Heart Failure Prediction: AI for Cardiovascular Risk

Description: Identify high-risk Heart Failure patients while reducing hospitalizations and costs.

Problem

Heart Failure (HF) affects approximately 1-2% of the  world's population, with rates of up to 10% in those over 65 years of age [1].

More than 20-25% of patients hospitalized  for HF are readmitted within 30 days due to inadequate or late management [2].

Diseases such as Chagas disease (endemic  in 21 Latin American countries) are responsible for up to 30% of HF cases in  certain rural and urban areas [3].

Ischemic heart disease is responsible for  70% of HF cases globally, standing out in Latin America as one of the main  comorbidities [4].

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Problem Size

• More than 20-25% of patients hospitalized  for HF are readmitted within 30 days due to inadequate or delayed management.
• Global direct and indirect costs of HF  exceed USD 108 billion per year, with 60% allocated to hospitalizations [5].
• In Latin America, it is estimated that  spending on HF can reach up to 1.5-2% of the total health budget of some  countries due to the high prevalence and frequent readmissions [2].

Solution

A proposed artificial intelligence (AI) model will address these issues by:

Predictive analytics: Early identification of at-risk patients using structured  clinical data (such as blood pressure, cholesterol, ejection fraction) and  unstructured data (medical records, electrocardiograms) [1].

Risk classification: Automated segmentation to prioritize personalized interventions.

Resource optimization: Reduction of unnecessary hospitalizations through remote  monitoring and evidence-based care recommendations.

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Opportunity Cost

Without AI:

High readmission rates (>25%) lead to  additional costs of $3,000-5,000 per hospitalization [5].

Increased mortality due to delayed  diagnoses and suboptimal treatment.

With AI:

Projected 15-25% reduction in  hospitalizations through early interventions.

Up to 20% reduction in overall treatment  costs by focusing on prevention and outpatient management.

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Impact

Clinical:‍

- Reduction in the annualmortality rate due to HF by 5-10% in 3 years, significantly improving thequality of life of patients [2].
- Decrease in the annual hospitalizationrate by at least 20%, with a direct impact on the sustainability of the healthsystem.

Economic:

Potential savings of up to USD50 million annually in national health systems in countries such as Brazil orMexico, based on a 20% reduction in hospitalizations due to HF [5].

Social:

Improved quality of life andincreased life expectancy, especially in vulnerable communities affected byChagas and other preventable conditions [3].

Data Sources

Data from electronic healthrecords (EHRs), medical imaging such as ECGs and echocardiograms, keybiomarkers (NT-proBNP, troponin), and epidemiological databases such as the GBDor national health surveys can be used. Genomic data, cardiovascular risk factors(hypertension, dyslipidemias, diabetes), and social determinants of health(socioeconomic status, medical access) are also relevant. Additional sourcesinclude longitudinal cohorts, local cardiology registries, and public databasessuch as MIMIC-CXR or PhysioNet. All of this should be managed ensuringdiversity, balance, and compliance with privacy regulations such as GDPR orLGPD.

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Citations

1. Roger VL, et al. Heartfailure statistics. PubMed: 23989710
2. Heidenreich PA, et al. Projectionsfor heart failure incidence. PubMed: 33983838
3. Tang Q, et al. Economicburden of heart failure. SCIRP: 2573802
4. Soto ZJ, et al. HeartFailure in Chagas Patients. ResearchGate: 369316489
5. GPC, Mexico. ClinicalPractice Guidelines for the Diagnosis of Dyslipidemia. ResearchGate: 359147316
6. Pérez CJ, et al. IschemicHeart Disease in Emergencies. ResearchGate: 384548571

Video

https://www.tella.tv/video/clvghe8kp01nw0cjo7zfb14t5