Diagnosis and Risk Prediction of Dilated Cardiomyopathy in the Era of Big Data and Genomics

Dilated cardiomyopathy (DCM) is a leading cause of heart failure and life-threatening ventricular arrhythmias (LTVA). Work-up and risk stratification of DCM is clinically challenging, as there is great heterogeneity in phenotype and genotype. Throughout the last decade, improved genetic testing of patients has identified genotype–phenotype associations and enhanced evaluation of at-risk relatives leading to better patient prognosis. The field is now ripe to explore opportunities to improve personalised risk assessments. Multivariable risk models presented as “risk calculators” can incorporate a multitude of clinical variables and predict outcome (such as heart failure hospitalisations or LTVA). In addition, genetic risk scores derived from genome/exome-wide association studies can estimate an individual’s lifetime genetic risk of developing DCM. The use of clinically granular investigations, such as late gadolinium enhancement on cardiac magnetic resonance imaging, is warranted in order to increase predictive performance. To this end, constructing big data infrastructures improves accessibility of data by using electronic health records, existing research databases, and disease registries. By applying methods such as machine and deep learning, we can model complex interactions, identify new phenotype clusters, and perform prognostic modelling. This review aims to provide an overview of the evolution of DCM definitions as well as its clinical work-up and considerations in the era of genomics. In addition, we present exciting examples in the field of big data infrastructures, personalised prognostic assessment, and artificial intelligence

Automatic multilabel detection of ICD10 codes in Dutch cardiology discharge letters using neural networks

Standard reference terminology of diagnoses and risk factors is crucial for billing, epidemiological studies, and inter/intranational comparisons of diseases. The International Classification of Disease (ICD) is a standardized and widely used method, but the manual classification is an enormously time-consuming endeavor. Natural language processing together with machine learning allows automated structuring of diagnoses using ICD-10 codes, but the limited performance of machine learning models, the necessity of gigantic datasets, and poor reliability of terminal parts of these codes restricted clinical usability. We aimed to create a high performing pipeline for automated classification of reliable ICD-10 codes in the free medical text in cardiology. We focussed on frequently used and well-defined three- and four-digit ICD-10 codes that still have enough granularity to be clinically relevant such as atrial fibrillation (I48), acute myocardial infarction (I21), or dilated cardiomyopathy (I42.0). Our pipeline uses a deep neural network known as a Bidirectional Gated Recurrent Unit Neural Network and was trained and tested with 5548 discharge letters and validated in 5089 discharge and procedural letters. As in clinical practice discharge letters may be labeled with more than one code, we assessed the single- and multilabel performance of main diagnoses and cardiovascular risk factors. We investigated using both the entire body of text and only the summary paragraph, supplemented by age and sex. Given the privacy-sensitive information included in discharge letters, we added a de-identification step. The performance was high, with F1 scores of 0.76–0.99 for three-character and 0.87–0.98 for four-character ICD-10 codes, and was best when using complete discharge letters. Adding variables age/sex did not affect results. For model interpretability, word coefficients were provided and qualitative assessment of classification was manually performed. Because of its high performance, this pipeline can be useful to decrease the administrative burden of classifying discharge diagnoses and may serve as a scaffold for reimbursement and research applications

A head-to-head comparison of speckle tracking echocardiography and feature tracking cardiovascular magnetic resonance imaging in right ventricular deformation

Aims: Speckle tracking echocardiography (STE) and feature tracking cardiovascular magnetic resonance imaging (FT-CMR) are advanced imaging techniques which are both used for quantification of global and regional myocardial strain. Direct comparisons of STE and FT-CMR regarding right ventricular (RV) strain analysis are limited. We aimed to study clinical performance, correlation and agreement of RV strain by these techniques, using arrhythmogenic right ventricular cardiomyopathy (ARVC) as a model for RV disease. // Methods and results: We enrolled 110 subjects, including 34 patients with definite ARVC, 30 preclinical relatives of ARVC patients, and 46 healthy control subjects. Global and regional RV longitudinal peak strain (PS) were measured by STE and FT-CMR. Both modalities showed reduced strain values in ARVC patients compared to ARVC relatives (STE global PS: P < 0.001; FT-CMR global PS: P < 0.001) and reduced strain values in ARVC relatives compared to healthy control subjects (STE global PS: P = 0.042; FT-CMR global PS: P = 0.084). There was a moderate, albeit significant correlation between RV strain values obtained by STE and FT-CMR [global PS r = 0.578 (95% confidence interval 0.427–0.697), P < 0.001]. Agreement between the techniques was weak (limits of agreement for global PS: ±11.8%). Correlation and agreement both deteriorated when regional strain was studied. // Conclusion: RV STE and FT-CMR show a similar trend within the spectrum of ARVC and have significant correlation, but inter-modality agreement is weak. STE and FT-CMR may therefore both individually have added value for assessment of RV function, but RV PS values obtained by these techniques currently cannot be used interchangeably in clinical practice

Quantitative Approach to Fragmented QRS in Arrhythmogenic Cardiomyopathy: From Disease towards Asymptomatic Carriers of Pathogenic Variants

Fragmented QRS complexes (fQRS) are common in patients with arrhythmogenic cardiomyopathy (ACM). A new method of fQRS quantification may aid early disease detection in pathogenic variant carriers and assessment of prognosis in patients with early stage ACM. Patients with definite ACM (n = 221, 66%), carriers of a pathogenic ACM-associated variant without a definite ACM diagnosis (n = 57, 17%) and control subjects (n = 58, 17%) were included. Quantitative fQRS (Q-fQRS) was defined as the total amount of deflections in the QRS complex in all 12 electrocardiography (ECG) leads. Q-fQRS was scored by a single observer and reproducibility was determined by three independent observers. Q-fQRS count was feasible with acceptable intra- and inter-observer agreement. Q-fQRS count is significantly higher in patients with definite ACM (54 ± 15) and pathogenic variant carriers (55 ± 10) compared to controls (35 ± 5) (p < 0.001). In patients with ACM, Q-fQRS was not associated with sustained ventricular arrhythmia (p = 0.701) at baseline or during follow-up (p = 0.335). Both definite ACM patients and pathogenic variant carriers not fulfilling ACM diagnosis have a higher Q-fQRS than controls. This may indicate that increased Q-fQRS is an early sign of disease penetrance. In concealed and early stages of ACM the role of Q-fQRS for risk stratification is limite

Comparing Non-invasive Inverse Electrocardiography With Invasive Endocardial and Epicardial Electroanatomical Mapping During Sinus Rhythm

This study presents a novel non-invasive equivalent dipole layer (EDL) based inverse electrocardiography (iECG) technique which estimates both endocardial and epicardial ventricular activation sequences. We aimed to quantitatively compare our iECG approach with invasive electro-anatomical mapping (EAM) during sinus rhythm with the objective of enabling functional substrate imaging and sudden cardiac death risk stratification in patients with cardiomyopathy. Thirteen patients (77% males, 48 ± 20 years old) referred for endocardial and epicardial EAM underwent 67-electrode body surface potential mapping and CT imaging. The EDL-based iECG approach was improved by mimicking the effects of the His-Purkinje system on ventricular activation. EAM local activation timing (LAT) maps were compared with iECG-LAT maps using absolute differences and Pearson’s correlation coefficient, reported as mean ± standard deviation [95% confidence interval]. The correlation coefficient between iECG-LAT maps and EAM was 0.54 ± 0.19 [0.49–0.59] for epicardial activation, 0.50 ± 0.27 [0.41–0.58] for right ventricular endocardial activation and 0.44 ± 0.29 [0.32–0.56] for left ventricular endocardial activation. The absolute difference in timing between iECG maps and EAM was 17.4 ± 7.2 ms for epicardial maps, 19.5 ± 7.7 ms for right ventricular endocardial maps, 27.9 ± 8.7 ms for left ventricular endocardial maps. The absolute distance between right ventricular endocardial breakthrough sites was 30 ± 16 mm and 31 ± 17 mm for the left ventricle. The absolute distance for latest epicardial activation was median 12.8 [IQR: 2.9–29.3] mm. This first in-human quantitative comparison of iECG and invasive LAT-maps on both the endocardial and epicardial surface during sinus rhythm showed improved agreement, although with considerable absolute difference and moderate correlation coefficient. Non-invasive iECG requires further refinements to facilitate clinical implementation and risk stratification

Predicting arrhythmic risk in arrhythmogenic right ventricular cardiomyopathy: A systematic review and meta-analysis

While many studies evaluate predictors of ventricular arrhythmias in arrhythmogenic right ventricular cardiomyopathy (ARVC), a systematic review consolidating this evidence is currently lacking. Therefore, we searched MEDLINE and Embase for studies analyzing predictors of ventricular arrhythmias (sustained ventricular tachycardia/fibrillation (VT/VF), appropriate implantable cardioverter-defibrillator therapy, or sudden cardiac death) in patients with definite ARVC, patients with borderline ARVC, and ARVC-associated mutation carriers. In the case of multiple publications on the same cohort, the study with the largest population was included. This yielded 45 studies with a median cohort size of 70 patients (interquartile range 60 patients) and a median follow-up of 5.0 years (interquartile range 3.3 - 6.7 years). The average proportion of arrhythmic events observed was 10.6%/y in patients with definite ARVC, 10.0%/y in patients with borderline ARVC, and 3.7%/y with mutation carriers. Predictors of ventricular arrhythmias were population dependent: consistently predictive risk factors in patients with definite ARVC were male sex, syncope, T-wave inversion in lead >V3, right ventricular dysfunction, and prior (non)sustained VT/VF; in patients with borderline ARVC, 2 additional predictors—inducibility during electrophysiology study and strenuous exercise—were identified; and with mutation carriers, all aforementioned predictors as well as ventricular ectopy, multiple ARVC-related pathogenic mutations, left ventricular dysfunction, and palpitations/presyncope determined arrhythmic risk. Most evidence originated from small observational cohort studies, with a moderate quality of evidence. In conclusion, the average risk of ventricular arrhythmia ranged from 3.7 to 10.6%/y depending on the population with ARVC. Male sex, syncope, T-wave inversion in lead >V3, right ventricular dysfunction, and prior (non)sustained VT/VF consistently predict ventricular arrhythmias in all populations with ARVC

Integrating Exercise Into Personalized Ventricular Arrhythmia Risk Prediction in Arrhythmogenic Right Ventricular Cardiomyopathy

BACKGROUND: Exercise is associated with sustained ventricular arrhythmias (VA) in Arrhythmogenic Right Ventricular Cardiomyopathy (ARVC) but is not included in the ARVC risk calculator (arvcrisk.com). The objective of this study is to quantify the influence of exercise at diagnosis on incident VA risk and evaluate whether the risk calculator needs adjustment for exercise. METHODS: We interviewed ARVC patients without sustained VA at diagnosis about their exercise history. The relationship between exercise dose 3 years preceding diagnosis (average METh/wk) and incident VA during follow-up was analyzed with time-to-event analysis. The incremental prognostic value of exercise to the risk calculator was evaluated by Cox models. RESULTS: We included 176 patients (male, 43.2%; age, 37.6±16.1 years) from 3 ARVC centers, of whom 53 (30.1%) developed sustained VA during 5.4 (2.7-9.7) years of follow-up. Exercise at diagnosis showed a dose-dependent nonlinear relationship with VA, with no significant risk increase 18, >24, and >36 METh/wk), was significantly associated with VA (hazard ratios, 2.53-2.91) but was also correlated with risk factors currently in the risk calculator model. Thus, adding athlete status to the model did not change the C index of 0.77 (0.71-0.84) and showed no significant improvement (Akaike information criterion change, <2). CONCLUSIONS: Exercise at diagnosis was dose dependently associated with risk of sustained VA in ARVC patients but only above 15 to 30 METh/wk. Exercise does not appear to have incremental prognostic value over the risk calculator. The ARVC risk calculator can be used accurately in athletic patients without modification

Risk stratification and subclinical phenotyping of dilated and/or arrhythmogenic cardiomyopathy mutation-positive relatives: CVON eDETECT consortium

In relatives of index patients with dilated cardiomyopathy and arrhythmogenic cardiomyopathy, early detection of disease onset is essential to prevent sudden cardiac death and facilitate early treatment of heart failure. However, the optimal screening interval and combination of diagnostic techniques are unknown. The clinical course of disease in index patients and their relatives is variable due to incomplete and age-dependent penetrance. Several biomarkers, electrocardiographic and imaging (echocardiographic deformation imaging and cardiac magnetic resonance imaging) techniques are promising non-invasive methods for detection of subclinical cardiomyopathy. However, these techniques need optimisation and integration into clinical practice. Furthermore, determining the optimal interval and intensity of cascade screening may require a personalised approach. To address this, the CVON-eDETECT (early detection of disease in cardiomyopathy mutation carriers) consortium aims to integrate electronic health record data from long-term follow-up, diagnostic data sets, tissue and plasma samples in a multidisciplinary biobank environment to provide personalised risk stratification for heart failure and sudden cardiac death. Adequate risk stratification may lead to personalised screening, treatment and optimal timing of implantable cardioverter defibrillator implantation. In this article, we describe non-invasive diagnostic techniques used for detection of subclinical disease in relatives of index patients with dilated cardiomyopathy and arrhythmogenic cardiomyopathy

Prognostic value of strain by feature-tracking cardiac magnetic resonance in arrhythmogenic right ventricular cardiomyopathy

AIMS: Arrhythmogenic right ventricular cardiomyopathy (ARVC) is characterized by ventricular dysfunction and ventricular arrhythmias (VA). Adequate arrhythmic risk assessment is important to prevent sudden cardiac death. We aimed to study the incremental value of strain by feature-tracking cardiac magnetic resonance imaging (FT-CMR) in predicting sustained VA in ARVC patients. METHODS AND RESULTS: CMR images of 132 ARVC patients (43% male, 40.6 ± 16.0 years) without prior VA were analysed for global and regional right and left ventricular (RV, LV) strain. Primary outcome was sustained VA during follow-up. We performed multivariable regression assessing strain, in combination with (i) RV ejection fraction (EF); (ii) LVEF; and (iii) the ARVC risk calculator. False discovery rate adjusted P-values were given to correct for multiple comparisons and c-statistics were calculated for each model. During 4.3 (2.0-7.9) years of follow-up, 19% of patients experienced sustained VA. Compared to patients without VA, those with VA had significantly reduced RV longitudinal (P ≤ 0.03) and LV circumferential (P ≤ 0.04) strain. In addition, patients with VA had significantly reduced biventricular EF (P ≤ 0.02). After correcting for RVEF, LVEF, and the ARVC risk calculator separately in multivariable analysis, both RV and LV strain lost their significance [hazard ratio 1.03-1.18, P > 0.05]. Likewise, while strain improved the c-statistic in combination with RVEF, LVEF, and the ARVC risk calculator separately, this did not reach statistical significance (P ≥ 0.18). CONCLUSION: Both RV longitudinal and LV circumferential strain are reduced in ARVC patients with sustained VA during follow-up. However, strain does not have incremental value over RVEF, LVEF, and the ARVC VA risk calculator

Diagnosing arrhythmogenic right ventricular cardiomyopathy by 2010 Task Force Criteria: clinical performance and simplified practical implementation

AIMS: Arrhythmogenic right ventricular cardiomyopathy (ARVC) is diagnosed by a complex set of clinical tests as per 2010 Task Force Criteria (TFC). Avoiding misdiagnosis is crucial to prevent sudden cardiac death as well as unnecessary implantable cardioverter-defibrillator implantations. This study aims to validate the overall performance of the TFC in a real-world cohort of patients referred for ARVC evaluation. METHODS AND RESULTS: We included patients consecutively referred to our centres for ARVC evaluation. Patients were diagnosed by consensus of three independent clinical experts. Using this as a reference standard, diagnostic performance was measured for each individual criterion as well as the overall TFC classification. Of 407 evaluated patients (age 38 ± 17 years, 51% male), the expert panel diagnosed 66 (16%) with ARVC. The clinically observed TFC was false negative in 7/66 (11%) patients and false positive in 10/69 (14%) patients. Idiopathic outflow tract ventricular tachycardia was the most common alternative diagnosis. While the TFC performed well overall (sensitivity and specificity 92%), signal-averaged electrocardiogram (SAECG, P = 0.43), and several family history criteria (P ≥ 0.17) failed to discriminate. Eliminating these criteria reduced false positives without increasing false negatives (net reclassification improvement 4.3%, P = 0.019). Furthermore, all ARVC patients met at least one electrocardiogram (ECG) or arrhythmia criterion (sensitivity 100%). CONCLUSION: The TFC perform well but are complex and can lead to misdiagnosis. Simplification by eliminating SAECG and several family history criteria improves diagnostic accuracy. Arrhythmogenic right ventricular cardiomyopathy can be ruled out using ECG and arrhythmia criteria alone, hence these tests may serve as a first-line screening strategy among at-risk individuals