Citations

Introduction: Ventricular arrhythmias, including ventricular tachycardia (VT) and ventricular fibrillation (VF), are associated with high mortality and morbidity. Sedation through agents like benzodiazepines, propofol, and opiates are mainstays of treatment but are limited by side effects. Dexmedetomidine is a α2-presynaptic receptor agonist with a safe and efficacious sedative profile. Furthermore, it has been recently proposed as a sedative agent in VT due its additional antiarrhythmic properties. Methods: A systemic literature review was conducted of MEDLINE, EMBASE, Cochrane, and Scopus. Randomized controlled trials comparing the incidence of ventricular arrhythmias of dexmedetomidine with placebo or an alternative sedative agent were included. Two researchers independently extracted the data and performed quality assessments. Results: We identified 6 studies that met the selection criteria. Our meta-analysis demonstrated significant reduction of the overall risk of ventricular arrhythmias in patients receiving dexmedetomidine (RR 0.35, 95% CI 0.16, 0.76). Furthermore, there was no significant difference in risk of mortality (RR 0.59 95% CI 0.12, 3.02). Dexmedetomidine also significantly increased the risk of bradycardia (RR 2.78 95% CI 2.00, 3.87). There was no significant difference in risk of tachycardia (RR 0.59 95% CI 0.12, 3.02) or hypotension (RR 1.07 95% CI 0.64, 1.81). Conclusions: Our study demonstrates that the dexmedetomidine reduces the risk of VA by 65% compared to control. Further prospective research is necessary to clarify the antiarrhythmic and sedative properties of dexmedetomidine and its potential effect on patient outcomes.

This novel study deciphered the expression and function of protein phosphatase 2Aα (PP2Aα) in airway smooth muscle (ASM) during asthma and/or inflammation. We showed robust expression of PP2Aα in human ASM while its downregulation in asthmatic ASM. Similarly, we demonstrated reduced PP2Aα expression in ASM exposed to proinflammatory cytokines. PP2Aα activation inhibited bronchoconstriction of isolated mouse bronchi. In addition, we unveiled that PP2Aα activation inhibits the intracellular calcium release and myosin light chain phosphorylation in human ASM.

Abstract Airway remodeling is a cardinal feature of asthma, associated with increased airway smooth muscle (ASM) cell mass and upregulation of extracellular matrix deposition. Exaggerated ASM cell migration contributes to excessive ASM mass. Previously, we demonstrated the alleviating role of Kp (kisspeptin) receptor (KISS1R) activation by Kp-10 in mitogen (PDGF [platelet-derived growth factor])-induced human ASM cell proliferation in vitro and airway remodeling in vivo in a mouse model of asthma. Here, we examined the mechanisms by which KISS1R activation regulates mitogen-induced ASM cell migration. KISS1R activation using Kp-10 significantly inhibited PDGF-induced ASM cell migration, further confirmed using KISS1R shRNA. Furthermore, KISS1R activation modulated F/G actin dynamics and the expression of promigration proteins like CDC42 (cell division control protein 42) and cofilin. Mechanistically, we observed reduced ASM RhoA-GTPAse with KISS1R activation. The antimigratory effect of KISS1R was abolished by PKA (protein kinase A)-inhibitory peptide. Conversely, KISS1R activation significantly increased cAMP and phosphorylation of CREB (cAMP-response element binding protein) in PDGF-exposed ASM cells. Overall, these results highlight the alleviating properties of Kp-10 in the context of airway remodeling.

Introduction Burnout in medicine is an occupational hazard and has emerged as a pressing concern in recent years. Organizational changes can be impactful in countering burnout (1). The factors leading to burnout in medicine are multifaceted, encompassing organizational factors such as workload, inadequate support, and inefficient administrative systems. The purpose of this study is to investigate current organizational measures to support physician well‐being. This study was an initiative led by the SVIN wellness committee. Methods A 39‐question online survey investigating current organizational well‐being practices was distributed to physicians both nationally and internationally practicing stroke, neurocritical care, and interventional neurology. Data analysis was performed using Python, utilizing the libraries “pandas” and “sklearn”. Results This study analyzed burnout among 109 healthcare professionals, predominantly from the U.S. (93.6%) and Canada (6.4%). The majority were neurointerventional specialists (53.2%), aged 35‐44 years (52.3%), and male (62.4%). Burnout frequency was measured on a 0 (Never) to 4 (Every day) scale. Using a Random Forest model, the study identified key burnout predictors from questions, which covered organizational leadership, wellness resources, compensation, and workload. The most influential predictors were: adequacy of compensation relative to specialty, workload, and stress (17.7% importance); leadership accountability for workforce wellbeing (9.1%); and establishment of wellness or burnout as a critical metric (7.5%). Other factors included provision of wellness screenings (7.1%), and dedication of resources toward professional well‐being (4.9%). Conclusion The study identifies compensation, leadership accountability, and wellness resources as key predictors of burnout among healthcare professionals. These findings highlight the need for targeted organizational strategies to enhance physician well‐being and mitigate burnout. Further research is warranted to validate and expand upon these findings.

AbstractNew‐onset atrial fibrillation (NOAF) in COVID‐19 raises significant clinical and public health issues. This systematic review and meta‐analysis aims to compile and analyze the current literature on NOAF in COVID‐19 and give a more comprehensive understanding of the prevalence and outcomes of NOAF in COVID‐19. A comprehensive literature search was carried out using several databases. The random effect model using inverse variance method and DerSimonian and Laird estimator of Tua2 was used to calculate the pooled prevalence and associated 95% confidence interval (CI). Results for outcome analysis were presented as odds ratios (ORs) with 95% CI and pooled using the Mantel‐Haenszel random‐effects model. The pooled prevalence of NOAF in COVID‐19 was 7.8% (95% CI: 6.54%–9.32%),a pooled estimate from 30 articles (81 929 COVID‐19 patients). Furthermore, our analysis reported that COVID‐19 patients with NOAF had a higher risk of developing severe disease compared with COVID‐19 patients without a history of atrial fibrillation (OR = 4.78, 95% CI: 3.75–6.09) and COVID‐19 patients with a history of pre‐existing atrial fibrillation (OR = 2.75, 95% CI: 2.10–3.59). Similarly, our analysis also indicated that COVID‐19 patients with NOAF had a higher risk of all‐cause mortality compared with, COVID‐19 patients without a history of atrial fibrillation (OR = 3.83, 95% CI: 2.99–4.92) and COVID‐19 patients with a history of pre‐existing atrial fibrillation (OR = 2.32, 95% CI: 1.35–3.96). The meta‐analysis did not reveal any significant publication bias. The results indicate a strong correlation between NOAF and a higher risk of severe illness and mortality. These results emphasize the importance of careful surveillance, early detection, and customized NOAF management strategies to improve clinical outcomes for COVID‐19 patients.

To evaluate deep learning-based calcium segmentation and quantification on ECG-gated cardiac CT scans compared with manual evaluation. Automated calcium quantification was performed using a neural network based on mask regions with convolutional neural networks (R-CNNs) for multi-organ segmentation. Manual evaluation of calcium was carried out using proprietary software. This is a retrospective study of archived data. This study used 40 patients to train the segmentation model and 110 patients were used for the validation of the algorithm. The Pearson correlation coefficient between the reference actual and the computed predictive scores shows high level of correlation (0.84; P < .001) and high limits of agreement (±1.96 SD; −2000, 2000) in Bland–Altman plot analysis. The proposed method correctly classifies the risk group in 75.2% and classifies the subjects in the same group. In total, 81% of the predictive scores lie in the same categories and only seven patients out of 110 were more than one category off. For the presence/absence of coronary artery calcifications, the deep learning model achieved a sensitivity of 90% and a specificity of 94%. Fully automated model shows good correlation compared with reference standards. Automating process reduces evaluation time and optimizes clinical calcium scoring without additional resources.

Introduction: Intravenous heparin is strongly associated with pocket hematoma (PH) formation in patients undergoing cardiac implantable electronic device (CIED) implantation. However, there is no data on subcutaneous prophylactic heparin in this population Hypothesis: We hypothesized that the subcutaneous heparin may increase the odds of PH in patients undergoing CIED implantation using data from a large, nationally representative sample of the US population. Methods: The Premier Healthcare Database was analyzed from 2016-2019 to identify patients >18 years old undergoing CIED implantation. Patients with PH were then identified by using ICD-10 CM codes. A multivariable logistic regression model was used to adjust for confounders Results: A total of 267,650 patients underwent CIED implantation between January 1, 2016, and December 31, 2019, of which 4,496 (1.68%) \developed PH. Single chamber pacemaker in 7.22%, dual-chamber pacemaker was implanted in 58.3% of patients, and CRT-P in 4.59%, ICD in 18.58%, CRT-D in 11.31%. Of the total cohort, 559 Patients received subcutaneous heparin within 3 days of the CIED implant procedure. Of these 559 patients, 91 (16%) patients were on subcutaneous heparin (pre-procedural: 19; post-procedural: 57) and developed PH. Subcutaneous heparin is associated with increased odds (OR :13.8,p<0.01) of PH formation. Conclusions: This study suggests that subcutaneous heparin is a strong risk factor for the development of PH. This may independently drive up the rates of PH in the inpatient population. Further studies on the timing of subcutaneous heparin need to be conducted.

Background: New-onset atrial fibrillation (NOAF) in COVID-19 raises significant clinical and public health issues. This systematic review and meta-analysis aims to compile and analyze the current literature on NOAF in COVID-19 and give a more comprehensive understanding of the prevalence and outcomes of NOAF in COVID-19. Methods: A comprehensive literature search was carried out using several databases. The random effect model using inverse variance method and DerSimonian and Laird estimator of Tua2 was used to calculate the pooled prevalence and associated 95% confidence interval (CI). Results for outcome analysis were presented as odds ratios (ORs) with 95% CI and pooled using the Mantel-Haenszel random-effects model. Results: The pooled prevalence of NOAF in COVID-19 was 7.8% (95% CI, 6.54% to 9.32%), pooled estimate from 30 articles (81,929 COVID-19 patients). Furthermore, our analysis reported that COVID-19 patients with NOAF had a higher risk of developing severe disease compared with COVID–19 patients without a history of atrial fibrillation (OR= 4.78, 95% CI 3.75 to 6.09) and COVID-19 patients with a history of pre-existing atrial fibrillation (OR= 2.75, 95% CI 2.10 to 3.59) . Similarly, our analysis also indicated that COVID–19 patients with NOAF had a higher risk of all-cause mortality compared with, COVID–19 patients without a history of atrial fibrillation (OOR= 3.83, 95% CI 2.99 to 4.92) and COVID-19 patients with a history of pre-existing atrial fibrillation (OR= 2.32, 95% CI 1.35 to 3.96). The meta-analysis did not reveal any significant publication bias. Conclusion: The results of the current meta-analysis a high prevalence rate of NOAF among COVID-19 patients. Further the study reported higher disease severity with NOAF compared with COVID-19 patients without a history of atrial fibrillation and with a history of atrial fibrillation.

Background and motivation: Lung computed tomography (CT) techniques are high-resolution and are well adopted in the intensive care unit (ICU) for COVID-19 disease control classification. Most artificial intelligence (AI) systems do not undergo generalization and are typically overfitted. Such trained AI systems are not practical for clinical settings and therefore do not give accurate results when executed on unseen data sets. We hypothesize that ensemble deep learning (EDL) is superior to deep transfer learning (TL) in both non-augmented and augmented frameworks. Methodology: The system consists of a cascade of quality control, ResNet–UNet-based hybrid deep learning for lung segmentation, and seven models using TL-based classification followed by five types of EDL’s. To prove our hypothesis, five different kinds of data combinations (DC) were designed using a combination of two multicenter cohorts—Croatia (80 COVID) and Italy (72 COVID and 30 controls)—leading to 12,000 CT slices. As part of generalization, the system was tested on unseen data and statistically tested for reliability/stability. Results: Using the K5 (80:20) cross-validation protocol on the balanced and augmented dataset, the five DC datasets improved TL mean accuracy by 3.32%, 6.56%, 12.96%, 47.1%, and 2.78%, respectively. The five EDL systems showed improvements in accuracy of 2.12%, 5.78%, 6.72%, 32.05%, and 2.40%, thus validating our hypothesis. All statistical tests proved positive for reliability and stability. Conclusion: EDL showed superior performance to TL systems for both (a) unbalanced and unaugmented and (b) balanced and augmented datasets for both (i) seen and (ii) unseen paradigms, validating both our hypotheses.

AbstractBackgroundLeft ventricular assist device (LVAD) implantation is frequently employed in patients with end‐stage heart failure. The outcomes of addressing the repair of all substantial aortic valvular disease at the time of LVAD implantation remain unclear. We sought to assess the clinical outcomes in patients undergoing LVAD implantation concomitant with aortic valve procedures (AVPs) compared with isolated LVAD implantation.MethodsA literature search was performed using PubMed, Embase, and Cochrane library from inception till June 2022. Primary outcomes included short‐term mortality and long‐term survival. Random effects models were used to compute mean differences and odds ratios with 95% confidence intervals (CIs).ResultsA total of 14 observational studies (N = 52 693) met our inclusion criteria. Concomitant LVAD implantation and AVPs were associated with higher short‐term mortality (OR = 1.61 [95% CI, 1.06–2.42]; p = 0.02) and mean CPBt (MD = 43.25 [95% CI, 22.95–63.56]; p < 0.0001), and reduced long‐term survival (OR = 0.70 [95% CI, 0.55–0.88]; p = 0.003) compared with isolated LVAD implantation. No difference in the odds of cerebrovascular accident (OR = 1.05 [95% CI, 0.79–1.39]; p = 0.74) and mean length of hospital stay (MD = 2.89 [95% CI, −4.04 to 9.82]; p = 0.41) was observed between the two groups. On adjusted analysis, short‐term mortality was significantly higher in the LVAD group with concurrent AVPs when compared with the isolated LVAD group (aHR = 1.50 [95% CI, 1.20–1.87]; p = 0.0004).ConclusionsConcurrent AVPs were associated with higher short‐term mortality and reduced long‐term survival in patients undergoing LVAD implantation compared with isolated LVAD implantation.