Screening Patients with Familial Hypercholesterolemia using an Artificial Intelligence Method
Authors: А. Ivanova
Journal: Clinical Cardiology and Cardiovascular Interventions (2025)

Introduction: Familial hypercholesterolemia (FH) is an autosomal dominant genetic manifestation with early, aggressive, and recurrent cardiovascular events. According to literature data, this is seen in one in 200 individuals in the general population and is interpreted as the most common hereditary cardiovascular disease. Following the current trends in modern medicine, we used the artificial intelligence (AI) method to achieve greater efficiency in the recognition of FH patients. Objective: Our primary goal was to create, with the help of AI, an algorithm for FH screening in patients, admitted to the Clinics of the Bulgarian Cardiac Institute. A secondary task was to optimize the therapy of patients, according to the latest recommendations for behavior in dyslipidemias of the European Society of Cardiology. Methods: For a period of one year, we conducted a prospective study in which all hospitalized patients for any atherosclerotic event requiring treatment were followed in three hospitals in Bulgaria - Pleven, Burgas and Varna, covering Cardiology, Cardiac Surgery, Neurology and Vascular Surgery clinics. The electronic records were stored in the hospital's Gamma Cod Master (GCM) program. The algorithm for patient inclusion and data collection uses AI, through which, according to set parameters, patients are selected according to age, diagnosis, LDL-C value, type, and dose of antilipemic therapy. Each indicator gives certain points that are summed up. For each patient with a final score of ≥4, an alarm signal is created in the GSM, which can be easily seen by the attending physician. Despite AI, the main task is the attending physician to check the accuracy of the integrated data, to specify the physical status for the presence of stigmata such as xanthomas and corneal arcus at an early age, to collect additional family history for early atherosclerosis, to additionally recalculate the points of the DLCN score, and to optimize the therapy according to the recommendations of the European Society of Cardiology for dyslipidemias. Results: We prospectively followed 438 patients signaled by AI. The largest number of electronic files were analyzed in the cardiology clinics, with the most patients being screened for FH in the Cardiology Clinic in Pleven – 174, of which 79 patients (46%) had probable FH. In 49% of the total number of patients (438), we found a family history of an early atherosclerotic event, and 32% of the patients had a previous cardiovascular event. Only 1.5% of those hospitalized were on combined statin and ezetimibe therapy, and 15% of the total number of patients were completely missing lipid- lowering therapy. Through AI, patient therapy was optimized, also increasing the number of PCSK9 inhibitor prescriptions 3-to 4.5-fold (depending on the region). Conclusion: The development, implementation and use of AI for FH screening is possible and easily applicable. Despite the use of AI, many FH patients remain undiagnosed. This confirms the need for active participation of the attending physician, whose role in correct diagnosis and treatment, as well as in improving the patient's prognosis, remains leading.