Optimization of Early Detection Algorithms for Subclinical Arrhythmogenic Conditions in Military Personnel Based on Digital Electrocardiographic Screening and Risk Stratification of Sudden Cardiac Events

Subclinical arrhythmogenic conditions remain an important problem in military medicine due to their association with sudden cardiac events and decreased operational readiness among military personnel. Conventional cardiovascular screening methods often fail to identify latent electrophysiological abnormalities in asymptomatic individuals. The introduction of digital electrocardiographic technologies and automated risk stratification systems may improve early diagnosis and preventive cardiovascular surveillance. Objective The aim of this study was to optimize algorithms for the early detection of subclinical arrhythmogenic conditions in military personnel using digital electrocardiographic screening and integrated risk stratification of sudden cardiac events. Methods A prospective observational study was conducted between 2023 and 2025 at the Central Military Clinical Hospital of the Ministry of Defense of the Republic of Uzbekistan in collaboration with Tashkent State Medical University. A total of 286 active-duty military personnel aged 18–45 years underwent digital 12-lead electrocardiographic screening, heart rate variability assessment, and clinical cardiovascular evaluation. Electrocardiographic analysis included assessment of rhythm disturbances, QT/QTc intervals, fragmented QRS complexes, ventricular ectopic activity, and autonomic dysfunction. Participants were stratified into low-, intermediate-, and high-risk groups for sudden cardiac events using integrated clinical and electrocardiographic criteria. Statistical analysis was performed using SPSS Statistics 27.0. Results Subclinical arrhythmogenic abnormalities were identified in 61 (21.3%) participants. The most common findings included premature ventricular contractions in 24 (8.4%) individuals, early repolarization abnormalities in 19 (6.6%), QTc prolongation in 14 (4.9%), fragmented QRS complexes in 11 (3.8%), and conduction disturbances in 9 (3.1%) participants. Reduced heart rate variability parameters were detected in 52 (18.2%) military personnel and were significantly associated with elevated arrhythmogenic risk (p<0.001). According to integrated cardiovascular risk stratification, 168 (58.7%) participants were classified as low-risk, 84 (29.4%) as intermediate-risk, and 34 (11.9%) as high-risk. The optimized digital ECG algorithm demonstrated sensitivity of 90.2%, specificity of 86.4%, and an area under the ROC curve of 0.91 for identifying clinically significant arrhythmogenic conditions. Conclusion Digital electrocardiographic screening combined with integrated cardiovascular risk stratification demonstrates high diagnostic effectiveness for the early identification of subclinical arrhythmogenic conditions in military personnel. The proposed algorithm may improve preventive cardiological monitoring, facilitate early detection of high-risk individuals, and reduce the incidence of sudden cardiac events during military service.

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