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.

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.

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.

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.

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.

Smartphone ECG for evaluation of ST-segment elevation myocardial infarction (STEMI): Design of the ST LEUIS International Multicenter Study.

In patients experiencing an ST-elevation myocardial infarction (STEMI), rapid diagnosis and immediate access to reperfusion therapy leads to optimal clinical outcomes. The rate-limiting step in STEMI diagnosis is the availability and performance of a 12-lead ECG. Recent technology has provided access to a reliable means of obtaining an ECG reading through a smartphone application (app) that works with an attachment providing all 12-leads of a standard ECG system. The ST LEUIS study was designed to validate the smartphone ECG app and its ability to accurately assess the presence or absence of STEMI in patients presenting with chest pain compared with the gold standard 12-lead ECG. We aimed to support the diagnostic utility of smartphone technology to provide a timely diagnosis and treatment of STEMI. The study will take place over 12months at five institutions. Approximately 60 patients will be enrolled per institution, for a total recruitment of 300 patients.

History of periodontal treatment and risk for intrauterine growth restriction (IUGR).

BACKGROUND: To explore the hypothesis that maternal periodontitis is associated with increased risk for Intrauterine Growth Restriction (IUGR), we examined the risk of IUGR in relation to periodontal treatment before, during and after pregnancy. METHODS: We conducted a retrospective cohort analysis of insurance claims data from 2009 to 2012 for women who delivered a singleton live birth (n = 32,168). IUGR was examined as a function of type and timing of dental treatment, adjusting for potential confounders in logistic regression. Sensitivity analysis evaluated the potential effects of unmeasured confounding. RESULTS: Women who received periodontal treatment after delivery, indicating the presence of untreated periodontal disease during pregnancy, had significantly higher odds of IUGR compared to women who received no periodontal treatment (adjusted OR 1.5, 95% CI 1.2, 1.8). CONCLUSIONS: Periodontal treatment provided in the immediate postpartum period, a proxy for periodontitis during gestation, was associated with increased risk of IUGR.

Debatable issues in automated ECG reporting.

Although automated ECG analysis has been available for many years, there are some aspects which require to be re-assessed with respect to their value while newer techniques which are worthy of review are beginning to find their way into routine use. At the annual International Society of Computerized Electrocardiology conference held in April 2017, four areas in particular were debated. These were a) automated 12 lead resting ECG analysis; b) real time out of hospital ECG monitoring; c) ECG imaging; and d) single channel ECG rhythm interpretation. One speaker presented the positive aspects of each technique and another outlined the more negative aspects. Debate ensued. There were many positives set out for each technique but equally, more negative features were not in short supply, particularly for out of hospital ECG monitoring.

Comparison of QT Interval Readings in Normal Sinus Rhythm Between a Smartphone Heart Monitor and a 12-Lead ECG for Healthy Volunteers and Inpatients Receiving Sotalol or Dofetilide.

INTRODUCTION: A variety of medications ranging from antiarrhythmics to psychotropics, as well as conditions such as bradycardia, can prolong the QT interval, increasing the risk for life-threatening arrhythmias. Monitoring the corrected QT interval (QTc) is therefore critical for patient safety. The recent development of smart phone heart monitors (SHM) may allow for easier QTc monitoring. We sought to evaluate the accuracy of an SHM for assessing the QTc, as compared to the standard 12-lead ECG. METHODS AND RESULTS: We compared the QTc interval in lead-I and lead-II between an SHM and 12-lead ECG. Healthy volunteers and hospitalized patients in sinus rhythm being loaded on dofetilide or sotalol were included. Manual and automatic measurements were studied. Across 99 healthy volunteers, the SHM QTc demonstrated good agreement (bias = 4 milliseconds, standard deviation of bias = 11 milliseconds) compared to the 12-lead ECG, using the Bland-Altman method of agreement. Across all hospitalized patients, the SHM was capable of demonstrating QTc prolongation. Between the 12-lead ECG and SHM, lead-I measurements had reasonable agreement (bias = 3 milliseconds, standard deviation of bias = 46 milliseconds). A QTc of > 500 milliseconds was associated with a higher likelihood (OR = 12.0; 95% CI 1.5-111.4; P = 0.02) to not achieve perfect agreement. CONCLUSION: The SHM is accurate in measuring QTc interval in sinus rhythm when compared to 12-lead ECG in healthy volunteers. For patients receiving QT prolonging antiarrhythmics, SHM is capable of detecting QTc prolongation, and lead-I of the SHM is most accurate in measuring the QTc if < 500 milliseconds.

Smartphone ECG for evaluation of STEMI: results of the ST LEUIS Pilot Study.

BACKGROUND: 12-lead ECG is a critical component of initial evaluation of cardiac ischemia, but has traditionally been limited to large, dedicated equipment in medical care environments. Smartphones provide a potential alternative platform for the extension of ECG to new care settings and to improve timeliness of care. OBJECTIVE: To gain experience with smartphone electrocardiography prior to designing a larger multicenter study evaluating standard 12-lead ECG compared to smartphone ECG. METHODS: 6 patients for whom the hospital STEMI protocol was activated were evaluated with traditional 12-lead ECG followed immediately by a smartphone ECG using right (VnR) and left (VnL) limb leads for precordial grounding. The AliveCor™ Heart Monitor was utilized for this study. All tracings were taken prior to catheterization or immediately after revascularization while still in the catheterization laboratory. RESULTS: The smartphone ECG had excellent correlation with the gold standard 12-lead ECG in all patients. Four out of six tracings were judged to meet STEMI criteria on both modalities as determined by three experienced cardiologists, and in the remaining two, consensus indicated a non-STEMI ECG diagnosis. No significant difference was noted between VnR and VnL. CONCLUSIONS: Smartphone based electrocardiography is a promising, developing technology intended to increase availability and speed of electrocardiographic evaluation. This study confirmed the potential of a smartphone ECG for evaluation of acute ischemia and the feasibility of studying this technology further to define the diagnostic accuracy, limitations and appropriate use of this new technology.

Impact of a Web-based intervention on maternal caries transmission and prevention knowledge, and oral health attitudes.

Poor oral health knowledge is thought to significantly contribute to the incidence of early childhood caries, the most common childhood disease in the U.S. This study assessed the effectiveness of a web-based educational program in increasing oral health and caries transmission knowledge, attitudes and planned behavior among mothers and primary caregivers. Study participants were recruited from subscribers to an online health information newsletter distributed by a national dental insurance company and from visitors to a health information website sponsored by the same company. Participants completed pre- and post-intervention surveys and viewed a brief educational program. Results were analyzed for pre- to post-test changes in knowledge and attitudes. Planned behavior change was also assessed. 459 participants completed pre-and post-test surveys. The sample was typically more insured (91.3 %), and college educated (76.9 %), than the general population. At baseline, respondents were knowledgeable about caries and its prevention; however, their specific knowledge about caries transmission was limited. There was a significant increase in caries knowledge from baseline to follow-up, particularly regarding caries transmission. At baseline less than half of the participants (48.8 %) knew that mothers/primary caregivers play a large role in passing cavity causing germs to children and 43.1 % knew that there is a defined period of time when the risk of transmission of cariogenic bacteria is greatest; however in post-testing 99.6 % and 98.3 % answered these question correctly respectively (p < 0.001). Results indicate that a web-delivered oral health intervention can increase dental knowledge and increase participants' plans to prevent maternal caries transmission. The provision of web-delivered education to primary caregivers can be an effective and low cost strategy for promoting maternal and infant oral health.

Some bats are here: Reducing the Wallacean shortfall of bats in the amazon.

The Amazon rainforest has approximately 23% of its sampled area dedicated to bats, making it one of the least sampled and most diverse regions for bats in Brazil. The lack of sampling results in a lack of knowledge regarding the accurate geographical distribution of bat species. This lack is referred to as the Wallacean shortfall, which should be addressed with primary data obtained from in situ collections. However, the use of Species Distribution Models (SDMs) can help alleviate this gap. The states of Pará and Acre are located in the Brazilian Amazon. So, our objective is to decrease the Wallacean shortfall concerning Amazonian bat species. To achieve this, we provide (i) a list of bat species sampled in the states of Pará and Acre in the last 5 years (2017 to 2022); (ii) the potential distribution of species considered as new occurrences for the region; and (iii) the potential distribution of species classified as Data Deficient (DD) and Near Threatened (NT) according to the IUCN classification. With 96 nights of collection and 129,600 m2h of mist netting, we obtained 75 bat species, with an estimated total of 94.78 species. Additionally, 21 species were considered as range extensions. The Brazilian Amazon region has a vast geographic expanse and few established research centers, resulting in a limited sampling of bats and other biological groups. Furthermore, we draw attention to the significant number of bat species with expanded geographical distributions, with 21 out of the 75 sampled species. This should be a reminder that primary biogeographic data is still necessary for the neotropical region.

Use of historical logging patterns to identify disproportionately logged ecosystems within temperate rainforests of southeastern Alaska.

The forests of southeastern Alaska remain largely intact and contain a substantial proportion of Earth's remaining old-growth temperate rainforest. Nonetheless, industrial-scale logging has occurred since the 1950s within a relatively narrow range of forest types that has never been quantified at a regional scale. We analyzed historical patterns of logging from 1954 through 2004 and compared the relative rates of change among forest types, landform associations, and biogeographic provinces. We found a consistent pattern of disproportionate logging at multiple scales, including large-tree stands and landscapes with contiguous productive old-growth forests. The highest rates of change were among landform associations and biogeographic provinces that originally contained the largest concentrations of productive old growth (i.e., timber volume >46.6 m³/ha). Although only 11.9% of productive old-growth forests have been logged region wide, large-tree stands have been reduced by at least 28.1%, karst forests by 37%, and landscapes with the highest volume of contiguous old growth by 66.5%. Within some island biogeographic provinces, loss of rare forest types may place local viability of species dependent on old growth at risk of extirpation. Examination of historical patterns of change among ecological forest types can facilitate planning for conservation of biodiversity and sustainable use of forest resources.

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 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.

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.

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.