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.

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.

Acute Appendicitis Treatment Strategies and Mortality Based on Critical Illness on Admission: An Observational Study

Background: Trials have shown non-inferiority of non-operative management (NOM) for appendicitis, although critically ill patients have been often excluded. The purpose of this study is to evaluate surgical versus NOM outcomes in critically ill patients with appendicitis by measuring mortality and hospital length of stay (LOS). Patients and Methods: The Healthcare Cost and Utilization Project's (HCUP) Database was utilized to analyze data from 10 states between 2008 and 2015. All patients with acute appendicitis by International Classification of Diseases, Ninth Revision (ICD-9) codes over the age of 18 were included. Negative binomial and logistic regression were used to determine the association of acute renal failure (ARF), cardiovascular failure (CVF), pulmonary failure (PF), and sepsis by treatment strategy (laparoscopic, open, both, or no surgery) on mortality and hospital LOS. Results: Among 464,123 patients, 67.5%, 23.3%, 8.2%, and 0.8% underwent laparoscopic, open, NOM, or both laparoscopic and open surgery, respectively. Patients who underwent surgery had 58% lower odds of mortality and 34% shorter hospital LOS compared with NOM patients. Patients with ARF, CVF, PF, and sepsis had 102%, 383%, 475%, and 666% higher odds of mortality and a 47%, 46%, 71%, and 163% longer hospital LOS, respectively, compared with patients without these diagnoses on admission. Conclusions: Critical illness on admission increases mortality and hospital LOS. Patients who underwent laparoscopic, and to a lesser extent, open appendectomy had improved mortality compared with those who did not undergo surgery regardless of critical illness status.

A Symptom-Based Natural Language Processing Surveillance Pipeline for Post-COVID-19 Patients

Post-acute sequelae of SARS CoV-2 (PASC) are a group of conditions in which patients previously infected with COVID-19 experience symptoms weeks/months post-infection. PASC has substantial societal burden, including increased healthcare costs and disabilities. This study presents a natural language processing (NLP) based pipeline for identification of PASC symptoms and demonstrates its ability to estimate the proportion of suspected PASC cases. A manual case review to obtain this estimate indicated our sample incidence of PASC (13%) was representative of the estimated population proportion (95% CI: 19±6.22%). However, the high number of cases classified as indeterminate demonstrates the challenges in classifying PASC even among experienced clinicians. Lastly, this study developed a dashboard to display views of aggregated PASC symptoms and measured its utility using the System Usability Scale. Overall comments related to the dashboard's potential were positive. This pipeline is crucial for monitoring post-COVID-19 patients with potential for use in clinical settings.

Development of a proteomic signature associated with severe disease for patients with COVID-19 using data from 5 multicenter, randomized, controlled, and prospective studies

Significant progress has been made in preventing severe COVID-19 disease through the development of vaccines. However, we still lack a validated baseline predictive biologic signature for the development of more severe disease in both outpatients and inpatients infected with SARS-CoV-2. The objective of this study was to develop and externally validate, via 5 international outpatient and inpatient trials and/or prospective cohort studies, a novel baseline proteomic signature, which predicts the development of moderate or severe (vs mild) disease in patients with COVID-19 from a proteomic analysis of 7000 + proteins. The secondary objective was exploratory, to identify (1) individual baseline protein levels and/or (2) protein level changes within the first 2 weeks of acute infection that are associated with the development of moderate/severe (vs mild) disease. For model development, samples collected from 2 randomized controlled trials were used. Plasma was isolated and the SomaLogic SomaScan platform was used to characterize protein levels for 7301 proteins of interest for all studies. We dichotomized 113 patients as having mild or moderate/severe COVID-19 disease. An elastic net approach was used to develop a predictive proteomic signature. For validation, we applied our signature to data from three independent prospective biomarker studies. We found 4110 proteins measured at baseline that significantly differed between patients with mild COVID-19 and those with moderate/severe COVID-19 after adjusting for multiple hypothesis testing. Baseline protein expression was associated with predicted disease severity with an error rate of 4.7% (AUC = 0.964). We also found that five proteins (Afamin, I-309, NKG2A, PRS57, LIPK) and patient age serve as a signature that separates patients with mild COVID-19 and patients with moderate/severe COVID-19 with an error rate of 1.77% (AUC = 0.9804). This panel was validated using data from 3 external studies with AUCs of 0.764 (Harvard University), 0.696 (University of Colorado), and 0.893 (Karolinska Institutet). In this study we developed and externally validated a baseline COVID-19 proteomic signature associated with disease severity for potential use in both outpatients and inpatients with COVID-19.

Strategies used for the COVID-OUT decentralized trial of outpatient treatment of SARS-CoV-2

The COVID-19 pandemic accelerated the development of decentralized clinical trials (DCT). DCT's are an important and pragmatic method for assessing health outcomes yet comprise only a minority of clinical trials, and few published methodologies exist. In this report, we detail the operational components of COVID-OUT, a decentralized, multicenter, quadruple-blinded, randomized trial that rapidly delivered study drugs nation-wide. The trial examined three medications (metformin, ivermectin, and fluvoxamine) as outpatient treatment of SARS-CoV-2 for their effectiveness in preventing severe or long COVID-19. Decentralized strategies included HIPAA-compliant electronic screening and consenting, prepacking investigational product to accelerate delivery after randomization, and remotely confirming participant-reported outcomes. Of the 1417 individuals with the intention-to-treat sample, the remote nature of the study caused an additional 94 participants to not take any doses of study drug. Therefore, 1323 participants were in the modified intention-to-treat sample, which was the a priori primary study sample. Only 1.4% of participants were lost to follow-up. Decentralized strategies facilitated the successful completion of the COVID-OUT trial without any in-person contact by expediting intervention delivery, expanding trial access geographically, limiting contagion exposure, and making it easy for participants to complete follow-up visits. Remotely completed consent and follow-up facilitated enrollment.

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.

Developing A Baseline Metabolomic Signature Associated with COVID-19 Severity: Insights from Prospective Trials Encompassing 13 U.S. Centers

Metabolic disease is a significant risk factor for severe COVID-19 infection, but the contributing pathways are not yet fully elucidated. Using data from two randomized controlled trials across 13 U.S. academic centers, our goal was to characterize metabolic features that predict severe COVID-19 and define a novel baseline metabolomic signature. Individuals (n = 133) were dichotomized as having mild or moderate/severe COVID-19 disease based on the WHO ordinal scale. Blood samples were analyzed using the Biocrates platform, providing 630 targeted metabolites for analysis. Resampling techniques and machine learning models were used to determine metabolomic features associated with severe disease. Ingenuity Pathway Analysis (IPA) was used for functional enrichment analysis. To aid in clinical decision making, we created baseline metabolomics signatures of low-correlated molecules. Multivariable logistic regression models were fit to associate these signatures with severe disease on training data. A three-metabolite signature, lysophosphatidylcholine a C17:0, dihydroceramide (d18:0/24:1), and triacylglyceride (20:4_36:4), resulted in the best discrimination performance with an average test AUROC of 0.978 and F1 score of 0.942. Pathways related to amino acids were significantly enriched from the IPA analyses, and the mitogen-activated protein kinase kinase 5 (MAP2K5) was differentially activated between groups. In conclusion, metabolites related to lipid metabolism efficiently discriminated between mild vs. moderate/severe disease. SDMA and GABA demonstrated the potential to discriminate between these two groups as well. The mitogen-activated protein kinase kinase 5 (MAP2K5) regulator is differentially activated between groups, suggesting further investigation as a potential therapeutic pathway.

American Association for the Surgery of Trauma/American College of Surgeons Committee on Trauma clinical protocol for management of acute respiratory distress syndrome and severe hypoxemia

LEVEL OF EVIDENCE

Therapeutic/Care Management: Level V.

Outpatient treatment of COVID-19 and incidence of post-COVID-19 condition over 10 months (COVID-OUT): a multicentre, randomised, quadruple-blind, parallel-group, phase 3 trial

BACKGROUND

Post-COVID-19 condition (also known as long COVID) is an emerging chronic illness potentially affecting millions of people. We aimed to evaluate whether outpatient COVID-19 treatment with metformin, ivermectin, or fluvoxamine soon after SARS-CoV-2 infection could reduce the risk of long COVID.

METHODS

We conducted a decentralised, randomised, quadruple-blind, parallel-group, phase 3 trial (COVID-OUT) at six sites in the USA. We included adults aged 30-85 years with overweight or obesity who had COVID-19 symptoms for fewer than 7 days and a documented SARS-CoV-2 positive PCR or antigen test within 3 days before enrolment. Participants were randomly assigned via 2 × 3 parallel factorial randomisation (1:1:1:1:1:1) to receive metformin plus ivermectin, metformin plus fluvoxamine, metformin plus placebo, ivermectin plus placebo, fluvoxamine plus placebo, or placebo plus placebo. Participants, investigators, care providers, and outcomes assessors were masked to study group assignment. The primary outcome was severe COVID-19 by day 14, and those data have been published previously. Because the trial was delivered remotely nationwide, the a priori primary sample was a modified intention-to-treat sample, meaning that participants who did not receive any dose of study treatment were excluded. Long COVID diagnosis by a medical provider was a prespecified, long-term secondary outcome. This trial is complete and is registered with ClinicalTrials.gov, NCT04510194.

FINDINGS

Between Dec 30, 2020, and Jan 28, 2022, 6602 people were assessed for eligibility and 1431 were enrolled and randomly assigned. Of 1323 participants who received a dose of study treatment and were included in the modified intention-to-treat population, 1126 consented for long-term follow-up and completed at least one survey after the assessment for long COVID at day 180 (564 received metformin and 562 received matched placebo; a subset of participants in the metformin vs placebo trial were also randomly assigned to receive ivermectin or fluvoxamine). 1074 (95%) of 1126 participants completed at least 9 months of follow-up. 632 (56·1%) of 1126 participants were female and 494 (43·9%) were male; 44 (7·0%) of 632 women were pregnant. The median age was 45 years (IQR 37-54) and median BMI was 29·8 kg/m2 (IQR 27·0-34·2). Overall, 93 (8·3%) of 1126 participants reported receipt of a long COVID diagnosis by day 300. The cumulative incidence of long COVID by day 300 was 6·3% (95% CI 4·2-8·2) in participants who received metformin and 10·4% (7·8-12·9) in those who received identical metformin placebo (hazard ratio [HR] 0·59, 95% CI 0·39-0·89; p=0·012). The metformin beneficial effect was consistent across prespecified subgroups. When metformin was started within 3 days of symptom onset, the HR was 0·37 (95% CI 0·15-0·95). There was no effect on cumulative incidence of long COVID with ivermectin (HR 0·99, 95% CI 0·59-1·64) or fluvoxamine (1·36, 0·78-2·34) compared with placebo.

INTERPRETATION

Outpatient treatment with metformin reduced long COVID incidence by about 41%, with an absolute reduction of 4·1%, compared with placebo. Metformin has clinical benefits when used as outpatient treatment for COVID-19 and is globally available, low-cost, and safe.

FUNDING

Parsemus Foundation; Rainwater Charitable Foundation; Fast Grants; UnitedHealth Group Foundation; National Institute of Diabetes, Digestive and Kidney Diseases; National Institutes of Health; and National Center for Advancing Translational Sciences.

Striking a match between FHIR-based patient data and FHIR-based eligibility criteria

Inputs and Outputs

The Strike-a-Match Function, written in JavaScript version ES6+, accepts the input of two datasets (one dataset defining eligibility criteria for research studies or clinical decision support, and one dataset defining characteristics for an individual patient). It returns an output signaling whether the patient characteristics are a match for the eligibility criteria.

Purpose

Ultimately, such a system will play a "matchmaker" role in facilitating point-of-care recognition of patient-specific clinical decision support.

Specifications

The eligibility criteria are defined in HL7 FHIR (version R5) EvidenceVariable Resource JSON structure. The patient characteristics are provided in an FHIR Bundle Resource JSON including one Patient Resource and one or more Observation and Condition Resources which could be obtained from the patient's electronic health record.

Application

The Strike-a-Match Function determines whether or not the patient is a match to the eligibility criteria and an Eligibility Criteria Matching Software Demonstration interface provides a human-readable display of matching results by criteria for the clinician or patient to consider. This is the first software application, serving as proof of principle, that compares patient characteristics and eligibility criteria with all data exchanged using HL7 FHIR JSON. An Eligibility Criteria Matching Software Library at https://fevir.net/110192 provides a method for sharing functions using the same information model.

Association of Discontinuing Preinjury Beta-Adrenergic Blockade Medications With Mortality in Severe Blunt Traumatic Brian Injury

Background

Beta-adrenergic receptor blocker (BB) administration has been shown to improve survival after traumatic brain injury (TBI). However, studies to date that observe a benefit did not distinguish between continuation of preinjury BB versus de novo initiation of BB.

Objectives

To determine the effect of continuation of preinjury BB and de novo initiation of BB on risk-adjusted mortality and complications for patients with TBI.

Methods

Trauma quality collaborative data (2016-2021) were analyzed. Patients were excluded with hospitalization <48 hours, direct admission, or penetrating injury. Severe TBI was identified as a head abbreviated injury scale (AIS) value of 3 to 5. Patients were placed into 4 groups based on the preinjury BB use and administration of BB during hospitalization. Propensity score matching was used to create 1:1 matched cohorts of patients for comparisons. Odd ratios of mortality accounting for hospital clustering were calculated. A sensitivity analysis was performed excluding patients with AIS >2 injuries in all other body regions to create a cohort of isolated TBI patients.

Results

A total of 15,153 patients treated at 35 trauma centers were available for analysis. Patients were divided into 4 cohort groupings related to preinjury BB use and postinjury receipt of BB. The odds of mortality was significantly reduced for patients with a TBI on a preinjury BB who had the medication continued in the acute setting (as compared with patients on preinjury BB who did not) (odds ratio [OR], 0.73; 95% confidence interval [CI], 0.54-0.98; P = 0.04). Patients with a TBI who were not on preinjury BB did not benefit from de novo initiation of BB with regard to mortality (OR, 0.83; 95% CI, 0.64-1.08; P = 0.2). In the sensitivity analysis, excluding polytrauma patients, patients on preinjury BB who had BB continued had a reduction in mortality when compared with patients in which BB was stopped following a TBI (OR, 0.65; 95% CI, 0.47-0.91; P = 0.01).

Conclusions

Continuing BB is associated with reduced odds of mortality in patients with a TBI on preinjury BB. We were unable to demonstrate benefit from instituting beta blockade in patients who are not on a BB preinjury.

A novel, evidence-based, comprehensive clinical decision support system improves outcomes for patients with traumatic rib fractures

BACKGROUND

Traumatic rib fractures are associated with high morbidity and mortality. Clinical decision support systems (CDSS) have been shown to improve adherence to evidence-based (EB) practice and improve clinical outcomes. The objective of this study was to investigate if a rib fracture CDSS reduced hospital length of stay (LOS), 90-day and 1-year mortality, unplanned ICU transfer, and the need for mechanical ventilation. The independent association of two process measures, an admission EB order set and a pain-inspiratory-cough score early warning system, with LOS were investigated.

METHODS

The CDSS was scaled across nine US trauma centers. Following multiple imputation, multivariable regression models were fit to evaluate the association of the CDSS on primary and secondary outcomes. As a sensitivity analysis, propensity score matching was also performed to confirm regression findings.

RESULTS

Overall, 3,279 patients met inclusion criteria. Rates of EB practices increased following implementation. On risk-adjusted analysis, in-hospital LOS preintervention versus postintervention was unchanged (incidence rate ratio [IRR], 1.06; 95% confidence interval [CI], 0.97-1.15, p = 0.2) but unplanned transfer to the ICU was reduced (odds ratio, 0.28; 95% CI, 0.09-0.84, p = 0.024), as was 1-year mortality (hazard ratio, 0.6; 95% CI, 0.4-0.89, p = 0.01). Provider utilization of the admission order bundle was 45.3%. Utilization was associated with significantly reduced LOS (IRR, 0.87; 95% CI, 0.77-0.98; p = 0.019). The early warning system triggered on 34.4% of patients; however, was not associated with a significant reduction in hospital LOS (IRR, 0.76; 95% CI, 0.55-1.06; p = 0.1).

CONCLUSION

A novel, user-centered, comprehensive CDSS improves adherence to EB practice and is associated with a significant reduction in unplanned ICU admissions and possibly mortality, but not hospital LOS.

LEVEL OF EVIDENCE

Therapeutic/Care Management; Level III.

Validation of a Proprietary Deterioration Index Model and Performance in Hospitalized Adults

Importance

The Deterioration Index (DTI), used by hospitals for predicting patient deterioration, has not been extensively validated externally, raising concerns about performance and equitable predictions.

Objective

To locally validate DTI performance and assess its potential for bias in predicting patient clinical deterioration.

Design, Setting, and Participants

This retrospective prognostic study included 13 737 patients admitted to 8 heterogenous Midwestern US hospitals varying in size and type, including academic, community, urban, and rural hospitals. Patients were 18 years or older and admitted between January 1 and May 31, 2021.

Exposure

DTI predictions made every 15 minutes.

Main Outcomes and Measures

Deterioration, defined as the occurrence of any of the following while hospitalized: mechanical ventilation, intensive care unit transfer, or death. Performance of the DTI was evaluated using area under the receiver operating characteristic curve (AUROC) and area under the precision recall curve (AUPRC). Bias measures were calculated across demographic subgroups.

Results

A total of 5 143 513 DTI predictions were made for 13 737 patients across 14 834 hospitalizations. Among 13 918 encounters, the mean (SD) age of patients was 60.3 (19.2) years; 7636 (54.9%) were female, 11 345 (81.5%) were White, and 12 392 (89.0%) were of other ethnicity than Hispanic or Latino. The prevalence of deterioration was 10.3% (n = 1436). The DTI produced AUROCs of 0.759 (95% CI, 0.756-0.762) at the observation level and 0.685 (95% CI, 0.671-0.700) at the encounter level. Corresponding AUPRCs were 0.039 (95% CI, 0.037-0.040) at the observation level and 0.248 (95% CI, 0.227-0.273) at the encounter level. Bias measures varied across demographic subgroups and were 14.0% worse for patients identifying as American Indian or Alaska Native and 19.0% worse for those who chose not to disclose their ethnicity.

Conclusions and Relevance

In this prognostic study, the DTI had modest ability to predict patient deterioration, with varying degrees of performance at the observation and encounter levels and across different demographic groups. Disparate performance across subgroups suggests the need for more transparency in model training data and reinforces the need to locally validate externally developed prediction models.

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.

Losartan in hospitalized patients with COVID-19 in North America: An individual participant data meta-analysis

BACKGROUND

Angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers (ARBs) have been hypothesized to benefit patients with COVID-19 via the inhibition of viral entry and other mechanisms. We conducted an individual participant data (IPD) meta-analysis assessing the effect of starting the ARB losartan in recently hospitalized COVID-19 patients.

METHODS

We searched ClinicalTrials.gov in January 2021 for U.S./Canada-based trials where an angiotensin-converting enzyme inhibitors/ARB was a treatment arm, targeted outcomes could be extrapolated, and data sharing was allowed. Our primary outcome was a 7-point COVID-19 ordinal score measured 13 to 16 days post-enrollment. We analyzed data by fitting multilevel Bayesian ordinal regression models and standardizing the resulting predictions.

RESULTS

325 participants (156 losartan vs 169 control) from 4 studies contributed IPD. Three were randomized trials; one used non-randomized concurrent and historical controls. Baseline covariates were reasonably balanced for the randomized trials. All studies evaluated losartan. We found equivocal evidence of a difference in ordinal scores 13-16 days post-enrollment (model-standardized odds ratio [OR] 1.10, 95% credible interval [CrI] 0.76-1.71; adjusted OR 1.15, 95% CrI 0.15-3.59) and no compelling evidence of treatment effect heterogeneity among prespecified subgroups. Losartan had worse effects for those taking corticosteroids at baseline after adjusting for covariates (ratio of adjusted ORs 0.29, 95% CrI 0.08-0.99). Hypotension serious adverse event rates were numerically higher with losartan.

CONCLUSIONS

In this IPD meta-analysis of hospitalized COVID-19 patients, we found no convincing evidence for the benefit of losartan versus control treatment, but a higher rate of hypotension adverse events with losartan.

Metformin reduces SARS-CoV-2 in a Phase 3 Randomized Placebo Controlled Clinical Trial

Current antiviral treatment options for SARS-CoV-2 infections are not available globally, cannot be used with many medications, and are limited to virus-specific targets.1-3 Biophysical modeling of SARS-CoV-2 replication predicted that protein translation is an especially attractive target for antiviral therapy.4 Literature review identified metformin, widely known as a treatment for diabetes, as a potential suppressor of protein translation via targeting of the host mTor pathway.5 In vitro, metformin has antiviral activity against RNA viruses including SARS-CoV-2.6,7 In the COVID-OUT phase 3, randomized, placebo-controlled trial of outpatient treatment of COVID-19, metformin had a 42% reduction in ER visits/hospitalizations/death through 14 days; a 58% reduction in hospitalizations/death through 28 days, and a 42% reduction in Long COVID through 10 months.8,9 Here we show viral load analysis of specimens collected in the COVID-OUT trial that the mean SARS-CoV-2 viral load was reduced 3.6-fold with metformin relative to placebo (-0.56 log10 copies/mL; 95%CI, -1.05 to -0.06, p=0.027) while there was no virologic effect for ivermectin or fluvoxamine vs placebo. The metformin effect was consistent across subgroups and with emerging data.10,11 Our results demonstrate, consistent with model predictions, that a safe, widely available,12 well-tolerated, and inexpensive oral medication, metformin, can be repurposed to significantly reduce SARS-CoV-2 viral load.

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.

Changes in patterns of traumatic brain injury in the Michigan Trauma Quality Improvement Program database early in the COVID-19 pandemic

OBJECTIVE

The authors' objective was to investigate the impact of the global COVID-19 pandemic on hospital presentation and process of care for the treatment of traumatic brain injuries (TBIs). Improved understanding of these effects will inform sociopolitical and hospital policies in response to future pandemics.

METHODS

The Michigan Trauma Quality Improvement Program (MTQIP) database, which contains data from 36 level I and II trauma centers in Michigan and Minnesota, was queried to identify patients who sustained TBI on the basis of head/neck Abbreviated Injury Scale (AIS) codes during the periods of March 13 through July 2 of 2017-2019 (pre-COVID-19 period) and March 13, 2020, through July 2, 2020 (COVID-19 period). Analyses were performed to detect differences in incidence, patient characteristics, injury severity, and outcomes.

RESULTS

There was an 18% decrease in the rate of encounters with TBI in the first 8 weeks (March 13 through May 7), followed by a 16% increase during the last 8 weeks (May 8 through July 2), of our COVID-19 period compared with the pre-COVID-19 period. Cumulatively, there was no difference in the rates of encounters with TBI between the COVID-19 and pre-COVID-19 periods. Severity of TBI, as measured with maximum AIS score for the head/neck region and Glasgow Coma Scale score, was also similar between periods. During the COVID-19 period, a greater proportion of patients with TBI presented more than a day after sustaining their injuries (p = 0.046). COVID-19 was also associated with a doubling in the decubitus ulcer rate from 1.0% to 2.1% (p = 0.002) and change in the distribution of discharge status (p = 0.01). Multivariable analysis showed no differences in odds of death/hospice discharge, intensive care unit stay of at least a day, or need for a ventilator for at least a day between the COVID-19 and pre-COVID-19 periods.

CONCLUSIONS

During the early months of the COVID-19 pandemic, the number of patients who presented with TBI was initially lower than in the years 2017-2019 prior to the pandemic. However, there was a subsequent increase in the rate of encounters with TBI, resulting in overall similar rates of TBI between March 13 through July 2 during the COVID-19 period and during the pre-COVID-19 period. The COVID-19 cohort was also associated with negative impacts on time to presentation, rate of decubitus ulcers, and discharge with supervision. Policies in response to future pandemics must consider the resources necessary to care for patients with TBI.

Predictors of discharge disposition and mortality following hospitalization with SARS-CoV-2 infection

IMPORTANCE

The SARS-CoV-2 pandemic has overwhelmed hospital capacity, prioritizing the need to understand factors associated with type of discharge disposition.

OBJECTIVE

Characterization of disposition associated factors following SARS-CoV-2.

DESIGN

Retrospective study of SARS-CoV-2 positive patients from March 7th, 2020, to May 4th, 2022, requiring hospitalization.

SETTING

Midwest academic health-system.

PARTICIPANTS

Patients above the age 18 years admitted with PCR + SARS-CoV-2.

INTERVENTION

None.

MAIN OUTCOMES

Discharge to home versus PAC (inpatient rehabilitation facility (IRF), skilled-nursing facility (SNF), long-term acute care (LTACH)), or died/hospice while hospitalized (DH).

RESULTS

We identified 62,279 SARS-CoV-2 PCR+ patients; 6,248 required hospitalizations, of whom 4611(73.8%) were discharged home, 985 (15.8%) to PAC and 652 (10.4%) died in hospital (DH). Patients discharged to PAC had a higher median age (75.7 years, IQR: 65.6-85.1) compared to those discharged home (57.0 years, IQR: 38.2-69.9), and had longer mean length of stay (LOS) 14.7 days, SD: 14.0) compared to discharge home (5.8 days, SD: 5.9). Older age (RRR:1.04, 95% CI:1.041-1.055), and higher Elixhauser comorbidity index [EI] (RRR:1.19, 95% CI:1.168-1.218) were associated with higher rate of discharge to PAC versus home. Older age (RRR:1.069, 95% CI:1.060-1.077) and higher EI (RRR:1.09, 95% CI:1.071-1.126) were associated with more frequent DH versus home. Blacks, Asians, and Hispanics were less likely to be discharged to PAC (RRR, 0.64 CI 0.47-0.88), (RRR 0.48 CI 0.34-0.67) and (RRR 0.586 CI 0.352-0.975). Having alpha variant was associated with less frequent PAC discharge versus home (RRR 0.589 CI 0.444-780). The relative risks for DH were lower with a higher platelet count 0.998 (CI 0.99-0.99) and albumin levels 0.342 (CI 0.26-0.45), and higher with increased CRP (RRR 1.006 CI 1.004-1.007) and D-Dimer (RRR 1.070 CI 1.039-1.101). Increased albumin had lower risk to PAC discharge (RRR 0.630 CI 0.497-0.798. An increase in D-Dimer (RRR1.033 CI 1.002-1.064) and CRP (RRR1.002 CI1.001-1.004) was associated with higher risk of PAC discharge. A breakthrough (BT) infection was associated with lower likelihood of DH and PAC.

CONCLUSION

Older age, higher EI, CRP and D-Dimer are associated with PAC and DH discharges following hospitalization with COVID-19 infection. BT infection reduces the likelihood of being discharged to PAC and DH.

Evaluation of federated learning variations for COVID-19 diagnosis using chest radiographs from 42 US and European hospitals

OBJECTIVE

Federated learning (FL) allows multiple distributed data holders to collaboratively learn a shared model without data sharing. However, individual health system data are heterogeneous. "Personalized" FL variations have been developed to counter data heterogeneity, but few have been evaluated using real-world healthcare data. The purpose of this study is to investigate the performance of a single-site versus a 3-client federated model using a previously described Coronavirus Disease 19 (COVID-19) diagnostic model. Additionally, to investigate the effect of system heterogeneity, we evaluate the performance of 4 FL variations.

MATERIALS AND METHODS

We leverage a FL healthcare collaborative including data from 5 international healthcare systems (US and Europe) encompassing 42 hospitals. We implemented a COVID-19 computer vision diagnosis system using the Federated Averaging (FedAvg) algorithm implemented on Clara Train SDK 4.0. To study the effect of data heterogeneity, training data was pooled from 3 systems locally and federation was simulated. We compared a centralized/pooled model, versus FedAvg, and 3 personalized FL variations (FedProx, FedBN, and FedAMP).

RESULTS

We observed comparable model performance with respect to internal validation (local model: AUROC 0.94 vs FedAvg: 0.95, P = .5) and improved model generalizability with the FedAvg model (P < .05). When investigating the effects of model heterogeneity, we observed poor performance with FedAvg on internal validation as compared to personalized FL algorithms. FedAvg did have improved generalizability compared to personalized FL algorithms. On average, FedBN had the best rank performance on internal and external validation.

CONCLUSION

FedAvg can significantly improve the generalization of the model compared to other personalization FL algorithms; however, at the cost of poor internal validity. Personalized FL may offer an opportunity to develop both internal and externally validated algorithms.

Massive blood transfusion following older adult trauma: The effect of blood ratios on mortality

BACKGROUND

Massive blood transfusion (MBT) following older adult trauma poses unique challenges. Despite extensive evidence on optimal resuscitative strategies in the younger adult patients, there is limited research in the older adult population.

METHODS

We used the Trauma Quality Improvement Program (TQIP) database from 2013 to 2017 to identify all patients over 65 years old who received a MBT. We stratified our population into six fresh-frozen plasma:packed red blood cell (FFP:pRBC) ratio cohorts (1:1, 1:2, 1:3, 1:4, 1:5, 1:6+). Our primary outcomes were 24-h and 30-day mortality. We constructed multivariable regression models with 1:1 group as the baseline and adjusted for confounders to estimate the independent effect of blood ratios on mortality.

RESULTS

A total of 3134 patients met our inclusion criteria (median age 73 ± 7.6 years, 65% male). On risk-adjusted multivariable analysis, 1:1 FFP:pRBC ratio was independently associated with lowest 24-h mortality (1:2 odds ratio [OR] 1.60, 95% confidence interval [CI] 1.25-2.06, p < 0.001) and 30-day mortality (1:2 OR 1.44, 95% CI 1.15-1.80, p = 0.002).

CONCLUSIONS

Compared to all other ratios, the 1:1 FFP:pRBC ratio had the lowest 24-h and 30-day mortality following older adult trauma consistent with findings in the younger adult population.

Factors Associated with Mortality in Patients with COVID-19 Receiving Prolonged Ventilatory Support

Background: Since its emergence in early 2020, coronavirus disease 2019 (COVID-19)-associated pneumonia has caused a global strain on intensive care unit (ICU) resources with many intubated patients requiring prolonged ventilatory support. Outcomes for patients with COVID-19 who receive prolonged intubation (>21 days) and possible predictors of mortality in this group are not well established. Patients and Methods: Data were prospectively collected from adult patients with COVID-19 requiring mechanical ventilation from March 2020 through December 2021 across a system of 11 hospitals. The primary end point was in-hospital mortality. Factors associated with mortality were evaluated using univariable and multivariable logistic regression analyses. Results: Six hundred six patients were placed on mechanical ventilation for COVID-19 pneumonia during the study period, with in-hospital mortality of 40.3% (n = 244). Increased age (odds ratio [OR], 1.06; 95% confidence interval [CI], 1.03-1.09), increased creatinine (OR, 1.40; 95% CI, 1.08-1.82), and receiving corticosteroids (OR, 2.68; 95% CI, 1.20-5.98) were associated with mortality. Intubations lasting longer than 21 days (n = 140) had a lower in-hospital mortality of 25.7% (n = 36; p < 0.001). Increasing Elixhauser comorbidity index (OR, 1.12; 95% CI, 1.04-1.19) and receiving corticosteroids (OR, 1.92; 95% CI, 1.06-3.47) were associated with need for prolonged ventilation. In this group, increased age (OR, 1.06; 95% CI, 1.01-1.08) and non-English speaking (OR, 3.74; 95% CI, 1.13-12.3) were associated with mortality. Conclusions: In-hospital mortality in mechanically ventilated patients with COVID-19 pneumonia occurs primarily in the first 21 days after intubation, possibly related to the early active inflammatory process. In patients on prolonged mechanical ventilation, increased age and being non-English speaking were associated with mortality.

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.

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.

Combined Expert and User-Driven Usability Assessment of Trauma Decision Support Systems Improves User-Centered Design

BACKGROUND

Trauma clinical decision support systems improve adherence with evidence-based practice but suffer from poor usability and the lack of a user-centered design. The objective of this study was to compare the effectiveness of user and expert-driven usability testing methods to detect usability issues in a rib fracture clinical decision support system and identify guiding principles for trauma clinical decision support systems.

METHODS

A user-driven and expert-driven usability investigation was conducted using a clinical decision support system developed for patients with rib fractures. The user-driven usability evaluation was as follows: 10 clinicians were selected for simulation-based usability testing using snowball sampling, and each clinician completed 3 simulations using a video-conferencing platform. End-users participated in a novel team-based approach that simulated realistic clinical workflows. The expert-driven heuristic evaluation was as follows: 2 usability experts conducted a heuristic evaluation of the clinical decision support system using 10 common usability heuristics. Usability issues were identified, cataloged, and ranked for severity using a 4-level ordinal scale. Thematic analysis was utilized to categorize the identified usability issues.

RESULTS

Seventy-nine usability issues were identified; 63% were identified by experts and 48% by end-users. Notably, 58% of severe usability issues were identified by experts alone. Only 11% of issues were identified by both methods. Five themes were identified that could guide the design of clinical decision support systems-transparency, functionality and integration into workflow, automated and noninterruptive, flexibility, and layout and appearance. Themes were preferentially identified by different methods.

CONCLUSION

We found that a dual-method usability evaluation involving usability experts and end-users drastically improved detection of usability issues over single-method alone. We identified 5 themes to guide trauma clinical decision support system design. Performing usability testing via a remote video-conferencing platform facilitated multi-site involvement despite a global pandemic.

Renin-Angiotensin System Inhibitors in Patients With COVID-19: A Meta-Analysis of Randomized Controlled Trials Led by the International Society of Hypertension

Background

Published randomized controlled trials are underpowered for binary clinical end points to assess the safety and efficacy of renin‐angiotensin system inhibitors (RASi) in adults with COVID‐19. We therefore performed a meta‐analysis to assess the safety and efficacy of RASi in adults with COVID‐19.

Methods and Results

MEDLINE, EMBASE, ClinicalTrials.gov, and the Cochrane Controlled Trial Register were searched for randomized controlled trials that randomly assigned patients with COVID‐19 to RASi continuation/commencement versus no RASi therapy. The primary outcome was all‐cause mortality at ≤30 days. A total of 14 randomized controlled trials met the inclusion criteria and enrolled 1838 participants (aged 59 years, 58% men, mean follow‐up 26 days). Of the trials, 11 contributed data. We found no effect of RASi versus control on all‐cause mortality (7.2% versus 7.5%; relative risk [RR], 0.95; [95% CI, 0.69–1.30]) either overall or in subgroups defined by COVID‐19 severity or trial type. Network meta‐analysis identified no difference between angiotensin‐converting enzyme inhibitors versus angiotensin II receptor blockers. RASi users had a nonsignificant reduction in acute myocardial infarction (2.1% versus 3.6%; RR, 0.59; [95% CI, 0.33–1.06]), but increased risk of acute kidney injury (7.0% versus 3.6%; RR, 1.82; [95% CI, 1.05–3.16]), in trials that initiated and continued RASi. There was no increase in need for dialysis or differences in congestive cardiac failure, cerebrovascular events, venous thromboembolism, hospitalization, intensive care admission, inotropes, or mechanical ventilation.

Conclusions

This meta‐analysis of randomized controlled trials evaluating angiotensin‐converting enzyme inhibitors/angiotensin II receptor blockers versus control in patients with COVID‐19 found no difference in all‐cause mortality, a borderline decrease in myocardial infarction, and an increased risk of acute kidney injury with RASi. Our findings provide strong evidence that RASi can be used safely in patients with COVID‐19.

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.).

Prevalence of medical contraindications to nirmatrelvir/ritonavir in a cohort of hospitalized and non-hospitalized patients with COVID-19

This analysis describes the prevalence of contraindications to nirmatrelvir/ritonavir among 66 007 patients with coronavirus disease 2019 in a large health care system. A possible contradiction was present in 9830 patients (14.8%), with the prevalence of contraindications increasing with higher acuity of illness.

Performance of a Chest Radiograph AI Diagnostic Tool for COVID-19: A Prospective Observational Study

PURPOSE

To conduct a prospective observational study across 12 U.S. hospitals to evaluate real-time performance of an interpretable artificial intelligence (AI) model to detect COVID-19 on chest radiographs.

MATERIALS AND METHODS

A total of 95 363 chest radiographs were included in model training, external validation, and real-time validation. The model was deployed as a clinical decision support system, and performance was prospectively evaluated. There were 5335 total real-time predictions and a COVID-19 prevalence of 4.8% (258 of 5335). Model performance was assessed with use of receiver operating characteristic analysis, precision-recall curves, and F1 score. Logistic regression was used to evaluate the association of race and sex with AI model diagnostic accuracy. To compare model accuracy with the performance of board-certified radiologists, a third dataset of 1638 images was read independently by two radiologists.

RESULTS

Participants positive for COVID-19 had higher COVID-19 diagnostic scores than participants negative for COVID-19 (median, 0.1 [IQR, 0.0-0.8] vs 0.0 [IQR, 0.0-0.1], respectively; P < .001). Real-time model performance was unchanged over 19 weeks of implementation (area under the receiver operating characteristic curve, 0.70; 95% CI: 0.66, 0.73). Model sensitivity was higher in men than women (P = .01), whereas model specificity was higher in women (P = .001). Sensitivity was higher for Asian (P = .002) and Black (P = .046) participants compared with White participants. The COVID-19 AI diagnostic system had worse accuracy (63.5% correct) compared with radiologist predictions (radiologist 1 = 67.8% correct, radiologist 2 = 68.6% correct; McNemar P < .001 for both).

CONCLUSION

AI-based tools have not yet reached full diagnostic potential for COVID-19 and underperform compared with radiologist prediction.Keywords: Diagnosis, Classification, Application Domain, Infection, Lung Supplemental material is available for this article.. © RSNA, 2022.

Responding to COVID-19 Through Interhospital Resource Coordination: A Mixed-Methods Evaluation

OBJECTIVES

The COVID-19 pandemic stressed hospital operations, requiring rapid innovations to address rise in demand and specialized COVID-19 services while maintaining access to hospital-based care and facilitating expertise. We aimed to describe a novel hospital system approach to managing the COVID-19 pandemic, including multihospital coordination capability and transfer of COVID-19 patients to a single, dedicated hospital.

METHODS

We included patients who tested positive for SARS-CoV-2 by polymerase chain reaction admitted to a 12-hospital network including a dedicated COVID-19 hospital. Our primary outcome was adherence to local guidelines, including admission risk stratification, anticoagulation, and dexamethasone treatment assessed by differences-in-differences analysis after guideline dissemination. We evaluated outcomes and health care worker satisfaction. Finally, we assessed barriers to safe transfer including transfer across different electronic health record systems.

RESULTS

During the study, the system admitted a total of 1209 patients. Of these, 56.3% underwent transfer, supported by a physician-led System Operations Center. Patients who were transferred were older (P = 0.001) and had similar risk-adjusted mortality rates. Guideline adherence after dissemination was higher among patients who underwent transfer: admission risk stratification (P < 0.001), anticoagulation (P < 0.001), and dexamethasone administration (P = 0.003). Transfer across electronic health record systems was a perceived barrier to safety and reduced quality. Providers positively viewed our transfer approach.

CONCLUSIONS

With standardized communication, interhospital transfers can be a safe and effective method of cohorting COVID-19 patients, are well received by health care providers, and have the potential to improve care quality.

Predictors of Post-Acute 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.

Barriers to and Facilitators for Acceptance of Comprehensive Clinical Decision Support System-Driven Care Maps for Patients With Thoracic Trauma: Interview Study Among Health Care Providers and Nurses

BACKGROUND

Comprehensive clinical decision support (CDS) care maps can improve the delivery of care and clinical outcomes. However, they are frequently plagued by usability problems and poor user acceptance.

OBJECTIVE

This study aims to characterize factors influencing successful design and use of comprehensive CDS care maps and identify themes associated with end-user acceptance of a thoracic trauma CDS care map earlier in the process than has traditionally been done. This was a planned adaptive redesign stage of a User Acceptance and System Adaptation Design development and implementation strategy for a CDS care map. This stage was based on a previously developed prototype CDS care map guided by the Unified Theory of Acceptance and Use of Technology.

METHODS

A total of 22 multidisciplinary end users (physicians, advanced practice providers, and nurses) were identified and recruited using snowball sampling. Qualitative interviews were conducted, audio-recorded, and transcribed verbatim. Generation of prespecified codes and the interview guide was informed by the Unified Theory of Acceptance and Use of Technology constructs and investigative team experience. Interviews were blinded and double-coded. Thematic analysis of interview scripts was conducted and yielded descriptive themes about factors influencing the construction and potential use of an acceptable CDS care map.

RESULTS

A total of eight dominant themes were identified: alert fatigue (theme 1), automation (theme 2), redundancy (theme 3), minimalistic design (theme 4), evidence based (theme 5), prevent errors (theme 6), comprehensive across the spectrum of disease (theme 7), and malleability (theme 8). Themes 1 to 4 addressed factors directly affecting end users, and themes 5 to 8 addressed factors affecting patient outcomes. More experienced providers prioritized a system that is easy to use. Nurses prioritized a system that incorporated evidence into decision support. Clinicians across specialties, roles, and ages agreed that the amount of extra work generated should be minimal and that the system should help them administer optimal care efficiently.

CONCLUSIONS

End user feedback reinforces attention toward factors that improve the acceptance and use of a CDS care map for patients with thoracic trauma. Common themes focused on system complexity, the ability of the system to fit different populations and settings, and optimal care provision. Identifying these factors early in the development and implementation process may facilitate user-centered design and improve adoption.

Efficacy of Losartan in Hospitalized Patients With COVID-19-Induced Lung Injury: A Randomized Clinical Trial

Importance

SARS-CoV-2 viral entry may disrupt angiotensin II (AII) homeostasis, contributing to COVID-19 induced lung injury. AII type 1 receptor blockade mitigates lung injury in preclinical models, although data in humans with COVID-19 remain mixed.

Objective

To test the efficacy of losartan to reduce lung injury in hospitalized patients with COVID-19.

Design, Setting, and Participants

This blinded, placebo-controlled randomized clinical trial was conducted in 13 hospitals in the United States from April 2020 to February 2021. Hospitalized patients with COVID-19 and a respiratory sequential organ failure assessment score of at least 1 and not already using a renin-angiotensin-aldosterone system (RAAS) inhibitor were eligible for participation. Data were analyzed from April 19 to August 24, 2021.

Interventions

Losartan 50 mg orally twice daily vs equivalent placebo for 10 days or until hospital discharge.

Main Outcomes and Measures

The primary outcome was the imputed arterial partial pressure of oxygen to fraction of inspired oxygen (Pao2:Fio2) ratio at 7 days. Secondary outcomes included ordinal COVID-19 severity; days without supplemental o2, ventilation, or vasopressors; and mortality. Losartan pharmacokinetics and RAAS components (AII, angiotensin-[1-7] and angiotensin-converting enzymes 1 and 2)] were measured in a subgroup of participants.

Results

A total of 205 participants (mean [SD] age, 55.2 [15.7] years; 123 [60.0%] men) were randomized, with 101 participants assigned to losartan and 104 participants assigned to placebo. Compared with placebo, losartan did not significantly affect Pao2:Fio2 ratio at 7 days (difference, -24.8 [95%, -55.6 to 6.1]; P = .12). Compared with placebo, losartan did not improve any secondary clinical outcomes and led to fewer vasopressor-free days than placebo (median [IQR], 9.4 [9.1-9.8] vasopressor-free days vs 8.7 [8.2-9.3] vasopressor-free days).

Conclusions and Relevance

This randomized clinical trial found that initiation of orally administered losartan to hospitalized patients with COVID-19 and acute lung injury did not improve Pao2:Fio2 ratio at 7 days. These data may have implications for ongoing clinical trials.

Trial Registration

ClinicalTrials.gov Identifier: NCT04312009.

Vaccination against SARS-CoV-2 is associated with a lower viral load and likelihood of systemic symptoms

Background

Data conflict on whether vaccination decreases severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral load. The objective of this analysis was to compare baseline viral load and symptoms between vaccinated and unvaccinated adults enrolled in a randomized trial of outpatient coronavirus disease 2019 (COVID-19) treatment.

Methods

Baseline data from the first 433 sequential participants enrolling into the COVID-OUT trial were analyzed. Adults aged 30–85 with a body mass index (BMI) ≥25 kg/m² were eligible within 3 days of a positive SARS-CoV-2 test and <7 days of symptoms. Log₁₀ polymerase chain reaction viral loads were normalized to human RNase P by vaccination status, by time from vaccination, and by symptoms.

Results

Two hundred seventy-four participants with known vaccination status contributed optional nasal swabs for viral load measurement: median age, 46 years; median (interquartile range) BMI 31.2 (27.4–36.4) kg/m². Overall, 159 (58%) were women, and 217 (80%) were White. The mean relative log₁₀ viral load for those vaccinated <6 months from the date of enrollment was 0.11 (95% CI, –0.48 to 0.71), which was significantly lower than the unvaccinated group (P = .01). Those vaccinated ≥6 months before enrollment did not differ from the unvaccinated with respect to viral load (mean, 0.99; 95% CI, –0.41 to 2.40; P = .85). The vaccinated group had fewer moderate/severe symptoms of subjective fever, chills, myalgias, nausea, and diarrhea (all P < .05).

Conclusions

These data suggest that vaccination within 6 months of infection is associated with a lower viral load, and vaccination was associated with a lower likelihood of having systemic symptoms.

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.

A 12-hospital prospective evaluation of a clinical decision support prognostic algorithm based on logistic regression as a form of machine learning to facilitate decision making for patients with suspected COVID-19

OBJECTIVE

To prospectively evaluate a logistic regression-based machine learning (ML) prognostic algorithm implemented in real-time as a clinical decision support (CDS) system for symptomatic persons under investigation (PUI) for Coronavirus disease 2019 (COVID-19) in the emergency department (ED).

METHODS

We developed in a 12-hospital system a model using training and validation followed by a real-time assessment. The LASSO guided feature selection included demographics, comorbidities, home medications, vital signs. We constructed a logistic regression-based ML algorithm to predict "severe" COVID-19, defined as patients requiring intensive care unit (ICU) admission, invasive mechanical ventilation, or died in or out-of-hospital. Training data included 1,469 adult patients who tested positive for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) within 14 days of acute care. We performed: 1) temporal validation in 414 SARS-CoV-2 positive patients, 2) validation in a PUI set of 13,271 patients with symptomatic SARS-CoV-2 test during an acute care visit, and 3) real-time validation in 2,174 ED patients with PUI test or positive SARS-CoV-2 result. Subgroup analysis was conducted across race and gender to ensure equity in performance.

RESULTS

The algorithm performed well on pre-implementation validations for predicting COVID-19 severity: 1) the temporal validation had an area under the receiver operating characteristic (AUROC) of 0.87 (95%-CI: 0.83, 0.91); 2) validation in the PUI population had an AUROC of 0.82 (95%-CI: 0.81, 0.83). The ED CDS system performed well in real-time with an AUROC of 0.85 (95%-CI, 0.83, 0.87). Zero patients in the lowest quintile developed "severe" COVID-19. Patients in the highest quintile developed "severe" COVID-19 in 33.2% of cases. The models performed without significant differences between genders and among race/ethnicities (all p-values > 0.05).

CONCLUSION

A logistic regression model-based ML-enabled CDS can be developed, validated, and implemented with high performance across multiple hospitals while being equitable and maintaining performance in real-time validation.

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.

Outpatient and inpatient anticoagulation therapy and the risk for hospital admission and death among COVID-19 patients

BACKGROUND

Coronavirus disease 2019 (COVID-19) is associated with a hypercoagulable state. Limited data exist informing the relationship between anticoagulation therapy and risk for COVID-19 related hospitalization and mortality.

METHODS

We evaluated all patients over the age of 18 diagnosed with COVID-19 in a prospective cohort study from March 4th to August 27th, 2020 among 12 hospitals and 60 clinics of M Health Fairview system (USA). We investigated the relationship between (1) 90-day anticoagulation therapy among outpatients before COVID-19 diagnosis and the risk for hospitalization and mortality and (2) Inpatient anticoagulation therapy and mortality risk.

FINDINGS

Of 6195 patients, 598 were immediately hospitalized and 5597 were treated as outpatients. The overall case-fatality rate was 2•8% (n = 175 deaths). Among the patients who were hospitalized, the inpatient mortality was 13%. Among the 5597 COVID-19 patients initially treated as outpatients, 160 (2.9%) were on anticoagulation and 331 were eventually hospitalized (5.9%). In a multivariable analysis, outpatient anticoagulation use was associated with a 43% reduction in risk for hospital admission, HR (95% CI = 0.57, 0.38-0.86), p = 0.007, but was not associated with mortality, HR (95% CI=0.88, 0.50 - 1.52), p = 0.64. Inpatients who were not on anticoagulation (before or after hospitalization) had an increased risk for mortality, HR (95% CI = 2.26, 1.17-4.37), p = 0.015.

INTERPRETATION

Outpatients with COVID-19 who were on outpatient anticoagulation at the time of diagnosis experienced a 43% reduced risk of hospitalization. Failure to initiate anticoagulation upon hospitalization or maintaining outpatient anticoagulation in hospitalized COVID-19 patients was associated with increased mortality risk.

FUNDING

No funding was obtained for this study.

NLP Methods for Extraction of Symptoms from Unstructured Data for Use in Prognostic COVID-19 Analytic Models

Statistical modeling of outcomes based on a patient's presenting symptoms (symptomatology) can help deliver high quality care and allocate essential resources, which is especially important during the COVID-19 pandemic. Patient symptoms are typically found in unstructured notes, and thus not readily available for clinical decision making. In an attempt to fill this gap, this study compared two methods for symptom extraction from Emergency Department (ED) admission notes. Both methods utilized a lexicon derived by expanding The Center for Disease Control and Prevention's (CDC) Symptoms of Coronavirus list. The first method utilized a word2vec model to expand the lexicon using a dictionary mapping to the Uni ed Medical Language System (UMLS). The second method utilized the expanded lexicon as a rule-based gazetteer and the UMLS. These methods were evaluated against a manually annotated reference (f1-score of 0.87 for UMLS-based ensemble; and 0.85 for rule-based gazetteer with UMLS). Through analyses of associations of extracted symptoms used as features against various outcomes, salient risks among the population of COVID-19 patients, including increased risk of in-hospital mortality (OR 1.85, p-value < 0.001), were identified for patients presenting with dyspnea. Disparities between English and non-English speaking patients were also identified, the most salient being a concerning finding of opposing risk signals between fatigue and in-hospital mortality (non-English: OR 1.95, p-value = 0.02; English: OR 0.63, p-value = 0.01). While use of symptomatology for modeling of outcomes is not unique, unlike previous studies this study showed that models built using symptoms with the outcome of in-hospital mortality were not significantly different from models using data collected during an in-patient encounter (AUC of 0.9 with 95% CI of [0.88, 0.91] using only vital signs; AUC of 0.87 with 95% CI of [0.85, 0.88] using only symptoms). These findings indicate that prognostic models based on symptomatology could aid in extending COVID-19 patient care through telemedicine, replacing the need for in-person options. The methods presented in this study have potential for use in development of symptomatology-based models for other diseases, including for the study of Post-Acute Sequelae of COVID-19 (PASC).

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.

Comparison of a trauma comorbidity index with other measures of comorbidities to estimate risk of trauma mortality

BACKGROUND

Comorbidities influence the outcomes of injured patients, yet a lack of consensus exists regarding how to quantify that association. This study details the development and internal validation of a trauma comorbidity index (TCI) designed for use with trauma registry data and compares its performance to other existing measures to estimate the association between comorbidities and mortality.

METHODS

Indiana state trauma registry data (2013-2015) were used to compare the TCI with the Charlson and Elixhauser comorbidity indices, a count of comorbidities, and comorbidities as separate variables. The TCI approach utilized a randomly selected training cohort and was internally validated in a distinct testing cohort. The C-statistic of the adjusted models was tested using each comorbidity measure in the testing cohort to assess model discrimination. C-statistics were compared using a Wald test, and stratified analyses were performed based on predicted risk of mortality. Multiple imputation was used to address missing data.

RESULTS

The study included 84,903 patients (50% each in training and testing cohorts). The Indiana TCI model demonstrated no significant difference between testing and training cohorts (p = 0.33). It produced a C-statistic of 0.924 in the testing cohort, which was significantly greater than that of models using the other indices (p < 0.05). The C-statistics of models using the Indiana TCI and the inclusion of comorbidities as separate variables-the method used by the American College of Surgeons Trauma Quality Improvement Program-were comparable (p = 0.11) but use of the TCI approach reduced the number of comorbidity-related variables in the mortality model from 19 to one.

CONCLUSIONS

When examining trauma mortality, the TCI approach using Indiana state trauma registry data demonstrated superior model discrimination and/or parsimony compared to other measures of comorbidities.

A multi-center phase II randomized clinical trial of losartan on symptomatic outpatients with COVID-19

BACKGROUND

The SARS-CoV-2 virus enters cells via Angiotensin-converting enzyme 2 (ACE2), disrupting the renin-angiotensin-aldosterone axis, potentially contributing to lung injury. Treatment with angiotensin receptor blockers (ARBs), such as losartan, may mitigate these effects, though induction of ACE2 could increase viral entry, replication, and worsen disease.

METHODS

This study represents a placebo-controlled blinded randomized clinical trial (RCT) to test the efficacy of losartan on outpatients with COVID-19 across three hospital systems with numerous community sites in Minnesota, U.S. Participants included symptomatic outpatients with COVID-19 not already taking ACE-inhibitors or ARBs, enrolled within 7 days of symptom onset. Patients were randomized to 1:1 losartan (25 mg orally twice daily unless estimated glomerular filtration rate, eGFR, was reduced, when dosing was reduced to once daily) versus placebo for 10 days, and all patients and outcome assesors were blinded. The primary outcome was all-cause hospitalization within 15 days. Secondary outcomes included functional status, dyspnea, temperature, and viral load. (clinicatrials.gov, NCT04311177, closed to new participants).

FINDINGS

From April to November 2020, 117 participants were randomized 58 to losartan and 59 to placebo, and all were analyzed under intent to treat principles. The primary outcome did not differ significantly between the two arms based on Barnard's test [losartan arm: 3 events (5.2% 95% CI 1.1, 14.4%) versus placebo arm: 1 event (1.7%; 95% CI 0.0, 9.1%)]; proportion difference -3.5% (95% CI -13.2, 4.8%); p = 0.32]. Viral loads were not statistically different between treatment groups at any time point. Adverse events per 10 patient days did not differ signifcantly [0.33 (95% CI 0.22-0.49) for losartan vs. 0.37 (95% CI 0.25-0.55) for placebo]. Due to a lower than expected hospitalization rate and low likelihood of a clinically important treatment effect, the trial was terminated early.

INTERPRETATION

In this multicenter blinded RCT for outpatients with mild symptomatic COVID-19 disease, losartan did not reduce hospitalizations, though assessment was limited by low event rate. Importantly, viral load was not statistically affected by treatment. This study does not support initiation of losartan for low-risk outpatients.

A fast, resource efficient, and reliable rule-based system for COVID-19 symptom identification

OBJECTIVE

With COVID-19, there was a need for a rapidly scalable annotation system that facilitated real-time integration with clinical decision support systems (CDS). Current annotation systems suffer from a high-resource utilization and poor scalability limiting real-world integration with CDS. A potential solution to mitigate these issues is to use the rule-based gazetteer developed at our institution.

MATERIALS AND METHODS

Performance, resource utilization, and runtime of the rule-based gazetteer were compared with five annotation systems: BioMedICUS, cTAKES, MetaMap, CLAMP, and MedTagger.

RESULTS

This rule-based gazetteer was the fastest, had a low resource footprint, and similar performance for weighted microaverage and macroaverage measures of precision, recall, and f1-score compared to other annotation systems.

DISCUSSION

Opportunities to increase its performance include fine-tuning lexical rules for symptom identification. Additionally, it could run on multiple compute nodes for faster runtime.

CONCLUSION

This rule-based gazetteer overcame key technical limitations facilitating real-time symptomatology identification for COVID-19 and integration of unstructured data elements into our CDS. It is ideal for large-scale deployment across a wide variety of healthcare settings for surveillance of acute COVID-19 symptoms for integration into prognostic modeling. Such a system is currently being leveraged for monitoring of postacute sequelae of COVID-19 (PASC) progression in COVID-19 survivors. This study conducted the first in-depth analysis and developed a rule-based gazetteer for COVID-19 symptom extraction with the following key features: low processor and memory utilization, faster runtime, and similar weighted microaverage and macroaverage measures for precision, recall, and f1-score compared to industry-standard annotation systems.

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.

Morbidity and Mortality Trends of Pancreatitis: An Observational Study

Background: Pancreatitis accounts for more than $2.5 billion of healthcare costs and remains the most common gastrointestinal (GI) admission. Few contemporary studies have assessed temporal trends of incidence, complications, management, and outcomes for acute pancreatitis in hospitalized patients at the national level. Methods: We used data from one of the largest hospital-based databases available in the United States, the Healthcare Cost and Utilization Project's (HCUP) State Inpatient Database, from 10 states between 2008 and 2015. We included patients with a diagnosis of acute pancreatitis (ICD-9 CM 577.0). Patient- and hospital-level data were used to estimate incidence and inpatient mortality rates. Results: From 80,736,256 hospitalizations, 929,914 (1.15%) cases of acute pancreatitis were identified, 186,226 (20.2%) of which were caused by gallbladder disease). The median age was 53 years (interquartile range [IQR], 41-67) and 50.8% were men. In-hospital mortality was 2.5% and crude mortality rates declined from 2.9% to 2.0% over the study period. Admission year remained significant after adjusting for patient demographics and comorbidities (odds ratio [OR], 0.90; 95% confidence interval [CI], 0.89-0.90; p < 0.001). Gallbladder disease was associated with decreased odds of mortality (OR, 0.60; 95% CI, 0.57-0.62). Median length of stay was four days (IQR, 2-7) and decreased over time. The rates of surgical and endoscopic interventions were highest in 2011 (peak incidence of 16.1% and 9.5%, respectively) and have been decreasing since. Surgical providers were, on average, more likely than medical providers to perform surgery in both those with and without gallbladder disease etiology (gallbladder disease OR, 7.11; 95% CI, 5.46-9.25; non-gallbladder disease OR, 20.50; 95% CI, 16.81-25.01), endoscopy (gallbladder disease OR, 1.22; 95% CI, 0.87-1.72; non-gallbladder disease OR, 1.60; 95% CI, 1.18-2.16), or both (gallbladder disease OR, 7.00; 95% CI, 5.22-9.37; non-gallbladder disease OR, 8.85; 95% CI, 5.61-13.96). Conclusions: The incidence of pancreatitis, from 2008 to 2015, has increased whereas inpatient mortality (i.e., case fatality) has decreased. Understanding temporal trends in outcomes and management along with provider, hospital, and regional variation can better identify areas for future research and collaboration in managing these patients.