Citations

Background: Rigorous study of ventricular fibrillation (VF) is not feasible in humans. The spatiotemporal characteristics of prolonged VF remain undefined, limiting our understanding of this lethal rhythm. Methods: VF was mapped in 4 canines. The endocardial and epicardial left ventricle (LV) and right ventricle (RV) were sequentially mapped at 0 to 15, 15 to 30, 30 to 45, and 45 to 60 minutes post-induction. Ten consecutive beats were used to determine the average cycle length and regularity index of ventricular and His-Purkinje system signals in discrete regions during each time interval. Results: Average VF time was 58±12 minutes. The shortest ventricular cycle length was present in the RV apical region (70±10 msec) at 0 to 15 minutes and at 15 to 30 minutes (89±31 msec) and LV apical region at 45 to 60 minutes (242±163 msec). The His-Purkinje system cycle length was the shortest at the RV outflow tract (75±3 msec) at 0 to 15 minutes, RV inflow and free wall (89±12 msec) at 15 to 30 minutes, LV apical region (83±14 msec) at 30 to 45 minutes, and inferior and inferolateral LV (145±23 msec) at 45 to 60 minutes. Regularity index was the highest in the RV inflow and free wall (78%) at 0 to 15 minutes, RV apical region (86%) at 15 to 30 minutes, LV septum and epicardial anterior RV (80%) at 30 to 45 minutes, and anterior and anterolateral LV (75%) at 45 to 60 minutes. Conclusions: These data suggest significant regional changes in electrical activity throughout VF in canines. A transition of fastest electrical activity from RV to LV apical regions across VF was observed. These novel insights of the temporal and spatial changes of VF merit further evaluation in diseased hearts to assess for reproducibility of findings.

Abstract Targeting interactions between α4β7 integrin and endothelial adhesion molecule MAdCAM-1 to inhibit lymphocyte migration to the gastrointestinal tract is an effective therapy in inflammatory bowel disease (IBD). Following lymphocyte entry into the mucosa, a subset of these cells expresses αEβ7 integrin, which is expressed on proinflammatory lymphocytes, to increase cell retention. The factors governing lymphocyte migration into the intestinal mucosa and αE integrin expression in healthy subjects and IBD patients remain incompletely understood. We evaluated changes in factors involved in lymphocyte migration and differentiation within tissues. Both ileal and colonic tissue from active IBD patients showed upregulation of ICAM-1, VCAM-1, and MAdCAM-1 at the gene and protein levels compared with healthy subjects and/or inactive IBD patients. β1 and β7 integrin expression on circulating lymphocytes was similar across groups. TGF-β1 treatment induced expression of αE on both β7+ and β7− T cells, suggesting that cells entering the mucosa independently of MAdCAM-1/α4β7 can become αEβ7+. ITGAE gene polymorphisms did not alter protein induction following TGF-β1 stimulation. Increased phospho-SMAD3, which is directly downstream of TGF-β, and increased TGF-β–responsive gene expression were observed in the colonic mucosa of IBD patients. Finally, in vitro stimulation experiments showed that baseline β7 expression had little effect on cytokine, chemokine, transcription factor, and effector molecule gene expression in αE+ and αE− T cells. These findings suggest cell migration to the gut mucosa may be altered in IBD and α4β7−, and α4β7+ T cells may upregulate αEβ7 in response to TGF-β once within the gut mucosa.

Research ObjectiveThroughout the spring of 2020, stay‐at‐home orders were imposed to curb the spread of the coronavirus disease 2019 (COVID‐19) in the United States. However, there is limited data and evidence on their effectiveness, especially in rural areas as compared to urban areas. Therefore, we examined the association between stay‐at‐home order implementation and the incidence of COVID‐19 cases in rural and urban counties.Study DesignWe conducted an interrupted time series analysis using a mixed effects zero‐inflated Poisson model with random intercept by county and standardized by population, which examined the association between stay‐at‐home orders and 14‐day lagged county‐level counts of daily new COVID‐19 cases in rural vs. urban counties between 1/22/2020 and 6/10/2020. To further bolster our findings, the association between stay‐at‐home orders and mobility in rural vs. urban counties was assessed using Google Community Mobility Reports.Population StudiedWe examined the COVID‐19 daily incident and stay‐at‐home orders for all 3142 United States counties. Each county was categorized as urban or rural using the 2013 county‐level Rural–Urban Continuum Codes per federal standards. There are 1976 rural and 1166 urban counties in the United States.Principal FindingsAs of June 10, 2020, there were 1,786,886 cases of COVID‐19 in the U.S., of which 9.0% (N = 161,452) were in rural counties. During the study period, 1854 (93.8%) rural counties and 1075 (92.2%) urban counties were covered by a stay‐at‐home order. Stay‐at‐home orders were implemented later (median March 30 vs. March 28) and were shorter (median 35 vs. 54 days) in rural than urban counties. Indoor mobility was, on average, 2.6–6.9% higher in rural than urban counties during and after stay‐at‐home orders. Compared to the pre‐stay‐at‐home baseline period, the number of new COVID‐19 cases increased under stay‐at‐home by IRR 1.60 (95% CI, 1.57–1.64) in rural and 1.36 (95% CI, 1.30–1.42) in urban counties. For each day under stay‐at‐home orders, the number of new cases changed by a factor of 0.982 (95% CI, 0.981–0.982) in rural and 0.952 (95% CI, 0.951–0.953) in urban counties. Each day after stay‐at‐home orders expired, the number of new cases changed by a factor of 0.995 (95% CI, 0.994–0.995) in rural and 0.997 (95% CI, 0.995–0.999) in urban counties.ConclusionsStay‐at‐home orders decreased mobility and slowed the spread of COVID‐19 less effectively in rural than urban counties.Implications for Policy or PracticeAs states are considering implementing a new round of stay‐at‐home orders, our findings reveal several ways to improve the implementation, enforcement, and adherence. First, they should be implemented earlier and maintained longer for optimal effectiveness. There was marked heterogeneity in the timing and duration of stay‐at‐home orders and our data reinforces the need for multi‐jurisdictional, ideally federal, infection control mandates. Any new stay‐at‐home orders should be gradually implemented to avoid the pre‐stay‐at‐home surge of mobility and subsequent spike in COVID‐19 cases. To better encourage and facilitate compliance, leaders at all levels need to use scientific evidence to advocate for the importance of stay‐at‐home orders, set personal examples, and develop employment, housing, educational, and healthcare assistance for the most vulnerable.Primary Funding SourceNational Institutes of Health.

Abstract Objective Social determinants of health (SDoH) are nonclinical dispositions that impact patient health risks and clinical outcomes. Leveraging SDoH in clinical decision-making can potentially improve diagnosis, treatment planning, and patient outcomes. Despite increased interest in capturing SDoH in electronic health records (EHRs), such information is typically locked in unstructured clinical notes. Natural language processing (NLP) is the key technology to extract SDoH information from clinical text and expand its utility in patient care and research. This article presents a systematic review of the state-of-the-art NLP approaches and tools that focus on identifying and extracting SDoH data from unstructured clinical text in EHRs. Materials and Methods A broad literature search was conducted in February 2021 using 3 scholarly databases (ACL Anthology, PubMed, and Scopus) following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 6402 publications were initially identified, and after applying the study inclusion criteria, 82 publications were selected for the final review. Results Smoking status (n = 27), substance use (n = 21), homelessness (n = 20), and alcohol use (n = 15) are the most frequently studied SDoH categories. Homelessness (n = 7) and other less-studied SDoH (eg, education, financial problems, social isolation and support, family problems) are mostly identified using rule-based approaches. In contrast, machine learning approaches are popular for identifying smoking status (n = 13), substance use (n = 9), and alcohol use (n = 9). Conclusion NLP offers significant potential to extract SDoH data from narrative clinical notes, which in turn can aid in the development of screening tools, risk prediction models, and clinical decision support systems.

Abstract Objective The study sought to test the feasibility of conducting a phenome-wide association study to characterize phenotypic abnormalities associated with individuals at high risk for lung cancer using electronic health records. Materials and Methods We used the beta release of the All of Us Researcher Workbench with clinical and survey data from a population of 225 000 subjects. We identified 3 cohorts of individuals at high risk to develop lung cancer based on (1) the 2013 U.S. Preventive Services Task Force criteria, (2) the long-term quitters of cigarette smoking criteria, and (3) the younger age of onset criteria. We applied the logistic regression analysis to identify the significant associations between individuals’ phenotypes and their risk categories. We validated our findings against a lung cancer cohort from the same population and conducted an expert review to understand whether these associations are known or potentially novel. Results We found a total of 214 statistically significant associations (P < .05 with a Bonferroni correction and odds ratio > 1.5) enriched in the high-risk individuals from 3 cohorts, and 15 enriched in the low-risk individuals. Forty significant associations enriched in the high-risk individuals and 13 enriched in the low-risk individuals were validated in the cancer cohort. Expert review identified 15 potentially new associations enriched in the high-risk individuals. Conclusions It is feasible to conduct a phenome-wide association study to characterize phenotypic abnormalities associated in high-risk individuals developing lung cancer using electronic health records. The All of Us Research Workbench is a promising resource for the research studies to evaluate and optimize lung cancer screening criteria.

AbstractBackgroundHis bundle pacing (HBP) is a physiological pacing strategy to preserve the electrical synchrony of ventricular conduction and left ventricular (LV) function. Left bundle branch pacing (LBBP) has emerged as an alternative physiological pacing technique.ObjectiveTo evaluate cardiac electrical and mechanical synchrony comparing LBBP and HBP in patients with permanent atrial fibrillation (AF).MethodsConsecutive patients with symptomatic bradycardia and AF were enrolled from January to June of 2019. The cardiac electrical and mechanical synchrony in different pacing mode were evaluated at baseline and after implantation.ResultsBoth HBP and LBBP were performed in 20 patients. LBBP significantly widened the QRS duration compared with the intrinsic conduction (113.2 ± 14.5 vs. 96.5 ± 16.2 ms; p = .01), while HBP did not (104.5 ± 22.3 vs. 96.5 ± 16.2 ms; p = .12). Both LBBP and HBP patients had similar LV myocardial strain measurements for the mechanical synchrony evaluation without significant change compared with baseline. There was no significant difference in right ventricular synchrony measurement between LBBP and HBP. Compared to HBP, LBBP had less interventricular synchrony (IMVD, 14.7 ± 9.2 vs. 3.1 ± 12.7 ms, p < .01; Ts‐LV‐RV, 37.9 ± 10.7 vs. 18.5 ± 10.8 ms, p < .001).ConclusionsAlthough LBBP's a physiological pacing mode can achieve a similar cardiac electrical and mechanical synchronization when compared to HBP, LBBP results in modest delay in RV activation, and the clinical implication remains to be studied.