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Multi-output machine learning for prediction of postoperative outcomes after cardiac surgery using patient blood management biomarkers

2026-06-01Artigo de investigação Acesso aberto
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dc.contributor.authorCoelho, Henrique
dc.contributor.authorPaupério, Diana
dc.contributor.authorSilva, Fernando
dc.contributor.authorBarbosa, Maria Inês
dc.contributor.authorRibeiro, Pedro
dc.contributor.authorCorreia, Marta
dc.contributor.authorRodrigues, Pedro Miguel
dc.date.accessioned2026-06-23T16:11:33Z
dc.date.available2026-06-23T16:11:33Z
dc.date.issued2026-06-01
dc.description.abstractBackground/Objectives: Postoperative complications following adult cardiac surgery are biologically interrelated, yet most machine learning models predict single outcomes. We developed an explainable multi-output model integrating routinely collected clinical variables and patient blood management (PBM) biomarkers to predict multiple postoperative outcomes simultaneously, with complementary mono-output analyses for selected endpoints. Methods: This retrospective single-center cohort included 1414 adults undergoing cardiac surgery. In total, 513 complete cases were analyzed. Thirteen outcomes were modeled, including major binary complications and ICU/ward length of stay. An initial 80:20 train–test split was used only for algorithm screening across six candidate multi-output regressors and training-set-defined feature subsets. The selected regressor was then evaluated across five random states, and global permutation feature importance was used for multi-output explainability. Mono-output binary analyses using the selected regressor and the same training-set-only feature-selection workflow were evaluated along with accuracy, precision, recall/sensitivity, and F1-scores. Results: The Decision Tree Regressor was selected. Across five random states, global multi-output performance was R2 = 0.83, MSE = 1.296, RMSE = 1.132, MAE = 0.298, and MAPE = 0.128. Based on global multi-output permutation importance, creatinine, ferritin, platelet count, estimated glomerular filtration rate, preoperative red blood cell units, and EuroSCORE II were ranked the highest. Atrial fibrillation had the lowest mono-output F1-score (0.719), whereas acute kidney injury, postoperative bleeding, infection, and 1-year hospital readmission yielded F1-scores of 0.928, 0.970, 0.963, and 0.975, respectively. Conclusions: This proof-of-concept study shows the feasibility of explainable multi-output modeling for postoperative outcomes after adult cardiac surgery using clinical and PBM variables. However, external validation is required prior to clinical use.eng
dc.identifier.doi10.3390/jcm15114221
dc.identifier.eid105041392720
dc.identifier.other422fb2d3-4a93-4670-a05d-8891c8c3ff63
dc.identifier.pmcPMC13258426
dc.identifier.pmid42279082
dc.identifier.urihttp://hdl.handle.net/10400.14/58235
dc.identifier.wos001790081900001
dc.language.isoeng
dc.peerreviewedyes
dc.publisherMDPI
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectCardiac surgeryeng
dc.subjectMachine learningeng
dc.subjectMulti-output regressioneng
dc.subjectPatient blood managementeng
dc.subjectPostoperative complicationseng
dc.titleMulti-output machine learning for prediction of postoperative outcomes after cardiac surgery using patient blood management biomarkers
dc.typeresearch article
dspace.entity.typePublication
oaire.citation.issue11
oaire.citation.volume15
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85

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