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.

Keywords: Cardiac resynchronization therapy (CRT); nomogram; phase analysis technique; single photon emission computed tomography (SPECT).

Figure 1

Study flow diagram. CAF, consistent atrial fibrillation; CRT, cardiac resynchronization therapy; LASSO, least absolute shrinkage and selection operator; LBBB, left bundle branch block; LVEF, left ventricular ejection fraction; MPI, myocardial perfusion imaging; NYHA, New York Heart Association; QRSd, QRS complex duration; RBBB, right bundle branch block; ROC, receiver operating characteristic; SPECT, single photon emission computed tomography.

Figure 2

Polar maps showing systolic match and diastolic match. (A) A patient who didn’t respond to CRT. The latest three contraction [162, 140, 137] and latest three relaxation segments [311, 309, 305] were in the septal and anterior wall. The LV lead was placed in the posterior vein (systolic no match and diastolic no match). (B) Another patient who responded to CRT. Both the latest three contraction [123, 120, 113] and latest three relaxation [310, 299, 297] segments were in the lateral wall. The LV lead was placed in the posterolateral vein (systolic match and diastolic match). For patient A, the QRSd is 150 ms, which corresponds to a score of 36 on the nomogram. The systolic match value is 0, which corresponds to a score of 0. The scores for all variables are then summed, resulting in a total score of 164. This total score is mapped to the probability curve on the nomogram, yielding a corresponding risk probability of 0.15, indicating that the probability of patient A being a CRT responder is 15%. Similarly, for patient B, the total score is 334, corresponding to a probability of over 90%, indicating a high likelihood of this patient being a CRT responder. CRT, cardiac resynchronization therapy; LV, left ventricle; QRSd, QRS complex duration.

Figure 3

Results of LASSO regression. (A) LASSO regression coefficient path diagram. (B) LASSO regression cross-validation graph. The two dotted vertical lines were drawn at minimum criteria and 1 − SE. criteria. (At 1 − SE. criteria including QRSd, NS-VT, NT-proBNP, LVESD, rest scar burden, diastolic PSD, diastolic PBW, systolic match, diastolic match, and NYHA). LASSO, least absolute shrinkage and selection operator; LVESD, left ventricular end-systolic diameter; NS-VT, non-sustained ventricular tachycardia; NT-proBNP, N-terminal pro-B-type natriuretic peptide; NYHA, New York Heart Association; PBW, phase histogram bandwidth; PSD, phase standard deviation; QRSd, QRS complex duration; SE, standard error.

Figure 4

Nomograms to predict CRT response. To estimate the probability of CRT response for a given patient, locate the patient’s value for each predictor variable; draw a vertical line from this value up to the points axis to determine the score associated with the variable; repeat the process for each variable in the nomogram; sum the total points and locate the total points on the total points axis. Then, draw a line down to the Probability axis and read off the estimated probability. CRT, cardiac resynchronization therapy; LVESD, left ventricular end-systolic diameter; NS-VT, non-sustained ventricular tachycardia; NT-proBNP, N-terminal pro-B-type natriuretic peptide; NYHA, New York Heart Association; PBW, phase histogram bandwidth; PSD, phase standard deviation; QRSd, QRS complex duration.

Figure 5

Discriminative performance and bootstrap internal validation of the prediction model. (A) ROC curves for the nomogram; AUC =0.845 (95% CI: 0.785–0.906). (B) Summary plot of the ROC curves from 1,000 internal samplings. (C) The frequency distribution diagram of the average AUC [mean AUC (the red line): 0.814; 95% CI (the blue dashed lines): 0.777–0.836)]. AUC, area under the ROC curve; CI, confidence interval; ROC, receiver operating characteristic.

Figure 6

Evaluation of the prediction model’s calibration and clinical utility. (A) Calibration curve of the nomogram. The calibration curves suggest good consistency between predicted probabilities estimated by the nomogram and the observed probabilities. (B) DCAs of the nomogram. DCAs demonstrate its high practical value in clinical decision-making. DCA, decision curve analysis; LASSO, least absolute shrinkage and selection operator; ROC, receiver operating characteristic.

Figure 7

Comparison of preoperative and postoperative polar maps. (A) Polar maps before surgery. Left: Left: systolic phase; right: diastolic phase. (B) Polar maps 6 months after CRT implantation. Left: systolic phase; right: diastolic phase. The preoperative and postoperative polar maps visually demonstrate that this patient’s systolic dyssynchrony and diastolic dyssynchrony have significantly improved. (This patient’s LVEF increased from 38% to 67%). CRT, cardiac resynchronization therapy; LVEF, left ventricular ejection fraction.