Evaluation of interventricular delay during cardiac resynchronization therapy in patients   with quadripolar systems in long-term postoperative follow-up

Chumarnaya, Tatiana; Lyubimtseva, Tamara; Lebedev, Dmitry; Gasimova, N; Solovyova, Olga

doi:10.15829/1560-4071-2022-5121

Cited by 2 publications

(1 citation statement)

References 16 publications

(28 reference statements)

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“…By contrast, we used clinical data on long-term EF improvement in a year after the procedure where the impact of the RV-LV delay could have been less important. This hypothesis is supported by our recent data from clinical observations (55).…”

Section: Which LV Lead Position Is Better?supporting

confidence: 84%

Combination of personalized computational modeling and machine-learning for optimization of left ventricular pacing site in cardiac resynchronization therapy

Dokuchaev

Chumarnaya

Bazhutina

et al. 2022

Preprint

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Background: Up to 30-50% of selected patients with chronic heart failure do not respond to cardiac resynchronization therapy (CRT). Optimization of pacing lead placement in ventricles remains a challenge. Objective: We utilize a machine learning (ML) classifier to predict the position of an optimal left ventricular (LV) pacing site maximizing the probability of CRT response for a certain patient. Materials and Methods: Retrospective data from 57 patients with implanted CRT devices were utilized. Positive response to CRT was defined by a 10% improvement in the LV ejection fraction in a year after implantation. For each patient, a personalized model of ventricular activation and ECG was developed based on MRI and CT images. The total ventricular activation time, QRS duration and electrical dyssynchrony indices during intrinsic rhythm and biventricular (BiV) pacing with clinical pacing lead position (ref-LP) were computed and used in combination with clinical data to train the ML algorithm. We built a logistic regression classifier predicting CRT response with a high ROC AUC=0.84 and an average accuracy of 0.77. It generates a ML-score estimating the probability of CRT response. ML-scores were computed from model-driven features for varying LV pacing sites. Then Bayesian optimization was used to interpolate the ML-score over the available LV surface and an optimal LV lead position for BiV pacing that maximizes ML-score (ML-LP) was defined. Results: The optimal LV pacing site position increased the average ML-score by 17% in the patient cohort. Moreover, 11 out of 34 (20%) non-responders classified as true negative at ref-LP were re-classified as positive at ML-LP. In a patient group (n=14, 25% of the cohort) with LV pacing lead deployed in close proximity to the optimal position, the ratio of responders to non-responders was three times higher than in the entire cohort. Conclusion: We have developed a new technique based on simulations and ML to define an optimal position for LV lead for BiV pacing maximizing the ML-score of CRT response on the available LV epicardial surface. This technique demonstrates a high potential for the improvement of CRT outcome with guided lead implantation.

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Section: Which LV Lead Position Is Better?supporting

confidence: 84%

Combination of personalized computational modeling and machine-learning for optimization of left ventricular pacing site in cardiac resynchronization therapy

Dokuchaev

Chumarnaya

Bazhutina

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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Combination of personalized computational modeling and machine learning for optimization of left ventricular pacing site in cardiac resynchronization therapy

et al. 2023

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Introduction: The 30–50% non-response rate to cardiac resynchronization therapy (CRT) calls for improved patient selection and optimized pacing lead placement. The study aimed to develop a novel technique using patient-specific cardiac models and machine learning (ML) to predict an optimal left ventricular (LV) pacing site (ML-PS) that maximizes the likelihood of LV ejection fraction (LVEF) improvement in a given CRT candidate. To validate the approach, we evaluated whether the distance DPS between the clinical LV pacing site (ref-PS) and ML-PS is associated with improved response rate and magnitude.Materials and methods: We reviewed retrospective data for 57 CRT recipients. A positive response was defined as a more than 10% LVEF improvement. Personalized models of ventricular activation and ECG were created from MRI and CT images. The characteristics of ventricular activation during intrinsic rhythm and biventricular (BiV) pacing with ref-PS were derived from the models and used in combination with clinical data to train supervised ML classifiers. The best logistic regression model classified CRT responders with a high accuracy of 0.77 (ROC AUC = 0.84). The LR classifier, model simulations and Bayesian optimization with Gaussian process regression were combined to identify an optimal ML-PS that maximizes the ML-score of CRT response over the LV surface in each patient.Results: The optimal ML-PS improved the ML-score by 17 ± 14% over the ref-PS. Twenty percent of the non-responders were reclassified as positive at ML-PS. Selection of positive patients with a max ML-score >0.5 demonstrated an improved clinical response rate. The distance DPS was shorter in the responders. The max ML-score and DPS were found to be strong predictors of CRT response (ROC AUC = 0.85). In the group with max ML-score > 0.5 and DPS< 30 mm, the response rate was 83% compared to 14% in the rest of the cohort. LVEF improvement in this group was higher than in the other patients (16 ± 8% vs. 7 ± 8%).Conclusion: A new technique combining clinical data, personalized heart modelling and supervised ML demonstrates the potential for use in clinical practice to assist in optimizing patient selection and predicting optimal LV pacing lead position in HF candidates for CRT.

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Evaluation of interventricular delay during cardiac resynchronization therapy in patients with quadripolar systems in long-term postoperative follow-up

Cited by 2 publications

References 16 publications

Combination of personalized computational modeling and machine-learning for optimization of left ventricular pacing site in cardiac resynchronization therapy

Combination of personalized computational modeling and machine-learning for optimization of left ventricular pacing site in cardiac resynchronization therapy

Combination of personalized computational modeling and machine learning for optimization of left ventricular pacing site in cardiac resynchronization therapy

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