Search

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.). [ABSTRACT FROM AUTHOR]

Background: There is conflicting evidence regarding emergency medical service (EMS) provider level of training and outcomes in trauma. We hypothesized that advanced life support (ALS) provider transport is associated with lower mortality compared with basic life support transport., Methods: We performed secondary analysis of a combined prehospital and in-hospital database of trauma patients utilizing ESO electronic medical records from 2018 to 2022. We included encounters with patients aged 15 years to 100 years transported by ground to a Level I or II trauma center with trauma-specific ICD-10-CM codes. Patients dead upon EMS arrival and transfers were excluded. We matched patients using 1:1 nearest neighbor propensity scores based on demographic, injury, and EMS characteristics, prehospital vitals, and trauma center designation. The exposure variable was EMS level of training and outcome was mortality. We conducted subgroup analyses on predefined cohorts (age > 50 years, mechanism of injury, prehospital EMS time > 30 minutes)., Results: We identified 30,735 ALS and 1,758 basic life support encounters, representing 1,154 pairs following propensity matching. Mortality was lower among patients transported by ALS providers (odds ratio [OR], 0.40; 95% confidence interval [CI], 0.18-0.88; p = 0.023). Mortality was also lower in the subgroups of patients aged > 50 years (OR, 0.35; 95% CI, 0.13-0.98; p = 0.046), and in patients with mechanisms of injury excluding falls (OR, 0.35; 95% CI, 0.13-0.98; p = 0.047). In those with prolonged prehospital time, the association approached significance (OR, 0.30; 95% CI, 0.08-1.08; p = 0.067). In those with mechanisms of injury of fall, the association was not significant., Conclusion: In this retrospective, propensity matched cohort study using a national sample of trauma patients, attendance by ALS providers was associated with reduced mortality. This was observed in the entire cohort, in those aged > 50 years, and those with a higher-risk mechanism of injury. It approached significance in those with prolonged prehospital time., Level of Evidence: Therapeutic/Care Management; Level III., (Copyright © 2025 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Association for the Surgery of Trauma.)

Background: The COVID-19 pandemic has led to the widespread use of anticoagulation (AC) and corticosteroids (CCS) for hospitalized patients, but real-world outcomes may differ from clinical trial findings due to diverse patient populations and treatment variability., Objective: To evaluate the real-world impact of AC and CCS therapies on key clinical outcomes in hospitalized COVID-19 patients., Design: Multicenter, retrospective observational cohort study conducted across 11 hospitals in a Midwest health system., Participants: The study included 4,754 hospitalized COVID-19 patients treated with AC, CCS, both (AC+CCS), or neither. The 'neither' group served as the reference for comparisons., Interventions: Interventions included administration of AC, CCS, both AC+CCS, or no intervention., Main Measures: Primary outcomes included thromboembolism (TE), bleeding events, ICU admissions, invasive mechanical ventilation (IMV), and in-hospital mortality., Key Results: Compared to the reference group, the AC+CCS group had significantly lower odds of TE (aOR 0.61, 95% CI 0.43-0.87) and bleeding events (aOR 0.15 95% CI (0.08, 0.27)). The AC-only group demonstrated the lowest ICU admission, IMV, and mortality rates (aHR 0.30 95% CI (0.17, 0.53)). The CCS-only group had the highest rates of adverse outcomes, likely reflecting greater baseline illness severity., Conclusions: This study emphasizes the importance of individualized treatment strategies in hospitalized COVID-19 patients, showing that real-world outcomes of AC and CCS can differ significantly from controlled trials. These findings provide crucial insights for adapting clinical guidelines to diverse patient settings., Competing Interests: Declaration of competing interest None of the authors have conflicts of interest to declare related to this work., (Copyright © 2025 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

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., Competing Interests: Declaration of Conflicting InterestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Purpose: Rib fractures are common after blunt thoracic trauma and can be associated with significant morbidity and mortality. We investigated trends of rib fracture injuries among adults presenting to United States (US) emergency departments, factors related to increased likelihood of hospitalization, and hospitalization practice patterns., Methods: We queried the National Electronic Injury Surveillance System database between 2012 and 2021 for all patients 18 years of age and older with rib fractures. These data were extrapolated to provide national estimates. Regression analysis was performed to identify trends for injury and risk factors for hospitalization., Results: We identified 32,233 adult patients with rib fractures; this extrapolated to a national estimate of 1,430,270 patients with rib fractures during the 10-year period. Between 2012 and 2021, there was a 52% increase in the incidence rate per 100,000 persons (R 2  = 0.94, p < 0.001). Males accounted for 58% of patients with rib fractures, and 50% of patients were 65 years or older. Hospitalization was required in 38% of patients, and the hospitalization rate increased by 96% during the study period (R 2  = 0.96, p < 0.001). When comparing hospitals of different sizes, a 20% greater increase in the odds of hospitalization over time was identified among patients presenting to "larger" hospitals compared to "smaller" hospitals., Conclusion: The incidence of rib fractures and the associated hospitalization rates are both increasing nationally, with half of cases occurring in patients aged 65 years or older. Our findings emphasize the urgent need to implement evidence-based preventive measures and current management guidelines when managing the increasing caseload of rib fracture injuries., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany.)

Abstract: Trauma patients are at an elevated risk for developing venous thromboembolism (VTE), which includes pulmonary embolism and deep vein thrombosis. In the inpatient setting, prompt pharmacologic prophylaxis is utilized to prevent VTE. For patients with lower extremity fractures or limited mobility, VTE risk does not return to baseline levels postdischarge. Currently, there are limited data to guide postdischarge VTE prophylaxis in trauma patients. The goal of these postdischarge VTE prophylaxis guidelines are to identify patients at the highest risk of developing VTE after discharge and to offer pharmacologic prophylaxis strategies to limit this risk., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Objectives: Occurrence of post-intubation laryngotracheal stenosis (LTS) with respect to COVID-19 status., Design: Retrospective cross-sectional inpatient database., Setting: Eleven Midwest academic and community hospitals, United States., Patients: Adults, mechanically ventilated, from January 2020 to August 2022, who were subsequently readmitted within 6 months with a new diagnosis of LTS., Interventions: None., Measurements and Main Results: Six thousand eight hundred fifty-one COVID-19 negative and 1316 COVID-19 positive patients were intubated and had similar distribution by age (median 63.77 vs. 63.16 yr old), sex (male, 60.8%; n = 4173 vs. 60%; n = 789), endotracheal tube size (≥ 7.5, 75.8%; n = 5192 vs. 75.5%; n = 994), and comorbidities. The ICU length of stay (median [interquartile range (IQR)], 7.23 d [2.13-16.67 d] vs. 3.95 d [1.91-8.88 d]) and mechanical ventilation days (median [IQR], 5.57 d [1.01-14.18 d] vs. 1.37 d [0.35-4.72 d]) were longer in the COVID-19 positive group. The occurrence of LTS was double in the COVID-19 positive group (12.7%, n = 168 vs. 6.4%, n = 440; p < 0.001) and was most commonly diagnosed within 60 days of intubation. In multivariate analysis, the risk of LTS increased by 2% with each additional ICU day (hazard ratio [HR], 1.02; 95% CI, 1.02-1.03; p < 0.001), by 3% with each additional day of ventilation (HR, 1.03; 95% CI, 1.02-1.04; p < 0.001), and by 52% for each additional reintubation (HR, 1.52; 95% CI, 1.36-1.71; p < 0.001). We observed no significant association COVID-19 status and risk of LTS., Conclusions: The occurrence of post-intubation LTS was double in a COVID-19 positive cohort, with higher risk with increasing number of days intubated, days in the ICU and especially with the number of reintubations. COVID-19 status was not an independent risk factor for LTS., Competing Interests: The authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2024 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.)

Background: Learning health systems (LHSs) iteratively generate evidence that can be implemented into practice to improve care and produce generalizable knowledge. Pragmatic clinical trials fit well within LHSs as they combine real-world data and experiences with a degree of methodological rigor which supports generalizability., Objectives: We established a pragmatic clinical trial unit ("RapidEval") to support the development of an LHS. To further advance the field of LHS, we sought to further characterize the role of health information technology (HIT), including innovative solutions and challenges that occur, to improve LHS project delivery., Methods: During the period from December 2021 to February 2023, eight projects were selected out of 51 applications to the RapidEval program, of which five were implemented, one is currently in pilot testing, and two are in planning. We evaluated pre-study planning, implementation, analysis, and study closure approaches across all RapidEval initiatives to summarize approaches across studies and identify key innovations and learnings by gathering data from study investigators, quality staff, and IT staff, as well as RapidEval staff and leadership., Implementation Results: Implementation approaches spanned a range of HIT capabilities including interruptive alerts, clinical decision support integrated into order systems, patient navigators, embedded micro-education, targeted outpatient hand-off documentation, and patient communication. Study approaches include pre-post with time-concordant controls (1), randomized stepped-wedge (1), cluster randomized across providers (1) and location (3), and simple patient level randomization (2)., Conclusions: Study selection, design, deployment, data collection, and analysis required close collaboration between data analysts, informaticists, and the RapidEval team., Competing Interests: The authors have no relevant conflicts of interest to report., (© 2024 The Authors. Learning Health Systems published by Wiley Periodicals LLC on behalf of University of Michigan.)

Electronic health records (EHRs) and other real-world data (RWD) are critical to accelerating and scaling care improvement and transformation. To efficiently leverage it for secondary uses, EHR/RWD should be optimally managed and mapped to industry standard concepts (ISCs). Inherent challenges in concept encoding usually result in inefficient and costly workflows and resultant metadata representation structures outside the EHR. Using three related projects to map data to ISCs, we describe the development of standard, repeatable processes for precisely and unambiguously representing EHR data using appropriate ISCs within the EHR platform lifecycle and mappings specific to SNOMED-CT for Demographics, Specialty and Services. Mappings in these 3 areas resulted in ISC mappings of 779 data elements requiring 90 new concept requests to SNOMED-CT and 738 new ISCs mapped into the workflow within an accessible, enterprise-wide EHR resource with supporting processes.

Purpose: Prior studies show convolutional neural networks predicting self-reported race using x-rays of chest, hand and spine, chest computed tomography, and mammogram. We seek an understanding of the mechanism that reveals race within x-ray images, investigating the possibility that race is not predicted using the physical structure in x-ray images but is embedded in the grayscale pixel intensities., Approach: Retrospective full year 2021, 298,827 AP/PA chest x-ray images from 3 academic health centers across the United States and MIMIC-CXR, labeled by self-reported race, were used in this study. The image structure is removed by summing the number of each grayscale value and scaling to percent per image (PPI). The resulting data are tested using multivariate analysis of variance (MANOVA) with Bonferroni multiple-comparison adjustment and class-balanced MANOVA. Machine learning (ML) feed-forward networks (FFN) and decision trees were built to predict race (binary Black or White and binary Black or other) using only grayscale value counts. Stratified analysis by body mass index, age, sex, gender, patient type, make/model of scanner, exposure, and kilovoltage peak setting was run to study the impact of these factors on race prediction following the same methodology., Results: MANOVA rejects the null hypothesis that classes are the same with 95% confidence ( F 7.38, P < 0.0001 ) and balanced MANOVA ( F 2.02, P < 0.0001 ). The best FFN performance is limited [area under the receiver operating characteristic (AUROC) of 69.18%]. Gradient boosted trees predict self-reported race using grayscale PPI (AUROC 77.24%)., Conclusions: Within chest x-rays, pixel intensity value counts alone are statistically significant indicators and enough for ML classification tasks of patient self-reported race., (© 2023 Society of Photo-Optical Instrumentation Engineers (SPIE).)

We report long-term follow-up of a patient who underwent a tailored laparoscopic procedure for symptomatic cholelithiasis, massive splenomegaly, and a planned pregnancy. There were no complications, and the patient remained symptom-free at the 5-year follow-up. We supplemented our case report with national surgical data demonstrating the safety of laparoscopic splenectomy., Competing Interests: No conflicts of interest to report., (© 2023 The Authors. Clinical Case Reports published by John Wiley & Sons Ltd.)

Background: Long Covid is an emerging chronic illness potentially affecting millions, sometimes preventing the ability to work or participate in normal daily activities. COVID-OUT was an investigator-initiated, multi-site, phase 3, randomized, quadruple-blinded placebo-controlled clinical trial (NCT04510194). The design simultaneously assessed three oral medications (metformin, ivermectin, fluvoxamine) using two by three parallel treatment factorial assignment to efficiently share placebo controls and assessed Long Covid outcomes for 10 months to understand whether early outpatient treatment of SARS-CoV-2 with metformin, ivermectin, or fluvoxamine prevents Long Covid., Methods: This was a decentralized, remotely delivered trial in the US of 1,125 adults age 30 to 85 with overweight or obesity, fewer than 7 days of symptoms, and enrolled within three days of a documented SARS-CoV-2 infection. Immediate release metformin titrated over 6 days to 1,500mg per day 14 days total; ivermectin 430mcg/kg/day for 3 days; fluvoxamine, 50mg on day one then 50mg twice daily through 14 days. Medical-provider diagnosis of Long Covid, reported by participant by day 300 after randomization was a pre-specified secondary outcome; the primary outcome of the trial was severe Covid by day 14., Result: The median age was 45 years (IQR 37 to 54), 56% female of whom 7% were pregnant. Two percent identified as Native American; 3.7% as Asian; 7.4% as Black/African American; 82.8% as white; and 12.7% as Hispanic/Latino. The median BMI was 29.8 kg/m 2 (IQR 27 to 34); 51% had a BMI >30kg/m 2 . Overall, 8.4% reported having received a diagnosis of Long Covid from a medical provider: 6.3% in the metformin group and 10.6% in the metformin control; 8.0% in the ivermectin group and 8.1% in the ivermectin control; and 10.1% in the fluvoxamine group and 7.5% in the fluvoxamine control. The Hazard Ratio (HR) for Long Covid in the metformin group versus control was 0.58 (95% CI 0.38 to 0.88); 0.99 (95% CI 0.592 to 1.643) in the ivermectin group; and 1.36 in the fluvoxamine group (95% CI 0.785 to 2.385)., Conclusions: There was a 42% relative decrease in the incidence of Long Covid in the metformin group compared to its blinded control in a secondary outcome of this randomized phase 3 trial.

Generalizability, external validity, and reproducibility are high priorities for artificial intelligence applications in healthcare. Traditional approaches to addressing these elements involve sharing patient data between institutions or practice settings, which can compromise data privacy (individuals' right to prevent the sharing and disclosure of information about themselves) and data security (simultaneously preserving confidentiality, accuracy, fidelity, and availability of data). This article describes insights from real-world implementation of federated learning techniques that offer opportunities to maintain both data privacy and availability via collaborative machine learning that shares knowledge, not data. Local models are trained separately on local data. As they train, they send local model updates (e.g. coefficients or gradients) for consolidation into a global model. In some use cases, global models outperform local models on new, previously unseen local datasets, suggesting that collaborative learning from a greater number of examples, including a greater number of rare cases, may improve predictive performance. Even when sharing model updates rather than data, privacy leakage can occur when adversaries perform property or membership inference attacks which can be used to ascertain information about the training set. Emerging techniques mitigate risk from adversarial attacks, allowing investigators to maintain both data privacy and availability in collaborative healthcare research. When data heterogeneity between participating centers is high, personalized algorithms may offer greater generalizability by improving performance on data from centers with proportionately smaller training sample sizes. Properly applied, federated learning has the potential to optimize the reproducibility and performance of collaborative learning while preserving data security and privacy., (© The Author(s) 2022.)

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.

Abstract: Trauma patients are at increased risk of venous thromboembolism (VTE), which includes both deep vein thrombosis and pulmonary embolism. Pharmacologic VTE prophylaxis is a critical component of optimal trauma care that significantly decreases VTE risk. Optimal VTE prophylaxis protocols must manage the risk of VTE with the competing risk of hemorrhage in patients following significant trauma. Currently, there is variability in VTE prophylaxis protocols across trauma centers. In an attempt to optimize VTE prophylaxis for the injured patient, stakeholders from the American Association for the Surgery of Trauma and the American College of Surgeons-Committee on Trauma collaborated to develop a group of consensus recommendations as a resource for trauma centers. The primary goal of these recommendations is to help standardize VTE prophylaxis strategies for adult trauma patients (age ≥15 years) across all trauma centers. This clinical protocol has been developed to (1) provide standardized medication dosing for VTE prophylaxis in the injured patient; and (2) promote evidence-based, prompt VTE prophylaxis in common, high-risk traumatic injuries., Level of Evidence: Therapeutic/Care Management; Level V., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Importance: Early in the SARS-CoV-2 pandemic, the M Health Fairview Hospital System established dedicated hospitals for establishing cohorts and caring for patients with COVID-19, yet the association between treatment at COVID-19-dedicated hospitals and mortality and complications is not known., Objective: To analyze the mortality rate and complications associated with treatment at the COVID-19-dedicated hospitals., Design, Setting, and Participants: This retrospective cohort study evaluated data prospectively collected from March 1, 2020, through June 30, 2021, from 11 hospitals in Minnesota, including 2 hospitals created solely to care for patients with COVID-19. Data obtained included demographic characteristics, treatments, and outcomes of interest for all patients with a confirmed COVID-19 infection admitted to this hospital system during the study period., Exposures: Patients were grouped based on whether they received treatment from 1 of the 2 COVID-19-dedicated hospitals compared with the remainder of the hospitals within the hospital system., Main Outcomes and Measures: Multivariate analyses, including risk-adjusted logistic regression and propensity score matching, were performed to evaluate the primary outcome of in-hospital mortality and secondary outcomes, including complications and use of COVID-specific therapeutics., Results: There were 5504 patients with COVID-19 admitted during the study period (median age, 62.5 [IQR, 45.0-75.6] years; 2854 women [51.9%]). Of these, 2077 patients (37.7%) (median age, 63.4 [IQR, 50.7-76.1] years; 1080 men [52.0%]) were treated at 1 of the 2 COVID-19-dedicated hospitals compared with 3427 (62.3%; median age, 62.0 [40.0-75.1] years; 1857 women (54.2%) treated at other hospitals. The mortality rate was 11.6% (n = 241) at the dedicated hospitals compared with 8.0% (n = 274) at the other hospitals (P < .001). However, risk-adjusted in-hospital mortality was significantly lower for patients in the COVID-19-dedicated hospitals in both the unmatched group (n = 2077; odds ratio [OR], 0.75; 95% CI, 0.59-0.95) and the propensity score-matched group (n = 1317; OR, 0.78; 95% CI, 0.58-0.99). The rate of overall complications in the propensity score-matched group was significantly lower (OR, 0.81; 95% CI, 0.66-0.99) and the use of COVID-19-specific therapeutics including deep vein thrombosis prophylaxis (83.9% vs 56.9%; P < .001), high-dose corticosteroids (56.1% vs 22.2%; P < .001), remdesivir (61.5% vs 44.5%; P < .001), and tocilizumab (7.9% vs 2.0; P < .001) was significantly higher., Conclusions and Relevance: In this cohort study, COVID-19-dedicated hospitals had multiple benefits, including providing high-volume repetitive treatment and isolating patients with the infection. This experience suggests improved in-hospital mortality for patients treated at dedicated hospitals owing to improved processes of care and supports the use of establishing cohorts for future pandemics.

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., Competing Interests: The authors report no conflicts of interest., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Background: Older adult trauma is associated with high morbidity and mortality. Individuals older than 65 years are expected to make up more than 21% of the total population and almost 39% of trauma admissions by 2050. Our objective was to perform a national review of older adult trauma mortality and identify associated risk factors to highlight potential areas for improvement in quality of care., Materials and Methods: This is a retrospective cohort study of the National Trauma Data Bank including all patients age ≥65 years with at least one International Classification of Diseases, Ninth Revision, Clinical Modification trauma code admitted to a Level I or II US trauma center between 2007 and 2015. Variables examined included demographics, comorbidities, emergency department vitals, injury characteristics, and trauma center characteristics. Multilevel mixed-effect logistic regression was performed to identify independent risk factors of in-hospital mortality., Results: There were 1,492,759 patients included in this study. The number of older adult trauma patients increased from 88,056 in 2007 to 158,929 in 2015 (p > 0.001). Adjusted in-hospital mortality decreased in 2014 to 2015 (odds ratio [OR], 0.88; 95% confidence interval [CI], 0.86-0.91) when compared with 2007 to 2009. Admission to a university hospital was protective (OR, 0.83; 95% CI, 0.74-0.93) as compared with a community hospital admission. There was no difference in mortality risk between Level II and Level I admission (OR, 1.00; 95% CI, 0.92-1.08). The strongest trauma-related risk factor for in-patient mortality was pancreas/bowel injury (OR, 2.25; 95% CI, 2.04-2.49)., Conclusion: Mortality in older trauma patients is decreasing over time, indicating an improvement in the quality of trauma care. The outcomes of university based hospitals can be used as national benchmarks to guide quality metrics., Level of Evidence: Therapeutic/Care Management, Level IV., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Metformin is the first-line medication for type 2 diabetes, but it also has a long history of improved outcomes in infectious diseases, such as influenza, hepatitis C, and in-vitro assays of zika. In the current Covid-19 pandemic, which has rapidly spread throughout the world, 4 observational studies have been published showing reduced mortality among individuals with home metformin use. There are several potential overlapping mechanisms by which metformin may reduce mortality from Covid-19. Metformin's past anti-infectious benefits have been both against the infectious agent directly, as well as by improving the underlying health of the human host. It is unknown if the lower mortality suggested by observational studies in patients infected with Covid-19 who are on home metformin is due to direct activity against the virus itself, improved host substrate, or both., Competing Interests: DV is employed by the company UnitedHealth Group. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Ibrahim, Lowe, Bramante, Shah, Klatt, Sherwood, Aronne, Puskarich, Tamariz, Palacio, Bomberg, Usher, King, Benson, Vojta, Tignanelli and Ingraham.)

Background: Age and disease prevalence are the 2 biggest risk factors for Coronavirus disease 2019 (COVID-19) symptom severity and death. We therefore hypothesized that increased biological age, beyond chronological age, may be driving disease-related trends in COVID-19 severity., Methods: Using the UK Biobank England data, we tested whether a biological age estimate (PhenoAge) measured more than a decade prior to the COVID-19 pandemic was predictive of 2 COVID-19 severity outcomes (inpatient test positivity and COVID-19-related mortality with inpatient test-confirmed COVID-19). Logistic regression models were used with adjustment for age at the pandemic, sex, ethnicity, baseline assessment centers, and preexisting diseases/conditions., Results: Six hundred and thirteen participants tested positive at inpatient settings between March 16 and April 27, 2020, 154 of whom succumbed to COVID-19. PhenoAge was associated with increased risks of inpatient test positivity and COVID-19-related mortality (ORMortality = 1.63 per 5 years, 95% CI: 1.43-1.86, p = 4.7 × 10-13) adjusting for demographics including age at the pandemic. Further adjustment for preexisting diseases/conditions at baseline (ORM = 1.50, 95% CI: 1.30-1.73 per 5 years, p = 3.1 × 10-8) and at the early pandemic (ORM = 1.21, 95% CI: 1.04-1.40 per 5 years, p = .011) decreased the association., Conclusions: PhenoAge measured in 2006-2010 was associated with COVID-19 severity outcomes more than 10 years later. These associations were partly accounted for by prevalent chronic diseases proximate to COVID-19 infection. Overall, our results suggest that aging biomarkers, like PhenoAge may capture long-term vulnerability to diseases like COVID-19, even before the accumulation of age-related comorbid conditions., (© The Author(s) 2021. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Objective: Ensuring an efficient response to COVID-19 requires a degree of inter-system coordination and capacity management coupled with an accurate assessment of hospital utilization including length of stay (LOS). We aimed to establish optimal practices in inter-system data sharing and LOS modeling to support patient care and regional hospital operations., Materials and Methods: We completed a retrospective observational study of patients admitted with COVID-19 followed by 12-week prospective validation, involving 36 hospitals covering the upper Midwest. We developed a method for sharing de-identified patient data across systems for analysis. From this, we compared 3 approaches, generalized linear model (GLM) and random forest (RF), and aggregated system level averages to identify features associated with LOS. We compared model performance by area under the ROC curve (AUROC)., Results: A total of 2068 patients were included and used for model derivation and 597 patients for validation. LOS overall had a median of 5.0 days and mean of 8.2 days. Consistent predictors of LOS included age, critical illness, oxygen requirement, weight loss, and nursing home admission. In the validation cohort, the RF model (AUROC 0.890) and GLM model (AUROC 0.864) achieved good to excellent prediction of LOS, but only marginally better than system averages in practice., Conclusion: Regional sharing of patient data allowed for effective prediction of LOS across systems; however, this only provided marginal improvement over hospital averages at the aggregate level. A federated approach of sharing aggregated system capacity and average LOS will likely allow for effective capacity management at the regional level., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Importance: An artificial intelligence (AI)-based model to predict COVID-19 likelihood from chest x-ray (CXR) findings can serve as an important adjunct to accelerate immediate clinical decision making and improve clinical decision making. Despite significant efforts, many limitations and biases exist in previously developed AI diagnostic models for COVID-19. Utilizing a large set of local and international CXR images, we developed an AI model with high performance on temporal and external validation., Objective: Investigate real-time performance of an AI-enabled COVID-19 diagnostic support system across a 12-hospital system., Design: Prospective observational study., Setting: Labeled frontal CXR images (samples of COVID-19 and non-COVID-19) from the M Health Fairview (Minnesota, USA), Valencian Region Medical ImageBank (Spain), MIMIC-CXR, Open-I 2013 Chest X-ray Collection, GitHub COVID-19 Image Data Collection (International), Indiana University (Indiana, USA), and Emory University (Georgia, USA)., Participants: Internal (training, temporal, and real-time validation): 51,592 CXRs; Public: 27,424 CXRs; External (Indiana University): 10,002 CXRs; External (Emory University): 2002 CXRs., Main Outcome and Measure: Model performance assessed via receiver operating characteristic (ROC), Precision-Recall curves, and F1 score., Results: Patients that were COVID-19 positive had significantly higher COVID-19 Diagnostic Scores (median .1 [IQR: 0.0-0.8] vs median 0.0 [IQR: 0.0-0.1], p < 0.001) than patients that were COVID-19 negative. Pre-implementation the AI-model performed well on temporal validation (AUROC 0.8) and external validation (AUROC 0.76 at Indiana U, AUROC 0.72 at Emory U). The model was noted to have unrealistic performance (AUROC > 0.95) using publicly available databases. Real-time model performance was unchanged over 19 weeks of implementation (AUROC 0.70). On subgroup analysis, the model had improved discrimination for patients with "severe" as compared to "mild or moderate" disease, p < 0.001. Model performance was highest in Asians and lowest in whites and similar between males and females., Conclusions and Relevance: AI-based diagnostic tools may serve as an adjunct, but not replacement, for clinical decision support of COVID-19 diagnosis, which largely hinges on exposure history, signs, and symptoms. While AI-based tools have not yet reached full diagnostic potential in COVID-19, they may still offer valuable information to clinicians taken into consideration along with clinical signs and symptoms.

Several studies have shown that COVID-19 patients with prior comorbidities have a higher risk for adverse outcomes, resulting in a disproportionate impact on older adults and minorities that fit that profile. However, although there is considerable heterogeneity in the comorbidity profiles of these populations, not much is known about how prior comorbidities co-occur to form COVID-19 patient subgroups, and their implications for targeted care. Here we used bipartite networks to quantitatively and visually analyze heterogeneity in the comorbidity profiles of COVID-19 inpatients, based on electronic health records from 12 hospitals and 60 clinics in the greater Minneapolis region. This approach enabled the analysis and interpretation of heterogeneity at three levels of granularity (cohort, subgroup, and patient), each of which enabled clinicians to rapidly translate the results into the design of clinical interventions. We discuss future extensions of the multigranular heterogeneity framework, and conclude by exploring how the framework could be used to analyze other biomedical phenomena including symptom clusters and molecular phenotypes, with the goal of accelerating translation to targeted clinical care., (©2021 AMIA - All rights reserved.)

Background: The increasing incidence of COVID-19 infection has challenged health care systems to increase capacity while conserving personal protective equipment (PPE) supplies and minimizing nosocomial spread. Telemedicine shows promise to address these challenges but lacks comprehensive evaluation in the inpatient environment., Objective: The aim of this study is to evaluate an intrahospital telemedicine program (virtual care), along with its impact on exposure risk and communication., Methods: We conducted a natural experiment of virtual care on patients admitted for COVID-19. The primary exposure variable was documented use of virtual care. Patient characteristics, PPE use rates, and their association with virtual care use were assessed. In parallel, we conducted surveys with patients and clinicians to capture satisfaction with virtual care along the domains of communication, medical treatment, and exposure risk., Results: Of 137 total patients in our primary analysis, 43 patients used virtual care. In total, there were 82 inpatient days of use and 401 inpatient days without use. Hospital utilization and illness severity were similar in patients who opted in versus opted out. Virtual care was associated with a significant reduction in PPE use and physical exam rate. Surveys of 41 patients and clinicians showed high rates of recommendation for further use, and subjective improvements in communication. However, providers and patients expressed limitations in usability, medical assessment, and empathetic communication., Conclusions: In this pilot natural experiment, only a subset of patients used inpatient virtual care. When used, virtual care was associated with reductions in PPE use, reductions in exposure risk, and patient and provider satisfaction., (©Sean Legler, Matthew Diehl, Brian Hilliard, Andrew Olson, Rebecca Markowitz, Christopher Tignanelli, Genevieve B Melton, Alain Broccard, Jonathan Kirsch, Michael Usher. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 29.04.2021.)

With no known treatments or vaccine, COVID-19 presents a major threat, particularly to older adults, who account for the majority of severe illness and deaths. The age-related susceptibility is partly explained by increased comorbidities including dementia and type II diabetes [1]. While it is unclear why these diseases predispose risk, we hypothesize that increased biological age, rather than chronological age, may be driving disease-related trends in COVID-19 severity with age. To test this hypothesis, we applied our previously validated biological age measure (PhenoAge) [2] composed of chronological age and nine clinical chemistry biomarkers to data of 347,751 participants from a large community cohort in the United Kingdom (UK Biobank), recruited between 2006 and 2010. Other data included disease diagnoses (to 2017), mortality data (to 2020), and the UK national COVID-19 test results (to May 31, 2020) [3]. Accelerated aging 10-14 years prior to the start of the COVID-19 pandemic was associated with test positivity (OR=1.15 per 5-year acceleration, 95% CI: 1.08 to 1.21, p=3.2×10 -6 ) and all-cause mortality with test-confirmed COVID-19 (OR=1.25, per 5-year acceleration, 95% CI: 1.09 to 1.44, p=0.002) after adjustment for demographics including current chronological age and pre-existing diseases or conditions. The corresponding areas under the curves were 0.669 and 0.803, respectively. Biological aging, as captured by PhenoAge, is a better predictor of COVID-19 severity than chronological age, and may inform risk stratification initiatives, while also elucidating possible underlying mechanisms, particularly those related to inflammaging.

Introduction: Perforated peptic ulcers are a surgical emergency that can be repaired using either laparoscopic surgery (LS) or open surgery (OS). No consensus has been reached on the comparative outcomes and safety of each approach., Methods: Using the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) database, we conducted a 12-year retrospective review (2005-2016) and identified 6260 adult patients who underwent either LS (n = 616) or OS (n = 5644) to repair perforated peptic ulcers. To mitigate selection bias and adjust for the inherent heterogeneity between groups, we used propensity-score matching with a case (LS):control (OS) ratio of 1:3. We then compared intraoperative outcomes such as operative time, and 30-day postoperative outcomes including infectious and non-infectious complications, and mortality., Results: Propensity-score matching created a total of 2462 matched pairs (616 in the LS group, 1846 in the OS group). Univariate analysis demonstrated successful matching of patient characteristics and baseline clinical variables. We found that OS was associated with a shorter operative time (67.0 ± 28.6 min, OS versus 86.9 ± 57.5 min, LS; P < 0.001) but a longer hospital stay (8.6 ± 6.2 days, OS versus 7.8 ± 5.9 days, LS; P = 0.001). LS was associated with a lower rate of superficial surgical site infections (1.5%, LS versus 4.2%, OS; P = 0.032), wound dehiscence (0.3%, LS versus 1.6%, OS; P = 0.030), and mortality (3.2%, LS versus 5.4%, OS; P = 0.009)., Conclusion: Fewer than 10% of patients with perforated peptic ulcers underwent LS, which was associated with reduced length of stay, lower rate of superficial surgical site infections, wound dehiscence, and mortality. Given our results, a greater emphasis should be provided to a minimally invasive approach for the surgical repair of perforated peptic ulcers.

Background: Simulation is quickly becoming vital to resident education, but commercially available central line models are costly and little information exists to evaluate their realism. This study compared an inexpensive homemade simulator to three commercially available simulators and rated model characteristics., Materials and Methods: Seventeen physicians, all having placed >50 lines in their lifetime, completed blinded central line insertions on three commercial and one homemade model (made of silicone, tubing, and a pressurized pump system). Participants rated each model on the realism of its ultrasound image, cannulation feel, manometry, and overall. They then ranked the models based on the same variables. Rankings were assessed with Friedman's and post hoc Conover's tests, using alphas 0.05 and 0.008 (Bonferroni corrected), respectively., Results: The models significantly differed (P < 0.0004) in rankings across all dimensions. The homemade model was ranked best on ultrasound image, manometry measurement, cannulation feel, and overall quality by 71%, 67%, 53%, and 77% of raters, respectively. It was found to be statistically superior to the second rated model in all (P < 0.003) except cannulation feel (P = 0.134). Ultrasound image and manometry measurement received the lowest ratings across all models, indicating less realistic simulation. The cost of the homemade model was $400 compared to $1000-$8000 for commercial models., Conclusions: Our data suggest that an inexpensive, homemade central line model is as good or better than commercially available models. Areas for potential improvement within models include the ultrasound image and ability to appropriately measure manometry of accessed vessels., (Copyright © 2017 Elsevier Inc. All rights reserved.)