Layer-specific fast strain-encoded cardiac magnetic resonance imaging aids in the identification and discrimination of acute myocardial injury: a prospective proof-of-concept study.

BACKGROUND: Acute myocardial injury is a common diagnosis in the emergency department and differential diagnoses are numerous. Cardiac magnetic resonance (CMR) strain sequences, such as fast strain ENCoded (fSENC), are early predictors of myocardial function loss. This study assessed the potential diagnostic and prognostic benefits of a layer-specific approach. METHODS: For this prospective study, patients in the emergency department fulfilling rule-in criteria for non-ST-elevation myocardial infarction (NSTEMI) received an ultra-fast fSENC CMR. Volunteers without cardiac diseases (controls) were recruited for comparison. Measurements were performed in a single heartbeat acquisition to measure global longitudinal strain (GLS) and segmental longitudinal strain and dysfunctional segments. The GLS was measured in two layers and a difference (GLSdifference = GLSepicardial - GLSendocardial) was calculated. The performance of those strain features was compared to standard care (physical examination, cardiac biomarkers, electrocardiogram). According to the final diagnosis after discharge, patients were divided into groups and followed up for 2 years. RESULTS: A total of 114 participants, including 50 controls, were included. The 64 patients (51 male) were divided into a NSTEMI (25), myocarditis (16), and other myocardial injury group (23). GLS served as a potent predictor of myocardial injury (area under the curve (AUC) 91.8%). The GLSdifference provided an excellent diagnostic performance to identify a NSTEMI (AUC 83.2%), further improved by including dysfunctional segments (AUC 87.5%, p = 0.01). An optimal test was achieved by adding fSENC to standard care (AUC 95.5%, sensitivity 96.0%, specificity 86.5%, p = 0.03). No death occurred in 2 years for patients with normal GLS and ≤5 dysfunctional segments, while three patients died that showed abnormal GLS or >5 dysfunctional segments. CONCLUSIONS: Layer-specific strain is a potential new marker with high diagnostic performance in the identification and differentiation of acute myocardial injuries.

A randomized control trial to support smoke-free policy compliance in public housing.

BACKGROUND: Smoke-free housing policies in multiunit housing are increasingly widespread interventions to reduce smoking and secondhand smoke exposure. Little research has identified factors that impede compliance with smoke-free housing policies in low-income multiunit housing and test corresponding solutions. METHODS: We are using an experimental design to test two compliance support interventions: (A) a "compliance through reduction (via relocation and reduction in personal smoking) and cessation" intervention targets households with smokers and involves support to shift smoking practices to areas beyond the apartment or building setting, reduce personal smoking, and deliver in-residence smoking cessation support services via trained peer educators and (B) a "compliance through resident endorsement" intervention involving voluntary adoption of smoke-free living environments through personal pledges, visible door markers, and/or via social media. We will compare randomly sampled participants in buildings that receive A or B or A plus B to the NYCHA standard approach. DISCUSSION: This RCT addresses key gaps in knowledge and capitalizes on key scientific opportunities by (1) leveraging the federal mandate to ban smoking in a public housing system of more than sufficient size to conduct an adequately powered RCT; (2) expanding our understanding of smoke-free policy compliance beyond policy implementation by testing two novel treatments: (a) in-residence smoking cessation and (b) resident endorsement, while (3) addressing population and location-specific tobacco-related disparities. At the conclusion of the study, this RCT will have leveraged a monumental policy shift affecting nearly half a million NYC public housing residents, many of whom disproportionately experience chronic illness and are more likely to smoke and be exposed to secondhand smoke than other city residents. This first-ever RCT will test the effects of much-needed compliance strategies on resident smoking behavior and secondhand smoke exposure in multiunit housing. TRIAL REGISTRATION: Clinical Trials Registered, NCT05016505. Registered on August 23, 2021.

ECG Interpretation Proficiency of Healthcare Professionals.

ECG interpretation is essential in modern medicine, yet achieving and maintaining competency can be challenging for healthcare professionals. Quantifying proficiency gaps can inform educational interventions for addressing these challenges. Medical professionals from diverse disciplines and training levels interpreted 30 12-lead ECGs with common urgent and nonurgent findings. Average accuracy (percentage of correctly identified findings), interpretation time per ECG, and self-reported confidence (rated on a scale of 0 [not confident], 1 [somewhat confident], or 2 [confident]) were evaluated. Among the 1206 participants, there were 72 (6%) primary care physicians (PCPs), 146 (12%) cardiology fellows-in-training (FITs), 353 (29%) resident physicians, 182 (15%) medical students, 84 (7%) advanced practice providers (APPs), 120 (10%) nurses, and 249 (21%) allied health professionals (AHPs). Overall, participants achieved an average overall accuracy of 56.4% ± 17.2%, interpretation time of 142 ± 67 seconds, and confidence of 0.83 ± 0.53. Cardiology FITs demonstrated superior performance across all metrics. PCPs had a higher accuracy compared to nurses and APPs (58.1% vs 46.8% and 50.6%; P < 0.01), but a lower accuracy than resident physicians (58.1% vs 59.7%; P < 0.01). AHPs outperformed nurses and APPs in every metric and showed comparable performance to resident physicians and PCPs. Our findings highlight significant gaps in the ECG interpretation proficiency among healthcare professionals.

A Randomized Control Trial to support smoke-free policy compliance in public housing.

• Background Smoke-free housing policies in multiunit housing are increasingly widespread interventions to reduce smoking and secondhand smoke exposure. Little research has identified factors that impede compliance with smoke-free housing policies in low-income multiunit housing and test corresponding solutions. • Methods We are using an experimental design to test two compliance support interventions: (A) a "compliance through reduction (via relocation and reduction in personal smoking) and cessation" intervention targets households with smokers and involves support to shift smoking practices to designated areas, reduce personal smoking, and deliver in-residence smoking cessation support services via trained peer educators and (B) a "compliance through resident endorsement" intervention involving voluntary adoption of smoke-free living environments through personal pledges, visible door markers and/or via social media. We will compare randomly sampled participants in buildings that receive A or B or A plus B to the NYCHA standard approach, • Discussion This RCT addresses key gaps in knowledge and capitalizes on key scientific opportunities by: 1) leveraging the federal mandate to ban smoking in a public housing system of more than sufficient size to conduct an adequately powered RCT; 2) expanding our understanding of smoke-free policy compliance beyond policy implementation by testing two novel treatments: a) in-residence smoking cessation and b) resident endorsement, while 3) addressing population and location-specific tobacco-related disparities. At the conclusion of the study, this RCT will have leveraged a monumental policy shift affecting nearly half a million NYC public housing residents, many of whom disproportionately experience chronic illness and are more likely to smoke and be exposed to secondhand smoke than other city residents. This first-ever RCT will test the effects of much-needed compliance strategies on resident smoking behavior and secondhand smoke exposure in multiunit housing. Trial registration: Clinical Trials Registered, NCT05016505 Registered: August 23, 2021 https://clinicaltrials.gov/ct2/show/NCT05016505.

Artificial intelligence-enabled mobile electrocardiograms for event prediction in paroxysmal atrial fibrillation.

Background: Paroxysmal atrial fibrillation (AF) often eludes early diagnosis, resulting in significant morbidity and mortality. Artificial intelligence (AI) has been used to predict AF from sinus rhythm electrocardiograms (ECGs), but AF prediction using sinus rhythm mobile electrocardiograms (mECG) remains unexplored. Objective: The purpose of this study was to investigate the utility of AI to predict AF events prospectively and retrospectively using sinus rhythm mECG data. Methods: We trained a neural network to predict AF events from sinus rhythm mECGs obtained from users of the Alivecor KardiaMobile 6L device. We tested our model on sinus rhythm mECGs within ±0-2 days, ±3-7 days, and ±8-30 days from AF events to determine the optimal screening window. Finally, we tested our model on mECGs from before an AF event to determine whether AF can be predicted prospectively. Results: We included 73,861 users with 267,614 mECGs (mean age 58.14 years; 35% women). Users with paroxysmal AF contributed 60.15% of mECGs. Model performance on the test set comprising control and study samples across all windows of interest showed an area under the curve (AUC) score of 0.760 (95% confidence interval [CI] 0.759-0.760), sensitivity of 0.703 (95% CI 0.700-0.705), specificity of 0.684 (95% CI 0.678-0.685), and accuracy of 69.4% (95% CI 0.692-0.700). Model performance was better on ±0-2 day samples (sensitivity 0.711; 95% CI 0.709-0.713) and worse on the ±8-30 day window (sensitivity 0.688; 95% CI 0.685-0.690), with performance on the ±3-7 day window falling in between (sensitivity 0.708; 95% CI 0.704-0.710). Conclusion: Neural networks can predict AF using a widely scalable and cost-effective mobile technology prospectively and retrospectively.

A head-to-head comparison of fast-SENC and feature tracking to LV long axis strain for assessment of myocardial deformation in chest pain patients.

BACKGROUND: Myocardial strain imaging has gained importance in cardiac magnetic resonance (CMR) imaging in recent years as an even more sensitive marker of early left ventricular dysfunction than left-ventricular ejection fraction (LVEF). fSENC (fast strain encoded imaging) and FT (feature tracking) both allow for reproducible assessment of myocardial strain. However, left-ventricular long axis strain (LVLAS) might enable an equally sensitive measurement of myocardial deformation as global longitudinal or circumferential strain in a more rapid and simple fashion. METHODS: In this study we compared the diagnostic performance of fSENC, FT and LVLAS for identification of cardiac pathology (ACS, cardiac-non-ACS) in patients presenting with chest pain (initial hscTnT 5-52 ng/l). Patients were prospectively recruited from the chest pain unit in Heidelberg. The CMR scan was performed within 1 h after patient presentation. Analysis of LVLAS was compared to the GLS and GCS as measured by fSENC and FT. RESULTS: In total 40 patients were recruited (ACS n = 6, cardiac-non-ACS n = 6, non-cardiac n = 28). LVLAS was comparable to fSENC for differentiation between healthy myocardium and myocardial dysfunction (GLS-fSENC AUC: 0.882; GCS-fSENC AUC: 0.899; LVLAS AUC: 0.771; GLS-FT AUC: 0.740; GCS-FT: 0.688), while FT-derived strain did not allow for differentiation between ACS and non-cardiac patients. There was significant variability between the three techniques. Intra- and inter-observer variability (OV) was excellent for fSENC and FT, while for LVLAS the agreement was lower and levels of variability higher (intra-OV: Pearson > 0.7, ICC > 0.8; inter-OV: Pearson > 0.65, ICC > 0.8; CoV > 25%). CONCLUSIONS: While reproducibility was excellent for both FT and fSENC, it was only fSENC and the LVLAS which allowed for significant identification of myocardial dysfunction, even before LVEF, and therefore might be used as rapid supporting parameters for assessment of left-ventricular function.

Comparison of electrocardiograms (ECG) waveforms and centralized ECG measurements between a simple 6-lead mobile ECG device and a standard 12-lead ECG.

BACKGROUND: Interval duration measurements (IDMs) were compared between standard 12-lead electrocardiograms (ECGs) and 6-lead ECGs recorded with AliveCor's KardiaMobile 6L, a hand-held mobile device designed for use by patients at home. METHODS: Electrocardiograms were recorded within, on average, 15 min from 705 patients in Mayo Clinic's Windland Smith Rice Genetic Heart Rhythm Clinic. Interpretable 12-lead and 6-lead recordings were available for 685 out of 705 (97%) eligible patients. The most common diagnosis was congenital long QT syndrome (LQTS, 343/685 [50%]), followed by unaffected relatives and patients (146/685 [21%]), and patients with other genetic heart diseases, including hypertrophic cardiomyopathy (36 [5.2%]), arrhythmogenic cardiomyopathy (23 [3.4%]), and idiopathic ventricular fibrillation (14 [2.0%]). IDMs were performed by a central ECG laboratory using lead II with a semi-automated technique. RESULTS: Despite differences in patient position (supine for 12-lead ECGs and sitting for 6-lead ECGs), mean IDMs were comparable, with mean values for the 12-lead and 6-lead ECGs for QTcF, heart rate, PR, and QRS differing by 2.6 ms, -5.5 beats per minute, 1.0 and 1.2 ms, respectively. Despite a modest difference in heart rate, intervals were close enough to allow a detection of clinically meaningful abnormalities. CONCLUSIONS: The 6-lead hand-held device is potentially useful for a clinical follow-up of remote patients, and for a safety follow-up of patients participating in clinical trials who cannot visit the investigational site. This technology may extend the use of 12-lead ECG recordings during the current COVID-19 pandemic as remote patient monitoring becomes more common in virtual or hybrid-design clinical studies.

Use of Artificial Intelligence and Deep Neural Networks in Evaluation of Patients With Electrocardiographically Concealed Long QT Syndrome From the Surface 12-Lead Electrocardiogram.

Importance: Long QT syndrome (LQTS) is characterized by prolongation of the QT interval and is associated with an increased risk of sudden cardiac death. However, although QT interval prolongation is the hallmark feature of LQTS, approximately 40% of patients with genetically confirmed LQTS have a normal corrected QT (QTc) at rest. Distinguishing patients with LQTS from those with a normal QTc is important to correctly diagnose disease, implement simple LQTS preventive measures, and initiate prophylactic therapy if necessary. Objective: To determine whether artificial intelligence (AI) using deep neural networks is better than the QTc alone in distinguishing patients with concealed LQTS from those with a normal QTc using a 12-lead electrocardiogram (ECG). Design, Setting, and Participants: A diagnostic case-control study was performed using all available 12-lead ECGs from 2059 patients presenting to a specialized genetic heart rhythm clinic. Patients were included if they had a definitive clinical and/or genetic diagnosis of type 1, 2, or 3 LQTS (LQT1, 2, or 3) or were seen because of an initial suspicion for LQTS but were discharged without this diagnosis. A multilayer convolutional neural network was used to classify patients based on a 10-second, 12-lead ECG, AI-enhanced ECG (AI-ECG). The convolutional neural network was trained using 60% of the patients, validated in 10% of the patients, and tested on the remaining patients (30%). The study was conducted from January 1, 1999, to December 31, 2018. Main Outcomes and Measures: The goal of the study was to test the ability of the convolutional neural network to distinguish patients with LQTS from those who were evaluated for LQTS but discharged without this diagnosis, especially among patients with genetically confirmed LQTS but a normal QTc value at rest (referred to as genotype positive/phenotype negative LQTS, normal QT interval LQTS, or concealed LQTS). Results: Of the 2059 patients included, 1180 were men (57%); mean (SD) age at first ECG was 21.6 (15.6) years. All 12-lead ECGs from 967 patients with LQTS and 1092 who were evaluated for LQTS but discharged without this diagnosis were included for AI-ECG analysis. Based on the ECG-derived QTc alone, patients were classified with an area under the curve (AUC) value of 0.824 (95% CI, 0.79-0.858); using AI-ECG, the AUC was 0.900 (95% CI, 0.876-0.925). Furthermore, in the subset of patients who had a normal resting QTc (<450 milliseconds), the QTc alone distinguished those with LQTS from those without LQTS with an AUC of 0.741 (95% CI, 0.689-0.794), whereas the AI-ECG increased this discrimination to an AUC of 0.863 (95% CI, 0.824-0.903). In addition, the AI-ECG was able to distinguish the 3 main genotypic subgroups (LQT1, LQT2, and LQT3) with an AUC of 0.921 (95% CI, 0.890-0.951) for LQT1 compared with LQT2 and 3, 0.944 (95% CI, 0.918-0.970) for LQT2 compared with LQT1 and 3, and 0.863 (95% CI, 0.792-0.934) for LQT3 compared with LQT1 and 2. Conclusions and Relevance: In this study, the AI-ECG was found to distinguish patients with electrocardiographically concealed LQTS from those discharged without a diagnosis of LQTS and provide a nearly 80% accurate pregenetic test anticipation of LQTS genotype status. This model may aid in the detection of LQTS in patients presenting to an arrhythmia clinic and, with validation, may be the stepping stone to similar tools to be developed for use in the general population.

Artificial Intelligence-Enabled Assessment of the Heart Rate Corrected QT Interval Using a Mobile Electrocardiogram Device.

BACKGROUND: Heart rate-corrected QT interval (QTc) prolongation, whether secondary to drugs, genetics including congenital long QT syndrome, and/or systemic diseases including SARS-CoV-2-mediated coronavirus disease 2019 (COVID-19), can predispose to ventricular arrhythmias and sudden cardiac death. Currently, QTc assessment and monitoring relies largely on 12-lead electrocardiography. As such, we sought to train and validate an artificial intelligence (AI)-enabled 12-lead ECG algorithm to determine the QTc, and then prospectively test this algorithm on tracings acquired from a mobile ECG (mECG) device in a population enriched for repolarization abnormalities. METHODS: Using >1.6 million 12-lead ECGs from 538 200 patients, a deep neural network (DNN) was derived (patients for training, n = 250 767; patients for testing, n = 107 920) and validated (n = 179 513 patients) to predict the QTc using cardiologist-overread QTc values as the "gold standard". The ability of this DNN to detect clinically-relevant QTc prolongation (eg, QTc ≥500 ms) was then tested prospectively on 686 patients with genetic heart disease (50% with long QT syndrome) with QTc values obtained from both a 12-lead ECG and a prototype mECG device equivalent to the commercially-available AliveCor KardiaMobile 6L. RESULTS: In the validation sample, strong agreement was observed between human over-read and DNN-predicted QTc values (-1.76±23.14 ms). Similarly, within the prospective, genetic heart disease-enriched dataset, the difference between DNN-predicted QTc values derived from mECG tracings and those annotated from 12-lead ECGs by a QT expert (-0.45±24.73 ms) and a commercial core ECG laboratory [10.52±25.64 ms] was nominal. When applied to mECG tracings, the DNN's ability to detect a QTc value ≥500 ms yielded an area under the curve, sensitivity, and specificity of 0.97, 80.0%, and 94.4%, respectively. CONCLUSIONS: Using smartphone-enabled electrodes, an AI DNN can predict accurately the QTc of a standard 12-lead ECG. QTc estimation from an AI-enabled mECG device may provide a cost-effective means of screening for both acquired and congenital long QT syndrome in a variety of clinical settings where standard 12-lead electrocardiography is not accessible or cost-effective.

Practical insights for paediatric otolaryngology surgical cases and performing microlaryngobronchoscopy during the COVID-19 pandemic.

Paediatric otolaryngology practice involves examining and operating in anatomical locations with high levels of aerosol generation and transmission of COVID-19 to treating clinicians, especially from the asymptomatic patient populations including children. During the COVID-19 pandemic all emergent otolaryngological conditions affecting the airway, oral, and nasal cavities should be managed medically where possible and any operating deferred. We present guidelines for operating on paediatric otolaryngological patients when necessary during the COVID-19 pandemic, and incorporate experience gathered during microlaryngobronchoscopy on a COVID-19 positive infant at our institution.

Case Report: Optical Coherence Tomography Angiography of Idiopathic Foveal Hypoplasia and Its Correlation With Visual Acuity.

SIGNIFICANCE: Foveal hypoplasia is described clinically by the absence of a foveal pit and subsequent reduction in visual acuity. Optical coherence tomography angiography provides precise segmentation of the retinal vascular supply demonstrating the vascular perfusion in affected patients. Preservation of perfusion is linked to visual acuity and function. PURPOSE: This case report describes a patient with foveal hypoplasia and preservation of visual acuity with preserved retinal capillary density of the superficial and deep capillary plexuses on optical coherence tomography angiography. In addition, the diagnostic findings of foveal hypoplasia as seen on optical coherence tomography angiography will be described. CASE REPORT: A 25-year-old Caucasian female with history of foveal hypoplasia presented to the clinic for evaluation. She had no other visual, ocular, or systemic complaints. Her ocular history included Duane syndrome, accommodative insufficiency, and traumatic brain injury. Her medical history included cardiac ablation secondary to supraventricular tachycardia, gall bladder removal, maxillary sinus cyst, and a history of migraines. Best-corrected visual acuity was 20/15 in the right and left eyes. Funduscopic examination was unremarkable. Spectral domain optical coherence tomography revealed absence of the anatomical foveal pit with normal inner retinal morphology. Optical coherence tomography angiography confirmed a decreased foveal avascular zone; however, a vascular density analysis showed normal perfusion to the inner retinal plexuses. CONCLUSIONS: Optical coherence tomography angiography is a rapid, noninvasive imaging modality that provides excellent insight into the microvasculature supply to the retina and choroid. As such, it allows for an in-depth analysis into the pathophysiology behind certain conditions such as foveal hypoplasia.

Feasibility of combining serial smartphone single-lead electrocardiograms for the diagnosis of ST-elevation myocardial infarction.

BACKGROUND: The rate-limiting step in STEMI diagnosis often is the availability of a 12-lead electrocardiogram (ECG) and its interpretation. The potential may exist to speed the availability of 12-lead ECG information by using commonly available mobile technologies. We sought to test whether combining serial smartphone single-lead ECGs to create a virtual 12-lead ECG can accurately diagnose STEMI. METHODS: Consenting patients presenting with symptoms consistent with a possible STEMI had contemporaneous standard 12-lead and smartphone '12-lead equivalent' ECG (produced by electronically combining serial single-lead ECGs) recordings obtained. Matched ECGs were evaluated qualitatively and quantitatively by a panel of blinded readers and classified as STEMI/STEMI equivalent (LBBB), Not-STEMI, or uninterpretable. Interpretable ECG pairs were graded as showing good, fair, or poor correlation. RESULTS: Two hundred four subjects (age = 60 years, males = 57%, STEMI activation = 45%) were enrolled from 5 international sites. Smartphone ECG quality was graded as good in 151 (74.0%), fair in 32 (15.7%), poor in 8 (3.9%), and uninterpretable in 13 (6.4%). A STEMI/STEMI equivalent diagnosis was identified by standard 12-lead ECG in 57/204 (27.9%) recordings. For all interpretable pairs of smartphone ECGs compared with standard ECGs (n = 190), the sensitivity, specificity, and positive and negative predictive values for STEMI/STEMI equivalent by smartphone were 0.89, 0.84, 0.70 and 0.95, respectively. CONCLUSIONS: A '12-lead equivalent' ECG obtained from multiple serial single-lead ECGs from a smartphone can identify STEMI with good correlation to a standard 12-lead ECG. This technology holds promise to improve outcomes in STEMI by enhancing the reach and speed of diagnosis and thereby early treatment.

Long-term Dextromethorphan Use and Acute Intoxication Results in an Episode of Mania and Autoenucleation.

: The pharmacologic and neuropsychiatric sequelae of long-term dextromethorphan use and acute dextromethorphan intoxication are reviewed in this case report. Dextromethorphan ingestion at the high end of toxicity, although rare, can cause violence to oneself and others, even in those previously without any history of such behaviors. In this article, the neuropsychiatric consequences of dextromethorphan toxicity are highlighted in a case report of a 37-year-old woman who had been using dextromethorphan for 5 years. She presented to a large urban emergency department in a psychotic and manic state after attempting autoenucleation. She reported to consult liaison psychiatry staff that she had taken a total of 1400 mg of dextromethorphan over the course of 3 days with intent to experience altered state of consciousness. Toxicology screens on admission did not reveal any other substances in her system. She had no formal psychiatric history and no history of mania, psychosis, or self-harm. To our knowledge, this is the first case of autoenucleation resulting from dextromethorphan-induced mania with psychotic features.

First record of predation of Nyctinomops laticaudatus (É. Geoffroy, 1805) by Chrotopterus auritus (Peters, 1856) (Mammalia: Chiroptera).

The Wooly False Vampire Bat, Chrotopterus auritus (Peters, 1856) (Chiroptera: Phyllostomidae), feeds on small mammals, birds, lizards, frogs and occasionally large insects and fruits. In this paper we report an additional evidence of bat predation by C. auritus. A male of this species was captured with a partially eaten Broad-eared Free-tailed Bat, Nyctinomops laticaudatus (É. Geoffroy, 1805) (Chiroptera: Molossidae). This record was obtained during a research project conducted in the Biological Reserve of Sooretama, Southeastern Brazil.

First record of pigmentation disorder in the Fringe-lipped Bat Trachops cirrhosus (Spix, 1823) (Chiroptera: Phyllostomidae) from southeast Brazil.

Piebaldism is a genetic pigmentation disorder, which is caused by absence of melanocytes in parts of the skin and/or hair follicles, with eyes and claws normally pigmented. The occurrence of piebaldism in natural populations is rare and the effects on fitness are still unknown. This article reports the first case of pigmentation disorders in the Fringe-lipped Bat Trachops cirrhosus (Spix, 1823) (Chiroptera: Phyllostomidae) caught in Barra do Triunfo, city of João Neiva, northeastern state of Espírito Santo, southeast Brazil.