Dataset Information

A Biopsychosocial Model Predicting Myocardial Infarction.

ABSTRACT: Myocardial infarction is one of the main causes of death, and cardiovascular risk factors (CVRFs) are always considered when studying it. However, although it is known that other social and psychological variables, and especially frailty, can increase the risk of infarction, their simultaneous effect has not been extensively studied. This study is based on data from the SHARE project (latest wave, Wave 8), with a representative sample of 46,498 participants aged 50 or older (M = 70.40, SD = 9.33), of whom 57.4% were females. Statistical analyses included a full structural equation model that predicts 27% of infarction occurrence and evidences the significant effect of well-being, depression, and social connectedness on frailty. Frailty, in turn, explains 15.5% of the variability of CVRFs. This work supports the need to study these physical, social, and mental health factors together to intervene on frailty and, in turn, improve cardiovascular outcomes.

SUBMITTER: Tomas JM

PROVIDER: S-EPMC10489059 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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A Biopsychosocial Model Predicting Myocardial Infarction.

Tomás José M JM Oliver Amparo A Torres Zaira Z Parker Janhavi J Marques-Sule Elena E Sentandreu-Mañó Trinidad T

Journal of clinical medicine 20230901 17

Myocardial infarction is one of the main causes of death, and cardiovascular risk factors (CVRFs) are always considered when studying it. However, although it is known that other social and psychological variables, and especially frailty, can increase the risk of infarction, their simultaneous effect has not been extensively studied. This study is based on data from the SHARE project (latest wave, Wave 8), with a representative sample of 46,498 participants aged 50 or older (M = 70.40, SD = 9.33 ...[more]

PMID: 37685782

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Project description:BackgroundMachine learning (ML) risk prediction models, although much more accurate than traditional statistical methods, are inconvenient to use in clinical practice due to their nontransparency and requirement of a large number of input variables.ObjectiveWe aimed to develop a precise, explainable, and flexible ML model to predict the risk of in-hospital mortality in patients with ST-segment elevation myocardial infarction (STEMI).MethodsThis study recruited 18,744 patients enrolled in the 2013 China Acute Myocardial Infarction (CAMI) registry and 12,018 patients from the China Patient-Centered Evaluative Assessment of Cardiac Events (PEACE)-Retrospective Acute Myocardial Infarction Study. The Extreme Gradient Boosting (XGBoost) model was derived from 9616 patients in the CAMI registry (2014, 89 variables) with 5-fold cross-validation and validated on both the 9125 patients in the CAMI registry (89 variables) and the independent China PEACE cohort (10 variables). The Shapley Additive Explanations (SHAP) approach was employed to interpret the complex relationships embedded in the proposed model.ResultsIn the XGBoost model for predicting all-cause in-hospital mortality, the variables with the top 8 most important scores were age, left ventricular ejection fraction, Killip class, heart rate, creatinine, blood glucose, white blood cell count, and use of angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin II receptor blockers (ARBs). The area under the curve (AUC) on the CAMI validation set was 0.896 (95% CI 0.884-0.909), significantly higher than the previous models. The AUC for the Global Registry of Acute Coronary Events (GRACE) model was 0.809 (95% CI 0.790-0.828), and for the TIMI model, it was 0.782 (95% CI 0.763-0.800). Despite the China PEACE validation set only having 10 available variables, the AUC reached 0.840 (0.829-0.852), showing a substantial improvement to the GRACE (0.762, 95% CI 0.748-0.776) and TIMI (0.789, 95% CI 0.776-0.803) scores. Several novel and nonlinear relationships were discovered between patients' characteristics and in-hospital mortality, including a U-shape pattern of high-density lipoprotein cholesterol (HDL-C).ConclusionsThe proposed ML risk prediction model was highly accurate in predicting in-hospital mortality. Its flexible and explainable characteristics make the model convenient to use in clinical practice and could help guide patient management.Trial registrationClinicalTrials.gov NCT01874691; https://clinicaltrials.gov/study/NCT01874691.

Dataset Information

A Biopsychosocial Model Predicting Myocardial Infarction.

Publications

A Biopsychosocial Model Predicting Myocardial Infarction.

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