BACKGROUND: Prognosis models based on stepwise regression methods show modest performance in patients with cardiogenic shock (CS). Automated variable selection allows data-driven risk evaluation by recognizing distinct patterns in data. We sought to evaluate an automated variable selection method (least absolute shrinkage and selection operator, LASSO) for predicting 30-day mortality in patients with acute myocardial infarction and CS (AMICS) receiving acute percutaneous coronary intervention (PCI) compared to two established scores.
METHODS AND RESULTS: Consecutive patients with AMICS receiving acute PCI at one of two tertiary heart centres in Denmark 2010-2017. Patients were divided according to treatment with mechanical circulatory support (MCS); PCI-MCS cohort (n = 220) versus PCI cohort (n = 1180). The latter was divided into a development (2010-2014) and a temporal validation cohort (2015-2017). Cohort-specific LASSO models were based on data obtained before PCI. LASSO models outperformed IABP-SHOCK II and CardShock risk scores in discriminative ability for 30-day mortality in the PCI validation [receiver operating characteristics area under the curve (ROC AUC) 0.80 (95% CI 0.76-0.84) vs 0.73 (95% CI 0.69-0.77) and 0.70 (95% CI 0.65-0.75), respectively, P < 0.01 for both] and PCI-MCS development cohort [ROC AUC 0.77 (95% CI 0.70-0.83) vs 0.64 (95% CI 0.57-0.71) and 0.64 (95% CI 0.57-0.71), respectively, P < 0.01 for both]. Variable influence differed depending on MCS, with age being the most influential factor in the LASSO-PCI model, whereas haematocrit and estimated glomerular filtration rate were the highest-ranking factors in the LASSO-PCI-MCS model.
CONCLUSION: Data-driven prognosis models outperformed established risk scores in patients with AMICS receiving acute PCI and exhibited good discriminative abilities. Observations indicate a potential use of machinelearning to facilitate individualized patient care and targeted interventions in the future.