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Machine Learning-Enabled 30-Day Readmission Model for Stroke Patients

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dc.contributor.authorDarabi, Negaren
dc.contributor.authorHosseinichimeh, Niyoushaen
dc.contributor.authorNoto, Anthonyen
dc.contributor.authorZand, Raminen
dc.contributor.authorAbedi, Vidaen
dc.date.accessioned2021-09-02T17:44:46Zen
dc.date.available2021-09-02T17:44:46Zen
dc.date.issued2021-03-31en
dc.description.abstractBackground 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.en
dc.description.sponsorshipVA had financial research support from the National Institute of Health (NIH) grant no. R56HL116832 subawarded to Geisinger during the study period. RZ had financial research support from Bucknell University Initiative Program, Roche – Genentech Biotechnology Company, the Geisinger Health Plan Quality fund, and receives institutional support from Geisinger Health System during the study period.en
dc.description.versionPublished versionen
dc.format.extent10 pagesen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationDarabi N, Hosseinichimeh N, Noto A, Zand R and Abedi V (2021) Machine Learning-Enabled 30-Day Readmission Model for Stroke Patients. Front. Neurol. 12:638267. doi: 10.3389/fneur.2021.638267en
dc.identifier.doihttps://doi.org/10.3389/fneur.2021.638267en
dc.identifier.urihttp://hdl.handle.net/10919/104913en
dc.identifier.volume12en
dc.language.isoenen
dc.publisherFrontiersen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectischemic strokeen
dc.subject30-day readmissionsen
dc.subjectMachine learningen
dc.subjectstatistical analysisen
dc.subjectpatient readmissionen
dc.titleMachine Learning-Enabled 30-Day Readmission Model for Stroke Patientsen
dc.title.serialFrontiers in Neurologyen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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