Using Machine-Learning for Prediction of the Response to Cardiac Resynchronization Therapy

Abstract Background The relative importance of variables explaining sex differences in outcomes is scarcely explored in patients undergoing cardiac resynchronization therapy (CRT). Purpose We sought to implement and evaluate machine learning (ML) algorithms for the prediction of 1- and 3-year all-cause mortality in patients undergoing CRT implantation. We also aimed to assess the sex-specific differences and similarities in the predictors of mortality using ML approaches. Methods A retrospective registry of 2191 CRT patients (75% males) was used in the current analysis. ML models were implemented in 6 partially overlapping patient subsets (all patients, females or males with 1- or 3-year follow-up data available). Each cohort was randomly split into a training (80%) and a test set (20%). After hyperparameter tuning with 10-fold cross-validation in the training set, the best performing algorithm was also evaluated in the test set. Model discrimination was quantified using the area under the receiver-operating characteristic curves (AUC) and the associated 95% confidence intervals. The most important predictors were identified using the permutation feature importances method. Results Conditional inference random forest exhibited the best performance with AUCs of 0.728 [0.645–0.802] and 0.732 [0.681–0.784] for the prediction of 1- and 3-year mortality, respectively. Etiology of heart failure, NYHA class, left ventricular ejection fraction and QRS morphology had higher predictive power in females, whereas hemoglobin was less important than in males. The importance of atrial fibrillation and age increased, whereas the relevance of serum creatinine decreased from 1- to 3-year follow-up in both sexes. Conclusions Using advanced ML techniques in combination with easily obtainable clinical features, our models effectively predicted 1- and 3-year all-cause mortality in patients undergoing CRT implantation. The in-depth analysis of features has revealed marked sex differences in mortality predictors. These results support the use of ML-based approaches for the risk stratification of patients undergoing CRT implantation. Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): National Research, Development and Innovation Office of Hungary

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Importance of systematic right ventricular assessment in cardiac resynchronization therapy candidates: a machine-learning approach

European Heart Journal - Cardiovascular Imaging ◽

10.1093/ehjci/jeaa356.044 ◽

2021 ◽

Vol 22 (Supplement_1) ◽

Author(s):

E Galli ◽

V Le Rolle ◽

OA Smiseth ◽

J Duchenne ◽

JM Aalen ◽

...

Keyword(s):

Machine Learning ◽

Heart Failure ◽

Cardiac Resynchronization Therapy ◽

Right Ventricular ◽

Systolic Heart Failure ◽

Left Ventricular ◽

Supervised Machine Learning ◽

Cardiac Resynchronization ◽

Resynchronization Therapy ◽

Crt Response

Abstract Funding Acknowledgements Type of funding sources: None. Background Despite having all a systolic heart failure and broad QRS, patients proposed for cardiac resynchronization therapy (CRT) are highly heterogeneous and it remains extremely complicated to predict the impact of the device on left ventricular (LV) function and outcomes. Objectives We sought to evaluate the relative impact of clinical, electrocardiographic, and echocardiographic data on the left ventricular (LV) remodeling and prognosis of CRT-candidates by the application of machine learning (ML) approaches. Methods 193 patients with systolic heart failure undergoing CRT according to current recommendations were prospectively included in this multicentre study. We used a combination of the Boruta algorithm and random forest methods to identify features predicting both CRT volumetric response and prognosis (Figure 1). The model performance was tested by the area under the receiver operating curve (AUC). We also applied the K-medoid method to identify clusters of phenotypically-similar patients. Results From 28 clinical, electrocardiographic, and echocardiographic-derived variables, 16 features were predictive of CRT-response; 11 features were predictive of prognosis. Among the predictors of CRT-response, 7 variables (44%) pertained to right ventricular (RV) size or function. Tricuspid annular plane systolic excursion was the main feature associated with prognosis. The selected features were associated with a very good prediction of both CRT response (AUC 0.81, 95% CI: 0.74-0.87) and outcomes (AUC 0.84, 95% CI: 0.75-0.93) (Figure 1, Supervised Machine Learning Panel). An unsupervised ML approach allowed the identifications of two phenogroups of patients who differed significantly in clinical and parameters, biventricular size and RV function. The two phenogroups had significant different prognosis (HR 4.70, 95% CI: 2.1-10.0, p < 0.0001; log –rank p < 0.0001; Figure 1, Unsupervised Machine Learning Panel). Conclusions Machine learning can reliably identify clinical and echocardiographic features associated with CRT-response and prognosis. The evaluation of both RV-size and function parameters has pivotal importance for the risk stratification of CRT-candidates and should be systematically assessed in patients undergoing CRT. Abstract Figure 1

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Can machine learning improve patient selection for cardiac resynchronization therapy?

PLoS ONE ◽

10.1371/journal.pone.0222397 ◽

2019 ◽

Vol 14 (10) ◽

pp. e0222397 ◽

Cited By ~ 1

Author(s):

Szu-Yeu Hu ◽

Enrico Santus ◽

Alexander W. Forsyth ◽

Devvrat Malhotra ◽

Josh Haimson ◽

...

Keyword(s):

Machine Learning ◽

Cardiac Resynchronization Therapy ◽

Patient Selection ◽

Cardiac Resynchronization ◽

Resynchronization Therapy ◽

Improve Patient Selection ◽

Selection For ◽

Improve Patient

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Prediction of response after cardiac resynchronization therapy with machine learning

International Journal of Cardiology ◽

10.1016/j.ijcard.2021.09.049 ◽

2021 ◽

Author(s):

Yixiu Liang ◽

Ruifeng Ding ◽

Jingfeng Wang ◽

Xue Gong ◽

Ziqing Yu ◽

...

Keyword(s):

Machine Learning ◽

Cardiac Resynchronization Therapy ◽

Cardiac Resynchronization ◽

Resynchronization Therapy ◽

Prediction Of Response

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Machine learning-based mortality prediction of patients undergoing cardiac resynchronization therapy: the SEMMELWEIS-CRT score

European Heart Journal ◽

10.1093/eurheartj/ehz902 ◽

2020 ◽

Vol 41 (18) ◽

pp. 1747-1756 ◽

Cited By ~ 9

Author(s):

Márton Tokodi ◽

Walter Richard Schwertner ◽

Attila Kovács ◽

Zoltán Tősér ◽

Levente Staub ◽

...

Keyword(s):

Machine Learning ◽

Cardiac Resynchronization Therapy ◽

Mortality Prediction ◽

Cardiac Resynchronization ◽

Risk Scores ◽

Resynchronization Therapy ◽

Discriminative Ability ◽

All Cause Mortality ◽

Estimated Risk ◽

Stratification System

Abstract Aims Our aim was to develop a machine learning (ML)-based risk stratification system to predict 1-, 2-, 3-, 4-, and 5-year all-cause mortality from pre-implant parameters of patients undergoing cardiac resynchronization therapy (CRT). Methods and results Multiple ML models were trained on a retrospective database of 1510 patients undergoing CRT implantation to predict 1- to 5-year all-cause mortality. Thirty-three pre-implant clinical features were selected to train the models. The best performing model [SEMMELWEIS-CRT score (perSonalizEd assessMent of estiMatEd risk of mortaLity With machinE learnIng in patientS undergoing CRT implantation)], along with pre-existing scores (Seattle Heart Failure Model, VALID-CRT, EAARN, ScREEN, and CRT-score), was tested on an independent cohort of 158 patients. There were 805 (53%) deaths in the training cohort and 80 (51%) deaths in the test cohort during the 5-year follow-up period. Among the trained classifiers, random forest demonstrated the best performance. For the prediction of 1-, 2-, 3-, 4-, and 5-year mortality, the areas under the receiver operating characteristic curves of the SEMMELWEIS-CRT score were 0.768 (95% CI: 0.674–0.861; P < 0.001), 0.793 (95% CI: 0.718–0.867; P < 0.001), 0.785 (95% CI: 0.711–0.859; P < 0.001), 0.776 (95% CI: 0.703–0.849; P < 0.001), and 0.803 (95% CI: 0.733–0.872; P < 0.001), respectively. The discriminative ability of our model was superior to other evaluated scores. Conclusion The SEMMELWEIS-CRT score (available at semmelweiscrtscore.com) exhibited good discriminative capabilities for the prediction of all-cause death in CRT patients and outperformed the already existing risk scores. By capturing the non-linear association of predictors, the utilization of ML approaches may facilitate optimal candidate selection and prognostication of patients undergoing CRT implantation.

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Sex-Specific Patterns of Mortality Predictors Among Patients Undergoing Cardiac Resynchronization Therapy: A Machine Learning Approach

Frontiers in Cardiovascular Medicine ◽

10.3389/fcvm.2021.611055 ◽

2021 ◽

Vol 8 ◽

Author(s):

Márton Tokodi ◽

Anett Behon ◽

Eperke Dóra Merkel ◽

Attila Kovács ◽

Zoltán Tősér ◽

...

Keyword(s):

Machine Learning ◽

Cardiac Resynchronization Therapy ◽

Left Ventricular ◽

Conditional Inference ◽

Cardiac Resynchronization ◽

Left Ventricular Ejection ◽

Resynchronization Therapy ◽

All Cause Mortality ◽

Test Sets

Background: The relative importance of variables explaining sex-related differences in outcomes is scarcely explored in patients undergoing cardiac resynchronization therapy (CRT). We sought to implement and evaluate machine learning (ML) algorithms for the prediction of 1- and 3-year all-cause mortality in CRT patients. We also aimed to assess the sex-specific differences in predictors of mortality utilizing ML.Methods: Using a retrospective registry of 2,191 CRT patients, ML models were implemented in 6 partially overlapping patient subsets (all patients, females, or males with 1- or 3-year follow-up). Each cohort was randomly split into training (80%) and test sets (20%). After hyperparameter tuning in the training sets, the best performing algorithm was evaluated in the test sets. Model discrimination was quantified using the area under the receiver-operating characteristic curves (AUC). The most important predictors were identified using the permutation feature importances method.Results: Conditional inference random forest exhibited the best performance with AUCs of 0.728 (0.645–0.802) and 0.732 (0.681–0.784) for the prediction of 1- and 3-year mortality, respectively. Etiology of heart failure, NYHA class, left ventricular ejection fraction, and QRS morphology had higher predictive power, whereas hemoglobin was less important in females compared to males. The importance of atrial fibrillation and age increased, while the importance of serum creatinine decreased from 1- to 3-year follow-up in both sexes.Conclusions: Using ML techniques in combination with easily obtainable clinical features, our models effectively predicted 1- and 3-year all-cause mortality in CRT patients. Sex-specific patterns of predictors were identified, showing a dynamic variation over time.

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MACHINE LEARNING WITH BIOMARKERS IMPROVES THE PREDICTION OF LONG-TERM OUTCOMES IN HEART FAILURE PATIENTS TREATED WITH CARDIAC RESYNCHRONIZATION THERAPY

Journal of the American College of Cardiology ◽

10.1016/s0735-1097(20)31017-2 ◽

2020 ◽

Vol 75 (11) ◽

pp. 390

Author(s):

Shengwen Yang ◽

Wei Hua

Keyword(s):

Machine Learning ◽

Heart Failure ◽

Cardiac Resynchronization Therapy ◽

Cardiac Resynchronization ◽

Resynchronization Therapy ◽

Long Term Outcomes ◽

Heart Failure Patients

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Using Machine-Learning for Prediction of the Response to Cardiac Resynchronization Therapy: the SMART-AV Study

10.22541/au.160680804.42971359/v1 ◽

2020 ◽

Author(s):

Stacey Howell ◽

Tim Stivland ◽

Kenneth Stein ◽

Kenneth Ellenbogen ◽

Larisa Tereshchenko

Keyword(s):

Machine Learning ◽

Cardiac Resynchronization Therapy ◽

Left Ventricular ◽

Adaptive Lasso ◽

Volume Index ◽

Cardiac Resynchronization ◽

Resynchronization Therapy ◽

Systolic Volume ◽

End Systolic Volume ◽

Crt Response

Introduction—We aimed to apply machine learning (ML) to develop a prediction model for cardiac resynchronization therapy (CRT) response. Methods and Results—Participants from the SmartDelay Determined AV Optimization (SMART-AV) trial (n=741; age, 66 ±11 yrs; 33% female; 100% NYHA III-IV; 100% EF≤35%) were randomly split into training & testing (80%; n=593), and validation (20%; n=148) samples. Baseline clinical, ECG, echocardiographic and biomarker characteristics, and left ventricular (LV) lead position (43 variables) were included in 6 ML models (random forests, convolutional neural network, lasso, adaptive lasso, plugin lasso, elastic net, ridge, and logistic regression). A composite of freedom from death and heart failure hospitalization and a >15% reduction in LV end-systolic volume index at 6-months post-CRT was the endpoint. The primary endpoint was met by 337 patients (45.5%). The adaptive lasso model was more accurate than class I ACC/AHA guidelines criteria (AUC 0.759; 95%CI 0.678-0.840 versus 0.639; 95%CI 0.554-0.722; P<0.0001), well-calibrated, and parsimonious (19 predictors; nearly half are potentially modifiable). The model predicted CRT response with 70% accuracy, 70% sensitivity, and 70% specificity, and should be further validated in prospective studies. Conclusions—ML predicts short-term CRT response and thus may help with CRT procedure planning.

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