Explainable AI predicts cardiovascular events in hospitalised COVID-19 patients

A Brazilian multicentre study used explainable artificial intelligence to forecast cardiovascular events among hospitalised COVID-19 patients, identifying age, urea, platelet count and oxygenation as key predictors.
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In the article published by BMC Infectious Diseases, researchers developed predictive models using machine learning (LightGBM) to identify cardiovascular complications (such as arrhythmia, acute heart failure, myocardial infarction) in 10,700 hospitalised COVID-19 patients across Brazil.

The study reports moderate discriminatory performance, with AUROC values of 0.752 and 0.760 for the two models, and high overall accuracy (~94.5%) due to the large majority of non-event cases.

However, due to the rarity of cardiovascular events (~5.3% of cases), the F1-scores for detecting the event class remained very low (5.2% and 4.2%, respectively), signalling that the models struggle to reliably identify the minority class despite efforts to rebalance the data.

Using SHAP (Shapley Additive exPlanations) values, the researchers identified the most influential predictors: age, urea level, platelet count and SatO₂/FiO₂ (oxygen saturation to inspired oxygen fraction) ratio.

The authors emphasise that while the approach shows promise for resource-constrained settings and contributes to risk stratification, the limitations around class imbalance and generalisability remain significant obstacles for clinical use.

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