Identifying unmet clinical need in hypertrophic cardiomyopathy using national electronic health records

A cost-effectiveness analysis of hypertrophic cardiomyopathy sudden cardiac death risk algorithms for implantable cardioverter defibrillator decision-making

AIMS

To conduct a contemporary cost-effectiveness analysis examining the use of implantable cardioverter defibrillators (ICDs) for primary prevention in patients with hypertrophic cardiomyopathy (HCM).

METHODS

A discrete-time Markov model was used to determine the cost-effectiveness of different ICD decision-making rules for implantation. Several scenarios were investigated, including the reference scenario of implantation rates according to observed real-world practice. A 12-year time horizon with an annual cycle length was used. Transition probabilities used in the model were obtained using Bayesian analysis. The study has been reported according to the Consolidated Health Economic Evaluation Reporting Standards checklist.

RESULTS

Using a 5-year SCD risk threshold of 6% was cheaper than current practice and has marginally better total quality adjusted life years (QALYs). This is the most cost-effective of the options considered, with an incremental cost-effectiveness ratio of £834 per QALY. Sensitivity analyses highlighted that this decision is largely driven by what health-related quality of life (HRQL) is attributed to ICD patients and time horizon.

CONCLUSION

We present a timely new perspective on HCM-ICD cost-effectiveness, using methods reflecting real-world practice. While we have shown that a 6% 5-year SCD risk cut-off provides the best cohort stratification to aid ICD decision-making, this will also be influenced by the particular values of costs and HRQL for subgroups or at a local level. The process of explicitly demonstrating the main factors, which drive conclusions from such an analysis will help to inform shared decision-making in this complex area for all stakeholders concerned.

The Diagnostic and Therapeutic Implications of Phenocopies and Mimics of Hypertrophic Cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is a common myocardial disease defined by increased left ventricular wall thickness unexplained by loading conditions. HCM frequently is caused by pathogenic variants in sarcomeric protein genes, but several other syndromic, metabolic, infiltrative, and neuromuscular diseases can result in HCM phenocopies. This review summarizes the current understanding of these HCM mimics, highlighting their importance across the life course. The central role of a comprehensive, multiparametric diagnostic approach and the potential of precision medicine in tailoring treatment strategies are emphasized.

Emerging Themes in Genetics of Hypertrophic Cardiomyopathy: Current Status and Clinical Application

Hypertrophic cardiomyopathy (HCM), defined clinically by the presence of unexplained left ventricular hypertrophy (LVH), with wall thickness ≥ 1.5 cm, is a phenotype in search of a diagnosis, which is most often a genetically determined, cardiac exclusive, or systemic disorder. Familial evaluation and genetic testing are required for definitive diagnosis. The role of genetic findings in predicting development of disease, outcomes, and increasingly to guide management is evolving with access to larger data sets. The specific mutation and sex of the patient are important determinants that ultimately are likely to guide management. The genetic/familial evaluation is influenced by the accuracy of the clinical diagnosis and the extent/expertise of the genetic laboratory. Genetic testing in a patient with unexplained LVH without systemic manifestations will yield a definite/likely pathogenetic mutation in a sarcomere (30%-50%), regulatory/functional (10%-15%) or metabolic/syndromic (< 5%) gene associated with Mendelian inheritance. The importance of oligo- and polygenic determinants, usually in the absence of Mendelian inheritance, is under investigation with important implications, particularly related to familial evaluation and definition of risk of disease development in relatives of probands. The results of genetic testing are increasingly important in management strategies related to the use of the implantable cardioverter defibrillator for prevention of sudden death, use of myosin inhibitors for refractory symptoms in patients with and without outflow tract obstruction, and-on the immediate horizon-gene therapy. This review will focus on genetic and outcome data in sarcomeric HCM, and minor causative genes with robust evidence of their association will also be considered.

Artificial intelligence for dementia-Applied models and digital health

INTRODUCTION

The use of applied modeling in dementia risk prediction, diagnosis, and prognostics will have substantial public health benefits, particularly as "deep phenotyping" cohorts with multi-omics health data become available.

METHODS

This narrative review synthesizes understanding of applied models and digital health technologies, in terms of dementia risk prediction, diagnostic discrimination, prognosis, and progression. Machine learning approaches show evidence of improved predictive power compared to standard clinical risk scores in predicting dementia, and the potential to decompose large numbers of variables into relatively few critical predictors.

RESULTS

This review focuses on key areas of emerging promise including: emphasis on easier, more transparent data sharing and cohort access; integration of high-throughput biomarker and electronic health record data into modeling; and progressing beyond the primary prediction of dementia to secondary outcomes, for example, treatment response and physical health.

DISCUSSION

Such approaches will benefit also from improvements in remote data measurement, whether cognitive (e.g., online), or naturalistic (e.g., watch-based accelerometry).

How common is hypertrophic cardiomyopathy… really?: Disease prevalence revisited 27 years after CARDIA

Hypertrophic cardiomyopathy (HCM) is a heterogeneous albeit treatable cardiac disease of variable severity, with the potential for heart failure, atrial fibrillation and arrhythmic sudden death, characterized by otherwise unexplained left ventricular (LV) hypertrophy and affecting all ages and races. Over the last 30 years, several studies have estimated the prevalence of HCM in the general population, employing echocardiography and cardiac magnetic resonance imaging (CMR), as well electronic health records and billing databases for clinical diagnosis. The estimated prevalence in the general population based on the disease phenotype of LV hypertrophy by imaging is 1:500 (0.2%). This prevalence was initially proposed in 1995 in the population-based CARDIA study employing echocardiography, and more recently confirmed by automated CMR analysis in the large UK Biobank cohort. The 1:500 prevalence appears most relevant to clinical assessment and management of HCM. These available data suggest that HCM is not a rare condition but likely underdiagnosed clinically and by extrapolation potentially affects about 700,000 Americans and possibly 15 million people worldwide.

Clinical Outcomes, Resource Utilization, and Treatment Over the Disease Course of Symptomatic Obstructive Hypertrophic Cardiomyopathy in the United States

We sought to describe the clinical outcomes, resource utilization, and treatment options for patients with symptomatic obstructive hypertrophic cardiomyopathy (HCM) over the course of their disease. Adults with obstructive HCM who were symptomatic were identified from the IBM MarketScan Commercial and Medicare supplemental database (January 2009 to March 2019). The index date was the initial obstructive HCM diagnosis date. Patients were required to have ≥12-month continuous eligibility before and after the index date. Incidence rates (IRs) and cumulative risk of cardiovascular events, healthcare resource utilization, and pharmacotherapy were assessed after index and compared with matched controls. Among 4,617 eligible patients with obstructive HCM, 2,917 (63.2%, mean age 60, 47.2% women) were symptomatic at index date. The most common cardiovascular events were atrial fibrillation/flutter (IR:1.421 per person-year [PPY], heart failure (IR: 0.895 PPY), and dyspnea (IR:0.797 PPY). Patients incurred higher resource use with frequent tests and monitoring, hospitalizations (0.454 PPY), and emergency room visits (0.611 PPY). The use of pharmacotherapy increased from 61.2% in the 6-month preindex period to 83.9% in the 6-month postindex period and remained stable after diagnosis. These events ranged from 3 to over 60-fold higher compared with controls, with the largest difference observed in arrhythmic events. The majority of patients were symptomatic at the time of obstructive HCM diagnosis, resulting in significantly increased cardiovascular complications and frequent disease monitoring after diagnosis versus controls. In conclusion, healthcare resource utilization was substantial, and these findings suggest a higher clinical and economic burden over the disease course among patients with symptomatic obstructive HCM, despite current treatment.

Natural language processing for identification of hypertrophic cardiomyopathy patients from cardiac magnetic resonance reports

Background

Cardiac magnetic resonance (CMR) imaging is important for diagnosis and risk stratification of hypertrophic cardiomyopathy (HCM) patients. However, collection of information from large numbers of CMR reports by manual review is time-consuming, error-prone and costly. Natural language processing (NLP) is an artificial intelligence method for automated extraction of information from narrative text including text in CMR reports in electronic health records (EHR). Our objective was to assess whether NLP can accurately extract diagnosis of HCM from CMR reports.

Methods

An NLP system with two tiers was developed for information extraction from narrative text in CMR reports; the first tier extracted information regarding HCM diagnosis while the second extracted categorical and numeric concepts for HCM classification. We randomly allocated 200 HCM patients with CMR reports from 2004 to 2018 into training (100 patients with 185 CMR reports) and testing sets (100 patients with 206 reports).

Results

NLP algorithms demonstrated very high performance compared to manual annotation. The algorithm to extract HCM diagnosis had accuracy of 0.99. The accuracy for categorical concepts included HCM morphologic subtype 0.99, systolic anterior motion of the mitral valve 0.96, mitral regurgitation 0.93, left ventricular (LV) obstruction 0.94, location of obstruction 0.92, apical pouch 0.98, LV delayed enhancement 0.93, left atrial enlargement 0.99 and right atrial enlargement 0.98. Accuracy for numeric concepts included maximal LV wall thickness 0.96, LV mass 0.99, LV mass index 0.98, LV ejection fraction 0.98 and right ventricular ejection fraction 0.99.

Conclusions

NLP identified and classified HCM from CMR narrative text reports with very high performance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-022-02017-y.

Changing concepts in heart muscle disease: the evolving understanding of hypertrophic cardiomyopathy

Sixty years ago, hypertrophic cardiomyopathy (HCM) was considered a rare lethal disease that affected predominantly young adults and for which there were few treatment options. Today, it is recognised to be a relatively common disorder that presents throughout the life course with a heterogeneous clinical phenotype that can be managed effectively in the majority of individuals. A greater awareness of the condition and less reluctance from healthcare practitioners to make the diagnosis, coupled with improvements in cardiac imaging, including greater use of artificial intelligence and improved yields from screening efforts, have all helped facilitate a more precise and timely diagnosis. This enhanced ability to diagnose HCM early is being paired with innovations in treatment, which means that the majority of patients receiving a contemporary diagnosis of HCM can anticipate a normal life expectancy and expect to maintain a good functional status and quality of life. Indeed, with increasing translation of molecular genetics from bench to bedside associated with a growing number of randomised clinical trials of novel therapies aimed at ameliorating or perhaps even preventing the disease, the next chapter in the story for HCM will provide much excitement and more importantly, offer much anticipated reward for our patients.

Comparison of mortality and cause of death between adults with and without hypertrophic cardiomyopathy

Insufficient evidence is available comparing mortality and cause of death between general hypertrophic cardiomyopathy (HCM) and general non-HCM populations. We aimed to investigate how causes of death and mortality differ in subjects with and without HCM. Using the National Health Insurance Service database from 2009 to 2016, individuals who underwent health check-up(s) with or without a history of HCM were identified. Participants in the HCM group were matched at a 1:1 ratio with those in the non-HCM group using propensity scores calculated from the baseline covariates. Mortality rates and risks were compared between the groups. In total, 14,858 participants (7,429 each in the HCM and non-HCM groups) were followed up over a mean 4.4 ± 2.2 years (mean age, 61.0 years; male proportion, 66.8%). Compared to the non-HCM group, the HCM group showed a higher risk of all-cause and HCM-related mortality and a similar risk for non-cardiovascular mortality (hazard ratio [95% confidence interval] 1.57 [1.38–1.78], 2.71 [1.92–3.83], and 1.04 [0.88–1.23], respectively). The sensitivity analyses consistently showed that the HCM group showed higher risks of all-cause and HCM-related mortality than the non-HCM group. The female participants with HCM were associated with an increasing trend of the risks of all-cause mortality but not HCM-related mortality compared to their male counterparts (p for interaction < 0.001 and 0.185, respectively). In conclusion, compared to the non-HCM population, the general HCM population showed higher risks of both all-cause and HCM-related mortality, but had a similar risk of non-cardiovascular mortality.

Epidemiology of cardiomyopathies and incident heart failure in a population-based cohort study

AIMS

The population prevalence of cardiomyopathies and the natural history of symptomatic heart failure (HF) and arrhythmia across cardiomyopathy phenotypes is poorly understood. Study aims were to estimate the population-diagnosed prevalence of cardiomyopathies and describe the temporal relationship between a diagnosis of cardiomyopathy with HF and arrhythmia.

METHODS

People with cardiomyopathy (n=4116) were identified from linked electronic health records (~9 million individuals; 2000-2018) and categorised into hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM), arrhythmogenic right ventricular cardiomyopathy (ARVC), restrictive cardiomyopathy (RCM) and cardiac amyloidosis (CA). Cardiomyopathy point prevalence, rates of symptomatic HF and arrhythmia and timing relative to a diagnosis of cardiomyopathy were determined.

RESULTS

In 2018, DCM was the most common cardiomyopathy. DCM and HCM were twice as common among men, with the reverse trend for ARVC. Between 2010 and 2018, prevalence increased for ARVC by 180% and HCM by 9%. At diagnosis, more patients with CA (66%), DCM (56%) and RCM (62%) had pre-existing HF compared with ARVC (29%) and HCM (27%). Among those free of HF at diagnosis of cardiomyopathy, annualised HF incidence was greatest in CA and DCM. Diagnoses of all cardiomyopathies clustered around the time of HF onset.

CONCLUSIONS

The recorded prevalence of all cardiomyopathies increased over the past decade. Recognition of CA is generally preceded by HF, whereas individuals with ARVC or HCM more often developed HF after their cardiomyopathy diagnosis suggesting a more indolent course or better asymptomatic recognition. The clustering of HF and cardiomyopathy diagnoses suggests opportunities for presymptomatic or earlier diagnosis.

The Impact of Demographic, Socio-Economic and Geographic Factors on Mortality Risk among People Living with Dementia in England (2002-2016)

Increasing numbers of people living with dementia (PLWD), and a pressured health and social care system, will exacerbate inequalities in mortality for PLWD. There is a dearth of research examining multiple factors in mortality risk among PLWD, including application of large administrative datasets to investigate these issues. This study explored variation mortality risk variation among people diagnosed with dementia between 2002-2016, based on: age, sex, ethnicity, deprivation, geography and general practice (GP) contacts. Data were derived from electronic health records from a cohort of Clinical Practice Research Datalink GP patients in England (n = 142,340). Cox proportional hazards regression modelled mortality risk separately for people with early- and later- onset dementia. Few social inequalities were observed in early-onset dementia; men had greater risk of mortality. For early- and later-onset, higher rates of GP observations-and for later-onset only dementia medications-are associated with increased mortality risk. Social inequalities were evident in later-onset dementia. Accounting for other explanatory factors, Black and Mixed/Other ethnicity groups had lower mortality risk, more deprived areas had greater mortality risk, and higher mortality was observed in North East, South Central and South West GP regions. This study provides novel evidence of the extent of mortality risk inequalities among PLWD. Variance in mortality risk was observed by social, demographic and geographic factors, and frequency of GP contact. Findings illustrate need for greater person-centred care discussions, prioritising tackling inequalities among PLWD. Future research should explore more outcomes for PLWD, and more explanatory factors of health outcomes.

Discovery of Aficamten (CK-274), a Next-Generation Cardiac Myosin Inhibitor for the Treatment of Hypertrophic Cardiomyopathy

Hypercontractility of the cardiac sarcomere may be essential for the underlying pathological hypertrophy and fibrosis in genetic hypertrophic cardiomyopathies. Aficamten (CK-274) is a novel cardiac myosin inhibitor that was discovered from the optimization of indoline compound 1. The important advancement of the optimization was discovery of an Indane analogue (12) with a less restrictive structure-activity relationship that allowed for the rapid improvement of drug-like properties. Aficamten was designed to provide a predicted human half-life (t_1/2) appropriate for once a day (qd) dosing, to reach steady state within two weeks, to have no substantial cytochrome P450 induction or inhibition, and to have a wide therapeutic window in vivo with a clear pharmacokinetic/pharmacodynamic relationship. In a phase I clinical trial, aficamten demonstrated a human t_1/2 similar to predictions and was able to reach steady state concentration within the desired two-week window.

NATURAL LANGUAGE PROCESSING BASED MACHINE LEARNING MODEL USING CARDIAC MRI REPORTS TO IDENTIFY HYPERTROPHIC CARDIOMYOPATHY PATIENTS

Hypertrophic Cardiomyopathy (HCM) is the most common genetic heart disease in the US and is known to cause sudden death (SCD) in young adults. While significant advancements have been made in HCM diagnosis and management, there is a need to identify HCM cases from electronic health record (EHR) data to develop automated tools based on natural language processing guided machine learning (ML) models for accurate HCM case identification to improve management and reduce adverse outcomes of HCM patients. Cardiac Magnetic Resonance (CMR) Imaging, plays a significant role in HCM diagnosis and risk stratification. CMR reports, generated by clinician annotation, offer rich data in the form of cardiac measurements as well as narratives describing interpretation and phenotypic description. The purpose of this study is to develop an NLP-based interpretable model utilizing impressions extracted from CMR reports to automatically identify HCM patients. CMR reports of patients with suspected HCM diagnosis between the years 1995 to 2019 were used in this study. Patients were classified into three categories of yes HCM, no HCM and, possible HCM. A random forest (RF) model was developed to predict the performance of both CMR measurements and impression features to identify HCM patients. The RF model yielded an accuracy of 86% (608 features) and 85% (30 features). These results offer promise for accurate identification of HCM patients using CMR reports from EHR for efficient clinical management transforming health care delivery for these patients.

Explanatory Analysis of a Machine Learning Model to Identify Hypertrophic Cardiomyopathy Patients from EHR Using Diagnostic Codes

Hypertrophic cardiomyopathy (HCM) is a genetic heart disease that is the leading cause of sudden cardiac death (SCD) in young adults. Despite the well-known risk factors and existing clinical practice guidelines, HCM patients are underdiagnosed and sub-optimally managed. Developing machine learning models on electronic health record (EHR) data can help in better diagnosis of HCM and thus improve hundreds of patient lives. Automated phenotyping using HCM billing codes has received limited attention in the literature with a small number of prior publications. In this paper, we propose a novel predictive model that helps physicians in making diagnostic decisions, by means of information learned from historical data of similar patients. We assembled a cohort of 11,562 patients with known or suspected HCM who have visited Mayo Clinic between the years 1995 to 2019. All existing billing codes of these patients were extracted from the EHR data warehouse. Target ground truth labeling for training the machine learning model was provided by confirmed HCM diagnosis using the gold standard imaging tests for HCM diagnosis echocardiography (echo), or cardiac magnetic resonance (CMR) imaging. As the result, patients were labeled into three categories of "yes definite HCM", "no HCM phenotype", and "possible HCM" after a manual review of medical records and imaging tests. In this study, a random forest was adopted to investigate the predictive performance of billing codes for the identification of HCM patients due to its practical application and expected accuracy in a wide range of use cases. Our model performed well in finding patients with "yes definite", "possible" and "no" HCM with an accuracy of 71%, weighted recall of 70%, the precision of 75%, and weighted F1 score of 72%. Furthermore, we provided visualizations based on multidimensional scaling and the principal component analysis to provide insights for clinicians' interpretation. This model can be used for the identification of HCM patients using their EHR data, and help clinicians in their diagnosis decision making.

Mortality Among Referral Patients With Hypertrophic Cardiomyopathy vs the General European Population

Importance

It is unclear whether hypertrophic cardiomyopathy (HCM) conveys excess mortality when compared with the general population.

Objective

To compare the survival of patients with HCM with that of the general European population.

Design, Setting, and Participants

Retrospective cohort study of 4893 consecutive adult patients with HCM presenting at 7 European referral centers between 1980 and 2013. The data were analyzed between April 2018 and August 2019.

Main Outcomes and Measures

Survival was compared using standardized mortality ratios (SMRs) calculated with data from Eurostat, stratified by study period, country, sex, and age, and using a composite end point in the HCM cohort of all-cause mortality, aborted sudden cardiac death, and heart transplant.

Results

Of 4893 patients with HCM, 3126 (63.9%) were male, and the mean (SD) age at presentation was 49.2 (16.4) years. During a median follow-up of 6.2 years (interquartile range, 3.1-9.8 years), 721 patients (14.7%) reached the composite end point. Compared with the general population, patients with HCM had excess mortality throughout the age spectrum (SMR, 2.0, 95% CI, 1.48-2.63). Excess mortality was highest among patients presenting prior to the year 2000 but persisted in the cohort presenting between 2006 and 2013 (SMR, 1.84; 95% CI, 1.55-2.18). Women had higher excess mortality than men (SMR, 2.66; 95% CI, 2.38-2.97; vs SMR, 1.68; 95% CI, 1.52-1.85; P < .001).

Conclusions and Relevance

Among patients referred to European specialty centers, HCM was associated with significant excess mortality through the life course. Although there have been improvements in survival with time, potentially reflecting improved treatments for HCM, these findings highlight the need for more research into the causes of excess mortality among patients with HCM and for better risk stratification.

Dose-dependent oral glucocorticoid cardiovascular risks in people with immune-mediated inflammatory diseases: A population-based cohort study

BACKGROUND

Glucocorticoids are widely used to reduce disease activity and inflammation in patients with a range of immune-mediated inflammatory diseases. It is uncertain whether or not low to moderate glucocorticoid dose increases cardiovascular risk. We aimed to quantify glucocorticoid dose-dependent cardiovascular risk in people with 6 immune-mediated inflammatory diseases.

METHODS AND FINDINGS

We conducted a population-based cohort analysis of medical records from 389 primary care practices contributing data to the United Kingdom Clinical Practice Research Datalink (CPRD), linked to hospital admissions and deaths in 1998-2017. We estimated time-variant daily and cumulative glucocorticoid prednisolone-equivalent dose-related risks and hazard ratios (HRs) of first all-cause and type-specific cardiovascular diseases (CVDs). There were 87,794 patients with giant cell arteritis and/or polymyalgia rheumatica (n = 25,581), inflammatory bowel disease (n = 27,739), rheumatoid arthritis (n = 25,324), systemic lupus erythematosus (n = 3,951), and/or vasculitis (n = 5,199), and no prior CVD. Mean age was 56 years and 34.1% were men. The median follow-up time was 5.0 years, and the proportions of person-years spent at each level of glucocorticoid daily exposure were 80% for non-use, 6.0% for <5 mg, 11.2% for 5.0-14.9 mg, 1.6% for 15.0-24.9 mg, and 1.2% for ≥25.0 mg. Incident CVD occurred in 13,426 (15.3%) people, including 6,013 atrial fibrillation, 7,727 heart failure, and 2,809 acute myocardial infarction events. One-year cumulative risks of all-cause CVD increased from 1.4% in periods of non-use to 8.9% for a daily prednisolone-equivalent dose of ≥25.0 mg. Five-year cumulative risks increased from 7.1% to 28.0%, respectively. Compared to periods of non-glucocorticoid use, those with <5.0 mg daily prednisolone-equivalent dose had increased all-cause CVD risk (HR = 1.74; 95% confidence interval [CI] 1.64-1.84; range 1.52 for polymyalgia rheumatica and/or giant cell arteritis to 2.82 for systemic lupus erythematosus). Increased dose-dependent risk ratios were found regardless of disease activity level and for all type-specific CVDs. HRs for type-specific CVDs and <5.0-mg daily dose use were: 1.69 (95% CI 1.54-1.85) for atrial fibrillation, 1.75 (95% CI 1.56-1.97) for heart failure, 1.76 (95% CI 1.51-2.05) for acute myocardial infarction, 1.78 (95% CI 1.53-2.07) for peripheral arterial disease, 1.32 (95% CI 1.15-1.50) for cerebrovascular disease, and 1.93 (95% CI 1.47-2.53) for abdominal aortic aneurysm. The lack of hospital medication records and drug adherence data might have led to underestimation of the dose prescribed when specialists provided care and overestimation of the dose taken during periods of low disease activity. The resulting dose misclassification in some patients is likely to have reduced the size of dose-response estimates.

CONCLUSIONS

In this study, we observed an increased risk of CVDs associated with glucocorticoid dose intake even at lower doses (<5 mg) in 6 immune-mediated diseases. These results highlight the importance of prompt and regular monitoring of cardiovascular risk and use of primary prevention treatment at all glucocorticoid doses.

Atrial fibrillation, anticoagulation management and risk of stroke in the Cardiomyopathy/Myocarditis registry of the EURObservational Research Programme of the European Society of Cardiology

Aims

Cardiomyopathies are a heterogeneous group of disorders that increase the risk for atrial fibrillation (AF). The aim of the study is to assess the prevalence of AF, anticoagulation management, and risk of stroke/transient ischaemic attack (TIA) in patients with cardiomyopathy.

Methods and results

Three thousand two hundred eight consecutive adult patients with cardiomyopathy (34.9% female; median age: 55.0 years) were prospectively enrolled as part of the EURObservational Research Programme Cardiomyopathy/Myocarditis Registry. At baseline, 903 (28.2%) patients had AF (29.4% dilated, 27.5% hypertrophic, 51.5% restrictive, and 14.7% arrhythmogenic right ventricular cardiomyopathy, P < 0.001). AF was associated with more advanced New York Heart Association class (P < 0.001), increased prevalence of cardiovascular risk factors and co‐morbidities, and a history of stroke/TIA (P < 0.001). Oral anticoagulation was administered in 71.7% of patients with AF (vitamin K antagonist: 51.6%; direct oral anticoagulant: 20.1%). At 1 year follow‐up, the incidence of cardiovascular endpoints was as follows: stroke/TIA 1.85% (AF vs. non‐AF: 3.17% vs. 1.19%, P < 0.001), death from any cause 3.43% (AF vs. non‐AF: 5.39% vs. 2.50%, P < 0.001), and death from heart failure 1.67% (AF vs. non‐AF: 2.44% vs. 1.31%, P = 0.033). The independent predictors for stroke/TIA were as follows: AF [odds ratio (OR) 2.812, P = 0.005], history of stroke (OR 7.311, P = 0.010), and anaemia (OR 3.119, P = 0.006).

Conclusions

The study reveals a high prevalence and diverse distribution of AF in patients with cardiomyopathies, inadequate anticoagulation regimen, and high risk of stroke/TIA in this population.

Effect of body surface area and gender on wall thickness thresholds in hypertrophic cardiomyopathy

BACKGROUND

Family screening for hypertrophic cardiomyopathy (HCM) is based on genetic testing and clinical evaluation (maximal left ventricular wall thickness (MWT) ≥15 mm, or ≥13 mm in first-degree relatives of HCM patients). The aim of this study was to assess the effect of gender and body size on diagnosis of HCM and prediction of clinical outcome.

METHODS

This study includes 199 genotype-positive subjects (age 44 ± 15 years, 50% men) referred for cardiac screening. Gender-specific reference values for MWT indexed by body surface area (BSA), height and weight were derived from 147 healthy controls. Predictive accuracy of each method for HCM-related events was assessed by comparing areas under the receiver operating characteristic curves (AUC).

RESULTS

Men had a higher absolute, but similar BSA- and weight-indexed MWT compared with women (14.0 ± 3.9 mm vs 11.5 ± 3.8 mm, p < 0.05; 6.8 ± 2.1 mm/m² vs 6.6 ± 2.4 mm/m²; 0.17 ± 0.06 mm/kg vs 0.17 ± 0.06 mm/kg, both p > 0.05). Applying BSA- and weight-indexed cut-off values decreased HCM diagnoses in the study group (48% vs 42%; 48% vs 39%, both p < 0.05), reclassified subjects in the largest, lightest and heaviest tertiles (≥2.03 m²: 58% vs 45%; ≤70 kg: 37% vs 46%; ≥85 kg: 53% vs 25%, all p < 0.05) and improved predictive accuracy (AUC 0.76 [95% CI 0.69-0.82] vs 0.78 [0.72-0.85]; and vs 0.80 [0.74-0.87]; both p < 0.05).

CONCLUSIONS

In genotype-positive subjects referred for family screening, differences in MWT across gender are mitigated after indexation by BSA or weight. Indexation decreases the prevalence of HCM, particularly in larger men, and improves the predictive accuracy for HCM-related events.

Factors influencing long-term heart failure mortality in patients with obstructive hypertrophic cardiomyopathy in Western Sweden: probable dose-related protection from beta-blocker therapy

Objective

In order to avoid effects of referral bias, we assessed risk factors for disease-related mortality in a geographical cohort of patients with hypertrophic obstructive cardiomyopathy (HOCM), and any therapy effect on survival.

Methods

Diagnostic databases in 10 hospitals in the West Götaland Region yielded 251 adult patients with HOCM (128 male, 123 female). Case notes were reviewed for clinical data and ECG and ultrasound findings. Beta-blockers were used in 71.3% of patients from diagnosis (median metoprolol-equivalent dose of 125 mg/day), and at latest follow-up in 86.1%; 121 patients had medical therapy alone, 88 short atrioventricular delay pacing and 42 surgical myectomy. Mean follow-up was 14.4±8.9 (mean±SD) years. Primary endpoint was disease-related death, and secondary endpoint heart failure deaths.

Results

There were 65 primary endpoint events. Independent risk factors for disease-related death on multivariate Cox hazard regression were: female sex (p=0.005), age at diagnosis (p<0.001), outflow gradient ≥50 mm Hg at diagnosis (p=0.036) and at follow-up (p=0.001). Heart failure caused 62% of deaths, and sudden cardiac death 17%. Late independent predictors of heart failure death were: female sex (p=0.003), outflow gradient ≥50 mm Hg at latest follow-up (p=0.032), verapamil/diltiazem therapy (p=0.012) and coexisting hypertension (p=0.031), but not other comorbidities. Neither myectomy nor pacing modified survival, but early and maintained beta-blocker therapy was associated with dose-dependent reduction in disease-related mortality in the multivariate model (p=0.028), and final dose was also associated with reduced heart failure mortality (p=0.008). Kaplan-Meier survival curves analysed in initial dose bands of 0–74, 75–149 and ≥150 mg metoprolol/day showed 10-year freedom from disease-related deaths of 83.1%, 90.7% and 97.0%, respectively (p_trend=0.00008). Even after successful relief of outflow obstruction by intervention, there was survival benefit of metoprolol doses ≥100 mg/day (p=0.01).

Conclusions

In population-based HOCM cohorts heart failure is a dominant cause of death and on multivariate analysis beta-blocker therapy was associated with a dose-dependent cardioprotective effect on total, disease-related as well as heart failure-related mortality.

Automated extraction of sudden cardiac death risk factors in hypertrophic cardiomyopathy patients by natural language processing

BACKGROUND

The management of hypertrophic cardiomyopathy (HCM) patients requires the knowledge of risk factors associated with sudden cardiac death (SCD). SCD risk factors such as syncope and family history of SCD (FH-SCD) as well as family history of HCM (FH-HCM) are documented in electronic health records (EHRs) as clinical narratives. Automated extraction of risk factors from clinical narratives by natural language processing (NLP) may expedite management workflow of HCM patients. The aim of this study was to develop and deploy NLP algorithms for automated extraction of syncope, FH-SCD, and FH-HCM from clinical narratives.

METHODS AND RESULTS

We randomly selected 200 patients from the Mayo HCM registry for development (n = 100) and testing (n = 100) of NLP algorithms for extraction of syncope, FH-SCD as well as FH-HCM from clinical narratives of EHRs. The clinical reference standard was manually abstracted by 2 independent annotators. Performance of NLP algorithms was compared to aggregation and summarization of data entries in the HCM registry for syncope, FH-SCD, and FH-HCM. We also compared the NLP algorithms with billing codes for syncope as well as responses to patient survey questions for FH-SCD and FH-HCM. These analyses demonstrated NLP had superior sensitivity (0.96 vs 0.39, p < 0.001) and comparable specificity (0.90 vs 0.92, p = 0.74) and PPV (0.90 vs 0.83, p = 0.37) compared to billing codes for syncope. For FH-SCD, NLP outperformed survey responses for all parameters (sensitivity: 0.91 vs 0.59, p = 0.002; specificity: 0.98 vs 0.50, p < 0.001; PPV: 0.97 vs 0.38, p < 0.001). NLP also achieved superior sensitivity (0.95 vs 0.24, p < 0.001) with comparable specificity (0.95 vs 1.0, p-value not calculable) and positive predictive value (PPV) (0.92 vs 1.0, p = 0.09) compared to survey responses for FH-HCM.

CONCLUSIONS

Automated extraction of syncope, FH-SCD and FH-HCM using NLP is feasible and has promise to increase efficiency of workflow for providers managing HCM patients.