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November 28, 2025Journal of Cardiovascular Medicine

Enhancing differential diagnosis of IHD and DCM using interpretable machine learning in mildly reduced ejection fraction

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Authors

KIKaterina IscraLMLaura MunarettoJRJacopo Giulio Rizzi

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Overview

Retrospective exploratory study shows a predictive model distinguishes IHD from DCM with 76% accuracy in patients with left ventricular dysfunction, indicating improved diagnostic approaches.

Key Points

  • Logistic regression achieved 76% classification accuracy and an area under the curve of 83%.
  • The model utilized echocardiographic imaging and HRV parameters to enhance differential diagnosis.
  • Key features included mean RR, pNN50, and GLS for reliably differentiating IHD and DCM.
  • This approach supports early diagnosis in patients with left ventricular dysfunction, as it provides valuable clinical insights.

Cite This Study

Iscra et al. (2025) studied this question.

synapsesocial.com/papers/6928f0f9a65b730b9ea797e0https://doi.org/10.2459/jcm.0000000000001794
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