Forecasting myocardial infarction and subsequent behavioural outcomes

Abstract: This thesis is compiled from four studies dealing with the prediction of myocardial infarction (MI) and some associated risk behaviours post MI.Study 1 extends the field of possible psychosocial stress-triggering of MI to Sweden, and to the phenomenon of temporal crests and troughs in national MI rates. These findings are in the present thesis integrated into a more comprehensive theoretical framework than provided by previous studies. By controlling for different confounders, analysis in subgroups, and more, the probable effect of psychosocial stress on the triggering of MI producing slight oscillations in daily MI rates at different temporal cycles was supported.Study 2 extends the existing literature of cognitive epidemiology to secondary preventive cardiology. Males with higher cognitive ability (CA), as assessed at mandatory military conscription in young adulthood, were found to be more adherent to their statin medication post MI, approximately 30 years later. The association is likely causal, given the fundamental importance of CA as a predictor for our individual ability to understand, plan, and execute everyday behaviour, including such health promoting behaviour as adhering to statin medication after MI.Study 3 continues the thesis thread of predicting clinically relevant health-promoting behaviour. It generated important hypotheses of what predicts adherence to internet-based cognitive behaviour therapy (ICBT) for symptoms of anxiety and/or depression after MI. In particular, the linguistic variables which were derived from what the patients actually wrote online to their ICBT therapist, predicted adherence. Using a flexible random forest model with a moderately sized sample, the aim was to handle a range of predictors and possible higher order effects in the relative strength estimation of these predictors.Study 4 presents the derivation and external validation of a new risk model, STOPSMOKE. Developed as a linear support vector machine with robust resampling, STOPSMOKE proved accurate in the unseen validation cohort for predicting one-year smoking abstinence at the start of cardiac rehabilitation (CR) post MI. STOPSMOKE predictions may inform the targeting of more elaborate interventions to high risk patients. Today, such intervention is not systematic as standard counselling does not account for the individual probability of future smoking abstinence failure. STOPSMOKE thus provides a novel real-world probabilistic basis for the risk of future smoking abstinence failure after MI. This basis may then be used by clinicians, patients, and organisations to tailor smoking intervention as best suited the particular individual or high-risk group. Implemented as part of a spectrum of models in a semi-automatic system, cost-effective tailored risk assessment could allow for augmented CR for future patients.