Randomized Trial of Metformin, Ivermectin, and Fluvoxamine for Covid-19.

BACKGROUND: Early treatment to prevent severe coronavirus disease 2019 (Covid-19) is an important component of the comprehensive response to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. METHODS: In this phase 3, double-blind, randomized, placebo-controlled trial, we used a 2-by-3 factorial design to test the effectiveness of three repurposed drugs - metformin, ivermectin, and fluvoxamine - in preventing serious SARS-CoV-2 infection in nonhospitalized adults who had been enrolled within 3 days after a confirmed diagnosis of infection and less than 7 days after the onset of symptoms. The patients were between the ages of 30 and 85 years, and all had either overweight or obesity. The primary composite end point was hypoxemia (≤93% oxygen saturation on home oximetry), emergency department visit, hospitalization, or death. All analyses used controls who had undergone concurrent randomization and were adjusted for SARS-CoV-2 vaccination and receipt of other trial medications. RESULTS: A total of 1431 patients underwent randomization; of these patients, 1323 were included in the primary analysis. The median age of the patients was 46 years; 56% were female (6% of whom were pregnant), and 52% had been vaccinated. The adjusted odds ratio for a primary event was 0.84 (95% confidence interval [CI], 0.66 to 1.09; P = 0.19) with metformin, 1.05 (95% CI, 0.76 to 1.45; P = 0.78) with ivermectin, and 0.94 (95% CI, 0.66 to 1.36; P = 0.75) with fluvoxamine. In prespecified secondary analyses, the adjusted odds ratio for emergency department visit, hospitalization, or death was 0.58 (95% CI, 0.35 to 0.94) with metformin, 1.39 (95% CI, 0.72 to 2.69) with ivermectin, and 1.17 (95% CI, 0.57 to 2.40) with fluvoxamine. The adjusted odds ratio for hospitalization or death was 0.47 (95% CI, 0.20 to 1.11) with metformin, 0.73 (95% CI, 0.19 to 2.77) with ivermectin, and 1.11 (95% CI, 0.33 to 3.76) with fluvoxamine. CONCLUSIONS: None of the three medications that were evaluated prevented the occurrence of hypoxemia, an emergency department visit, hospitalization, or death associated with Covid-19. (Funded by the Parsemus Foundation and others; COVID-OUT ClinicalTrials.gov number, NCT04510194.).

Favorable Antiviral Effect of Metformin on Severe Acute Respiratory Syndrome Coronavirus 2 Viral Load in a Randomized, Placebo-Controlled Clinical Trial of Coronavirus Disease 2019.

BACKGROUND: Metformin has antiviral activity against RNA viruses including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The mechanism appears to be suppression of protein translation via targeting the host mechanistic target of rapamycin pathway. In the COVID-OUT randomized trial for outpatient coronavirus disease 2019 (COVID-19), metformin reduced the odds of hospitalizations/death through 28 days by 58%, of emergency department visits/hospitalizations/death through 14 days by 42%, and of long COVID through 10 months by 42%. METHODS: COVID-OUT was a 2 × 3 randomized, placebo-controlled, double-blind trial that assessed metformin, fluvoxamine, and ivermectin; 999 participants self-collected anterior nasal swabs on day 1 (n = 945), day 5 (n = 871), and day 10 (n = 775). Viral load was quantified using reverse-transcription quantitative polymerase chain reaction. RESULTS: The mean SARS-CoV-2 viral load was reduced 3.6-fold with metformin relative to placebo (-0.56 log10 copies/mL; 95% confidence interval [CI], -1.05 to -.06; P = .027). Those who received metformin were less likely to have a detectable viral load than placebo at day 5 or day 10 (odds ratio [OR], 0.72; 95% CI, .55 to .94). Viral rebound, defined as a higher viral load at day 10 than day 5, was less frequent with metformin (3.28%) than placebo (5.95%; OR, 0.68; 95% CI, .36 to 1.29). The metformin effect was consistent across subgroups and increased over time. Neither ivermectin nor fluvoxamine showed effect over placebo. CONCLUSIONS: In this randomized, placebo-controlled trial of outpatient treatment of SARS-CoV-2, metformin significantly reduced SARS-CoV-2 viral load, which may explain the clinical benefits in this trial. Metformin is pleiotropic with other actions that are relevant to COVID-19 pathophysiology. CLINICAL TRIALS REGISTRATION: NCT04510194.

Beyond Unplanned ICU Transfers: Linking a Revised Definition of Deterioration to Patient Outcomes.

OBJECTIVES: To develop an electronic descriptor of clinical deterioration for hospitalized patients that predicts short-term mortality and identifies patient deterioration earlier than current standard definitions. DESIGN: A retrospective study using exploratory record review, quantitative analysis, and regression analyses. SETTING: Twelve-hospital community-academic health system. PATIENTS: All adult patients with an acute hospital encounter between January 1, 2018, and December 31, 2022. INTERVENTIONS: Not applicable. MEASUREMENTS AND MAIN RESULTS: Clinical trigger events were selected and used to create a revised electronic definition of deterioration, encompassing signals of respiratory failure, bleeding, and hypotension occurring in proximity to ICU transfer. Patients meeting the revised definition were 12.5 times more likely to die within 7 days (adjusted odds ratio 12.5; 95% CI, 8.9-17.4) and had a 95.3% longer length of stay (95% CI, 88.6-102.3%) compared with those who were transferred to the ICU or died regardless of meeting the revised definition. Among the 1812 patients who met the revised definition of deterioration before ICU transfer (52.4%), the median detection time was 157.0 min earlier (interquartile range 64.0-363.5 min). CONCLUSIONS: The revised definition of deterioration establishes an electronic descriptor of clinical deterioration that is strongly associated with short-term mortality and length of stay and identifies deterioration over 2.5 hours earlier than ICU transfer. Incorporating the revised definition of deterioration into the training and validation of early warning system algorithms may enhance their timeliness and clinical accuracy.

Predictors of Functional Decline Among Critically Ill Surgical Patients: A National Analysis.

INTRODUCTION: Functional decline is associated with critical illness, though this relationship in surgical patients is unclear. This study aims to characterize functional decline after intensive care unit (ICU) admission among surgical patients. METHODS: We performed a retrospective analysis of surgical patients admitted to the ICU in the Cerner Acute Physiology and Chronic Health Evaluation database, which includes 236 hospitals, from 2007 to 2017. Patients with and without functional decline were compared. Predictors of decline were modeled. RESULTS: A total of 52,838 patients were included; 19,310 (36.5%) experienced a functional decline. Median ages of the decline and nondecline groups were 69 (interquartile range 59-78) and 63 (interquartile range 52-72) years, respectively (P < 0.01). The nondecline group had a larger proportion of males (59.1% versus 55.3% in the decline group, P < 0.01). After controlling for sociodemographic covariates, comorbidities, and disease severity upon ICU admission, patients undergoing pulmonary (odds ratio [OR] 6.54, 95% confidence interval [CI] 2.67-16.02), musculoskeletal (OR 4.13, CI 3.51-4.87), neurological (OR 2.67, CI 2.39-2.98), gastrointestinal (OR 1.61, CI 1.38-1.88), and skin and soft tissue (OR 1.35, CI 1.08-1.68) compared to cardiovascular surgeries had increased odds of decline. CONCLUSIONS: More than one in three critically ill surgical patients experienced a functional decline. Pulmonary, musculoskeletal, and neurological procedures conferred the greatest risk. Additional resources should be targeted toward the rehabilitation of these patients.

Independent Risk Factors for Prolonged Tube Feeding After Endotracheal Intubation and Ventilation.

PURPOSE: Postextubation dysphagia (PED) can lead to prolonged tube feeding, but risk factors associated with prolonged tube feeding in this population are largely unknown. The purpose of this study was to identify factors independently associated with prolonged tube feeding in adult inpatients who required intubation and mechanical ventilation. MATERIALS AND METHODS: Retrospective observational cohort study in a dataset of 1.3 million inpatients. Extubated adults without preventilation dysphagia or tube feeding who underwent instrumental swallowing assessment were included. To characterize factors independently associated with prolonged tube feeding, we compiled a set of potential factors, completed factor selection using a random forest algorithm, and performed logistic regression. RESULTS: In total, 206 of 987 (20.9%) patients had prolonged tube feeding. The regression model produced an area under the curve of 0.79. Factors with the greatest influence on prolonged tube feeding included dysphagia with thickened liquids, dysphagia with soft/solid foods, preadmission weight loss, number of intubations, admission for neurologic disorder, and hospital of admission. CONCLUSIONS: Several factors predicted prolonged tube feeding after extubation. The strongest were some, but not all, aspects of swallowing function and clinical practice pattern variability. Clinical decision-making should consider bolus-specific data from instrumental swallowing evaluation rather than binary presence or absence of dysphagia.

Impact of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Vaccination and Booster on Coronavirus Disease 2019 (COVID-19) Symptom Severity Over Time in the COVID-OUT Trial.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination has decreasing protection from acquiring any infection with emergence of new variants; however, vaccination continues to protect against progression to severe coronavirus disease 2019 (COVID-19). The impact of vaccination status on symptoms over time is less clear. METHODS: Within a randomized trial on early outpatient COVID-19 therapy testing metformin, ivermectin, and/or fluvoxamine, participants recorded symptoms daily for 14 days. Participants were given a paper symptom diary allowing them to circle the severity of 14 symptoms as none (0), mild (1), moderate (2), or severe (3). This is a secondary analysis of clinical trial data on symptom severity over time using generalized estimating equations comparing those unvaccinated, SARS-CoV-2 vaccinated with primary vaccine series only, or vaccine-boosted. RESULTS: The parent clinical trial prospectively enrolled 1323 participants, of whom 1062 (80%) prospectively recorded some daily symptom data. Of these, 480 (45%) were unvaccinated, 530 (50%) were vaccinated with primary series only, and 52 (5%) vaccine-boosted. Overall symptom severity was least for the vaccine-boosted group and most severe for unvaccinated at baseline and over the 14 days (P < .001). Individual symptoms were least severe in the vaccine-boosted group including cough, chills, fever, nausea, fatigue, myalgia, headache, and diarrhea, as well as smell and taste abnormalities. Results were consistent over Delta and Omicron variant time periods. CONCLUSIONS: SARS-CoV-2 vaccine-boosted participants had the least severe symptoms during COVID-19, which abated the quickest over time. Clinical Trial Registration. NCT04510194.

Re-Aiming Equity Evaluation in Clinical Decision Support: A Scoping Review of Equity Assessments in Surgical Decision Support Systems.

OBJECTIVE: We critically evaluated the surgical literature to explore the prevalence and describe how equity assessments occur when using clinical decision support systems. BACKGROUND: Clinical decision support (CDS) systems are increasingly used to facilitate surgical care delivery. Despite formal recommendations to do so, equity evaluations are not routinely performed on CDS systems and underrepresented populations are at risk of harm and further health disparities. We explored surgical literature to determine frequency and rigor of CDS equity assessments and offer recommendations to improve CDS equity by appending existing frameworks. METHODS: We performed a scoping review up to Augus 25, 2021 using PubMed and Google Scholar for the following search terms: clinical decision support, implementation, RE-AIM, Proctor, Proctor's framework, equity, trauma, surgery, surgical. We identified 1415 citations and 229 abstracts met criteria for review. A total of 84 underwent full review after 145 were excluded if they did not assess outcomes of an electronic CDS tool or have a surgical use case. RESULTS: Only 6% (5/84) of surgical CDS systems reported equity analyses, suggesting that current methods for optimizing equity in surgical CDS are inadequate. We propose revising the RE-AIM framework to include an Equity element (RE 2 -AIM) specifying that CDS foundational analyses and algorithms are performed or trained on balanced datasets with sociodemographic characteristics that accurately represent the CDS target population and are assessed by sensitivity analyses focused on vulnerable subpopulations. CONCLUSION: Current surgical CDS literature reports little with respect to equity. Revising the RE-AIM framework to include an Equity element (RE 2 -AIM) promotes the development and implementation of CDS systems that, at minimum, do not worsen healthcare disparities and possibly improve their generalizability.

Artificial Intelligence-enabled Decision Support in Surgery: State-of-the-art and Future Directions.

OBJECTIVE: To summarize state-of-the-art artificial intelligence-enabled decision support in surgery and to quantify deficiencies in scientific rigor and reporting. BACKGROUND: To positively affect surgical care, decision-support models must exceed current reporting guideline requirements by performing external and real-time validation, enrolling adequate sample sizes, reporting model precision, assessing performance across vulnerable populations, and achieving clinical implementation; the degree to which published models meet these criteria is unknown. METHODS: Embase, PubMed, and MEDLINE databases were searched from their inception to September 21, 2022 for articles describing artificial intelligence-enabled decision support in surgery that uses preoperative or intraoperative data elements to predict complications within 90 days of surgery. Scientific rigor and reporting criteria were assessed and reported according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines. RESULTS: Sample size ranged from 163-2,882,526, with 8/36 articles (22.2%) featuring sample sizes of less than 2000; 7 of these 8 articles (87.5%) had below-average (<0.83) area under the receiver operating characteristic or accuracy. Overall, 29 articles (80.6%) performed internal validation only, 5 (13.8%) performed external validation, and 2 (5.6%) performed real-time validation. Twenty-three articles (63.9%) reported precision. No articles reported performance across sociodemographic categories. Thirteen articles (36.1%) presented a framework that could be used for clinical implementation; none assessed clinical implementation efficacy. CONCLUSIONS: Artificial intelligence-enabled decision support in surgery is limited by reliance on internal validation, small sample sizes that risk overfitting and sacrifice predictive performance, and failure to report confidence intervals, precision, equity analyses, and clinical implementation. Researchers should strive to improve scientific quality.

Arrhythmias in Critically Ill Surgical and Non-surgical Patients: A National Propensity-Matched Study.

BACKGROUND: Arrhythmias are common in critically ill patients, though the impact of arrhythmias on surgical patients is not well delineated. We aimed to characterize mortality following arrhythmias in critically ill patients. METHODS: We performed a propensity-matched retrospective analysis of intensive care unit (ICU) patients from 2007 to 2017 in the Cerner Acute Physiology and Chronic Health Evaluation database. We compared outcomes between patients with and without arrhythmias and those with and without surgical indications for ICU admission. We also modeled predictors of arrhythmias in surgical patients. RESULTS: 467,951 patients were included; 97,958 (20.9%) were surgical patients. Arrhythmias occurred in 1.4% of the study cohorts. Predictors of arrhythmias in surgical patients included a history of cardiovascular disease (odds ratio [OR] 1.35, 95% confidence interval [CI95] 1.11-1.63), respiratory failure (OR 1.48, CI95 1.12-1.96), pneumonia (OR 3.17, CI95 1.98-5.10), higher bicarbonate level (OR 1.03, CI95 1.01-1.05), lower albumin level (OR 0.79, CI95 0.68-0.91), and vasopressor requirement (OR 27.2, CI95 22.0-33.7). After propensity matching, surgical patients with arrhythmias had a 42% mortality risk reduction compared to non-surgical patients (risk ratio [RR] 0.58, CI 95 0.43-0.79). Predicted probabilities of mortality for surgical patients were lower at all ages. CONCLUSIONS: Surgical patients with arrhythmias are at lower risk of mortality than non-surgical patients. In this propensity-matched analysis, predictors of arrhythmias in critically ill surgical patients included a history of cardiovascular disease, respiratory complications, increased bicarbonate levels, decreased albumin levels, and vasopressor requirement. These findings highlight the differential effect of arrhythmias on different cohorts of critically ill populations.

Extending Trauma Quality Improvement Beyond Trauma Centers: Hospital Variation in Outcomes Among Nontrauma Hospitals.

OBJECTIVE: The American College of Surgeons (ACS) conducts a robust quality improvement program for ACS-verified trauma centers, yet many injured patients receive care at non-accredited facilities. This study tested for variation in outcomes across non-trauma hospitals and characterized hospitals associated with increased mortality. SUMMARY BACKGROUND DATA: The study included state trauma registry data of 37,670 patients treated between January 1, 2013, and December 31, 2015. Clinical data were supplemented with data from the American Hospital Association and US Department of Agriculture, allowing comparisons among 100 nontrauma hospitals. METHODS: Using Bayesian techniques, risk-adjusted and reliability-adjusted rates of mortality and interfacility transfer, as well as Emergency Departments length-of-stay (ED-LOS) among patients transferred from EDs were calculated for each hospital. Subgroup analyses were performed for patients ages >55 years and those with decreased Glasgow coma scores (GCS). Multiple imputation was used to address missing data. RESULTS: Mortality varied 3-fold (0.9%-3.1%); interfacility transfer rates varied 46-fold (2.1%-95.6%); and mean ED-LOS varied 3-fold (81-231 minutes). Hospitals that were high and low statistical outliers were identified for each outcome, and subgroup analyses demonstrated comparable hospital variation. Metropolitan hospitals were associated increased mortality [odds ratio (OR) 1.7, P = 0.004], decreased likelihood of interfacility transfer (OR 0.7, P ≤ 0.001), and increased ED-LOS (coef. 0.1, P ≤ 0.001) when compared with nonmetropolitan hospitals and risk-adjusted. CONCLUSIONS: Wide variation in trauma outcomes exists across nontrauma hospitals. Efforts to improve trauma quality should include engagement of nontrauma hospitals to reduce variation in outcomes of injured patients treated at those facilities.

Patient Heterogeneity and the J-Curve Relationship Between Time-to-Antibiotics and the Outcomes of Patients Admitted With Bacterial Infection.

OBJECTIVES: Sepsis remains a leading and preventable cause of hospital utilization and mortality in the United States. Despite updated guidelines, the optimal definition of sepsis as well as optimal timing of bundled treatment remain uncertain. Identifying patients with infection who benefit from early treatment is a necessary step for tailored interventions. In this study, we aimed to illustrate clinical predictors of time-to-antibiotics among patients with severe bacterial infection and model the effect of delay on risk-adjusted outcomes across different sepsis definitions. DESIGN: A multicenter retrospective observational study. SETTING: A seven-hospital network including academic tertiary care center. PATIENTS: Eighteen thousand three hundred fifteen patients admitted with severe bacterial illness with or without sepsis by either acute organ dysfunction (AOD) or systemic inflammatory response syndrome positivity. MEASUREMENTS AND MAIN RESULTS: The primary exposure was time to antibiotics. We identified patient predictors of time-to-antibiotics including demographics, chronic diagnoses, vitals, and laboratory results and determined the impact of delay on a composite of inhospital death or length of stay over 10 days. Distribution of time-to-antibiotics was similar across patients with and without sepsis. For all patients, a J-curve relationship between time-to-antibiotics and outcomes was observed, primarily driven by length of stay among patients without AOD. Patient characteristics provided good to excellent prediction of time-to-antibiotics irrespective of the presence of sepsis. Reduced time-to-antibiotics was associated with improved outcomes for all time points beyond 2.5 hours from presentation across sepsis definitions. CONCLUSIONS: Antibiotic timing is a function of patient factors regardless of sepsis criteria. Similarly, we show that early administration of antibiotics is associated with improved outcomes in all patients with severe bacterial illness. Our findings suggest identifying infection is a rate-limiting and actionable step that can improve outcomes in septic and nonseptic patients.

Improved Prediction of Older Adult Discharge After Trauma Using a Novel Machine Learning Paradigm.

BACKGROUND: The ability to reliably predict outcomes after trauma in older adults (age ≥ 65 y) is critical for clinical decision making. Using novel machine-learning techniques, we sought to design a nonlinear, competing risks paradigm for prediction of older adult discharge disposition following injury. MATERIALS AND METHODS: The National Trauma Databank (NTDB) was used to identify patients 65+ y between 2007 and 2014. Training was performed on an enriched cohort of diverse patients. Factors included age, comorbidities, length of stay, and physiologic parameters to predict in-hospital mortality and discharge disposition (home versus skilled nursing/long-term care facility). Length of stay and discharge status were analyzed via competing risks survival analysis with Bayesian additive regression trees and a multinomial mixed model. RESULTS: The resulting sample size was 47,037 patients. Admission GCS and age were important in predicting mortality and discharge disposition. As GCS decreased, patients were more likely to die (risk ratio increased by average of 1.4 per 2-point drop in GCS, P < 0.001). As GCS decreased, patients were also more likely to be discharged to a skilled nursing or long-term care facility (risk ratio decreased by 0.08 per 2-point decrease in GCS, P< 0.001). The area under curve for prediction of discharge home was improved in the competing risks model 0.73 versus 0.43 in the traditional multinomial mixed model. CONCLUSIONS: Predicting older adult discharge disposition after trauma is improved using machine learning over traditional regression analysis. We confirmed that a nonlinear, competing risks paradigm enhances prediction on any given hospital day post injury.

A serial cross-sectional study of trends and predictors of prescription controlled substance-related traumatic injury.

Pre-injury drug use is a key contributor to traumatic injury. However, limited research has examined trends and predictors of controlled substance-related trauma. The present study aims to provide better clarity on the specific role of prescription-controlled substances (PCS) in traumatic injury events. The data source was the American College of Surgeons National Trauma Data Bank. Trends by injury mechanism and intent for patients with PCS and no-confirmed substances were compared from 2007 to 2014. Logistic regression models were also performed to examine the association between substance use and injury mechanism and intent for data across the study period. Of 405,334 trauma patients, 328,623 (81.1%) had no-confirmed substances and 76,711 (18.9%) had PCS detected. The majority of events in the PCS and no-confirmed substance groups were classified as unintentional. Motor vehicle traffic (MVT), falls, other transport, and cut/pierce injuries accounted for approximately 80% of all injuries. From 2007 to 2014, the proportion of injuries with PCS increased for all injury mechanisms and injury intents. The injury mechanisms of fire/burn, firearm, machinery, poisoning, and other transport were significantly more likely to have PCS relative to MVT injuries. For injury intent, self-harm was more likely to have a toxicology test positive for PCS, while assault was less likely to have a toxicology test positive for PCS compared to unintentional injuries. PCS-related traumatic injuries increased significantly over time and across injury mechanisms and intents. These findings can be used to inform prescribing and understand risk factors to reduce the likelihood of PCS-related traumatic injury.

Recent Trends in Admission Diagnosis and Related Mortality in the Medically Critically Ill.

PURPOSE: With decades of declining ICU mortality, we hypothesized that the outcomes and distribution of diseases cared for in the ICU have changed and we aimed to further characterize them. STUDY DESIGN AND METHODS: A retrospective cohort analysis of 287,154 nonsurgical-critically ill adults, from 237 U.S. ICUs, using the manually abstracted Cerner APACHE Outcomes database from 2008 to 2016 was performed. Surgical patients, rare admission diagnoses (<100 occurrences), and low volume hospitals (<100 total admissions) were excluded. Diagnoses were distributed into mutually exclusive organ system/disease-based categories based on admission diagnosis. Multi-level mixed-effects negative binomial regression was used to assess temporal trends in admission, in-hospital mortality, and length of stay (LOS). RESULTS: The number of ICU admissions remained unchanged (IRR 0.99, 0.98-1.003) while certain organ system/disease groups increased (toxicology [25%], hematologic/oncologic [55%] while others decreased (gastrointestinal [31%], pulmonary [24%]). Overall risk-adjusted in-hospital mortality was unchanged (IRR 0.98, 0.96-1.0004). Risk-adjusted ICU LOS (Estimate -0.06 days/year, -0.07 to -0.04) decreased. Risk-adjusted mortality varied significantly by disease. CONCLUSION: Risk-adjusted ICU mortality rate did not change over the study period, but there was evidence of shifting disease burden across the critical care population. Our data provides useful information regarding future ICU personnel and resource needs.

Predictors of Postacute Sequelae of COVID-19 Development and Rehabilitation: A Retrospective Study.

OBJECTIVE: To examine the frequency of postacute sequelae of SARS-CoV-2 (PASC) and the factors associated with rehabilitation utilization in a large adult population with PASC. DESIGN: Retrospective study. SETTING: Midwest hospital health system. PARTICIPANTS: 19,792 patients with COVID-19 from March 10, 2020, to January 17, 2021. INTERVENTION: Not applicable. MAIN OUTCOME MEASURES: Descriptive analyses were conducted across the entire cohort along with an adult subgroup analysis. A logistic regression was performed to assess factors associated with PASC development and rehabilitation utilization. RESULTS: In an analysis of 19,792 patients, the frequency of PASC was 42.8% in the adult population. Patients with PASC compared with those without had a higher utilization of rehabilitation services (8.6% vs 3.8%, P<.001). Risk factors for rehabilitation utilization in patients with PASC included younger age (odds ratio [OR], 0.99; 95% confidence interval [CI], 0.98-1.00; P=.01). In addition to several comorbidities and demographics factors, risk factors for rehabilitation utilization solely in the inpatient population included male sex (OR, 1.24; 95% CI, 1.02-1.50; P=.03) with patients on angiotensin-converting-enzyme inhibitors or angiotensin-receptor blockers 3 months prior to COVID-19 infections having a decreased risk of needing rehabilitation (OR, 0.80; 95% CI, 0.64-0.99; P=.04). CONCLUSIONS: Patients with PASC had higher rehabilitation utilization. We identified several clinical and demographic factors associated with the development of PASC and rehabilitation utilization.

Need for Emergent Intervention within 6 Hours: A Novel Prediction Model for Hospital Trauma Triage.

Objective: A tiered trauma team activation system allocates resources proportional to patients' needs based upon injury burden. Previous trauma hospital-triage models are limited to predicting Injury Severity Score which is based on > 10% all-cause in-hospital mortality, rather than need for emergent intervention within 6 hours (NEI-6). Our aim was to develop a novel prediction model for hospital-triage that utilizes criteria available to the EMS provider to predict NEI-6 and the need for a trauma team activation.Methods: A regional trauma quality collaborative was used to identify all trauma patients ≥ 16 years from the American College of Surgeons-Committee on Trauma verified Level 1 and 2 trauma centers. Logistic regression and random forest were used to construct two predictive models for NEI-6 based on clinically relevant variables. Restricted cubic splines were used to model nonlinear predictors. The accuracy of the prediction model was assessed in terms of discrimination.Results: Using data from 12,624 patients for the training dataset (62.6% male; median age 61 years; median ISS 9) and 9,445 patients for the validation dataset (62.6% male; median age 59 years; median ISS 9), the following significant predictors were selected for the prediction models: age, gender, field GCS, vital signs, intentionality, and mechanism of injury. The final boosted tree model showed an AUC of 0.85 in the validation cohort for predicting NEI-6.Conclusions: The NEI-6 trauma triage prediction model used prehospital metrics to predict need for highest level of trauma activation. Prehospital prediction of major trauma may reduce undertriage mortality and improve resource utilization.

Risk of hospital admission related to scooter trauma injuries: a national emergency room database study.

BACKGROUND: We investigated key risk factors for hospital admission related to powered scooters, which are modes of transportation with increasing accessibility across the United States (US). METHODS: We queried the National Electronic Injury Surveillance System (NEISS) for injuries related to powered scooters, obtaining US population projections of injuries and hospital admissions. We determined mechanism of injury, characterized injury types, and performed multivariate regression analyses to determine factors associated with hospital admission. RESULTS: One thousand one hundred ninety-one patients sustained electric-motorized scooter (e-scooter) injuries and 10.9% (131) required hospitalization from 2013 to 2018. This extrapolated to a US annual total of 862 (95% CI:745-979) scooter injuries requiring hospitalization, with estimated annual mortality of 6.7 patients per year (95% CI:4.8-8.5). The incidence of hospital admissions increased by an average of 13.1% each year of the study period. Fall (79 [60%]) and motor vehicle collision (33 [25%]) were the most common mechanism. Injury locations included head (44 [34%]), lower extremity (22 [17%]), and lower trunk (16 [12%]). On multivariable analysis, significant factors associated with admission included increased age (OR 1.02, 95% CI:1.01-1.02), torso injuries (OR 6.19, 2.93-13.10), concussion (25.45, 5.88-110.18), fractures (21.98, 7.13-67.66), musculoskeletal injury (6.65, 1.20-36.99), and collision with vehicle (3.343, 2.009-5.562). Scooter speed, seasonality, and gender were not associated with risk of hospitalization. CONCLUSION: Our findings show increased hospital admissions and mortality from powered scooter trauma, with fall and motor vehicle collisions as the most common mechanisms resulting in hospitalization. This calls for improved rider safety measures and regulation surrounding vehicular collision scenarios.

mTOR inhibition in COVID-19: A commentary and review of efficacy in RNA viruses.

In this commentary, we shed light on the role of the mammalian target of rapamycin (mTOR) pathway in viral infections. The mTOR pathway has been demonstrated to be modulated in numerous RNA viruses. Frequently, inhibiting mTOR results in suppression of virus growth and replication. Recent evidence points towards modulation of mTOR in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We discuss the current literature on mTOR in SARS-CoV-2 and highlight evidence in support of a role for mTOR inhibitors in the treatment of coronavirus disease 2019.

Outpatient metformin use is associated with reduced severity of COVID-19 disease in adults with overweight or obesity.

Observational studies suggest outpatient metformin use is associated with reduced mortality from coronavirus disease-2019 (COVID-19). Metformin is known to decrease interleukin-6 and tumor-necrosis factor-α, which appear to contribute to morbidity in COVID-19. We sought to understand whether outpatient metformin use was associated with reduced odds of severe COVID-19 disease in a large US healthcare data set. Retrospective cohort analysis of electronic health record (EHR) data that was pooled across multiple EHR systems from 12 hospitals and 60 primary care clinics in the Midwest between March 4, 2020 and December 4, 2020. Inclusion criteria: data for body mass index (BMI) > 25 kg/m2 and a positive SARS-CoV-2 polymerase chain reaction test; age ≥ 30 and ≤85 years. Exclusion criteria: patient opt-out of research. Metformin is the exposure of interest, and death, admission, and intensive care unit admission are the outcomes of interest. Metformin was associated with a decrease in mortality from COVID-19, OR 0.32 (0.15, 0.66; p = .002), and in the propensity-matched cohorts, OR 0.38 (0.16, 0.91; p = .030). Metformin was associated with a nonsignificant decrease in hospital admission for COVID-19 in the overall cohort, OR 0.78 (0.58-1.04, p = .087). Among the subgroup with a hemoglobin HbA1c available (n = 1193), the adjusted odds of hospitalization (including adjustment for HbA1c) for metformin users was OR 0.75 (0.53-1.06, p = .105). Outpatient metformin use was associated with lower mortality and a trend towards decreased admission for COVID-19. Given metformin's low cost, established safety, and the mounting evidence of reduced severity of COVID-19 disease, metformin should be prospectively assessed for outpatient treatment of COVID-19.

Racial and Ethnic Disparities in Hospital Admissions from COVID-19: Determining the Impact of Neighborhood Deprivation and Primary Language.

BACKGROUND: Despite past and ongoing efforts to achieve health equity in the USA, racial and ethnic disparities persist and appear to be exacerbated by COVID-19. OBJECTIVE: Evaluate neighborhood-level deprivation and English language proficiency effect on disproportionate outcomes seen in racial and ethnic minorities diagnosed with COVID-19. DESIGN: Retrospective cohort study SETTING: Health records of 12 Midwest hospitals and 60 clinics in Minnesota between March 4, 2020, and August 19, 2020 PATIENTS: Polymerase chain reaction-positive COVID-19 patients EXPOSURES: Area Deprivation Index (ADI) and primary language MAIN MEASURES: The primary outcome was COVID-19 severity, using hospitalization within 45 days of diagnosis as a marker of severity. Logistic and competing-risk regression models assessed the effects of neighborhood-level deprivation (using the ADI) and primary language. Within race, effects of ADI and primary language were measured using logistic regression. RESULTS: A total of 5577 individuals infected with SARS-CoV-2 were included; 866 (n = 15.5%) were hospitalized within 45 days of diagnosis. Hospitalized patients were older (60.9 vs. 40.4 years, p < 0.001) and more likely to be male (n = 425 [49.1%] vs. 2049 [43.5%], p = 0.002). Of those requiring hospitalization, 43.9% (n = 381), 19.9% (n = 172), 18.6% (n = 161), and 11.8% (n = 102) were White, Black, Asian, and Hispanic, respectively. Independent of ADI, minority race/ethnicity was associated with COVID-19 severity: Hispanic patients (OR 3.8, 95% CI 2.72-5.30), Asians (OR 2.39, 95% CI 1.74-3.29), and Blacks (OR 1.50, 95% CI 1.15-1.94). ADI was not associated with hospitalization. Non-English-speaking (OR 1.91, 95% CI 1.51-2.43) significantly increased odds of hospital admission across and within minority groups. CONCLUSIONS: Minority populations have increased odds of severe COVID-19 independent of neighborhood deprivation, a commonly suspected driver of disparate outcomes. Non-English-speaking accounts for differences across and within minority populations. These results support the ongoing need to determine the mechanisms that contribute to disparities during COVID-19 while also highlighting the underappreciated role primary language plays in COVID-19 severity among minority groups.