Predicting cardiac resynchronization therapy response: development and validation of a single photon emission computed tomography-based nomogram.

Abstract

Background: Cardiac resynchronization therapy (CRT) is an effective treatment for patients with drug-refractory heart failure. However, more than thirty percent of patients do not benefit from CRT. This study aimed to develop and validate a novel model based on single photon emission computed tomography (SPECT) phase analysis features to predict CRT response.

Methods: We identified 163 CRT patients who received gated resting SPECT myocardial perfusion imaging (MPI) between 2010 and 2020 at The First Affiliated Hospital of Nanjing Medical University. All variables were first processed by univariate logistic regression, and those with a P value <0.05 were retained. The selected variables were subsequently used in the least absolute shrinkage and selection operator (LASSO) regression to construct a predictive model, which was then represented as a nomogram. Nomogram performance was assessed via receiver operating characteristic (ROC) curves, calibration curves, and decision curve analyses (DCAs). Internal validation was performed by bootstrapping with 1,000 replicates.

Results: Of the 163 patients, 93 (57.1%) responded to CRT during follow-up. Responders had a wider QRS complex duration (QRSd) (164.80 vs. 154.51 ms, P=0.003), fewer premature ventricular contractions (PVCs) (1,392.98 vs. 2,283.60, P=0.003), lower prevalence of non-sustained ventricular tachycardia (NS-VT) (45.2% vs. 77.1%, P<0.001), and better cardiac function [based on N-terminal pro-B-type natriuretic peptide (NT-proBNP), New York Heart Association (NYHA), and left ventricle (LV) parameters] compared to non-responders. Univariate logistic regression revealed 14 variables significantly associated with CRT response (all P<0.05). The area under the ROC curve (AUC) value for the nomogram was 0.845 [95% confidence interval (CI): 0.785-0.906; sensitivity: 0.771; specificity: 0.849]. Internal validation yielded a mean AUC of 0.814 (95% CI: 0.777-0.836). The calibration curve demonstrated strong consistency between the predicted and observed outcomes. DCA revealed that the nomogram consistently provides a net benefit over the baseline, demonstrating its high practical value in clinical decision-making. A web-based dynamic nomogram (https://jzw20000624.shinyapps.io/CRTpredictionmodel/) was developed for clinical application.

Conclusions: We developed and validated a SPECT-based prediction model for predicting CRT response, which can assist clinicians in optimizing CRT candidacy preoperatively. Pacing at the latest contraction and relaxation segments, while avoiding scarred regions and optimizing preoperative status, is anticipated to improve CRT response.