Prognostic stratification of familial hypercholesterolaemia patients using AI algorithms: a gender-specific approach

Identificador:  imarina:9468473

Handle: https://hdl.handle.net/20.500.11797/imarina9468473

Autors:  Zamora, A; Masana, L; Civeira, F; Ibarretxe, D; Fanlo-Maresma, M; Vila, A; Tembra, MS; Marco-Benedí, V; Alvarez-Sala-Walther, LA; Camacho-Ruiz, M

Resum:
Aims Familial hypercholesterolaemia (FH) is the most prevalent autosomal dominant disorder, affecting about 1 in 200-250 individuals. It is the leading cause of early and aggressive coronary artery disease. Methods and results We analysed patients with genetically confirmed FH or a score >8 on the Dutch Lipid Clinics Network criteria from the National Registry of the Spanish Atherosclerosis Society, including individuals enrolled from January 2010 to December 2017. The model utilized a dataset incorporating family history, clinical characteristics, laboratory results, genetic data, imaging studies, and lipid-lowering treatment details. Eighty per cent of the population was allocated for training the AI algorithm and 20% was used for testing. A Histogram-based Gradient Boosting Classification Tree was used. The stability of the AI system was assessed using K-fold cross-validation. Shapley additive explanations methodology analysed the influence of different variables by sex. Youden's J statistic established the optimal cut-off point. A total of 1764 patients were included (51.8% women), among whom 264 experienced major adverse cardiovascular events (MACEs), with 8% being women. The final model incorporated 82 variables, achieving metrics of precision for MACE accuracy (0.92), recall (0.89), F1-score (0.91), and receiver operating characteristic (0.88; 95% confidence interval, 0.85-0.90). In the model, age, gamma-glutamyl transferase levels, and subclinical disease significantly impacted risk for women, while year of birth, age at initiation of statin treatment, and HbA1c levels were more influential for men. The optimal risk threshold was 0.25. Conclusion Artificial intelligence-machine learning algorithms are promising tools for enhancing vascular risk stratification, revealing critical sex-based differences.