Abstract
Background and Purpose: Hospital readmissions impose a substantial burden on the healthcare system. Reducing readmissions after stroke could lead to improved quality of care especially since stroke is associated with a high rate of readmission. The goal of this study is to enhance our understanding of the predictors of 30-day readmission after ischemic stroke and develop models to identify high-risk individuals for targeted interventions.Methods: We used patient-level data from electronic health records (EHR), five machine learning algorithms (random forest, gradient boosting machine, extreme gradient boosting–XGBoost, support vector machine, and logistic regression-LR), data-driven feature selection strategy, and adaptive sampling to develop 15 models of 30-day readmission after ischemic stroke. We further identified important clinical variables.Results: We included 3,184 patients with ischemic stroke (mean age: 71 ± 13.90 years, men: 51.06%). Among the 61 clinical variables included in the model, the National Institutes of Health Stroke Scale score above 24, insert indwelling urinary catheter, hypercoagulable state, and percutaneous gastrostomy had the highest importance score. The Model's AUC (area under the curve) for predicting 30-day readmission was 0.74 (95%CI: 0.64–0.78) with PPV of 0.43 when the XGBoost algorithm was used with ROSE-sampling. The balance between specificity and sensitivity improved through the sampling strategy. The best sensitivity was achieved with LR when optimized with feature selection and ROSE-sampling (AUC: 0.64, sensitivity: 0.53, specificity: 0.69).Conclusions: Machine learning-based models can be designed to predict 30-day readmission after stroke using structured data from EHR. Among the algorithms analyzed, XGBoost with ROSE-sampling had the best performance in terms of AUC while LR with ROSE-sampling and feature selection had the best sensitivity. Clinical variables highly associated with 30-day readmission could be targeted for personalized interventions. Depending on healthcare systems' resources and criteria, models with optimized performance metrics can be implemented to improve outcomes.
Highlights
Hospital readmissions impose a substantial financial burden, costing Medicare about $26 billion annually [1]
There was a significant difference between patients who were readmitted and those who were not in terms of median length of stay (LOS), being married or previously married, discharged to SNF or against medical advice, and having Medicare or private insurance
Many studies considered readmission after 90 days or 1 year as a dependent variable which is a long follow-up period, as Centers for Medicare and Medicaid Services (CMS) penalizes healthcare systems for readmission under 30 days. We addressed these gaps and improved the prediction performance of readmissions in stroke patients using a wide range of potential risk factors and the proper Machine learning (ML) techniques
Summary
Hospital readmissions impose a substantial financial burden, costing Medicare about $26 billion annually [1]. Centers for Medicare and Medicaid Services (CMS) has made reducing 30day readmission rates a national healthcare reform goal [2] as a way to improve hospital care. Studies have found that stroke severity [3, 4], being discharged to skilled nursing, intermediate care facility, hospice, or left against doctor’s advice [2, 3, 5,6,7], being enrolled in Medicaid/Medicare [4, 6, 8, 9], and being married [5] were associated with higher readmissions. Hospital readmissions impose a substantial burden on the healthcare system. The goal of this study is to enhance our understanding of the predictors of 30-day readmission after ischemic stroke and develop models to identify high-risk individuals for targeted interventions
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