Late Mortality After Myocardial Injury in Critical Care Non-Cardiac Surgery Patients Using Machine Learning Analysis

Abstract

Myocardial injury after noncardiac surgery (MINS) increases mortality within 30 days. We aimed to evaluate the long-term impact of myocardial injury in a large cohort of patients admitted to intensive care after noncardiac surgery. All patients who stayed, at least, overnight with measurement of high-sensitive cardiac troponin were included. Clinical characteristics and occurrence of MINS were assessed between patients who died and survivors using chi-square test and Student t test. Variables with p <0.01 in the univariate model were included in the Cox regression model to identify predictor variables. Survival decision tree (SDT), a machine learning model, was also used to find the predictors and their correlations. We included 2,230 patients with mean age of 63.8±16.3 years, with most (55.6%) being women. The prevalence of MINS was 9.4% (209 patients) and there were 556 deaths (24.9%) in a median follow-up of 6.7 years. Univariate analysis showed variables associated with late mortality, namely: MINS, arterial hypertension, previous myocardial infarction, atrial fibrillation, dementia, urgent surgery, peripheral artery disease (PAD), chronic health status, and age. These variables were included in the Cox regression model and SDT. The predictor variables of all-cause death were MINS (hazard ratio [HR] 2.21; 95% confidence interval [CI] 1.77 to 2.76), previous myocardial infarction (HR 1.47; 95% CI 1.14 to 1.89); urgent surgery (HR 1.24; 95% CI 1.01 to 1.52), PAD (HR 1.83; 95% CI 1.23 to 2.73), dementia (HR 2.54; 95% CI 1.86 to 3.46) and age (HR 1.05; 95% CI 1.04 to 1.06). SDT had the same predictors, except PAD. In conclusion, increased high-sensitive troponin levels in patients who underwent noncardiac surgery raised the risk of short and late mortality.