Key result
Non-invasive telemonitoring, AI, and biomarkers help predict HF decompensation.
Why the study?
Heart failure decompensation increases hospitalizations and mortality, but reliable, validated risk scores for predicting worsening HF are lacking.
This review summarizes clinical comorbidities, biomarkers, and imaging parameters that predispose patients with chronic heart failure to acute decompensation, highlighting the role of non-invasive monitoring.
Answers are grounded in this paper and cite their sources.
Supports non-invasive monitoring to avert HF decompensation; extends integration of comorbidities, biomarkers, and imaging predictors.
PolandAbstractHeart failure (HF) remains the foremost global health problem. Decompensation of chronic HF, char-acterized by exacerbation of symptoms and signs of congestion, reduces functional capacity and quality of life, and increases the risk for hospitalizations and mortality. To reduce the HF burden, patients at elevated decompensation risk must be quickly identified. However, reliable, validated risk scores for the prediction of worsening HF are lacking. Therefore, this clinically oriented review aims to outline the clinical factors predisposing for HF decompensation and discuss modern strategies for the non-invasive monitoring of patients with chronic HF, including telemonitoring and artificial intelligence-based tools.
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Kłopecka et al. (2026) conducted a review in Chronic heart failure. Non-invasive monitoring (telemonitoring, AI, biomarkers) was evaluated. Non-invasive monitoring strategies, including telemonitoring and artificial intelligence, along with clinical factors and biomarkers, can help predict heart failure decompensation.
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