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

Howell, Stacey J.; Stivland, Tim; Steín, Kenneth M.; Ellenbogen, Kenneth A.; Tereshchenko, Larisa G.

doi:10.1016/j.jacep.2021.06.009

Cited by 17 publications

(12 citation statements)

References 47 publications

(55 reference statements)

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Section: Discussioncontrasting

confidence: 85%

“…Our study presents novel findings relative to 2 other recent papers on applications of machine learning for CRT 27 , 28 and is substantially different from these other studies in the following ways. First, the analysis based on the SMART-AV study uses a single binary response parameter based on a composite outcome, 27 and the other analysis uses a single binary response parameter based on whether LVEF improved by 10%. 28 In contrast, we used a multidimensional response outcome with 3 continuous variables representing different aspects of CRT response, which is then incorporated into a prediction model for long-term survival after CRT.…”

Section: Discussioncontrasting

confidence: 85%

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Machine learning for multidimensional response and survival after cardiac resynchronization therapy using features from cardiac magnetic resonance

et al. 2022

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Section: Discussioncontrasting

confidence: 85%

Section: Discussioncontrasting

confidence: 85%

Machine learning for multidimensional response and survival after cardiac resynchronization therapy using features from cardiac magnetic resonance

et al. 2022

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“…The datatypes used for these past reports have varied (electronic health records, clinical imaging, demographic data, electrocardiograms, etc. ), and the computational algorithms have spanned a range of simple regression models to more complicated approaches including gradient boosting [ 8 ], Naïve-Bayes [ 9 ], multiple kernel learning [ 10 ], random forest [ 11 ], adaptive lasso [ 12 ], and support vector machines [ 13 ]. In agreement with our results, the most important predictors from past studies have spanned different data types across comorbidity (e.g., ischemic cardiomyopathy and LBBB), electro-mechanical (e.g., systolic blood pressure, QRS width, and wall strains), and demographic data (e.g., age and sex) [ 8 , 10 , 12 ].…”

Section: Discussionmentioning

confidence: 99%

“…), and the computational algorithms have spanned a range of simple regression models to more complicated approaches including gradient boosting [ 8 ], Naïve-Bayes [ 9 ], multiple kernel learning [ 10 ], random forest [ 11 ], adaptive lasso [ 12 ], and support vector machines [ 13 ]. In agreement with our results, the most important predictors from past studies have spanned different data types across comorbidity (e.g., ischemic cardiomyopathy and LBBB), electro-mechanical (e.g., systolic blood pressure, QRS width, and wall strains), and demographic data (e.g., age and sex) [ 8 , 10 , 12 ]. This diversity of predictor type further supports our underlying hypothesis that various data sources are not necessarily redundant and can therefore provide additive benefit for identifying CRT response.…”

Section: Discussionmentioning

confidence: 99%

Interpretable machine learning predicts cardiac resynchronization therapy responses from personalized biochemical and biomechanical features

Haque

Stubbs

Hubig

et al. 2022

BMC Med Inform Decis Mak

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Background Cardiac Resynchronization Therapy (CRT) is a widely used, device-based therapy for patients with left ventricle (LV) failure. Unfortunately, many patients do not benefit from CRT, so there is potential value in identifying this group of non-responders before CRT implementation. Past studies suggest that predicting CRT response will require diverse variables, including demographic, biomarker, and LV function data. Accordingly, the objective of this study was to integrate diverse variable types into a machine learning algorithm for predicting individual patient responses to CRT. Methods We built an ensemble classification algorithm using previously acquired data from the SMART-AV CRT clinical trial (n = 794 patients). We used five-fold stratified cross-validation on 80% of the patients (n = 635) to train the model with variables collected at 0 months (before initiating CRT), and the remaining 20% of the patients (n = 159) were used as a hold-out test set for model validation. To improve model interpretability, we quantified feature importance values using SHapley Additive exPlanations (SHAP) analysis and used Local Interpretable Model-agnostic Explanations (LIME) to explain patient-specific predictions. Results Our classification algorithm incorporated 26 patient demographic and medical history variables, 12 biomarker variables, and 18 LV functional variables, which yielded correct prediction of CRT response in 71% of patients. Additional patient stratification to identify the subgroups with the highest or lowest likelihood of response showed 96% accuracy with 22 correct predictions out of 23 patients in the highest and lowest responder groups. Conclusion Computationally integrating general patient characteristics, comorbidities, therapy history, circulating biomarkers, and LV function data available before CRT intervention can improve the prediction of individual patient responses.

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“…The model predicted CRT response with 70% accuracy, 70% sensitivity, and 70% specificity. However, it has been stated that further prospective trials are required [6]. A call for referral and optimization of care in patients with CRT has been recently made by three European cardiac societies [7].…”

Section: Introductionmentioning

confidence: 99%

The Use of Machine Learning Algorithms in the Evaluation of the Effectiveness of Resynchronization Therapy

Krzowski

Rokicki

Główczyńska

et al. 2022

JCDD

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Background: Cardiovascular disease remains the leading cause of death in the European Union and worldwide. Constant improvement in cardiac care is leading to an increased number of patients with heart failure, which is a challenging condition in terms of clinical management. Cardiac resynchronization therapy is becoming more popular because of its grounded position in guidelines and clinical practice. However, some patients do not respond to treatment as expected. One way of assessing cardiac resynchronization therapy is with ECG analysis. Artificial intelligence is increasing in terms of everyday usability due to the possibility of everyday workflow improvement and, as a result, shortens the time required for diagnosis. A special area of artificial intelligence is machine learning. AI algorithms learn on their own based on implemented data. The aim of this study was to evaluate using artificial intelligence algorithms for detecting inadequate resynchronization therapy. Methods: A total of 1241 ECG tracings were collected from 547 cardiac department patients. All ECG signals were analyzed by three independent cardiologists. Every signal event (QRS-complex) and rhythm was manually classified by the medical team and fully reviewed by additional cardiologists. The results were divided into two parts: 80% of the results were used to train the algorithm, and 20% were used for the test (Cardiomatics, Cracow, Poland). Results: The required level of detection sensitivity of effective cardiac resynchronization therapy stimulation was achieved: 99.2% with a precision of 92.4%. Conclusions: Artificial intelligence algorithms can be a useful tool in assessing the effectiveness of resynchronization therapy.

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

Cited by 17 publications

References 47 publications

Machine learning for multidimensional response and survival after cardiac resynchronization therapy using features from cardiac magnetic resonance

Machine learning for multidimensional response and survival after cardiac resynchronization therapy using features from cardiac magnetic resonance

Interpretable machine learning predicts cardiac resynchronization therapy responses from personalized biochemical and biomechanical features

The Use of Machine Learning Algorithms in the Evaluation of the Effectiveness of Resynchronization Therapy

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