Sphingosine 1-Phosphate Stimulates Smooth Muscle Cell Differentiation and Proliferation by Activating Separate Serum Response Factor Co-factors

Sphingosine 1-phosphate (S1P) is a lipid agonist that regulates smooth muscle cell (SMC) and endothelial cell functions by activating several members of the S1P subfamily of G-protein-coupled Edg receptors. We have shown previously that SMC differentiation is regulated by RhoA-dependent activation of serum response factor (SRF). Because S1P is a strong activator of RhoA, we hypothesized that S1P would stimulate SMC differentiation. Treatment of primary rat aortic SMC cells with S1P activated RhoA as measured by precipitation with a glutathione S-transferase-rhotekin fusion protein. In SMC and 10T1/2 cells, S1P treatment up-regulated the activities of several transiently transfected SMC-specific promoters, and these effects were inhibited by the Rho-kinase inhibitor, Y-27632. S1P also increased smooth muscle alpha-actin protein levels in SMC but had no effect on SRF binding to the smooth muscle alpha-actin CArG B element. Quantitative reverse transcriptase-PCR showed that S1P treatment of SMC or 10T1/2 cells did not increase the mRNA level of either of the recently identified SRF co-factors, myocardin or myocardin-related transcription factor-A (MRTF-A). MRTF-A protein was expressed highly in SMC and 10T1/2 cultures, and importantly the effects of S1P were inhibited by a dominant negative form of MRTF-A indicating that S1P may regulate the transcriptional activity of MRTF-A. Indeed, S1P treatment increased the nuclear localization of FLAG-MRTF-A, and the effect of MRTF-A overexpression on smooth muscle alpha-actin promoter activity was inhibited by dominant negative RhoA. S1P also stimulated SMC growth by activating the early growth response gene, c-fos. This effect was not attenuated by Y-27632 but could be inhibited by the MEK inhibitor, UO126. S1P enhanced SMC growth through ERK-mediated phosphorylation of the SRF co-factor, Elk-1, as measured by gel shift and Elk-1 activation assays. Taken together these results demonstrate that S1P activates multiple signaling pathways in SMC and regulates proliferation by ERK-dependent activation of Elk-1 and differentiation by RhoA-dependent activation of MRTF-A

Update to the Vitamin C, Thiamine and Steroids in Sepsis (VICTAS) protocol: statistical analysis plan for a prospective, multicenter, double-blind, adaptive sample size, randomized, placebo-controlled, clinical trial.

BACKGROUND: Observational research suggests that combined therapy with Vitamin C, thiamine and hydrocortisone may reduce mortality in patients with septic shock. METHODS AND DESIGN: The Vitamin C, Thiamine and Steroids in Sepsis (VICTAS) trial is a multicenter, double-blind, adaptive sample size, randomized, placebo-controlled trial designed to test the efficacy of combination therapy with vitamin C (1.5 g), thiamine (100 mg), and hydrocortisone (50 mg) given every 6 h for up to 16 doses in patients with respiratory or circulatory dysfunction (or both) resulting from sepsis. The primary outcome is ventilator- and vasopressor-free days with mortality as the key secondary outcome. Recruitment began in August 2018 and is ongoing; 501 participants have been enrolled to date, with a planned maximum sample size of 2000. The Data and Safety Monitoring Board reviewed interim results at N = 200, 300, 400 and 500, and has recommended continuing recruitment. The next interim analysis will occur when N = 1000. This update presents the statistical analysis plan. Specifically, we provide definitions for key treatment and outcome variables, and for intent-to-treat, per-protocol, and safety analysis datasets. We describe the planned descriptive analyses, the main analysis of the primary end point, our approach to secondary and exploratory analyses, and handling of missing data. Our goal is to provide enough detail that our approach could be replicated by an independent study group, thereby enhancing the transparency of the study. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03509350. Registered on 26 April 2018

Regulation of myocardin factor protein stability by the LIM-only protein FHL2

Extensive evidence indicates that serum response factor (SRF) regulates muscle-specific gene expression and that myocardin family SRF cofactors are critical for smooth muscle cell differentiation. In a yeast two hybrid screen for novel SRF binding partners expressed in aortic SMC, we identified four and a half LIM domain protein 2 (FHL2) and confirmed this interaction by GST pull-down and coimmunoprecipitation assays. FHL2 also interacted with all three myocardin factors and enhanced myocardin and myocardin-related transcription factor (MRTF)-A-dependent transactivation of smooth muscle α-actin, SM22, and cardiac atrial natriuretic factor promoters in 10T1/2 cells. The expression of FHL2 increased myocardin and MRTF-A protein levels, and, importantly, this effect was due to an increase in protein stability not due to an increase in myocardin factor mRNA expression. Treatment of cells with proteasome inhibitors MG-132 and lactacystin strongly upregulated endogenous MRTF-A protein levels and resulted in a substantial increase in ubiquitin immunoreactivity in MRTF-A immunoprecipitants. Interestingly, the expression of FHL2 attenuated the effects of RhoA and MRTF-B on promoter activity, perhaps through decreased MRTF-B nuclear localization or decreased SRF-CArG binding. Taken together, these data indicate that myocardin factors are regulated by proteasome-mediated degradation and that FHL2 regulates SRF-dependent transcription by multiple mechanisms, including stabilization of myocardin and MRTF-A

Using the Electronic Medical Record to Reduce Unnecessary Ordering of Coagulation Studies for Patients with Chest Pain

Introduction: Our goal was to reduce ordering of coagulation studies in the emergency department (ED) that have no added value for patients presenting with chest pain. We hypothesized this could be achieved via implementation of a stopgap measure in the electronic medical record (EMR). Methods: We used a pre and post quasi-experimental study design to evaluate the impact of an EMRbased intervention on coagulation study ordering for patients with chest pain. A simple interactive prompt was incorporated into the EMR of our ED that required clinicians to indicate whether patients were on anticoagulation therapy prior to completion of orders for coagulation studies. Coagulation order frequency was measured via detailed review of randomly sampled encounters during two-month periods before and after intervention. We classified existing orders as clinically indicated or non-value added. Order frequencies were calculated as percentages, and we assessed differences between groups by chi-square analysis. Results: Pre-intervention, 73.8% (76/103) of patients with chest pain had coagulation studies ordered, of which 67.1% (51/76) were non-value added. Post-intervention, 38.5% (40/104) of patients with chest pain had coagulation studies ordered, of which 60% (24/40) were non-value added. There was an absolute reduction of 35.3% (95% confidence interval [CI]: 22.7%, 48.0%) in the total ordering of coagulation studies and 26.4% (95% CI: 13.8%, 39.0%) in non-value added order placement. Conclusion: Simple EMR-based interactive prompts can serve as effective deterrents to indiscriminate ordering of diagnostic studies. [West J Emerg Med. 2017;18(2)267-269.

Using the Electronic Medical Record to Reduce Unnecessary Ordering of Coagulation Studies for Patients with Chest Pain

Objectives: Our goal was to reduce ordering of coagulation studies in the emergency department (ED) that have no added value for patients presenting with chest pain. We hypothesized this could be achieved via implementation of a stopgap measure in the electronic medical record (EMR).Methods: A pre and post quasi-experimental study design was used to evaluate the impact of an EMR-based intervention on coagulation study ordering for patients with chest pain. A simple interactive prompt was incorporated into the EMR of our ED that required clinicians to indicate whether patients were on anticoagulation therapy prior to completion of orders for coagulation studies. Coagulation order frequency was measured before and after intervention via detailed review of randomly sampled encounters during two-month periods before and after intervention. Existing orders were classified as clinically indicated or non-value added. Order frequencies were calculated as percentages and differences between groups were assessed by chi-square analysis.Results: Pre-intervention, 73.8% (76/103) of patients with chest pain had coagulation studies ordered, of which 67.1% (51/76) were non-value added. Post-intervention, 38.5% (40/104) of patients with chest pain had coagulation studies ordered, of which 60% (24/40) were non-value added. There was an absolute reduction of 35.3% (95% CI: 22.7%, 48.0%) in the total ordering of coagulation studies and 26.4% (95% CI: 13.8%, 39.0%) in non-value added order placement.Conclusion: Simple EMR-based interactive prompts can serve as effective deterrents to indiscriminate ordering of diagnostic studies

Using the Electronic Medical Record to Reduce Unnecessary Ordering of Coagulation Studies for Patients with Chest Pain

Introduction: Our goal was to reduce ordering of coagulation studies in the emergency department (ED) that have no added value for patients presenting with chest pain. We hypothesized this could be achieved via implementation of a stopgap measure in the electronic medical record (EMR). Methods: We used a pre and post quasi-experimental study design to evaluate the impact of an EMRbased intervention on coagulation study ordering for patients with chest pain. A simple interactive prompt was incorporated into the EMR of our ED that required clinicians to indicate whether patients were on anticoagulation therapy prior to completion of orders for coagulation studies. Coagulation order frequency was measured via detailed review of randomly sampled encounters during two-month periods before and after intervention. We classified existing orders as clinically indicated or non-value added. Order frequencies were calculated as percentages, and we assessed differences between groups by chi-square analysis. Results: Pre-intervention, 73.8% (76/103) of patients with chest pain had coagulation studies ordered, of which 67.1% (51/76) were non-value added. Post-intervention, 38.5% (40/104) of patients with chest pain had coagulation studies ordered, of which 60% (24/40) were non-value added. There was an absolute reduction of 35.3% (95% confidence interval [CI]: 22.7%, 48.0%) in the total ordering of coagulation studies and 26.4% (95% CI: 13.8%, 39.0%) in non-value added order placement. Conclusion: Simple EMR-based interactive prompts can serve as effective deterrents to indiscriminate ordering of diagnostic studies. [West J Emerg Med. 2017;18(2)267-269.

Multisite implementation of a workflow-integrated machine learning system to optimize COVID-19 hospital admission decisions

Abstract Demand has outstripped healthcare supply during the coronavirus disease 2019 (COVID-19) pandemic. Emergency departments (EDs) are tasked with distinguishing patients who require hospital resources from those who may be safely discharged to the community. The novelty and high variability of COVID-19 have made these determinations challenging. In this study, we developed, implemented and evaluated an electronic health record (EHR) embedded clinical decision support (CDS) system that leverages machine learning (ML) to estimate short-term risk for clinical deterioration in patients with or under investigation for COVID-19. The system translates model-generated risk for critical care needs within 24 h and inpatient care needs within 72 h into rapidly interpretable COVID-19 Deterioration Risk Levels made viewable within ED clinician workflow. ML models were derived in a retrospective cohort of 21,452 ED patients who visited one of five ED study sites and were prospectively validated in 15,670 ED visits that occurred before (n = 4322) or after (n = 11,348) CDS implementation; model performance and numerous patient-oriented outcomes including in-hospital mortality were measured across study periods. Incidence of critical care needs within 24 h and inpatient care needs within 72 h were 10.7% and 22.5%, respectively and were similar across study periods. ML model performance was excellent under all conditions, with AUC ranging from 0.85 to 0.91 for prediction of critical care needs and 0.80–0.90 for inpatient care needs. Total mortality was unchanged across study periods but was reduced among high-risk patients after CDS implementation