Clinical features and outcomes in carriers of pathogenic desmoplakin variants.

BACKGROUND AND AIMS: Pathogenic variants in the desmoplakin (DSP) gene are associated with the development of a distinct arrhythmogenic cardiomyopathy phenotype not fully captured by either dilated cardiomyopathy (DCM), non-dilated left ventricular cardiomyopathy (NDLVC), or arrhythmogenic right ventricular cardiomyopathy (ARVC). Prior studies have described baseline DSP cardiomyopathy genetic, inflammatory, and structural characteristics. However, cohort sizes have limited full clinical characterization and identification of clinical and demographic predictors of sustained ventricular arrhythmias (VAs), heart failure (HF) hospitalizations, and transplant/death. In particular, the relevance of acute myocarditis-like episodes for subsequent disease course is largely unknown. METHODS: All patients with pathogenic/likely pathogenic (P/LP) DSP variants in the worldwide DSP-ERADOS Network (26 academic institutions across nine countries) were included. The primary outcomes were the development of sustained VA and HF hospitalizations during follow-up. Fine-Gray regressions were used to test association between clinical and instrumental parameters and the development of outcomes. RESULTS: Eight hundred patients [40.3 ± 17.5 years, 47.5% probands, left ventricular ejection fraction (LVEF) 49.5 ± 13.9%] were included. Over 3.7 [1.4-7.1] years, 139 (17.4%, 3.9%/year) and 72 (9.0%, 1.8%/year) patients experienced sustained VA and HF episodes, respectively. A total of 32.5% of individuals did not fulfil diagnostic criteria for ARVC, DCM, or NDLVC; their VA incidence was 0.5%/year. In multivariable regression, risk features associated with the development of VA were female sex [adjusted hazard ratio (aHR) 1.547; P = .025], prior non-sustained ventricular tachycardia (aHR 1.721; P = .009), prior sustained VA (aHR 1.923; P = .006), and LVEF ≤ 50% (aHR: 1.645; P = .032), while for HF, they were the presence of T-wave inversion in 3+ electrocardiogram leads (aHR 2.036, P = .007) and LVEF ≤ 50% (aHR 3.879; P < .001). Additionally, 70 (8.8%) patients experienced a myocardial injury episode at presentation or during follow-up. These episodes were associated with an increased risk of VA and HF thereafter (HR 2.394; P < .001, and HR 5.064, P < .001, respectively). CONCLUSIONS: Patients with P/LP DSP variants experience high rates of sustained VA and HF hospitalizations. These patients demonstrate a distinct clinical phenotype (DSP cardiomyopathy), whose most prominent risk features associated with adverse clinical outcomes are the presence of prior non-sustained ventricular tachycardia or sustained VA, T-wave inversion in 3+ leads on electrocardiogram, LVEF ≤ 50%, and myocardial injury events.

A novel tool for arrhythmic risk stratification in desmoplakin gene variant carriers.

BACKGROUND AND AIMS: Pathogenic desmoplakin (DSP) gene variants are associated with the development of a distinct form of arrhythmogenic cardiomyopathy known as DSP cardiomyopathy. Patients harbouring these variants are at high risk for sustained ventricular arrhythmia (VA), but existing tools for individualized arrhythmic risk assessment have proven unreliable in this population. METHODS: Patients from the multi-national DSP-ERADOS (Desmoplakin SPecific Effort for a RAre Disease Outcome Study) Network patient registry who had pathogenic or likely pathogenic DSP variants and no sustained VA prior to enrolment were followed longitudinally for the development of first sustained VA event. Clinically guided, step-wise Cox regression analysis was used to develop a novel clinical tool predicting the development of incident VA. Model performance was assessed by c-statistic in both the model development cohort (n = 385) and in an external validation cohort (n = 86). RESULTS: In total, 471 DSP patients [mean age 37.8 years, 65.6% women, 38.6% probands, 26% with left ventricular ejection fraction (LVEF) < 50%] were followed for a median of 4.0 (interquartile range: 1.6-7.3) years; 71 experienced first sustained VA events {2.6% [95% confidence interval (CI): 2.0, 3.5] events/year}. Within the development cohort, five readily available clinical parameters were identified as independent predictors of VA and included in a novel DSP risk score: female sex [hazard ratio (HR) 1.9 (95% CI: 1.1-3.4)], history of non-sustained ventricular tachycardia [HR 1.7 (95% CI: 1.1-2.8)], natural logarithm of 24-h premature ventricular contraction burden [HR 1.3 (95% CI: 1.1-1.4)], LVEF < 50% [HR 1.5 (95% CI: .95-2.5)], and presence of moderate to severe right ventricular systolic dysfunction [HR 6.0 (95% CI: 2.9-12.5)]. The model demonstrated good risk discrimination within both the development [c-statistic .782 (95% CI: .77-.80)] and external validation [c-statistic .791 (95% CI: .75-.83)] cohorts. The negative predictive value for DSP patients in the external validation cohort deemed to be at low risk for VA (<5% at 5 years; n = 26) was 100%. CONCLUSIONS: The DSP risk score is a novel model that leverages readily available clinical parameters to provide individualized VA risk assessment for DSP patients. This tool may help guide decision-making for primary prevention implantable cardioverter-defibrillator placement in this high-risk population and supports a gene-first risk stratification approach.

Improved diagnosis of arrhythmogenic right ventricular cardiomyopathy using electrocardiographic deep learning.

BACKGROUND: Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a rare genetic heart disease associated with life-threatening ventricular arrhythmias. Diagnosis of ARVC is based on the 2010 Task Force Criteria (TFC), application of which often requires clinical expertise at specialized centers. OBJECTIVE: The purpose of this study was to develop and validate an electrocardiogram (ECG) deep learning (DL) tool for ARVC diagnosis. METHODS: ECGs of patients referred for ARVC evaluation were used to develop (n = 551 [80.1%]) and test (n = 137 [19.9%]) an ECG-DL model for prediction of TFC-defined ARVC diagnosis. The ARVC ECG-DL model was externally validated in a cohort of patients with pathogenic or likely pathogenic (P/LP) ARVC gene variants identified through the Geisinger MyCode Community Health Initiative (N = 167). RESULTS: Of 688 patients evaluated at Johns Hopkins Hospital (JHH) (57.3% male, mean age 40.2 years), 329 (47.8%) were diagnosed with ARVC. Although ARVC diagnosis made by referring cardiologist ECG interpretation was unreliable (c-statistic 0.53; confidence interval [CI] 0.52-0.53), ECG-DL discrimination in the hold-out testing cohort was excellent (0.87; 0.86-0.89) and compared favorably to that of ECG interpretation by an ARVC expert (0.85; 0.84-0.86). In the Geisinger cohort, prevalence of ARVC was lower (n = 17 [10.2%]), but ECG-DL-based identification of ARVC phenotype remained reliable (0.80; 0.77-0.83). Discrimination was further increased when ECG-DL predictions were combined with non-ECG-derived TFC in the JHH testing (c-statistic 0.940; 95% CI 0.933-0.948) and Geisinger validation (0.897; 95% CI 0.883-0.912) cohorts. CONCLUSION: ECG-DL augments diagnosis of ARVC to the level of an ARVC expert and can differentiate true ARVC diagnosis from phenotype-mimics and at-risk family members/genotype-positive individuals.

Artificial Intelligence in Cardiovascular Care-Part 2: Applications: JACC Review Topic of the Week.

Recent artificial intelligence (AI) advancements in cardiovascular care offer potential enhancements in effective diagnosis, treatment, and outcomes. More than 600 U.S. Food and Drug Administration-approved clinical AI algorithms now exist, with 10% focusing on cardiovascular applications, highlighting the growing opportunities for AI to augment care. This review discusses the latest advancements in the field of AI, with a particular focus on the utilization of multimodal inputs and the field of generative AI. Further discussions in this review involve an approach to understanding the larger context in which AI-augmented care may exist, and include a discussion of the need for rigorous evaluation, appropriate infrastructure for deployment, ethics and equity assessments, regulatory oversight, and viable business cases for deployment. Embracing this rapidly evolving technology while setting an appropriately high evaluation benchmark with careful and patient-centered implementation will be crucial for cardiology to leverage AI to enhance patient care and the provider experience.

Artificial Intelligence for Cardiovascular Care-Part 1: Advances: JACC Review Topic of the Week.

Recent artificial intelligence (AI) advancements in cardiovascular care offer potential enhancements in diagnosis, treatment, and outcomes. Innovations to date focus on automating measurements, enhancing image quality, and detecting diseases using novel methods. Applications span wearables, electrocardiograms, echocardiography, angiography, genetics, and more. AI models detect diseases from electrocardiograms at accuracy not previously achieved by technology or human experts, including reduced ejection fraction, valvular heart disease, and other cardiomyopathies. However, AI's unique characteristics necessitate rigorous validation by addressing training methods, real-world efficacy, equity concerns, and long-term reliability. Despite an exponentially growing number of studies in cardiovascular AI, trials showing improvement in outcomes remain lacking. A number are currently underway. Embracing this rapidly evolving technology while setting a high evaluation benchmark will be crucial for cardiology to leverage AI to enhance patient care and the provider experience.

Drug-induced glaucomas: mechanism and management.

Glaucoma comprises a heterogeneous group of diseases that have in common a characteristic optic neuropathy and visual field defects, for which elevated intraocular pressure is the major risk factor. The level of intraocular pressure within the eye depends on the steady state of formation and drainage of the clear watery fluid, called the aqueous humour, in the anterior chamber of the eye. An obstruction in the circulatory pathway of aqueous humour causes an elevation in intraocular pressure. Because intraocular pressure is the most modifiable parameter, therapeutic measures (medical and surgical) are aimed at reducing the pressure to protect against optic nerve damage. Glaucomatous optic neuropathy results from degeneration of the axonal nerve fibres in the optic nerve and death of their cell bodies, the retinal ganglion cells. Clinical examination of the optic nerve head or disc and the peripapillary nerve fibre layer of the retina reveals specific changes, and the resulting visual field defects can be documented by perimetry. Glaucoma can be classified into four main groups: primary open-angle glaucoma; angle-closure glaucoma; secondary glaucoma; and developmental glaucoma. Drug-induced glaucoma should be considered as a form of secondary glaucoma because it is brought about by specific systemic or topical medications. Although there is a high prevalence of glaucoma worldwide, the incidence of drug-induced glaucoma is uncertain. Drugs that cause or exacerbate open-angle glaucoma are mostly glucocorticoids. Several classes of drugs, including adrenergic agonists, cholinergics, anticholinergics, sulpha-based drugs, selective serotonin reuptake inhibitors, tricyclic and tetracyclic antidepressants, anticoagulants and histamine H(1) and H(2) receptor antagonists, have been reported to induce or precipitate acute angle-closure glaucoma, especially in individuals predisposed with narrow angles of the anterior chamber. In some instances, bilateral involvement and even blindness have occurred. In this article, the mechanism and management of drug-induced glaucomatous disease of the eye are emphasised. Although the product package insert may mention glaucoma as a contraindication or as an adverse effect, the type of glaucoma is usually not specified. Clinicians should be mindful of the possibility of drug-induced glaucoma, whether or not it is listed as a contraindication and, if in doubt, consult an ophthalmologist.

Bimatoprost 0.03% gel for cosmetic eyelash growth and enhancement.

OBJECTIVE: To evaluate a bimatoprost gel for enhancing eyelash growth, adverse effects, and change in intraocular pressure (IOP). METHODS: Prospective, double-masked, randomized controlled study. Fifty-two patients without ocular disease were assigned a control or bimatoprost 0.03% gel to apply to the eyelid margin once a day. RESULTS: The adjusted mean change in eyelash length from baseline to 6 months was 0.77 mm in the bimatoprost group and -0.12 mm in the control group (P = 0.004). Adverse effects were experienced by 2 of 16 patients (12.5%) in the control group and 9 of 36 patients (25%) in the bimatoprost gel group. Mean change in IOP from baseline to 6 months was 0.685 mmHg in the control group and -2.04 mmHg in the bimatoprost group (P = 0.009). CONCLUSIONS: Bimatoprost gel was effective in enhancing eyelash growth. The most common adverse effect for the bimatoprost gel was conjunctival and eyelid hyperemia, while the most severe was recurrent anterior uveitis. Mean IOP was reduced in subjects using the bimatoprost gel.