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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...
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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...
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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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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...
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Updated: Feb 28, 2026

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Agrupación basada en inteligencia artificial para identificar fenotipos de riesgo funcional en insuficiencia cardíaca

Xunhan Qiu1, Jun Ma1, Li Xu2

  • 1Department of Cardiology, Shanghai Jiao Tong University School of Medicine Affiliated Renji Hospital, Shanghai, China.

Open heart
|February 26, 2026
PubMed
Resumen

La inteligencia artificial identificó tres fenotipos de insuficiencia cardíaca, revelando distintos riesgos de aptitud cardiorrespiratoria. Este modelo de IA permite una estratificación temprana del riesgo para mejorar los resultados del paciente.

Palabras clave:
Rehabilitación CardíacaEcocardiografíaRegistros Médicos ElectrónicosInsuficiencia Cardíaca

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Área de la Ciencia:

  • Cardiología
  • Inteligencia Artificial
  • Bioestadística

Sus antecedentes:

  • Los pacientes con insuficiencia cardíaca (IC) a menudo experimentan una disminución no detectada de la aptitud cardiorrespiratoria (ACR), lo que aumenta los riesgos de malos resultados.
  • Las prácticas clínicas actuales carecen de herramientas eficaces para la estratificación temprana del riesgo de ACR en pacientes con IC.

Objetivo del estudio:

  • Identificar fenotipos de riesgo novedosos de aptitud cardiorrespiratoria (ACR) en pacientes con insuficiencia cardíaca (IC) utilizando IA.
  • Desarrollar y validar un modelo de estratificación de riesgos interpretable y generalizable para la evaluación funcional temprana en la IC.

Principales métodos:

  • Se realizó un análisis de agrupación no supervisado impulsado por IA en 505 pacientes con IC utilizando 15 variables clínicas multimodales.
  • Se evaluaron las asociaciones entre los fenotipos identificados y el deterioro de la ACR (VO2 máx ≤20 mL/kg/min) utilizando regresión logística y modelos de aprendizaje automático.
  • El análisis SHapley Additive exPlanations (SHAP) garantizó la interpretabilidad del modelo, con validación externa en 201 pacientes con IC.

Principales resultados:

  • Se identificaron tres fenotipos distintos de IC: equilibrado, inflamatorio-sarcopénico y metabólicamente desregulado.
  • Ambos fenotipos no equilibrados demostraron una probabilidad significativamente mayor de deterioro de la aptitud cardiorrespiratoria (VO2 máx).
  • Los modelos de aprendizaje automático, incluidos random forest y XGBoost, mostraron un fuerte rendimiento discriminatorio (AUC ≈ 0.75) en las cohortes de derivación y validación.

Conclusiones:

  • La integración impulsada por IA de datos multimodales identificó con éxito nuevos fenotipos de riesgo de ACR en pacientes con IC.
  • Se estableció un modelo de estratificación de riesgos altamente interpretable y generalizable para la evaluación funcional temprana.
  • Estos hallazgos proporcionan un marco para estrategias de rehabilitación de precisión en el manejo de la insuficiencia cardíaca.