Ventricular geometry–regularized QRSd predicts cardiac resynchronization therapy response: machine learning from crosstalk between electrocardiography and echocardiography

Artificial intelligence models in prediction of response to cardiac resynchronization therapy: a systematic review

The aim of the presented review is to summarize the literature data on the accuracy and clinical applicability of artificial intelligence (AI) models as a valuable alternative to the current guidelines in predicting cardiac resynchronization therapy (CRT) response and phenotyping of patients eligible for CRT implantation. This systematic review was performed according to the PRISMA guidelines. After a search of Scopus, PubMed, Cochrane Library, and Embase databases, 675 records were identified. Twenty supervised (prediction of CRT response) and 9 unsupervised (clustering and phenotyping) AI models were analyzed qualitatively (22 studies, 14,258 patients). Fifty-five percent of AI models were based on retrospective studies. Unsupervised AI models were able to identify clusters of patients with significantly different rates of primary outcome events (death, heart failure event). In comparison to the guideline-based CRT response prediction accuracy of 70%, supervised AI models trained on cohorts with > 100 patients achieved up to 85% accuracy and an AUC of 0.86 in their prediction of response to CRT for echocardiographic and clinical outcomes, respectively. AI models seem to be an accurate and clinically applicable tool in phenotyping of patients eligible for CRT implantation and predicting potential responders. In the future, AI may help to increase CRT response rates to over 80% and improve clinical decision-making and prognosis of the patients, including reduction of mortality rates. However, these findings must be validated in randomized controlled trials.

Comparison of cardiac magnetic resonance imaging and fluorodeoxyglucose positron emission tomography in the assessment of cardiac sarcoidosis: Meta-analysis and systematic review

AIM

Fluorine-18 fluorodeoxyglucose-positron emission tomography (FDG-PET) and cardiac magnetic resonance (CMR) are frequently used advanced cardiac imaging to diagnose cardiac sarcoidosis (CS). We conducted a meta-analysis and systematic review to compare diagnostic parameters of FDG-PET and CMR in the diagnosis of cardiac sarcoidosis (CS).

METHODS

We searched PubMed, EMBASE, and Scopus databases from their inception to 9/30/2021 with search terms "cardiac sarcoidosis" AND "cardiac magnetic resonance imaging" AND "positronemission tomography". We extracted patient characteristics, results of the FDG-PET and CMR, and adverse outcomes from the included studies. Adverse outcomes served as a reference standard for the evaluation of FDG-PET and CMR.

RESULTS

We included 4 studies in the meta-analysis which provided adverse outcomes and all patients underwent FDG-PET and CMR. There were 237 patients, 60.3% male, and ages ranged from 50-53 years. There were 45 events in 237 patients from four studies included in the meta-analyses. The pooled sensitivity (95% confidence interval-CI) and specificity (CI) of CMR in predicting an adverse event were 0.94 (0.79-0.98) and 0.49 (0.40-0.59), respectively. The pooled sensitivity (CI) and specificity (CI) of FDG-PET in predicting an adverse event were 0.51 (0.26-0.75) and 0.60 (0.35-0.81), respectively.

CONCLUSION

CMR was more sensitive but less specific than FDG-PET in predicting adverse events; however, the study population and definition of a positive test need to be considered while interpreting the results.

Contemporary Applications of Machine Learning for Device Therapy in Heart Failure

Despite a better understanding of the underlying pathogenesis of heart failure (HF), pharmacotherapy, surgical, and percutaneous interventions do not prevent disease progression in all patients, and a significant proportion of patients end up requiring advanced therapies. Machine learning (ML) is gaining wider acceptance in cardiovascular medicine because of its ability to incorporate large, complex, and multidimensional data and to potentially facilitate the creation of predictive models not constrained by many of the limitations of traditional statistical approaches. With the coexistence of "big data" and novel advanced analytic techniques using ML, there is ever-increasing research into applying ML in the context of HF with the goal of improving patient outcomes. Through this review, the authors describe the basics of ML and summarize the existing published reports regarding contemporary applications of ML in device therapy for HF while highlighting the limitations to widespread implementation and its future promises.

Predicting adverse cardiac events in sarcoidosis: deep learning from automated characterization of regional myocardial remodeling

Recognizing early cardiac sarcoidosis (CS) imaging phenotypes can help identify opportunities for effective treatment before irreversible myocardial pathology occurs. We aimed to characterize regional CS myocardial remodeling features correlating with future adverse cardiac events by coupling automated image processing and data analysis on cardiac magnetic resonance (CMR) imaging datasets. A deep convolutional neural network (DCNN) was used to process a CMR database of a 10-year cohort of 117 consecutive biopsy-proven sarcoidosis patients. The maximum relevance - minimum redundancy method was used to select the best subset of all the features-24 (from manual processing) and 232 (from automated processing) left ventricular (LV) structural/functional features. Three machine learning (ML) algorithms, logistic regression (LogR), support vector machine (SVM) and multi-layer neural networks (MLP), were used to build classifiers to categorize endpoints. Over a median follow-up of 41.8 (inter-quartile range 20.4-60.5) months, 35 sarcoidosis patients experienced a total of 43 cardiac events. After manual processing, LV ejection fraction (LVEF), late gadolinium enhancement, abnormal segmental wall motion, LV mass (LVM), LVMI index (LVMI), septal wall thickness, lateral wall thickness, relative wall thickness, and wall thickness of 9 (out of 17) individual LV segments were significantly different between patients with and without endpoints. After automated processing, LVEF, end-diastolic volume, end-systolic volume, LV mass and wall thickness of 92 (out of 216) individual LV segments were significantly different between patients with and without endpoints. To achieve the best predictive performance, ML algorithms selected lateral wall thickness, abnormal segmental wall motion, septal wall thickness, and increased wall thickness of 3 individual segments after manual image processing, and selected end-diastolic volume and 7 individual segments after automated image processing. LogR, SVM and MLP based on automated image processing consistently showed better predictive accuracies than those based on manual image processing. Automated image processing with a DCNN improves data resolution and regional CS myocardial remodeling pattern recognition, suggesting that a framework coupling automated image processing with data analysis can help clinical risk stratification.

A multimodal deep learning model for cardiac resynchronisation therapy response prediction

We present a novel multimodal deep learning framework for cardiac resynchronisation therapy (CRT) response prediction from 2D echocardiography and cardiac magnetic resonance (CMR) data. The proposed method first uses the 'nnU-Net' segmentation model to extract segmentations of the heart over the full cardiac cycle from the two modalities. Next, a multimodal deep learning classifier is used for CRT response prediction, which combines the latent spaces of the segmentation models of the two modalities. At test time, this framework can be used with 2D echocardiography data only, whilst taking advantage of the implicit relationship between CMR and echocardiography features learnt from the model. We evaluate our pipeline on a cohort of 50 CRT patients for whom paired echocardiography/CMR data were available, and results show that the proposed multimodal classifier results in a statistically significant improvement in accuracy compared to the baseline approach that uses only 2D echocardiography data. The combination of multimodal data enables CRT response to be predicted with 77.38% accuracy (83.33% sensitivity and 71.43% specificity), which is comparable with the current state-of-the-art in machine learning-based CRT response prediction. Our work represents the first multimodal deep learning approach for CRT response prediction.

Distance-based Classification and Regression Trees for the analysis of complex predictors in health and medical research

Predicting patient outcomes based on patient characteristics and care processes is a common task in medical research. Such predictive features are often multifaceted and complex, and are usually simplified into one or more scalar variables to facilitate statistical analysis. This process, while necessary, results in a loss of important clinical detail. While this loss may be prevented by using distance-based predictive methods which better represent complex healthcare features, the statistical literature on such methods is limited, and the range of tools facilitating distance-based analysis is substantially smaller than those of other methods. Consequently, medical researchers must choose to either reduce complex predictive features to scalar variables to facilitate analysis, or instead use a limited number of distance-based predictive methods which may not fulfil the needs of the analysis problem at hand. We address this limitation by developing a Distance-Based extension of Classification and Regression Trees (DB-CART) capable of making distance-based predictions of categorical, ordinal and numeric patient outcomes. We also demonstrate how this extension is compatible with other extensions to CART, including a recently published method for predicting care trajectories in chronic disease. We demonstrate DB-CART by using it to expand upon previously published dose-response analysis of stroke rehabilitation data. Our method identified additional detail not captured by the previously published analysis, reinforcing previous conclusions. We also demonstrate how by combining DB-CART with other extensions to CART, the method is capable of making predictions about complex, multifaceted outcome data based on complex, multifaceted predictive features.

Machine Learning in Electrocardiography and Echocardiography: Technological Advances in Clinical Cardiology

PURPOSE OF REVIEW

Electrocardiography (ECG) and echocardiography are the most widely used diagnostic tools in clinical cardiology. This review focuses on recent advancements in applying machine learning (ML) in ECG and echocardiography and potential synergistic ML integration of ECG and echocardiography.

RECENT FINDINGS

ML algorithms have been used in ECG for technical quality assurance, arrhythmia identification, and prognostic predictions, and in echocardiography to recognize image views, quantify measurements, and identify pathologic patterns. Synergistic application of ML in ECG and echocardiograph has demonstrated the potential to optimize therapeutic response, improve risk stratification, and generate new disease classification. There is mounting evidence that ML potentially outperforms in disease diagnoses and outcome prediction with ECG and echocardiography when compared with trained healthcare professionals. The applications of ML in ECG and echocardiography are playing increasingly greater roles in medical research and clinical practice, particularly for their contributions to developing novel diagnostic/prognostic prediction models. The automation in data acquisition, processing, and interpretation help streamline the workflows of ECG and echocardiography in contemporary cardiology practice.

Differentiation between left bundle branch block (LBBB) preceded dilated cardiomyopathy and dilated cardiomyopathy preceded LBBB by cardiac magnetic resonance imaging

BACKGROUND

Dilated cardiomyopathy (DCM) may be a result of or the cause of left bundle branch block (LBBB) in patients with DCM and LBBB. It is almost impossible from the history alone to know which came first in clinical work.

METHODS

Patients with LBBB and DCM who had cardiac magnetic resonance (CMR) examination were analyzed. Occurrence sequence of DCM and LBBB was determined by chart reviewing. Diastolic lateral/septal wall thickness ratio (DLSWTR) and lateral wall thickening (LWT) were compared between patients with different time sequences. Response to CRT was analyzed according to medical history and CMR manifestation.

RESULTS

Sixty-three patients were divided into two groups by cluster analysis. DLSWTR and LWT were significantly higher in group 1 (preserved lateral wall thickness and function), compared to those in group 2 (reduced lateral wall thickness and function) (1.06 ± 0.13 vs. 0.8 ± 0.12, 34.57 ± 11.48% vs. 11.18 ± 5.56%, respectively, both P < .001). Occurrence sequence was clear in 14 patients and further analyzed. In group 1, seven patients were clearly having no evidence of DCM when LBBB was first diagnosed (defined as LBBB-precede-DCM) and in group 2, seven patients did not have LBBB when DCM was diagnosed (defined as DCM-precede-LBBB). Among 10 patients who received CRT therapy, all seven patients in group 1 responded well whereas none of three patients in group 2 responded well.

CONCLUSIONS

Occurrence sequence of DCM and LBBB can be discriminated by CMR. Preserved lateral wall morphology and function in CMR suggested LBBB preceded to DCM. Such features may be predictors of good response to CRT.