Impending Heart Failure : An Artificial Intellectual Reality

BackgroundHaving a prevalence of almost 64 million patients globally, Heart Failure (HF) remains a leading cause of cardiac death with high morbidity and mortality rates despite constant updates in diagnostic and therapeutic measures. One of the main reasons behind this might be a delay in initiating treatment. PurposeTo obtain a method of screening patients for Heart Failure even before they develop symptoms. MethodsAn Artificial Intelligence-based algorithm, named Heart Failure Predictor (HFP), born from mathematical calculations of a patented formula, came up with a cutting-edge solution that can predict the chances of HF in patients not suffering from any symptoms of Heart Failure. HFP was applied to data from the Framingham Heart Study as a retrospective analysis. LVEF and NT pro-BNP levels were used as a method of correlation. Asymptomatic patients who had been followed up extensively for at-least 24 months were included, while patients already diagnosed with HF were excluded. ResultsData from 20896 patients were analysed. 17 out of 1230 were false positive while 31 out of 19660 were false negative. Thus, HFP has a Positive Predictive Value of 98.6% and a Specificity of 99.9%. DiscussionEarly screening and detection may lead to vast improvements in HF treatment efficacy. HFP fills this need with a new classification of HF, "Impending Heart Failure", and synonymously "Rays Disease". Apart from screening HF patients, HFP can also be used for continuous monitoring of LVEF in cardiac beds, bringing about a revolution in the cardiac space.