Do positive psychosocial factors contribute to the prediction of coronary artery disease? A UK Biobank-based machine learning approach.

Journal: European journal of preventive cardiology
PMID: 39056264

Abstract

AIMS: Most prediction models for coronary artery disease (CAD) compile biomedical and behavioural risk factors using linear multivariate models. This study explores the potential of integrating positive psychosocial factors (PPFs), including happiness, satisfaction with life, and social support, into conventional and machine learning-based CAD-prediction models.

Authors

  • René Hefti
    Department of Psychosomatic Medicine, University Hospital Basel and University of Basel, Hebelstrasse 2, CH-4031 Basel, Switzerland.
  • Souad Guemghar
    Department of Psychosomatic Medicine, University Hospital Basel and University of Basel, Hebelstrasse 2, CH-4031 Basel, Switzerland.
  • Edouard Battegay
    International Center for Multimorbidity and Complexity in Medicine (ICMC), Universität Zürich, Zürich, Schweiz. edouard.battegay@uzh.ch.
  • Christian Mueller
    Department of Cardiology and Cardiovascular Research Institute Basel (CRIB), University Heart Center Basel, University Hospital Basel, University of Basel, Switzerland.
  • Harold G Koenig
    Department of Medicine and Psychiatry, Duke University Medical Center, 40 Duke Medicine Cir., Durham, NC 27710, USA.
  • Rainer Schaefert
    Department of Psychosomatic Medicine, University Hospital Basel and University of Basel, Hebelstrasse 2, CH-4031 Basel, Switzerland.
  • Gunther Meinlschmidt
    Division of Clinical Psychology and Cognitive Behavioural Therapy, International Psychoanalytic University (IPU) Berlin, Berlin, Germany. meinlschmidt@uni-trier.de.

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