Maanja, Maren

Abstract Background Hypertrophic cardiomyopathy (HCM) is a heritable cardiac disorder characterized by increased left ventricular (LV) wall thickness, often leading to heart failure (HF). The role of cardiovascular magnetic resonance (CMR) imaging in predicting new onset of HF symptoms in patients with HCM remains unknown. This study aimed to identify CMR predictors of new-onset HF symptoms in individuals with HCM. Methods This study was a single-centre retrospective cohort study of HCM patients treated at a tertiary referral centre in the USA who underwent CMR examination between 1998 and 2018, had no HF symptoms at baseline CMR, and at least 1 year of follow-up. Clinical data were collected by review of electronic medical records, and CMR images were analyzed by a blinded expert cardiac radiologis. The primary outcome was new onset of HF symptoms, defined as New York Heart Association (NYHA) class ≥ II at follow-up. Kaplan–Meier analyses and Cox proportional hazard analyses were performed. Results Of 1462 patients diagnosed with HCM who had at least 1 CMR, 276 HCM patients without HF symptoms (average age 52.7 years, 33.3% female),median maximum left ventricular (LV) wall thickness was 19 mm ([IQR] 17–22) with a and median LV ejection fraction of 71% (IQR 66–77). Late gadolinium enhancement was detected in 56.2%) patients (60.7% had mild; 30.7% moderate; 8.6% severe). During a median follow-up period of 6.3 years, 93 patients developed HF symptoms (NYHA class II in 56 (60.2%); class III in 31 (33.3%); and class IV in 6 (6.5%). Multivariable analysis adjusted for age showed that LA enlargement (HR 1.626; 95% CI 1.01–2.62; P = .045) and LV mass index (HR 1.014; 95% CI 1.007–1.022; P≤ .001) and sex (HR 1.7; 95% CI 1.074–2.691; P = .023) were independent predictors of new onset of HF symptoms in patients with HCM. Conclusions Nearly half of the patients with HCM developed HF symptoms within 6.3 years. Left atrial enlargement, LV mass index, and sex were independent predictors of new onset of HF symptoms in HCM patients. These findings emphasize the value of CMR in HF risk stratification.

Abstract Aims Recently, deep learning artificial intelligence (AI) models have been trained to detect cardiovascular conditions, including hypertrophic cardiomyopathy (HCM), from the 12-lead electrocardiogram (ECG). In this external validation study, we sought to assess the performance of an AI-ECG algorithm for detecting HCM in diverse international cohorts. Methods and results A convolutional neural network-based AI-ECG algorithm was developed previously in a single-centre North American HCM cohort (Mayo Clinic). This algorithm was applied to the raw 12-lead ECG data of patients with HCM and non-HCM controls from three external cohorts (Bern, Switzerland; Oxford, UK; and Seoul, South Korea). The algorithm’s ability to distinguish HCM vs. non-HCM status from the ECG alone was examined. A total of 773 patients with HCM and 3867 non-HCM controls were included across three sites in the merged external validation cohort. The HCM study sample comprised 54.6% East Asian, 43.2% White, and 2.2% Black patients. Median AI-ECG probabilities of HCM were 85% for patients with HCM and 0.3% for controls (P < 0.001). Overall, the AI-ECG algorithm had an area under the receiver operating characteristic curve (AUC) of 0.922 [95% confidence interval (CI) 0.910–0.934], with diagnostic accuracy 86.9%, sensitivity 82.8%, and specificity 87.7% for HCM detection. In age- and sex-matched analysis (case–control ratio 1:2), the AUC was 0.921 (95% CI 0.909–0.934) with accuracy 88.5%, sensitivity 82.8%, and specificity 90.4%. Conclusion The AI-ECG algorithm determined HCM status from the 12-lead ECG with high accuracy in diverse international cohorts, providing evidence for external validity. The value of this algorithm in improving HCM detection in clinical practice and screening settings requires prospective evaluation.

Background: An electrocardiogram (ECG)-based artificial intelligence (AI) algorithm has shown good performance in detecting hypertrophic cardiomyopathy (HCM AI-score). However, its clinical application may be challenging due to the low disease prevalence and potentially high false-positive rate when screening unenriched cohorts. Aims: To identify clinical characteristics associated with false-positive HCM AI-score results to improve screening tool application. Methods: From a cohort of 20,677 patients with a digital 12-lead ECG in routine clinical practice in 2021, we performed a detailed chart review of the 200 patients with highest HCM AI-score to determine their HCM status and collect clinical data. Stepwise forward logistic regression was used to create a clinical variable-based “candidacy for HCM screening” score (CHS score), differentiating true- and likely true-positive from likely false-positive HCM AI-score results. We then validated the CHS score in 200 patients with the highest HCM AI-scores from an independent cohort of 15,147 patients with an ECG in 2022. Results: Among the 200 patients in 2021 (median age 71 years, 48% female), 96 (48%) were likely false-positive HCM AI-score results, 71 (36%) definite or likely true-positive results, and 33 (16%) indeterminate. The CHS score included a history of surgical septal myectomy, coronary artery bypass graft surgery, cardiac valve replacement, and pacemaker, and had an AUC of 0.88 (95% CI 0.83-0.92) for the differentiation of true- versus false-positive AI results. In the 2022 cohort, the CHS score had an AUC of 0.82 (95% CI 0.76 - 0.88). After limiting the cohort to HCM candidates identified by the CHS score, the false-positive results rate was reduced to 27/114 (24%). Conclusions: By applying the CHS score to patients with positive HCM AI-screening results, the false-positive rate decreased from 49% to 24%. Utilizing a clinical model used in tandem with an AI model improved diagnostic yield.

Introduction: Cardiac resynchronization therapy (CRT) has known benefit for heart failure (HF) with reduced left ventricular ejection fraction (LVEF) and dyssynchrony. However, very little is known about its benefit in infiltrative cardiomyopathies such as cardiac amyloidosis (CA) due to relative rarity of these conditions. Methods: This retrospective case series (2003-2021) comprised patients with HF due to transthyretin wild-type CA with (case) or without (control) CRT implant. Only controls with an indication for CRT or LVEF <40% were included. To further match disease severity, cases were 1:1 matched (propensity score tolerance 0.04) to controls for age, NYHA class, and CA stage (UK Staging). Primary outcome was MACE (major adverse cardiac events) which included stroke, myocardial infarction, HF hospitalization and cardiac mortality during a 3-year follow-up. Loss to follow-up was censored as per last visit date. LVEF and NYHA were noted closest to 1-year follow-up. Survival analysis for MACE was performed with Kaplan-Meier (KM) and multivariable cox regression models. Results: Study Cohort (n=160) had a mean age of 75.5 (SD 7.8) years and 97.5% were males. A majority had NYHA class 2 or 3 (92.3%) and CA stage 2 (42.2.%). Notably, median LVEF at baseline was lower in controls (33.5 vs 36.5; P = .001) and 77.5% of controls had an indication for CRT device. A total of 79 MACE events occurred, most frequent being HF hospitalization (n=45). KM curves were comparable for MACE events in the two groups (log-rank test; P = .059). Even after adjusting, as in table, CRT device was not associated with MACE events (adjusted HR 0.66; 95% CI 0.40-1.09; P = .106). Lastly, change in LVEF ( P = .84) and NYHA ( P = .86) at follow-up was similar in cases vs controls. Conclusions: The findings of this largest yet observational study to examine this question suggest that CRT in patients with transthyretin wild-type CA does not favorably impact outcomes for MACE events, improve LVEF or NYHA functional class.

AF is the most common clinically relevant cardiac arrhythmia associated with multiple comorbidities, cardiovascular complications (e.g. stroke) and increased mortality. As artificial intelligence (AI) continues to transform the practice of medicine, this review article highlights specific applications of AI for the screening, diagnosis and treatment of AF. Routinely used digital devices and diagnostic technology have been significantly enhanced by these AI algorithms, increasing the potential for large-scale population-based screening and improved diagnostic assessments. These technologies have similarly impacted the treatment pathway of AF, identifying patients who may benefit from specific therapeutic interventions. While the application of AI to the diagnostic and therapeutic pathway of AF has been tremendously successful, the pitfalls and limitations of these algorithms must be thoroughly considered. Overall, the multifaceted applications of AI for AF are a hallmark of this emerging era of medicine.