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.

Network meta analysis to predict the efficacy of an approved treatment in a new indication.

Drug repurposing refers to the process of discovering new therapeutic uses for existing medicines. Compared to traditional drug discovery, drug repurposing is attractive for its speed, cost, and reduced risk of failure. However, existing approaches for drug repurposing involve complex, computationally-intensive analytical methods that are not widely used in practice. Instead, repurposing decisions are often based on subjective judgments from limited empirical evidence. In this article, we develop a novel Bayesian network meta-analysis (NMA) framework that can predict the efficacy of an approved treatment in a new indication and thereby identify candidate treatments for repurposing. We obtain predictions using two main steps: first, we use standard NMA modeling to estimate average relative effects from a network comprised of treatments studied in both indications in addition to one treatment studied in only one indication. Then, we model the correlation between relative effects using various strategies that differ in how they model treatments across indications and within the same drug class. We evaluate the predictive performance of each model using a simulation study and find that the model minimizing root mean squared error of the posterior median for the candidate treatment depends on the amount of available data, the level of correlation between indications, and whether treatment effects differ, on average, by drug class. We conclude by discussing an illustrative example in psoriasis and psoriatic arthritis and find that the candidate treatment has a high probability of success in a future trial.

Sigh Ventilation in Patients With Trauma: The SiVent Randomized Clinical Trial.

Importance: Among patients receiving mechanical ventilation, tidal volumes with each breath are often constant or similar. This may lead to ventilator-induced lung injury by altering or depleting surfactant. The role of sigh breaths in reducing ventilator-induced lung injury among trauma patients at risk of poor outcomes is unknown. Objective: To determine whether adding sigh breaths improves clinical outcomes. Design, Setting, and Participants: A pragmatic, randomized trial of sigh breaths plus usual care conducted from 2016 to 2022 with 28-day follow-up in 15 academic trauma centers in the US. Inclusion criteria were age older than 18 years, mechanical ventilation because of trauma for less than 24 hours, 1 or more of 5 risk factors for developing acute respiratory distress syndrome, expected duration of ventilation longer than 24 hours, and predicted survival longer than 48 hours. Interventions: Sigh volumes producing plateau pressures of 35 cm H2O (or 40 cm H2O for inpatients with body mass indexes >35) delivered once every 6 minutes. Usual care was defined as the patient's physician(s) treating the patient as they wished. Main Outcomes and Measures: The primary outcome was ventilator-free days. Prespecified secondary outcomes included all-cause 28-day mortality. Results: Of 5753 patients screened, 524 were enrolled (mean [SD] age, 43.9 [19.2] years; 394 [75.2%] were male). The median ventilator-free days was 18.4 (IQR, 7.0-25.2) in patients randomized to sighs and 16.1 (IQR, 1.1-24.4) in those receiving usual care alone (P = .08). The unadjusted mean difference in ventilator-free days between groups was 1.9 days (95% CI, 0.1 to 3.6) and the prespecified adjusted mean difference was 1.4 days (95% CI, -0.2 to 3.0). For the prespecified secondary outcome, patients randomized to sighs had 28-day mortality of 11.6% (30/259) vs 17.6% (46/261) in those receiving usual care (P = .05). No differences were observed in nonfatal adverse events comparing patients with sighs (80/259 [30.9%]) vs those without (80/261 [30.7%]). Conclusions and Relevance: In a pragmatic, randomized trial among trauma patients receiving mechanical ventilation with risk factors for developing acute respiratory distress syndrome, the addition of sigh breaths did not significantly increase ventilator-free days. Prespecified secondary outcome data suggest that sighs are well-tolerated and may improve clinical outcomes. Trial Registration: ClinicalTrials.gov Identifier: NCT02582957.

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.

Patient Outcomes and Unit Composition With Transition to a High-Intensity ICU Staffing Model: A Before-and-After Study.

Provider staffing models for ICUs are generally based on pragmatic necessities and historical norms at individual institutions. A better understanding of the role that provider staffing models play in determining patient outcomes and optimizing use of ICU resources is needed. OBJECTIVES: To explore the impact of transitioning from a low- to high-intensity intensivist staffing model on patient outcomes and unit composition. DESIGN SETTING AND PARTICIPANTS: This was a prospective observational before-and-after study of adult ICU patients admitted to a single community hospital ICU before (October 2016-May 2017) and after (June 2017-November 2017) the transition to a high-intensity ICU staffing model. MAIN OUTCOMES AND MEASURES: The primary outcome was 30-day all-cause mortality. Secondary outcomes included in-hospital mortality, ICU length of stay (LOS), and unit composition characteristics including type (e.g., medical, surgical) and purpose (ICU-specific intervention vs close monitoring only) of admission. RESULTS: For the primary outcome, 1,219 subjects were included (779 low-intensity, 440 high-intensity). In multivariable analysis, the transition to a high-intensity staffing model was not associated with a decrease in 30-day (odds ratio [OR], 0.90; 95% CI, 0.61-1.34; p = 0.62) or in-hospital (OR, 0.89; 95% CI, 0.57-1.38; p = 0.60) mortality, nor ICU LOS. However, the proportion of patients admitted to the ICU without an ICU-specific need did decrease under the high-intensity staffing model (27.2% low-intensity to 17.5% high-intensity; p < 0.001). CONCLUSIONS AND RELEVANCE: Multivariable analysis showed no association between transition to a high-intensity ICU staffing model and mortality or LOS outcomes; however, the proportion of patients admitted without an ICU-specific need decreased under the high-intensity model. Further research is needed to determine whether a high-intensity staffing model may lead to more efficient ICU bed usage.

An alternative metric for evaluating the potential patient benefit of response-adaptive randomization procedures.

When planning a two-arm group sequential clinical trial with a binary primary outcome that has severe implications for quality of life (e.g., mortality), investigators may strive to find the design that maximizes in-trial patient benefit. In such cases, Bayesian response-adaptive randomization (BRAR) is often considered because it can alter the allocation ratio throughout the trial in favor of the treatment that is currently performing better. Although previous studies have recommended using fixed randomization over BRAR based on patient benefit metrics calculated from the realized trial sample size, these previous comparisons have been limited by failures to hold type I and II error rates constant across designs or consider the impacts on all individuals directly affected by the design choice. In this paper, we propose a metric for comparing designs with the same type I and II error rates that reflects expected outcomes among individuals who would participate in the trial if enrollment is open when they become eligible. We demonstrate how to use the proposed metric to guide the choice of design in the context of two recent trials in persons suffering out of hospital cardiac arrest. Using computer simulation, we demonstrate that various implementations of group sequential BRAR offer modest improvements with respect to the proposed metric relative to conventional group sequential monitoring alone.

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.

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.).

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.

Outpatient treatment of Covid-19 with metformin, ivermectin, and fluvoxamine and the development of Long Covid over 10-month follow-up.

Background: Long Covid is an emerging chronic illness potentially affecting millions, sometimes preventing the ability to work or participate in normal daily activities. COVID-OUT was an investigator-initiated, multi-site, phase 3, randomized, quadruple-blinded placebo-controlled clinical trial (NCT04510194). The design simultaneously assessed three oral medications (metformin, ivermectin, fluvoxamine) using two by three parallel treatment factorial assignment to efficiently share placebo controls and assessed Long Covid outcomes for 10 months to understand whether early outpatient treatment of SARS-CoV-2 with metformin, ivermectin, or fluvoxamine prevents Long Covid. Methods: This was a decentralized, remotely delivered trial in the US of 1,125 adults age 30 to 85 with overweight or obesity, fewer than 7 days of symptoms, and enrolled within three days of a documented SARS-CoV-2 infection. Immediate release metformin titrated over 6 days to 1,500mg per day 14 days total; ivermectin 430mcg/kg/day for 3 days; fluvoxamine, 50mg on day one then 50mg twice daily through 14 days. Medical-provider diagnosis of Long Covid, reported by participant by day 300 after randomization was a pre-specified secondary outcome; the primary outcome of the trial was severe Covid by day 14. Result: The median age was 45 years (IQR 37 to 54), 56% female of whom 7% were pregnant. Two percent identified as Native American; 3.7% as Asian; 7.4% as Black/African American; 82.8% as white; and 12.7% as Hispanic/Latino. The median BMI was 29.8 kg/m2 (IQR 27 to 34); 51% had a BMI >30kg/m2. Overall, 8.4% reported having received a diagnosis of Long Covid from a medical provider: 6.3% in the metformin group and 10.6% in the metformin control; 8.0% in the ivermectin group and 8.1% in the ivermectin control; and 10.1% in the fluvoxamine group and 7.5% in the fluvoxamine control. The Hazard Ratio (HR) for Long Covid in the metformin group versus control was 0.58 (95% CI 0.38 to 0.88); 0.99 (95% CI 0.592 to 1.643) in the ivermectin group; and 1.36 in the fluvoxamine group (95% CI 0.785 to 2.385). Conclusions: There was a 42% relative decrease in the incidence of Long Covid in the metformin group compared to its blinded control in a secondary outcome of this randomized phase 3 trial.

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.

Alternative models and randomization techniques for Bayesian response-adaptive randomization with binary outcomes.

BACKGROUND: Bayesian response-adaptive designs, which data adaptively alter the allocation ratio in favor of the better performing treatment, are often criticized for engendering a non-trivial probability of a subject imbalance in favor of the inferior treatment, inflating type I error rate, and increasing sample size requirements. The implementation of these designs using the Thompson sampling methods has generally assumed a simple beta-binomial probability model in the literature; however, the effect of these choices on the resulting design operating characteristics relative to other reasonable alternatives has not been fully examined. Motivated by the Advanced R2 Eperfusion STrategies for Refractory Cardiac Arrest trial, we posit that a logistic probability model coupled with an urn or permuted block randomization method will alleviate some of the practical limitations engendered by the conventional implementation of a two-arm Bayesian response-adaptive design with binary outcomes. In this article, we discuss up to what extent this solution works and when it does not. METHODS: A computer simulation study was performed to evaluate the relative merits of a Bayesian response-adaptive design for the Advanced R2 Eperfusion STrategies for Refractory Cardiac Arrest trial using the Thompson sampling methods based on a logistic regression probability model coupled with either an urn or permuted block randomization method that limits deviations from the evolving target allocation ratio. The different implementations of the response-adaptive design were evaluated for type I error rate control across various null response rates and power, among other performance metrics. RESULTS: The logistic regression probability model engenders smaller average sample sizes with similar power, better control over type I error rate, and more favorable treatment arm sample size distributions than the conventional beta-binomial probability model, and designs using the alternative randomization methods have a negligible chance of a sample size imbalance in the wrong direction. CONCLUSION: Pairing the logistic regression probability model with either of the alternative randomization methods results in a much improved response-adaptive design in regard to important operating characteristics, including type I error rate control and the risk of a sample size imbalance in favor of the inferior treatment.

Metabolite profiles associated with disease progression in influenza infection.

BACKGROUND: We performed metabolomic profiling to identify metabolites that correlate with disease progression and death. METHODS: We performed a study of adults hospitalized with Influenza A(H1N1)pdm09. Cases (n = 32) were defined by a composite outcome of death or transfer to the intensive care unit during the 60-day follow-up period. Controls (n = 64) were survivors who did not require transfer to the ICU. Four hundred and eight metabolites from eight families were measured on plasma sample at enrollment using a mass spectrometry based Biocrates platform. Conditional logistic regression was used to summarize the association of the individual metabolites and families with the composite outcome and its major two components. RESULTS: The ten metabolites with the strongest association with disease progression belonged to five different metabolite families with sphingolipids being the most common. The acylcarnitines, glycerides, sphingolipids and biogenic metabolite families had the largest odds ratios based on the composite endpoint. The tryptophan odds ratio for the composite is largely associated with death (OR 17.33: 95% CI, 1.60-187.76). CONCLUSIONS: Individuals that develop disease progression when infected with Influenza H1N1 have a metabolite signature that differs from survivors. Low levels of tryptophan had a strong association with death. REGISTRY: ClinicalTrials.gov Identifier: NCT01056185.

Characterizing COVID-19 clinical phenotypes and associated comorbidities and complication profiles.

PURPOSE: Heterogeneity has been observed in outcomes of hospitalized patients with coronavirus disease 2019 (COVID-19). Identification of clinical phenotypes may facilitate tailored therapy and improve outcomes. The purpose of this study is to identify specific clinical phenotypes across COVID-19 patients and compare admission characteristics and outcomes. METHODS: This is a retrospective analysis of COVID-19 patients from March 7, 2020 to August 25, 2020 at 14 U.S. hospitals. Ensemble clustering was performed on 33 variables collected within 72 hours of admission. Principal component analysis was performed to visualize variable contributions to clustering. Multinomial regression models were fit to compare patient comorbidities across phenotypes. Multivariable models were fit to estimate associations between phenotype and in-hospital complications and clinical outcomes. RESULTS: The database included 1,022 hospitalized patients with COVID-19. Three clinical phenotypes were identified (I, II, III), with 236 [23.1%] patients in phenotype I, 613 [60%] patients in phenotype II, and 173 [16.9%] patients in phenotype III. Patients with respiratory comorbidities were most commonly phenotype III (p = 0.002), while patients with hematologic, renal, and cardiac (all p<0.001) comorbidities were most commonly phenotype I. Adjusted odds of respiratory, renal, hepatic, metabolic (all p<0.001), and hematological (p = 0.02) complications were highest for phenotype I. Phenotypes I and II were associated with 7.30-fold (HR:7.30, 95% CI:(3.11-17.17), p<0.001) and 2.57-fold (HR:2.57, 95% CI:(1.10-6.00), p = 0.03) increases in hazard of death relative to phenotype III. CONCLUSION: We identified three clinical COVID-19 phenotypes, reflecting patient populations with different comorbidities, complications, and clinical outcomes. Future research is needed to determine the utility of these phenotypes in clinical practice and trial design.

Characterizing COVID-19 Clinical Phenotypes and Associated Comorbidities and Complication Profiles.

BACKGROUND: There is limited understanding of heterogeneity in outcomes across hospitalized patients with coronavirus disease 2019 (COVID-19). Identification of distinct clinical phenotypes may facilitate tailored therapy and improve outcomes. OBJECTIVE: Identify specific clinical phenotypes across COVID-19 patients and compare admission characteristics and outcomes. DESIGN, SETTINGS, AND PARTICIPANTS: Retrospective analysis of 1,022 COVID-19 patient admissions from 14 Midwest U.S. hospitals between March 7, 2020 and August 25, 2020. METHODS: Ensemble clustering was performed on a set of 33 vitals and labs variables collected within 72 hours of admission. K-means based consensus clustering was used to identify three clinical phenotypes. Principal component analysis was performed on the average covariance matrix of all imputed datasets to visualize clustering and variable relationships. Multinomial regression models were fit to further compare patient comorbidities across phenotype classification. Multivariable models were fit to estimate the association between phenotype and in-hospital complications and clinical outcomes. Main outcomes and measures: Phenotype classification (I, II, III), patient characteristics associated with phenotype assignment, in-hospital complications, and clinical outcomes including ICU admission, need for mechanical ventilation, hospital length of stay, and mortality. RESULTS: The database included 1,022 patients requiring hospital admission with COVID-19 (median age, 62.1 [IQR: 45.9-75.8] years; 481 [48.6%] male, 412 [40.3%] required ICU admission, 437 [46.7%] were white). Three clinical phenotypes were identified (I, II, III); 236 [23.1%] patients had phenotype I, 613 [60%] patients had phenotype II, and 173 [16.9%] patients had phenotype III. When grouping comorbidities by organ system, patients with respiratory comorbidities were most commonly characterized by phenotype III (p=0.002), while patients with hematologic (p<0.001), renal (p<0.001), and cardiac (p<0.001) comorbidities were most commonly characterized by phenotype I. The adjusted odds of respiratory (p<0.001), renal (p<0.001), and metabolic (p<0.001) complications were highest for patients with phenotype I, followed by phenotype II. Patients with phenotype I had a far greater odds of hepatic (p<0.001) and hematological (p=0.02) complications than the other two phenotypes. Phenotypes I and II were associated with 7.30-fold (HR: 7.30, 95% CI: (3.11-17.17), p<0.001) and 2.57-fold (HR: 2.57, 95% CI: (1.10-6.00), p=0.03) increases in the hazard of death, respectively, when compared to phenotype III. CONCLUSION: In this retrospective analysis of patients with COVID-19, three clinical phenotypes were identified. Future research is urgently needed to determine the utility of these phenotypes in clinical practice and trial design.