Unraveling diagnostic co-morbidity makeup of each HF category as characteristically derived by ECG- and ECHO-findings, a prevalence analysis

Heart Failure (HF) relies mainly on measurements from Echocardiography, in particular Echo-Findings, to estimate Left Ventricle Ejection Fraction (LVEF) and evaluating structural heart disease criteria. As Echocardiography is not available in primary care, the key structural (heart chamber enlargements) and functional abnormality related measurements are not available precluding the ability to diagnose HF other than through mainly symptomatic means. The opportunity for earlier detection of HF is lost. In this work, we first explore each of the three HF categories, preserved EF, mild-reduced EF, and reduced EF, using various morphological and functional etiology-specific characteristics supported by a literature review and an extensive analysis of a large, dedicated database accumulated over 8 years. We then explore the typical signs and co-morbidities of HF using prevalence analysis to unravel the diagnostic makeup of each HF category as characteristically derived by ECG- and ECHO-findings. From this, we then conduct a principal component analysis (PCA) of the data to interpret patterns of comorbidities, showing groups of comorbidities frequently associated with each other. Lastly, we delve into the role of breakthrough methods for the analysis of bio-signals to replicate common ECHO-findings, as alternatives for detecting and diagnosing HF similarly to Echocardiography, thereby providing a simple device for the effective detection of HF for use in Primary Care.