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

Thirty years of heart transplantation at the University Medical Centre Utrecht.

PURPOSE: To analyse patient demographics, indications, survival and donor characteristics for heart transplantation (HTx) during the past 30 years at the University Medical Centre Utrecht (UMCU). METHODS: Data have been prospectively collected for all patients who underwent HTx at the UMCU from 1985 until 2015. Patients who were included underwent orthotopic HTx at an age >14 years. RESULTS: In total, 489 hearts have been transplanted since 1985; 120 patients (25%) had left ventricular assist device (LVAD) implantation prior to HTx. A shift from ischaemic heart disease to dilated cardiomyopathy has been seen as the leading indication for HTx since the year 2000. Median age at HTx was 49 years (range 16-68). Median waiting time and donor age have also increased from 40 to 513 days and from 27 to 44 years respectively (range 11-65). Donor cause of death is now primarily stroke, in contrast to head and brain injury in earlier years. Estimated median survival is 15.4 years (95% confidence interval 14.2-16.6) There is better survival throughout these years. CONCLUSION: Over the past 30 years, patient and donor demographics and underlying diseases have shifted substantially. Furthermore, the increase in waiting time due to lack of available donor hearts has led to a rise in the use of LVADs as bridge to transplant. Importantly, an improvement in survival rates is found over time which could be explained by better immunosuppressive therapy and improvements in follow-up care

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

Text-mining in electronic healthcare records can be used as efficient tool for screening and data collection in cardiovascular trials: a multicenter validation study

Objective: This study aimed to validate trial patient eligibility screening and baseline data collection using text-mining in electronic healthcare records (EHRs), comparing the results to those of an international trial. Study Design and Setting: In three medical centers with different EHR vendors, EHR-based text-mining was used to automatically screen patients for trial eligibility and extract baseline data on nineteen characteristics. First, the yield of screening with automated EHR text-mining search was compared with manual screening by research personnel. Second, the accuracy of extracted baseline data by EHR text mining was compared to manual data entry by research personnel. Results: Of the 92,466 patients visiting the out-patient cardiology departments, 568 (0.6%) were enrolled in the trial during its recruitment period using manual screening methods. Automated EHR data screening of all patients showed that the number of patients needed to screen could be reduced by 73,863 (79.9%). The remaining 18,603 (20.1%) contained 458 of the actual participants (82.4% of participants). In trial participants, automated EHR text-mining missed a median of 2.8% (Interquartile range [IQR] across all variables 0.4e8.5%) of all data points compared to manually collected data. The overall accuracy of automatically extracted data was 88.0% (IQR 84.7e92.8%). Conclusion: Automatically extracting data from EHRs using text-mining can be used to identify trial participants and to collect baseline informatio

Differences between familial and sporadic dilated cardiomyopathy: ESC EORP Cardiomyopathy & Myocarditis registry

Aims: Dilated cardiomyopathy (DCM) is a complex disease where genetics interplay with extrinsic factors. This study aims to compare the phenotype, management, and outcome of familial DCM (FDCM) and non‐familial (sporadic) DCM (SDCM) across Europe. / Methods and results: Patients with DCM that were enrolled in the prospective ESC EORP Cardiomyopathy & Myocarditis Registry were included. Baseline characteristics, genetic testing, genetic yield, and outcome were analysed comparing FDCM and SDCM; 1260 adult patients were studied (238 FDCM, 707 SDCM, and 315 not disclosed). Patients with FDCM were younger (P < 0.01), had less severe disease phenotype at presentation (P < 0.02), more favourable baseline cardiovascular risk profiles (P ≤ 0.007), and less medication use (P ≤ 0.042). Outcome at 1 year was similar and predicted by NYHA class (HR 0.45; 95% CI [0.25–0.81]) and LVEF per % decrease (HR 1.05; 95% CI [1.02–1.08]. Throughout Europe, patients with FDCM received more genetic testing (47% vs. 8%, P < 0.01) and had higher genetic yield (55% vs. 22%, P < 0.01). / Conclusions: We observed that FDCM and SDCM have significant differences at baseline but similar short‐term prognosis. Whether modification of associated cardiovascular risk factors provide opportunities for treatment remains to be investigated. Our results also show a prevalent role of genetics in FDCM and a non‐marginal yield in SDCM although genetic testing is largely neglected in SDCM. Limited genetic testing and heterogeneity in panels provides a scaffold for improvement of guideline adherence

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

Genetic Evaluation of A Nation-Wide Dutch Pediatric DCM Cohort:The Use of Genetic Testing in Risk Stratification

BACKGROUND: This study aimed to describe the current practice and results of genetic evaluation in Dutch children with dilated cardiomyopathy and to evaluate genotype-phenotype correlations that may guide prognosis. METHODS: We performed a multicenter observational study in children diagnosed with dilated cardiomyopathy, from 2010 to 2017. RESULTS: One hundred forty-four children were included. Initial diagnostic categories were idiopathic dilated cardiomyopathy in 67 children (47%), myocarditis in 23 (16%), neuromuscular in 7 (5%), familial in 18 (13%), inborn error of metabolism in 4 (3%), malformation syndrome in 2 (1%), and "other" in 23 (16%). Median follow-up time was 2.1 years [IQR 1.0-4.3]. Hundred-seven patients (74%) underwent genetic testing. We found a likely pathogenic or pathogenic variant in 38 children (36%), most often in MYH7 (n = 8). In 1 patient initially diagnosed with myocarditis, a pathogenic LMNA variant was found. During the study, 39 patients (27%) reached study endpoint (SE: all-cause death or heart transplantation). Patients with a likely pathogenic or pathogenic variant were more likely to reach SE compared with those without (hazard ratio 2.8; 95% CI 1.3-5.8, P = 0.007), while transplant-free survival was significantly lower (P = 0.006). Clinical characteristics at diagnosis did not differ between the 2 groups. CONCLUSIONS: Genetic testing is a valuable tool for predicting prognosis in children with dilated cardiomyopathy, with carriers of a likely pathogenic or pathogenic variant having a worse prognosis overall. Genetic testing should be incorporated in clinical work-up of all children with dilated cardiomyopathy regardless of presumed disease pathogenesis