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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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A nomogram for predicting CRT response based on multi-parameter features.

Yuxuan Lou1,2, Yang Hua2, Jiaming Yang2

  • 1Southeast University, Nanjing, 210009, Jiangsu, China.

BMC Cardiovascular Disorders
|July 19, 2024
PubMed
Summary
This summary is machine-generated.

This study developed a nomogram to predict cardiac resynchronization therapy (CRT) response in heart failure patients. The tool accurately identifies patients likely to benefit from CRT, aiding treatment decisions.

Keywords:
Cardiac resynchronization therapy (CRT)Heart failureMultiparameter featuresNomogram

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Area of Science:

  • Cardiology
  • Medical Imaging
  • Biostatistics

Background:

  • Chronic heart failure (CHF) poses a significant health burden.
  • Cardiac resynchronization therapy (CRT) is a treatment option for select CHF patients.
  • Predicting CRT responsiveness is crucial for optimizing patient outcomes.

Purpose of the Study:

  • To develop and validate a nomogram for predicting CRT responsiveness in CHF patients.
  • To identify key factors influencing CRT response.

Main Methods:

  • Retrospective analysis of 109 CHF patients who received CRT.
  • Utilized LASSO and multivariate logistic regression to identify predictive factors.
  • Constructed a nomogram and evaluated its performance using ROC, calibration curves, and DCA.

Main Results:

  • The nomogram incorporated left ventricular end-systolic volume, diffuse fibrosis, and left bundle branch block (LBBB).
  • Achieved an AUC of 0.865, indicating high predictive accuracy.
  • Demonstrated good calibration and excellent clinical net benefit.

Conclusions:

  • The developed nomogram effectively predicts CRT responsiveness in CHF patients.
  • The tool offers high discrimination and calibration for clinical application.
  • This nomogram can aid clinicians in patient selection for CRT.