Predicting cardiac resynchronization therapy response: development and validation of a single photon emission...

Area of Science:

• Cardiology
• Medical Imaging
• Biomedical Engineering

Background:

• Cardiac resynchronization therapy (CRT) is a key treatment for drug-refractory heart failure.
• Over 30% of patients do not respond to CRT, necessitating better predictive tools.
• Single photon emission computed tomography (SPECT) phase analysis offers potential for predicting CRT response.

Purpose of the Study:

• To develop and validate a novel predictive model for CRT response using SPECT phase analysis features.
• To improve patient selection for CRT and optimize treatment outcomes.
• To provide a practical tool for clinicians to assess CRT candidacy.

Main Methods:

• 163 CRT patients with gated resting SPECT MPI data were analyzed.
• Univariate logistic regression and LASSO regression were used to construct the predictive model.
• Model performance was evaluated using ROC curves, calibration curves, and decision curve analysis (DCA), with internal validation via bootstrapping.

Main Results:

• The predictive model achieved an AUC of 0.845 (95% CI: 0.785-0.906), with high sensitivity (0.771) and specificity (0.849).
• Internal validation confirmed the model's robustness with a mean AUC of 0.814.
• The model demonstrated strong calibration and practical clinical value through DCA, with a web-based tool developed.

Conclusions:

• A validated SPECT-based model can effectively predict CRT response.
• This tool assists clinicians in optimizing patient selection for CRT preoperatively.
• Targeted pacing strategies informed by imaging may enhance CRT efficacy.