| Title: | 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 |
| Published: | 26 Jul 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39056264/ |
| DOI: | https://doi.org/10.1093/eurjpc/zwae237 |
| URL: | https://academic.oup.com/eurjpc/advance-article-pdf/doi/10.1093/eurjpc/zwae237/58797452/zwae237.pdf |
Publication 12773
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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.</p>
METHODS AND RESULTS: We included UK Biobank (UKB) participants without CAD at baseline. First, we estimated associations of individual PPFs with subsequent acute myocardial infarction (AMI) and chronic ischaemic heart disease (CIHD) using logistic regression. Then, we compared the performances of logistic regression and eXtreme Gradient Boosting (XGBoost) prediction models when adding PPFs as predictors to the Framingham Risk Score (FRS). Based on a sample size between 160 226 and 441 419 of UKB participants, happiness, satisfaction with health and life, and participation in social activities were linked to lower AMI and CIHD risk (all P-for-trend ≤ 0.04), while social support was not. In a validation sample, adding PPFs to the FRS using logistic regression and XGBoost prediction models improved neither AMI [area under the receiver operating characteristic curve (AUC) change: 0.02 and 0.90%, respectively] nor CIHD (AUC change: -1.10 and -0.88%, respectively) prediction.</p>
CONCLUSION: Positive psychosocial factors were individually linked to CAD risk, in line with previous studies, and as reflected by the new European Society of Cardiology guidelines on cardiovascular disease prevention. However, including available PPFs in CAD-prediction models did not improve prediction compared with the FRS alone. Future studies should explore whether PPFs may act as CAD-risk modifiers, especially if the individual's risk is close to a decision threshold.</p>
17 Keywords
- Aged
- Biological Specimen Banks
- Coronary Artery Disease
- Female
- Happiness
- Humans
- Machine Learning
- Male
- Middle Aged
- Personal Satisfaction
- Predictive Value of Tests
- Prognosis
- Risk Assessment
- Risk Factors
- Social Support
- UK Biobank
- United Kingdom
7 Authors
- René Hefti
- Souad Guemghar
- Edouard Battegay
- Christian Mueller
- Harold G Koenig
- Rainer Schaefert
- Gunther Meinlschmidt
1 Application
| Application ID | Title |
|---|---|
| 85966 | Meaning, Happiness and Social Support as Protective Psychosocial Factors for Coronary Artery Disease and Related Mortality: A Personalised Artificial Intelligence Based Health Approach. |
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