Interpretable machine learning predicts cardiac resynchronization therapy responses from personalized biochemical and biomechanical features.
Background: Cardiac Resynchronization Therapy (CRT) is a widely used, device-based therapy for patients with left ventricle (LV) failure. Unfortunately, many patients do not benefit from CRT, so there is potential value in identifying this group of non-responders before CRT implementation. Past stud...
| Publicado en: | BMC Medical Informatics & Decision Making Vol. 22; no. 1; pp. 1 - 11 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
BioMed Central
10/31/2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=159972033&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 159972033 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726947 1CI0 jtl: BMC Medical Informatics & Decision Making issn: 14726947 maglogo: N pubinfo: dt: 10/31/2022 vid: 22 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 159972033 159972033 NLM36316772 10.1186/s12911-022-02015-0 NLM36316772 159972033 ppf: 1 ppct: 10 formats: tig: atl: Interpretable machine learning predicts cardiac resynchronization therapy responses from personalized biochemical and biomechanical features. aug: au: Haque, Anamul Stubbs, Doug Hubig, Nina C. Spinale, Francis G. Richardson, William J. affil: Biomedical Data Science & Informatics Program, Clemson University, Clemson, SC, USA sug: subj: Heart Failure Therapy Cardiac Resynchronization Therapy Adverse Effects Ventricular Function, Left Physiology Treatment Outcomes Clinical Assessment Tools Scales Questionnaires ab: Background: Cardiac Resynchronization Therapy (CRT) is a widely used, device-based therapy for patients with left ventricle (LV) failure. Unfortunately, many patients do not benefit from CRT, so there is potential value in identifying this group of non-responders before CRT implementation. Past studies suggest that predicting CRT response will require diverse variables, including demographic, biomarker, and LV function data. Accordingly, the objective of this study was to integrate diverse variable types into a machine learning algorithm for predicting individual patient responses to CRT.Methods: We built an ensemble classification algorithm using previously acquired data from the SMART-AV CRT clinical trial (n = 794 patients). We used five-fold stratified cross-validation on 80% of the patients (n = 635) to train the model with variables collected at 0 months (before initiating CRT), and the remaining 20% of the patients (n = 159) were used as a hold-out test set for model validation. To improve model interpretability, we quantified feature importance values using SHapley Additive exPlanations (SHAP) analysis and used Local Interpretable Model-agnostic Explanations (LIME) to explain patient-specific predictions.Results: Our classification algorithm incorporated 26 patient demographic and medical history variables, 12 biomarker variables, and 18 LV functional variables, which yielded correct prediction of CRT response in 71% of patients. Additional patient stratification to identify the subgroups with the highest or lowest likelihood of response showed 96% accuracy with 22 correct predictions out of 23 patients in the highest and lowest responder groups.Conclusion: Computationally integrating general patient characteristics, comorbidities, therapy history, circulating biomarkers, and LV function data available before CRT intervention can improve the prediction of individual patient responses. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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