Derivation and external validation of machine-learning models for risk stratification in chest pain with normal troponin.

ABSTRACT

AIMS: Risk stratification of patients with chest pain and a high-sensitivity cardiac troponin T (hs-cTnT) concentration risk prediction of 90-day death or myocardial infarction in patients presenting to the emergency department with chest pain and an initial hs-cTnT concentration METHODS AND

RESULTS:

Four machine-learning-based models and one logistic regression (LR) model were trained on 4075 patients (single-centre Spanish cohort) and externally validated on 3609 patients (international prospective Advantageous Predictors of Acute Coronary syndromes Evaluation cohort). Models were compared with GRACE and HEART scores and a single undetectable hs-cTnT-based strategy (u-cTn; hs-cTnT < 5 ng/L and time from symptoms onset >180 min). Probability thresholds for safe discharge were derived in the derivation cohort. The endpoint occurred in 105 (2.6%) patients in the training set and 98 (2.7%) in the external validation set. Gradient boosting full (GBf) showed the best discrimination (area under the curve = 0.808). Calibration was good for the reduced neural network and LR models. Gradient boosting full identified the highest proportion of patients for safe discharge (36.7 vs. 23.4 vs. 27.2%; GBf vs. LR vs. u-cTn, respectively) with similar safety (missed endpoint per 1000 patients 2.2 vs. 3.5 vs. 3.1, respectively). All derived models were superior to the HEART and GRACE scores (P < 0.001).

CONCLUSION:

Machine-learning and LR prediction models were superior to the HEART, GRACE, and u-cTn for risk stratification of patients with chest pain and a baseline hs-cTnT calibration, and efficacy, reducing the need for serial hs-cTnT determination by more than one-third. CLINICAL TRIAL REGISTRATION ClinicalTrials.gov number, NCT00470587, https//clinicaltrials.gov/ct2/show/NCT00470587.