Related Experiment Video
Updated: May 17, 2026
03:47
Workflow and Framework for Collecting and Implementing Point-of-Care Ultrasound Data in the Management of Heart Failure Patients
Published on: July 12, 2024
Unveiling the gap in heart failure: a Brazilian Unified Health System study.
Nathalia Volpi E Silva1, Vicky Nogueira-Pileggi1, Renato Lima Vitorasso1
1Oracle Life Sciences, São Paulo, Brazil.
Journal of Global Health
|May 15, 2026
Summary
Heart failure (HF) underreporting in Brazil is significant, with an estimated 12-41% underestimation and 200,000 linked deaths. Machine learning accurately predicts HF risk using routine data, aiding early detection and resource allocation.
Area of Science:
- Public Health
- Epidemiology
- Health Informatics
Background:
- Heart failure (HF) decompensation is a leading cause of hospitalization globally and a significant health challenge in Brazil.
- Underdiagnosis of HF leads to delayed treatment, inefficient health strategies, and inaccurate epidemiological data.
- Addressing the underreported HF population is crucial for improving surveillance, care pathways, and health system planning in Brazil.
Purpose of the Study:
- To quantify the underreporting of heart failure (HF) in Brazil.
- To estimate mortality trends associated with unreported HF cases.
- To develop and validate a machine learning model for early HF prediction using administrative data.
Main Methods:
- Identified potential HF patients using ICD-10 codes and HF-related procedures.
- Developed a CatBoost machine learning model trained on Brazilian health system data (2018-2022).
- Validated the model on independent cohorts and derived underreporting estimates via sensitivity analysis.
Main Results:
- Approximately 54,000 potential HF cases/year were unreported in ambulatory data, with 12-41% underestimation.
- An estimated 200,000 deaths could be linked to unreported HF cases.
- The CatBoost model achieved high discrimination (AUC 0.91 in balanced, 0.84 in low-prevalence cohorts) and accuracy.
Conclusions:
- Substantial HF underreporting in Brazil has significant mortality implications.
- The developed ML model accurately predicts HF risk using routine administrative data, supporting early detection and resource optimization.
- Ethical considerations for false positives require careful deployment; clinical validation and cost-effectiveness analysis are recommended for the Brazilian Unified Health System.
Related Concept Videos
01:29Heart Failure II: Pathophysiology
Systolic Heart Failure and Compensatory MechanismsSystolic heart failure (also termed HFrEF, Heart Failure with Reduced Ejection Fraction) is the most prevalent type of heart filure. It results in a decreased volume of blood being pumped from the ventricle. The aortic arch and carotid sinuses have baroreceptors that detect reduced blood pressure, triggering the sympathetic nervous system (SNS) to release epinephrine and norepinephrine. Initially, this response aims to boost heart rate and...
01:27Heart Failure I: Introduction
Heart failure refers to a clinical syndrome caused by structural or functional cardiac disorders that prevent the heart from pumping an adequate amount of blood to meet the body's metabolic needs. This condition often arises from myocardial infarction or ischemia, leading to decreased cardiac output, reduced tissue perfusion, impaired gas exchange, fluid volume imbalance, and decreased functional ability.Heart failure can result from disruptions in the mechanisms that regulate cardiac output...
01:30Heart Failure V: Medical Management
Medical Management of Acute Decompensated Heart Failure (ADHF)The primary goals of therapy for patients hospitalized with acute decompensated heart failure (ADHF) include:Relieving symptomsOptimizing volume statusSupporting oxygenation and ventilationMaintaining cardiac output (CO) and end-organ perfusionIdentifying and addressing the cause of ADHFPreventing complicationsProviding patient education on factors precipitating HF exacerbationPlanning for dischargeOngoing monitoring and assessment...
01:26Heart Failure III: Clinical Manifestations
Heart failure (HF) manifests primarily as dyspnea, fatigue, and fluid retention, resulting in peripheral and pulmonary edema. Symptoms may vary depending on which ventricle is more affected, left or right.Left-Sided Heart FailureAlso known as left ventricular failure, this condition results from the left ventricle's inability to fill or eject sufficient blood into the systemic circulation. It leads to pulmonary congestion, which occurs when the left ventricle fails to eject blood effectively...
01:17Pathophysiology of Heart Failure
Heart failure (HF) is a progressive syndrome involving ventricles that leads to inadequate cardiac output. It can be classified based on location and output or ejection fraction. Ejection fraction (EF) is an essential measurement in the diagnosis and surveillance of HF. Reduced EF corresponds to systolic heart failure (HFrEF). However, HF with preserved ejection fraction (HFpEF) is becoming increasingly prevalent. Also known as diastolic HF, this form of HF is related to aging. The...
01:30Heart Failure IV: Classification and Diagnostic Evaluation
Heart failure can be classified in various ways, with the most common classifications based on physical activity limitations, disease progression, severity, and treatment strategies.The Functional Classification of Heart Failure divides patients into four categories based on physical activity limitation due to symptom burden.Class I: Patients in this class have cardiac disease but no physical activity limitations. Ordinary activities like walking, climbing stairs, or routine tasks do not cause...