Can We Actually Predict Long-Term Patient Satisfaction After Traumatic Brain Injury?

INTRODUCTION: The Traumatic Brain Injury - Patient Reported Outcome (TBI-PRO) model was previously derived to predict long-term patient satisfaction as assessed by the Quality of Life After Brain Injury (QOLIBRI) score. The aim of this study is to externally and prospectively validate the TBI-PRO model to predict long-term patient-reported outcomes and to derive a new model using a larger dataset of older adults with TBI. METHODS: Patients admitted to a Level I trauma center with TBI were prospectively followed for 1 y after injury. Outcomes predicted by the TBI-PRO model based on admission findings were compared to actual QOLIBRI scores reported by patients at 3,6, and 12 mo. When deriving a new model, Collaborative European NeuroTrauma Effectiveness Research in TBI and the Transforming Research and Clinical Knowledge in Traumatic Brain Injury databases were used to identify older adults (≥50 y) with TBI from 2014 to 2018. Bayesian additive regression trees were used to identify predictive admission covariates. The coefficient of determination was used to identify the fitness of the model. RESULTS: For prospective validation, a total of 140 patients were assessed at 3 mo, with follow-up from 69 patients at 6 mo and 13 patients at 12 mo postinjury. The area under receiver operating curve of the TBI-PRO model for predicting favorable outcomes at 3, 6, and 12 mo were 0.65, 0.57, and 0.62, respectively. When attempting to derive a novel predictive model, a total of 1521 patients (80%) was used in the derivation dataset while 384 (20%) were used in the validation dataset. A past medical history of heart conditions, initial hospital length of stay, admission systolic blood pressure, age, number of reactive pupils on admission, and the need for craniectomy were most predictive of long-term QOLIBRI-Overall Scale. The coefficient of determination for the validation model including only the most predictive variables were 0.28, 0.19, and 0.27 at 3, 6, and 12 mo, respectively. CONCLUSIONS: In the present study, the prospective validation of a previously derived TBI-PRO model failed to accurately predict a long-term patient reported outcome measures in TBI. Additionally, the derivation of a novel model in older adults using a larger database showed poor accuracy in predicting long-term health-related quality of life. This study demonstrates limitations to current targeted approaches in TBI care. This study provides a framework for future studies and more targeted datasets looking to assess long-term quality of life based upon early hospital variables and can serve as a starting point for future predictive analysis.

Building to learn: Information technology innovations to enable rapid pragmatic evaluation in a learning health system.

Background: Learning health systems (LHSs) iteratively generate evidence that can be implemented into practice to improve care and produce generalizable knowledge. Pragmatic clinical trials fit well within LHSs as they combine real-world data and experiences with a degree of methodological rigor which supports generalizability. Objectives: We established a pragmatic clinical trial unit ("RapidEval") to support the development of an LHS. To further advance the field of LHS, we sought to further characterize the role of health information technology (HIT), including innovative solutions and challenges that occur, to improve LHS project delivery. Methods: During the period from December 2021 to February 2023, eight projects were selected out of 51 applications to the RapidEval program, of which five were implemented, one is currently in pilot testing, and two are in planning. We evaluated pre-study planning, implementation, analysis, and study closure approaches across all RapidEval initiatives to summarize approaches across studies and identify key innovations and learnings by gathering data from study investigators, quality staff, and IT staff, as well as RapidEval staff and leadership. Implementation Results: Implementation approaches spanned a range of HIT capabilities including interruptive alerts, clinical decision support integrated into order systems, patient navigators, embedded micro-education, targeted outpatient hand-off documentation, and patient communication. Study approaches include pre-post with time-concordant controls (1), randomized stepped-wedge (1), cluster randomized across providers (1) and location (3), and simple patient level randomization (2). Conclusions: Study selection, design, deployment, data collection, and analysis required close collaboration between data analysts, informaticists, and the RapidEval team.

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

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

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

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

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.

Longitudinal clinical decision support for assessing decisions over time: State-of-the-art and future directions.

Objective: Patients and clinicians rarely experience healthcare decisions as snapshots in time, but clinical decision support (CDS) systems often represent decisions as snapshots. This scoping review systematically maps challenges and facilitators to longitudinal CDS that are applied at two or more timepoints for the same decision made by the same patient or clinician. Methods: We searched Embase, PubMed, and Medline databases for articles describing development, validation, or implementation of patient- or clinician-facing longitudinal CDS. Validated quality assessment tools were used for article selection. Challenges and facilitators to longitudinal CDS are reported according to PRISMA-ScR guidelines. Results: Eight articles met inclusion criteria; each article described a unique CDS. None used entirely automated data entry, none used living guidelines for updating the evidence base or knowledge engine as new evidence emerged during the longitudinal study, and one included formal readiness for change assessments. Seven of eight CDS were implemented and evaluated prospectively. Challenges were primarily related to suboptimal study design (with unique challenges for each study) or user interface. Facilitators included use of randomized trial designs for prospective enrollment, increased CDS uptake during longitudinal exposure, and machine-learning applications that are tailored to the CDS use case. Conclusions: Despite the intuitive advantages of representing healthcare decisions longitudinally, peer-reviewed literature on longitudinal CDS is sparse. Existing reports suggest opportunities to incorporate longitudinal CDS frameworks, automated data entry, living guidelines, and user readiness assessments. Generating best practice guidelines for longitudinal CDS would require a greater depth and breadth of published work and expert opinion.

Prospective validation of a hospital triage predictive model to decrease undertriage: an EAST multicenter study.

Background: Tiered trauma team activation (TTA) allows systems to optimally allocate resources to an injured patient. Target undertriage and overtriage rates of <5% and <35% are difficult for centers to achieve, and performance variability exists. The objective of this study was to optimize and externally validate a previously developed hospital trauma triage prediction model to predict the need for emergent intervention in 6 hours (NEI-6), an indicator of need for a full TTA. Methods: The model was previously developed and internally validated using data from 31 US trauma centers. Data were collected prospectively at five sites using a mobile application which hosted the NEI-6 model. A weighted multiple logistic regression model was used to retrain and optimize the model using the original data set and a portion of data from one of the prospective sites. The remaining data from the five sites were designated for external validation. The area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC) were used to assess the validation cohort. Subanalyses were performed for age, race, and mechanism of injury. Results: 14 421 patients were included in the training data set and 2476 patients in the external validation data set across five sites. On validation, the model had an overall undertriage rate of 9.1% and overtriage rate of 53.7%, with an AUROC of 0.80 and an AUPRC of 0.63. Blunt injury had an undertriage rate of 8.8%, whereas penetrating injury had 31.2%. For those aged ≥65, the undertriage rate was 8.4%, and for Black or African American patients the undertriage rate was 7.7%. Conclusion: The optimized and externally validated NEI-6 model approaches the recommended undertriage and overtriage rates while significantly reducing variability of TTA across centers for blunt trauma patients. The model performs well for populations that traditionally have high rates of undertriage. Level of evidence: 2.

Calculation and Feedback of Risk-Adjusted Antibiotic Days as a Process Measure in a Statewide Trauma Collaborative.

BACKGROUND: Optimization of antibiotic stewardship requires determining appropriate antibiotic treatment and duration of use. Our current method of identifying infectious complications alone does not attempt to measure the resources actually utilized to treat infections in patients. We sought to develop a method accounting for treatment of infections and length of antibiotic administration to allow benchmarking of trauma hospitals with regard to days of antibiotic use. METHODS: Using trauma quality collaborative data from 35 American College of Surgeons (ACS)-verified level I and level II trauma centers between November 1, 2020, and January 31, 2023, a two-part model was created to account for (1) the odds of any antibiotic use, using logistic regression; and (2) the duration of usage, using negative binomial distribution. We adjusted for injury severity, presence/type of infection (eg, ventilator-acquired pneumonia), infectious complications, and comorbid conditions. We performed observed-to-expected adjustments to calculate each center's risk-adjusted antibiotic days, bootstrapped Observed/Expected (O/E) ratios to create confidence intervals, and flagged potential high or low outliers as hospitals whose confidence intervals lay above or below the overall mean. RESULTS: The mean antibiotic treatment days was 1.98°days with a total of 88,403 treatment days. A wide variation existed in risk-adjusted antibiotic treatment days (.76°days to 2.69°days). Several hospitals were identified as low (9 centers) or high (6 centers) outliers. CONCLUSION: There exists a wide variation in the duration of risk-adjusted antibiotic use amongst trauma centers. Further study is needed to address the underlying cause of variation and for improved antibiotic stewardship.

Incidence of adult rib fracture injuries and changing hospitalization practice patterns: a 10-year analysis.

PURPOSE: Rib fractures are common after blunt thoracic trauma and can be associated with significant morbidity and mortality. We investigated trends of rib fracture injuries among adults presenting to United States (US) emergency departments, factors related to increased likelihood of hospitalization, and hospitalization practice patterns. METHODS: We queried the National Electronic Injury Surveillance System database between 2012 and 2021 for all patients 18 years of age and older with rib fractures. These data were extrapolated to provide national estimates. Regression analysis was performed to identify trends for injury and risk factors for hospitalization. RESULTS: We identified 32,233 adult patients with rib fractures; this extrapolated to a national estimate of 1,430,270 patients with rib fractures during the 10-year period. Between 2012 and 2021, there was a 52% increase in the incidence rate per 100,000 persons (R2 = 0.94, p < 0.001). Males accounted for 58% of patients with rib fractures, and 50% of patients were 65 years or older. Hospitalization was required in 38% of patients, and the hospitalization rate increased by 96% during the study period (R2 = 0.96, p < 0.001). When comparing hospitals of different sizes, a 20% greater increase in the odds of hospitalization over time was identified among patients presenting to "larger" hospitals compared to "smaller" hospitals. CONCLUSION: The incidence of rib fractures and the associated hospitalization rates are both increasing nationally, with half of cases occurring in patients aged 65 years or older. Our findings emphasize the urgent need to implement evidence-based preventive measures and current management guidelines when managing the increasing caseload of rib fracture injuries.

Risk of Post-Intubation Laryngotracheal Stenosis With Respect to COVID-19 Status in a Large Multicenter Cohort Cross-Sectional Study.

OBJECTIVES: Occurrence of post-intubation laryngotracheal stenosis (LTS) with respect to COVID-19 status. DESIGN: Retrospective cross-sectional inpatient database. SETTING: Eleven Midwest academic and community hospitals, United States. PATIENTS: Adults, mechanically ventilated, from January 2020 to August 2022, who were subsequently readmitted within 6 months with a new diagnosis of LTS. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Six thousand eight hundred fifty-one COVID-19 negative and 1316 COVID-19 positive patients were intubated and had similar distribution by age (median 63.77 vs. 63.16 yr old), sex (male, 60.8%; n = 4173 vs. 60%; n = 789), endotracheal tube size (≥ 7.5, 75.8%; n = 5192 vs. 75.5%; n = 994), and comorbidities. The ICU length of stay (median [interquartile range (IQR)], 7.23 d [2.13-16.67 d] vs. 3.95 d [1.91-8.88 d]) and mechanical ventilation days (median [IQR], 5.57 d [1.01-14.18 d] vs. 1.37 d [0.35-4.72 d]) were longer in the COVID-19 positive group. The occurrence of LTS was double in the COVID-19 positive group (12.7%, n = 168 vs. 6.4%, n = 440; p < 0.001) and was most commonly diagnosed within 60 days of intubation. In multivariate analysis, the risk of LTS increased by 2% with each additional ICU day (hazard ratio [HR], 1.02; 95% CI, 1.02-1.03; p < 0.001), by 3% with each additional day of ventilation (HR, 1.03; 95% CI, 1.02-1.04; p < 0.001), and by 52% for each additional reintubation (HR, 1.52; 95% CI, 1.36-1.71; p < 0.001). We observed no significant association COVID-19 status and risk of LTS. CONCLUSIONS: The occurrence of post-intubation LTS was double in a COVID-19 positive cohort, with higher risk with increasing number of days intubated, days in the ICU and especially with the number of reintubations. COVID-19 status was not an independent risk factor for LTS.

American Association for the Surgery of Trauma/American College of Surgeons Committee on Trauma clinical protocol for postdischarge venous thromboembolism prophylaxis after trauma.

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

Promoting Learning Health System Cycles by Optimizing EHR Data Clinical Concept Encoding Processes.

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

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.

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.

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.

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.

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.