Cardiomyopathy Classification System

Abstract

Cardiomyopathy encompasses various types and refers to a condition affecting the heart muscle, which impairs the organ's normal pumping function. When diagnosing cardiomyopathy, it is crucial to identify the specific type of the disease. This paper outlines the benefits and implications of a new classification system for cardiomyopathy, highlighting its potential to improve diagnosis and treatment, as well as its impact on research and clinical trials. The methodology section details the research design, data collection methods, and sample selection process. The data analysis section explains the techniques used for data processing and statistical analysis. Finally, the evaluation and validation section describes the expert review process and the testing and validation methods applied. This research adopts an interpretive paradigm, which emphasizes the importance of individual experience in understanding social realities. The statistical analysis assessed the diagnostic capability of genetic testing across genders using ROC curves, revealing strong performance for both males (AUC=.856, p < .001) and females (AUC=.835, p < .001), surpassing pure chance. A one-way ANOVA test was conducted to examine age differences among four genetic patient groups. The assumption of homogeneity of variance was satisfied, as indicated by Levene's test for equality of variances, F(3,156) = 1.277, p > .05. Significant age differences were found among the groups, F(3,156) = 5.537, p < .005. An evidence-based classification system would enhance diagnostic accuracy, treatment efficacy, and research quality, thereby improving outcomes for patients with cardiomyopathy.