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Prediction of pacemaker-induced cardiomyopathy using a convolutional neural network based on clinical findings prior

Mitsunori Oida1, Takuya Mizutani2, Eriko Hasumi3

  • 1Department of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo, 113-8655, Japan.

Scientific Reports
|March 23, 2024
PubMed

Insights

A new convolutional neural network (CNN) model can predict pacemaker-induced cardiomyopathy (PICM) using pre-implantation clinical data. This tool helps identify patients at risk, enabling timely interventions to prevent heart muscle damage.

Area of Science:

  • Cardiology
  • Artificial Intelligence in Medicine
  • Biomedical Engineering

Background:

  • Pacemaker-induced cardiomyopathy (PICM) is a known complication of cardiac pacing.
  • Predicting PICM development is challenging despite identified risk factors like high right ventricular pacing burden and pre-existing left ventricular dysfunction.

Purpose of the Study:

  • To develop and validate a convolutional neural network (CNN) model for predicting PICM development.
  • To utilize pre-pacemaker implantation clinical data for early risk stratification of PICM.

Main Methods:

  • A CNN model was trained on a dataset of 31 pre-pacemaker implantation variables from 165 patients with dual-chamber pacemakers.
  • The dataset included demographic, clinical, echocardiographic, and laboratory findings.
  • Model performance was evaluated using accuracy, sensitivity, specificity, and area under the curve (AUC).

Main Results:

  • The CNN model achieved an accuracy of 75.8%, sensitivity of 55.6%, specificity of 83.3%, and an AUC of 0.78.
  • The model demonstrated the ability to predict PICM development using pre-implantation clinical data.
  • 47 out of 165 patients developed PICM during a mean follow-up of 1.7 years.

Conclusions:

  • A CNN model based on pre-pacemaker implantation clinical data can accurately predict the development of PICM.
  • This predictive model can aid in identifying high-risk patients for PICM.
  • Early identification facilitates timely interventions, such as upgrading to physiological pacing, to prevent adverse outcomes.

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