Rhabdomyolysis in Community Acquired Bacterial Sepsis – A Retrospective Cohort Study

BACKGROUND AND OBJECTIVES:Rhabdomyolysis is often associated with sepsis and gram positive bacterial pathogens are reported to be the most frequent cause of sepsis induced rhabdomyolysis. We report the pattern of infecting bacterial pathogens and associated causal factors in a South-Indian cohort. DESIGN, SETTING, PARTICIPANTS #ENTITYSTARTX00026; MEASUREMENTS:Retrospective cohort study of adult patients with community acquired bacterial sepsis complicated by rhabdomyolysis from March 2003--August 2008. Rhabdomyolysis was defined as serum creatine kinase >2000 IU/L. The study population was divided into group-I (sepsis with gram positive pathogens), group-II (sepsis with gram negative pathogens) and group-III (culture negative sepsis). RESULTS:103 patients (group I -15, group II- 34 and group III- 54) formed the study cohort. Mean age was 55 years and two-third had diabetes. Mean creatine kinase was 7114 IU/L and mean serum creatinine on admission was 2.4 mg/dl. Causative pathogen of sepsis was identified in 47.5%. Gram negative pathogens were more frequently (33%) associated with rhabdomyolysis than gram positive pathogens (14.5%). Lung was the commonest foci of sepsis (38.8%). 78.6% of the study population had one or more additional causal factor for rhabdomyolysis like statin intake, chronic alcoholism, hypokalemia, hypernatremia and hypophosphatemia. Mortality was 59%. CONCLUSIONS:Gram negative bacterial pathogens were more frequently associated with rhabdomyolysis than gram positive pathogens. Rhabdomyolysis in patients with sepsis is multifactorial and is associated with high mortality

The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers. MATERIALS AND METHODS: The Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics. RESULTS: Organized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access. CONCLUSIONS: The N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19

The National COVID Cohort Collaborative (N3C): Rationale, Design, Infrastructure, and Deployment

ObjectiveCoronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers.Materials and methodsThe Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics.ResultsOrganized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access.ConclusionsThe N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19

SARS-CoV-2 RNAemia predicts clinical deterioration and extrapulmonary complications from COVID-19

BackgroundThe determinants of coronavirus disease 2019 (COVID-19) disease severity and extrapulmonary complications (EPCs) are poorly understood. We characterized relationships between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNAemia and disease severity, clinical deterioration, and specific EPCs.MethodsWe used quantitative and digital polymerase chain reaction (qPCR and dPCR) to quantify SARS-CoV-2 RNA from plasma in 191 patients presenting to the emergency department with COVID-19. We recorded patient symptoms, laboratory markers, and clinical outcomes, with a focus on oxygen requirements over time. We collected longitudinal plasma samples from a subset of patients. We characterized the role of RNAemia in predicting clinical severity and EPCs using elastic net regression.ResultsOf SARS-CoV-2-positive patients, 23.0% (44 of 191) had viral RNA detected in plasma by dPCR, compared with 1.4% (2 of 147) by qPCR. Most patients with serial measurements had undetectable RNAemia within 10 days of symptom onset, reached maximum clinical severity within 16 days, and symptom resolution within 33 days. Initially RNAemic patients were more likely to manifest severe disease (odds ratio, 6.72 [95% confidence interval, 2.45-19.79]), worsening of disease severity (2.43 [1.07-5.38]), and EPCs (2.81 [1.26-6.36]). RNA loads were correlated with maximum severity (r = 0.47 [95% confidence interval, .20-.67]).ConclusionsdPCR is more sensitive than qPCR for the detection of SARS-CoV-2 RNAemia, which is a robust predictor of eventual COVID-19 severity and oxygen requirements, as well as EPCs. Because many COVID-19 therapies are initiated on the basis of oxygen requirements, RNAemia on presentation might serve to direct early initiation of appropriate therapies for the patients most likely to deteriorate

Using a 29-mRNA Host Response Classifier To Detect Bacterial Coinfections and Predict Outcomes in COVID-19 Patients Presenting to the Emergency Department

Clinicians in the emergency department (ED) face challenges in concurrently assessing patients with suspected COVID-19 infection, detecting bacterial coinfection, and determining illness severity since current practices require separate workflows. Here, we explore the accuracy of the IMX-BVN-3/IMX-SEV-3 29 mRNA host response classifiers in simultaneously detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and bacterial coinfections and predicting clinical severity of COVID-19. A total of 161 patients with PCR-confirmed COVID-19 (52.2% female; median age, 50.0 years; 51% hospitalized; 5.6% deaths) were enrolled at the Stanford Hospital ED. RNA was extracted (2.5 mL whole blood in PAXgene blood RNA), and 29 host mRNAs in response to the infection were quantified using Nanostring nCounter. The IMX-BVN-3 classifier identified SARS-CoV-2 infection in 151 patients with a sensitivity of 93.8%. Six of 10 patients undetected by the classifier had positive COVID tests more than 9 days prior to enrollment, and the remaining patients oscillated between positive and negative results in subsequent tests. The classifier also predicted that 6 (3.7%) patients had a bacterial coinfection. Clinical adjudication confirmed that 5/6 (83.3%) of the patients had bacterial infections, i.e., Clostridioides difficile colitis (n = 1), urinary tract infection (n = 1), and clinically diagnosed bacterial infections (n = 3), for a specificity of 99.4%. Two of 101 (2.8%) patients in the IMX-SEV-3 "Low" severity classification and 7/60 (11.7%) in the "Moderate" severity classification died within 30 days of enrollment. IMX-BVN-3/IMX-SEV-3 classifiers accurately identified patients with COVID-19 and bacterial coinfections and predicted patients' risk of death. A point-of-care version of these classifiers, under development, could improve ED patient management, including more accurate treatment decisions and optimized resource utilization. IMPORTANCE We assay the utility of the single-test IMX-BVN-3/IMX-SEV-3 classifiers that require just 2.5 mL of patient blood in concurrently detecting viral and bacterial infections as well as predicting the severity and 30-day outcome from the infection. A point-of-care device, in development, will circumvent the need for blood culturing and drastically reduce the time needed to detect an infection. This will negate the need for empirical use of broad-spectrum antibiotics and allow for antibiotic use stewardship. Additionally, accurate classification of the severity of infection and the prediction of 30-day severe outcomes will allow for appropriate allocation of hospital resources

Efficacy of levetiracetam, fosphenytoin, and valproate for established status epilepticus by age group (ESETT): a double-blind, responsive-adaptive, randomised controlled trial

BackgroundBenzodiazepine-refractory, or established, status epilepticus is thought to be of similar pathophysiology in children and adults, but differences in underlying aetiology and pharmacodynamics might differentially affect response to therapy. In the Established Status Epilepticus Treatment Trial (ESETT) we compared the efficacy and safety of levetiracetam, fosphenytoin, and valproate in established status epilepticus, and here we describe our results after extending enrolment in children to compare outcomes in three age groups.MethodsIn this multicentre, double-blind, response-adaptive, randomised controlled trial, we recruited patients from 58 hospital emergency departments across the USA. Patients were eligible for inclusion if they were aged 2 years or older, had been treated for a generalised convulsive seizure of longer than 5 min duration with adequate doses of benzodiazepines, and continued to have persistent or recurrent convulsions in the emergency department for at least 5 min and no more than 30 min after the last dose of benzodiazepine. Patients were randomly assigned in a response-adaptive manner, using Bayesian methods and stratified by age group (<18 years, 18-65 years, and >65 years), to levetiracetam, fosphenytoin, or valproate. All patients, investigators, study staff, and pharmacists were masked to treatment allocation. The primary outcome was absence of clinically apparent seizures with improved consciousness and without additional antiseizure medication at 1 h from start of drug infusion. The primary safety outcome was life-threatening hypotension or cardiac arrhythmia. The efficacy and safety outcomes were analysed by intention to treat. This study is registered in ClinicalTrials.gov, NCT01960075.FindingsBetween Nov 3, 2015, and Dec 29, 2018, we enrolled 478 patients and 462 unique patients were included: 225 children (aged <18 years), 186 adults (18-65 years), and 51 older adults (>65 years). 175 (38%) patients were randomly assigned to levetiracetam, 142 (31%) to fosphenyltoin, and 145 (31%) were to valproate. Baseline characteristics were balanced across treatments within age groups. The primary efficacy outcome was met in those treated with levetiracetam for 52% (95% credible interval 41-62) of children, 44% (33-55) of adults, and 37% (19-59) of older adults; with fosphenytoin in 49% (38-61) of children, 46% (34-59) of adults, and 35% (17-59) of older adults; and with valproate in 52% (41-63) of children, 46% (34-58) of adults, and 47% (25-70) of older adults. No differences were detected in efficacy or primary safety outcome by drug within each age group. With the exception of endotracheal intubation in children, secondary safety outcomes did not significantly differ by drug within each age group.InterpretationChildren, adults, and older adults with established status epilepticus respond similarly to levetiracetam, fosphenytoin, and valproate, with treatment success in approximately half of patients. Any of the three drugs can be considered as a potential first-choice, second-line drug for benzodiazepine-refractory status epilepticus.FundingNational Institute of Neurological Disorders and Stroke, National Institutes of Health

International comparisons of laboratory values from the 4CE collaborative to predict COVID-19 mortality

International audienceAbstract Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we evaluated the transportability of a mortality prediction algorithm using a multi-national network of healthcare systems. We predicted COVID-19 mortality using baseline commonly measured laboratory values and standard demographic and clinical covariates across healthcare systems, countries, and continents. Specifically, we trained a Cox regression model with nine measured laboratory test values, standard demographics at admission, and comorbidity burden pre-admission. These models were compared at site, country, and continent level. Of the 39,969 hospitalized patients with COVID-19 (68.6% male), 5717 (14.3%) died. In the Cox model, age, albumin, AST, creatine, CRP, and white blood cell count are most predictive of mortality. The baseline covariates are more predictive of mortality during the early days of COVID-19 hospitalization. Models trained at healthcare systems with larger cohort size largely retain good transportability performance when porting to different sites. The combination of routine laboratory test values at admission along with basic demographic features can predict mortality in patients hospitalized with COVID-19. Importantly, this potentially deployable model differs from prior work by demonstrating not only consistent performance but also reliable transportability across healthcare systems in the US and Europe, highlighting the generalizability of this model and the overall approach

Characterization of long COVID temporal sub-phenotypes by distributed representation learning from electronic health record data: a cohort studyResearch in Context

Summary: Background: Characterizing Post-Acute Sequelae of COVID (SARS-CoV-2 Infection), or PASC has been challenging due to the multitude of sub-phenotypes, temporal attributes, and definitions. Scalable characterization of PASC sub-phenotypes can enhance screening capacities, disease management, and treatment planning. Methods: We conducted a retrospective multi-centre observational cohort study, leveraging longitudinal electronic health record (EHR) data of 30,422 patients from three healthcare systems in the Consortium for the Clinical Characterization of COVID-19 by EHR (4CE). From the total cohort, we applied a deductive approach on 12,424 individuals with follow-up data and developed a distributed representation learning process for providing augmented definitions for PASC sub-phenotypes. Findings: Our framework characterized seven PASC sub-phenotypes. We estimated that on average 15.7% of the hospitalized COVID-19 patients were likely to suffer from at least one PASC symptom and almost 5.98%, on average, had multiple symptoms. Joint pain and dyspnea had the highest prevalence, with an average prevalence of 5.45% and 4.53%, respectively. Interpretation: We provided a scalable framework to every participating healthcare system for estimating PASC sub-phenotypes prevalence and temporal attributes, thus developing a unified model that characterizes augmented sub-phenotypes across the different systems. Funding: Authors are supported by National Institute of Allergy and Infectious Diseases, National Institute on Aging, National Center for Advancing Translational Sciences, National Medical Research Council, National Institute of Neurological Disorders and Stroke, European Union, National Institutes of Health, National Center for Advancing Translational Sciences

International Analysis of Electronic Health Records of Children and Youth Hospitalized With COVID-19 Infection in 6 Countries

International audienceAdditional sources of pediatric epidemiological and clinical data are needed to efficiently study COVID-19 in children and youth and inform infection prevention and clinical treatment of pediatric patients

Multinational characterization of neurological phenotypes in patients hospitalized with COVID-19

International audienceAbstract Neurological complications worsen outcomes in COVID-19. To define the prevalence of neurological conditions among hospitalized patients with a positive SARS-CoV-2 reverse transcription polymerase chain reaction test in geographically diverse multinational populations during early pandemic, we used electronic health records (EHR) from 338 participating hospitals across 6 countries and 3 continents (January–September 2020) for a cross-sectional analysis. We assessed the frequency of International Classification of Disease code of neurological conditions by countries, healthcare systems, time before and after admission for COVID-19 and COVID-19 severity. Among 35,177 hospitalized patients with SARS-CoV-2 infection, there was an increase in the proportion with disorders of consciousness (5.8%, 95% confidence interval [CI] 3.7–7.8%, p FDR < 0.001) and unspecified disorders of the brain (8.1%, 5.7–10.5%, p FDR < 0.001) when compared to the pre-admission proportion. During hospitalization, the relative risk of disorders of consciousness (22%, 19–25%), cerebrovascular diseases (24%, 13–35%), nontraumatic intracranial hemorrhage (34%, 20–50%), encephalitis and/or myelitis (37%, 17–60%) and myopathy (72%, 67–77%) were higher for patients with severe COVID-19 when compared to those who never experienced severe COVID-19. Leveraging a multinational network to capture standardized EHR data, we highlighted the increased prevalence of central and peripheral neurological phenotypes in patients hospitalized with COVID-19, particularly among those with severe disease