Adjusting the mathematical limits for cardiac dynamics analysis in Peruvian adults increased specificity from 65.3% to 83.9% but reduced sensitivity from 59.5% to 43.0%, improving overall accuracy from 63.6% to 72.1%.
Observational (n=272)
Yes
Does a mathematical methodology based on dynamical systems and fractal geometry accurately diagnose arrhythmias compared to conventional clinical diagnosis in a Peruvian population?
Adjusting the mathematical limits of a fractal geometry and dynamical systems-based diagnostic tool improved its specificity but reduced sensitivity for detecting arrhythmias in a Peruvian population.
Effect estimate: Sensitivity 0.595 and specificity 0.653 with original limits; sensitivity 0.430 and specificity 0.839 with adjusted limits (95% CI Cohen's Kappa for 2×2 table original limits 0.219 (95% CI, 0.104–0.334); adjusted limits 0.285 (95% CI, 0.163–0.409))
Introduction Cardiovascular diseases in Peru are a significant public health problem, and effective methods for risk stratification are needed. Using dynamical systems and fractal geometry shows promising results in other populations. Objective To adjust the limits between normality and disease in diagnoses according to the characteristics of the Peruvian population, using a methodology based on dynamical systems and fractal geometry. Methodology Heart rate and beats per hour were recorded over 24 h in 272 cases, 193 normal and 79 with arrhythmias, from Holter studies. Spatial occupation of attractors and their fractal dimensions were measured to determine its mathematical state. Results were compared using Cohen's Kappa coefficient with respect to conventional diagnosis. The limits of normality and disease were adjusted to improve concordance with the standard for the Peruvian population. Results With the original limits, sensitivity was 0.595, specificity was 0.653, positive predictive value was 0.412, negative predictive value was 0.797, and accuracy was 0.636. The Kappa for the 2 × 2 table was 0.219 (95% CI, 0.104–0.334) and for the 3 × 3 table, it was 0.141 (95% CI, 0.050–0.221). By adjusting the limits, sensibility was 0.430, specificity was 0.839, positive predictive value was 0.523, negative predictive value was 0.783, and accuracy was 0.721. The Kappa for the 2 × 2 table was 0.285 (95% CI, 0.164–0.409) and for the 3 × 3, 0.123 (95% CI, 0.036–0.209). Conclusion The agreement of the method improved with the new limits, demonstrating a fair level of alignment, characterized by enhanced specificity but reduced sensitivity. More studies are needed for clinical application.
Correa et al. (Thu,) conducted a observational in Adults over 18 years old in Peru undergoing Holter monitoring, including 193 with normal cardiac rhythm and 79 with arrhythmias (n=272). Mathematical method based on dynamical systems and fractal geometry to analyze cardiac dynamics from Holter data vs. Conventional clinical diagnosis by cardiologist from Holter reports (gold standard) was evaluated on Diagnostic accuracy of mathematical method (based on spatial occupation of attractors and fractal dimension) to distinguish normal cardiac rhythm from arrhythmias compared to clinical diagnosis (Sensitivity 0.595 and specificity 0.653 with original limits; sensitivity 0.430 and specificity 0.839 with adjusted limits, 95% CI Cohen's Kappa for 2×2 table original limits 0.219 (95% CI, 0.104–0.334); adjusted limits 0.285 (95% CI, 0.163–0.409)). Adjusting the mathematical limits for cardiac dynamics analysis in Peruvian adults increased specificity from 65.3% to 83.9% but reduced sensitivity from 59.5% to 43.0%, improving overall accuracy from 63.6% to 72.1%.