎的、代价高昂的结果，可能是可以预防的。住院治疗 糖尿病患者在30天内再入院(30天再入院)风险比 在美国，糖尿病患者中有100万人是非糖尿病患者。 每年一次。某些干预措施可以降低再入院风险，但应用这些干预措施 广泛的成本是令人望而却步的。一种提高干预效率的方法，减少 再入院风险是针对高危患者的。我们之前发表了一个模型，糖尿病 早期再入院风险指标(DERRITM)，预测全原因30天再入院风险 糖尿病患者的比例。然而，DERI具有适度的预测准确性(C-统计0.63- 0.69)，需要手工录入数据。最近，我们演示了将变量添加到 DERRI显著提高了预测精度(DERRIplus，C-统计量0.82)。但是，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太费力了 用于临床环境的强化治疗。事实上，大多数重新接纳风险预测模型都受到 在准确性和易用性之间权衡；缺乏翻译为集成了 临床工作流程；不太准确；缺乏验证；对仅有数据的依赖 出院后。 目前提案的目标是：1)制定更准确的全能事业 使用电子健康记录(EHR)数据的计划外30天再入院风险预测模型 糖尿病患者(EDERRI)；2)将模型转换为基于EHR的自动化工具 这可以预测住院患者的再入院风险百分比；以及3)前瞻性地验证 EDERRI模型和工具。新的eDERRI模型将在 基于EHR数据中的可用性的DERRIplus(例如，社会人口统计，相遇历史， 药物使用、实验室结果、合并症和住院时间)。为了开发这些模型，我们 将利用来自Path临床数据研究网络(CDRN)的数据，该网络是一个由40多个中心组成的多中心 医院是国家以患者为中心的临床研究网络(PCORnet)的成员。我们会 应用最先进的深度学习方法开发最优预测模型。这个项目 将以尖端技术分析近34万例患者的大型多中心队列 开发更好的模型并将其转换为预测的自动化工具的技术 糖尿病患者个体的再入院风险。拟议的工具将识别更高的风险 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.