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

开发和验证住院糖尿病患者的 EHR 集成再入院风险预测模型

基本信息

批准号：
10629295
负责人：
Daniel J Rubin
金额：
$ 54.84万
依托单位：
TEMPLE UNIV OF THE COMMONWEALTH
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-08-21 至 2025-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10629295
关键词：
Admission activity Caring Clinical Clinical Data Clinical Paths Clinical Research Collection Complement Data Data Set Dependence Diabetes Mellitus Electronic Health Record Goals Hospitalization Hospitals Institution Insulin Intervention Laboratories Length of Stay Machine Learning Manuals Modeling Participant Patient Readmission Patients Pharmaceutical Preparations Publishing Recording of previous events Research Risk Risk Factors Techniques Testing Translating Translations Validation Work clinical practice cohort comorbidity comorbidity Index cost cost outcomes deep learning demographics design diabetes risk electronic health record system experience high risk hospital readmission improved individual patient learning strategy member model development patient oriented patient subsets point of care predictive modeling predictive tools prospective readmission risk risk prediction risk prediction model sociodemographics statistics tool

项目摘要

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.

项目总结/文摘

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Hospital Readmission Risk and Risk Factors of People with a Primary or Secondary Discharge Diagnosis of Diabetes.

DOI：
10.3390/jcm12041274
发表时间：
2023-02-06
期刊：
Journal of clinical medicine
影响因子：
3.9
作者：
Rubin DJ;Maliakkal N;Zhao H;Miller EE
通讯作者：
Miller EE

Predicting and Preventing Acute Care Re-Utilization by Patients with Diabetes.

DOI：
10.1007/s11892-021-01402-7
发表时间：
2021-09-04
期刊：
Current diabetes reports
影响因子：
4.2
作者：
Rubin DJ;Shah AA
通讯作者：
Shah AA

The Diabetes Transition of Hospital Care (DiaTOHC) Pilot Study: A Randomized Controlled Trial of an Intervention Designed to Reduce Readmission Risk of Adults with Diabetes.

DOI：
10.3390/jcm11061471
发表时间：
2022-03-08
期刊：
Journal of clinical medicine
影响因子：
3.9
作者：
Rubin DJ;Gogineni P;Deak A;Vaz C;Watts S;Recco D;Dillard F;Wu J;Karunakaran A;Kondamuri N;Zhao H;Naylor MD;Golden SH;Allen S
通讯作者：
Allen S