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

被引:0
|
作者
Hefti, Rene [1 ,2 ]
Guemghar, Souad [1 ,2 ]
Battegay, Edouard [1 ,2 ,3 ,4 ]
Mueller, Christian [5 ]
Koenig, Harold G. [6 ]
Schaefert, Rainer [1 ,2 ]
Meinlschmidt, Gunther [1 ,2 ,7 ,8 ,9 ]
机构
[1] Univ Hosp Basel, Dept Psychosomat Med, Hebelstr 2, CH-4031 Basel, Switzerland
[2] Univ Basel, Hebelstr 2, CH-4031 Basel, Switzerland
[3] Univ Zurich, Int Ctr Multimorbid & Complex Med ICMC, Ramistr 71, CH-8006 Zurich, Switzerland
[4] Merian Iselin Klin, Fohrenstr 2, CH-4054 Basel, Switzerland
[5] Univ Hosp Basel, Cardiovasc Res Inst, Petersgraben 4, CH-4031 Basel, Switzerland
[6] Duke Univ, Med Ctr, Dept Med & Psychiat, 40 Duke Med Cir, Durham, NC 27710 USA
[7] Univ Hosp Basel, Dept Digital & Blended Psychosomat & Psychotherapy, Psychosomat Med, Hebelstrasse 2, CH-4031 Basel, Switzerland
[8] Trier Univ, Dept Psychol Clin Psychol & Psychotherapy Methods, Univ Sring 15, D-54296 Trier, Germany
[9] Int Psychoanalyt Univ IPU Berlin, Dept Clin Psychol & Cognit Behav Therapy, Stromstr 3b, D-10555 Berlin, Germany
关键词
Cardiovascular disease; Positive psychosocial factors; Disease prediction; Artificial intelligence; Preventive cardiology; HEART-DISEASE; CARDIOVASCULAR-DISEASE; RISK-FACTORS; MYOCARDIAL-INFARCTION; EUROPEAN-SOCIETY; SOCIAL-ISOLATION; TASK-FORCE; MORTALITY; ASSOCIATION; ADULTS;
D O I
10.1093/eurjpc/zwae237
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
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.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.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. Positive psychosocial factors (PPFs) like happiness, satisfaction with health and life, social support, and social activities can aid in successfully managing life's challenges, stress, and disease. Consequently, they may help lower the risk and progression of cardiovascular disease. The study confirmed that PPFs were associated with lower risks of myocardial infarction and chronic ischaemic heart disease. These findings underscore the role of PPFs as risk modifiers for coronary artery disease (CAD), as recommended by the 2021 ESC Guidelines on cardiovascular disease prevention. This means that the individual risk of getting a CAD can be shifted to the next lower risk category by higher levels of happiness, satisfaction with health and life, and social support. Graphical Abstract
引用
收藏
页数:10
相关论文
共 50 条
  • [41] Machine learning in coronary heart disease prediction: Structural equation modelling approach
    Rodrigues, Lewlyn L. R.
    Shetty, Dasharathraj K.
    Naik, Nithesh
    Maddodi, Chethana Balakrishna
    Rao, Anuradha
    Shetty, Ajith Kumar
    Bhat, Rama
    Hameed, Zeeshan
    COGENT ENGINEERING, 2020, 7 (01):
  • [42] Purine metabolite-based machine learning models for risk prediction, prognosis, and diagnosis of coronary artery disease
    Jung, Sunhee
    Ahn, Eunyong
    Koh, Sang Baek
    Lee, Sang-Hak
    Hwang, Geum-Sook
    BIOMEDICINE & PHARMACOTHERAPY, 2021, 139
  • [43] Use machine learning models to identify and assess risk factors for coronary artery disease
    Zhang, Mingyang
    Wang, Hongnian
    Zhao, Ju
    PLOS ONE, 2024, 19 (09):
  • [44] Do the Novel Pulmonary Risk Factors Identified by the Machine Learning Have an Impact on Outcomes of Revascularization in Complex Coronary Artery Disease?
    Kageyama, Shigetaka
    Ninomiya, Kai
    Jonik, Szymon
    Masuda, Shinichiro
    Kotoku, Nozomi
    Revaiah, Pruthvi Chenniganahosahalli
    O'Leary, Neil
    Serruys, Patrick
    Mazurek, Tomasz
    Onuma, Yoshinobu
    JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY, 2023, 82 (17) : B112 - B112
  • [45] A Machine Learning Model Based on Genetic and Traditional Cardiovascular Risk Factors to Predict Premature Coronary Artery Disease
    Liu, Benrong
    Fang, Lei
    Xiong, Yujuan
    Du, Qiqi
    Xiang, Yang
    Chen, Xiaohui
    Tian, Chao-Wei
    Liu, Shi-Ming
    FRONTIERS IN BIOSCIENCE-LANDMARK, 2022, 27 (07):
  • [46] Machine learning-based coronary artery disease diagnosis: A comprehensive review
    Alizadehsani, Roohallah
    Abdar, Moloud
    Roshanzamir, Mohamad
    Khosravi, Abbas
    Kebria, Parham M.
    Khozeimeh, Fahime
    Nahavandi, Saeid
    Sarrafzadegan, Nizal
    Acharya, U. Rajendra
    COMPUTERS IN BIOLOGY AND MEDICINE, 2019, 111
  • [47] Machine learning models of clinically relevant biomarkers for the prediction of stable obstructive coronary artery disease
    Kim, Juntae
    Lee, Su Yeon
    Cha, Byung Hee
    Lee, Wonseop
    Ryu, JiWung
    Chung, Young Hak
    Kim, Dongmin
    Lim, Seong-Hoon
    Kang, Tae Soo
    Park, Byoung-Eun
    Lee, Myung-Yong
    Cho, Sungsoo
    FRONTIERS IN CARDIOVASCULAR MEDICINE, 2022, 9
  • [48] Machine learning for atrial fibrillation risk prediction in patients with sleep apnea and coronary artery disease
    Silva, Carlos A. O.
    Morillo, Carlos A. A.
    Leite-Castro, Cristiano
    Gonzalez-Otero, Rafael
    Bessani, Michel
    Gonzalez, Rafael
    Castellanos, Julio C. C.
    Otero, Liliana
    FRONTIERS IN CARDIOVASCULAR MEDICINE, 2022, 9
  • [49] Machine learning-aided risk stratification system for the prediction o coronary artery disease
    Li, Dan
    Xiong, Guanglian
    Zeng, Hesong
    Zhou, Qiang
    Jiang, Jiangang
    Guo, Xiaomei
    INTERNATIONAL JOURNAL OF CARDIOLOGY, 2021, 326 : 30 - 34
  • [50] Prediction of Hidden Coronary Artery Disease Using Machine Learning in Patients With Acute Ischemic Stroke
    Heo, JoonNyung
    Yoo, Joonsang
    Lee, Hyungwoo
    Lee, Il Hyung
    Kim, Jung-Sun
    Park, Eunjeong
    Kim, Young Dae
    Nam, Hyo Suk
    NEUROLOGY, 2022, 99 (01) : E55 - E65