Optimising coronary imaging decisions with machine learning: an external validation study

Abstract

Background Exclusion of coronary stenosis in individuals with suggestive symptoms is challenging. Cardiac CT or coronary angiography is often used but is inefficient and costly and involves risks. Sex-stratified algorithms based on electronic health records (EHRs) could be a non-invasive alternative for excluding coronary stenosis, yet their performance may vary by healthcare settings. Thus, external validation is crucial for determining their generalisability. This study aimed to externally validate sex-stratified machine learning algorithms based on EHR data to predict the absence of coronary stenosis, evaluated in diverse clinical settings.Methods Sex-stratified XGBoost algorithms were trained on EHR data from patients who underwent coronary imaging at the University Medical Center Utrecht (n=14 674) and externally tested on EHR data of 13 Cardiology centres in the Netherlands (n=9252). The outcome was defined as the absence of coronary stenosis, identified through text mining of radiology report conclusions, and predictive performance was assessed by negative predictive values (NPVs) and specificities.Results On the training cohort (9298 men (median age 55 years, 73% no coronary stenosis) and 5376 women (median age 59 years, 83% no coronary stenosis)), the algorithms showed NPVs and specificities of 0.95 and 0.14 in men and 0.93 and 0.26 in women, respectively. On the testing cohort (4762 men (median age 60 years, 60% no coronary stenosis) and 4490 women (median age 60 years, 83% no coronary stenosis)), the algorithm showed NPVs and specificities of 0.89 and 0.07 in men and 0.87 and 0.18 in women, respectively.Conclusions This study externally validates sex‐stratified machine learning algorithms using EHR data to non-invasively predict the absence of coronary stenosis, with high NPVs observed across settings. However, given the modest specificity and study limitations, these findings should be considered preliminary, warranting further refinement before clinical adoption.