Draft:Ray's Disease

• Comment: A good start but needs more citations. Cite where you found facts, whole sections are currently unreferenced. Bobby Cohn (talk) 13:37, 22 November 2024 (UTC)

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Ray's Disease, also known as Impending Heart Failure (IHF), is a preclinical stage of heart failure in which asymptomatic patients are identified as having a high risk of developing symptomatic heart failure within a specific time frame. This stage is diagnosed through the Heart Failure Predictor (HFP), an AI-based software tool designed to detect early signs of heart failure using 12-lead ECG data alone.

Background

Heart failure (HF) is a chronic and progressive condition characterized by the heart's inability to pump blood efficiently, ultimately leading to symptoms such as shortness of breath, fatigue, and edema. Traditional diagnostics often rely on symptom-based assessments, which may miss early, asymptomatic phases of the disease. Ray's Disease represents an important clinical advantage by identifying patients at risk for both heart failure with reduced ejection fraction (HFrEF) and heart failure with preserved ejection fraction (HFpEF) even before symptoms develop, allowing for timely intervention.

Ray's Disease was introduced by Dr. Abhijit Ray following extensive research aimed at improving early detection and management of heart failure. This classification provides a more actionable approach for at-risk patients, as studies have shown that individuals identified with Ray's Disease have a high probability of experiencing symptomatic heart failure within a specific time frame—usually within 3 to 24 months.

Diagnosis

Ray's Disease is diagnosed using the Heart Failure Predictor (HFP) software, which assesses the left ventricular ejection fraction (LVEF) and other cardiac parameters from a standard 12-lead ECG. The HFP algorithm leverages machine learning and a patented formula to analyze cardiac indicators and assign a risk classification. Those categorized as "High Risk" by the HFP algorithm are diagnosed as Ray's Disease, or Impending Heart Failure.

Data Acquisition: A 12-lead ECG is obtained from the patient. Data Processing and Analysis: The ECG data undergoes signal quality checks, and the HFP algorithm calculates LVEF and analyzes additional indicators. Risk Prediction: Using machine learning, the algorithm classifies the patient's risk as either "High Risk" (Ray's Disease) or "Low Risk." Output: Results are displayed in an easy-to-interpret format for clinicians.

Clinical Performance

The Heart Failure Predictor (HFP) has been clinically validated to ensure reliable predictions. Studies have demonstrated high accuracy, with sensitivity at 97.5% and specificity at 99.9%, meaning it correctly identifies both those likely to develop heart failure and those at low risk. Patients classified as high risk were found to develop heart failure symptoms at rates consistent with HFP's positive predictive value of 98.6%.

Clinical Significance

Identifying patients with Ray's Disease provides an opportunity to prevent or delay heart failure progression through early interventions, potentially reducing healthcare costs and improving patient outcomes. As a predictive category, Ray's Disease shifts the focus of cardiac care from reactive treatment to proactive risk management.

Co-morbidities and Risk Factors

Patients identified with Ray's Disease commonly present with higher incidences of conditions such as hypertension, diabetes, coronary artery disease, and chronic kidney disease compared to the general population. This correlation underscores the importance of a holistic approach to treatment, addressing both Ray's Disease and associated co-morbidities.

Future Research

The HFP algorithm is currently being explored for possible integration with additional biomarkers, such as cardiac MRI, to enhance its predictive capabilities.