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Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
Published on: June 10, 2025
545
Artificial intelligence algorithm for predicting mortality of patients with acute heart failure.
Joon-Myoung Kwon1,2, Kyung-Hee Kim3, Ki-Hyun Jeon1,3
1Artificial Intelligence and Big Data Center, Sejong Medical Research Institute, Gyunggi, Korea.
Plos One
|July 9, 2019
Summary
A new deep learning algorithm, DAHF, accurately predicts mortality in acute heart failure (AHF) patients. DAHF outperformed existing scores and models, improving risk stratification for better patient outcomes.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Informatics
Background:
- Acute heart failure (AHF) poses significant mortality risks.
- Accurate prediction of AHF mortality is crucial for timely intervention and improved patient management.
- Existing risk stratification tools have limitations in predicting both short-term and long-term mortality.
Purpose of the Study:
- To develop and validate a deep-learning-based artificial intelligence algorithm for predicting mortality in patients with acute heart failure (AHF).
- To compare the predictive performance of the developed algorithm against established risk scores and other machine learning models.
Main Methods:
- A deep learning algorithm for predicting AHF mortality (DAHF) was developed using a large dataset (12,654 records) from two hospitals.
- The DAHF model's performance was validated on an independent dataset (4,759 records) from the Korean AHF registry.
- DAHF's predictive accuracy was compared against the Get with the Guidelines-Heart Failure (GWTG-HF) score and the Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) score.
Main Results:
- DAHF demonstrated superior performance in predicting in-hospital mortality with an AUC of 0.880, significantly outperforming GWTG-HF (0.728).
- For 12-month and 36-month mortality, DAHF (AUCs 0.782 and 0.813) significantly outperformed the MAGGIC score (0.718 and 0.729).
- Patients classified as high-risk by DAHF exhibited a significantly higher mortality rate over 36 months (p<0.001).
Conclusions:
- The DAHF algorithm provides a more accurate prediction of in-hospital and long-term mortality in AHF patients compared to existing risk scores and machine learning models.
- DAHF can enhance risk stratification for AHF patients, potentially leading to more personalized treatment strategies.
- This deep learning approach shows promise for improving outcomes in acute heart failure management.
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