Ensemble machine learning prediction and variable importance analysis of 5-year mortality after cardiac valve and CABG operations.

Journal: Scientific reports
PMID: 33568739

Abstract

Despite having a similar post-operative complication profile, cardiac valve operations are associated with a higher mortality rate compared to coronary artery bypass grafting (CABG) operations. For long-term mortality, few predictors are known. In this study, we applied an ensemble machine learning (ML) algorithm to 88 routinely collected peri-operative variables to predict 5-year mortality after different types of cardiac operations. The Super Learner algorithm was trained using prospectively collected peri-operative data from 8241 patients who underwent cardiac valve, CABG and combined operations. Model performance and calibration were determined for all models, and variable importance analysis was conducted for all peri-operative parameters. Results showed that the predictive accuracy was the highest for solitary mitral (0.846 [95% CI 0.812-0.880]) and solitary aortic (0.838 [0.813-0.864]) valve operations, confirming that ensemble ML using routine data collected perioperatively can predict 5-year mortality after cardiac operations with high accuracy. Additionally, post-operative urea was identified as a novel and strong predictor of mortality for several types of operation, having a seemingly additive effect to better known risk factors such as age and postoperative creatinine.

Authors

  • JosĂ© Castela Forte
    Department of Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, P.O. Box 30.001, 9700 RB, Groningen, The Netherlands. j.n.alves.castela.cardoso.forte@umcg.nl.
  • Hubert E Mungroop
    Department of Anesthesiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
  • Fred de Geus
    Department of Anesthesiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
  • Maureen L van der Grinten
    Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, The Netherlands.
  • Hjalmar R Bouma
    Department of Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, P.O. Box 30.001, 9700 RB, Groningen, The Netherlands.
  • Ville Pettilä
    Division of Intensive Care Medicine, Department of Anesthesiology, Intensive Care and Pain Medicine, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
  • Thomas W L Scheeren
    Department of Anaesthesiology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9700RB, Groningen, The Netherlands. t.w.l.scheeren@umcg.nl.
  • Maarten W N Nijsten
    Department of Critical Care, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
  • Massimo A Mariani
    Department of Cardiothoracic Surgery, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
  • Iwan C C van der Horst
    Department of Critical Care, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
  • Robert H Henning
    Department of Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, P.O. Box 30.001, 9700 RB, Groningen, The Netherlands.
  • Marco A Wiering
  • Anne H Epema
    Department of Anesthesiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.

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