Corticosteroids in COVID-19: Optimizing Observational Research through Target Trial Emulations

ImportanceCommunication and adoption of modern study design and analytical techniques is of high importance for the improvement of clinical research from observational data. ObjectiveTo compare (1) a modern method for causal inference including a target trial emulation framework and doubly robust estimation to (2) approaches common in the clinical literature such as Cox proportional hazards models. To do this, we estimate the effect of corticosteroids on mortality for moderate-to-severe coronavirus disease 2019 (COVID-19) patients. We use the World Health Organizations (WHO) meta-analysis of corticosteroid randomized controlled trials (RCTs) as a benchmark. DesignRetrospective cohort study using longitudinal electronic health record data for 28 days from time of hospitalization. SettingsMulti-center New York City hospital system. ParticipantsAdult patients hospitalized between March 1-May 15, 2020 with COVID-19 and not on corticosteroids for chronic use. InterventionCorticosteroid exposure defined as >0.5mg/kg methylprednisolone equivalent in a 24-hour period. For target trial emulation, interventions are (1) corticosteroids for six days if and when patient meets criteria for severe hypoxia and (2) no corticosteroids. For approaches common in clinical literature, treatment definitions used for variables in Cox regression models vary by study design (no time frame, one-, and five-days from time of severe hypoxia). Main outcome28-day mortality from time of hospitalization. Results3,298 patients (median age 65 (IQR 53-77), 60% male). 423 receive corticosteroids at any point during hospitalization, 699 die within 28 days of hospitalization. Target trial emulation estimates corticosteroids to reduce 28-day mortality from 32.2% (95% CI 30.9-33.5) to 25.7% (24.5-26.9). This estimate is qualitatively identical to the WHOs RCT meta-analysis odds ratio of 0.66 (0.53-0.82)). Hazard ratios using methods comparable to current corticosteroid research range in size and direction from 0.50 (0.41-0.62) to 1.08 (0.80-1.47). Conclusion and RelevanceClinical research based on observational data can unveil true causal relationships; however, the correctness of these effect estimates requires designing the study and analyzing the data based on principles which are different from the current standard in clinical research. Key PointsO_ST_ABSQuestionC_ST_ABSHow do modern methods for causal inference compare to approaches common in the clinical literature when estimating the effect of corticosteroids on mortality for moderate-to-severe coronavirus disease 2019 (COVID-19) patients? FindingsIn an analysis using retrospective data for 3,298 hospitalized COVID-19 patients, target trial emulation using a doubly robust estimation procedure successfully recovers a randomized controlled trial (RCT) meta-analysis benchmark. In contrast, analytic approaches common in the clinical research literature generally cannot recover the benchmark. MeaningClinical research based on observational data can unveil true causal relations. However, the correctness of these effect estimates requires designing and analyzing the data based on principles which are different from the current standard in clinical research. Widespread communication and adoption of these analytical techniques are of high importance for the improvement of clinical research.

Machine Learning for Identifying Data-Driven Subphenotypes of Incident Post-Acute SARS-CoV-2 Infection Conditions with Large Scale Electronic Health Records: Findings from the RECOVER Initiative

The post-acute sequelae of SARS-CoV-2 infection (PASC) refers to a broad spectrum of symptoms and signs that are persistent, exacerbated, or newly incident in the post-acute SARS-CoV-2 infection period of COVID-19 patients. Most studies have examined these conditions individually without providing concluding evidence on co-occurring conditions. To answer this question, this study leveraged electronic health records (EHRs) from two large clinical research networks from the national Patient-Centered Clinical Research Network (PCORnet) and investigated patients newly incident diagnoses that appeared within 30 to 180 days after a documented SARS-CoV-2 infection. Through machine learning, we identified four reproducible subphenotypes of PASC dominated by blood and circulatory system, respiratory, musculoskeletal and nervous system, and digestive system problems, respectively. We also demonstrated that these subphenotypes were associated with distinct patterns of patient demographics, underlying conditions present prior to SARS-CoV-2 infection, acute infection phase severity, and use of new medications in the post-acute period. Our study provides novel insights into the heterogeneity of PASC and can inform stratified decision-making in the treatment of COVID-19 patients with PASC conditions.

Understanding Post-Acute Sequelae of SARS-CoV-2 Infection through Data-Driven Analysis with the Longitudinal Electronic Health Records: Findings from the RECOVER Initiative

Recent studies have investigated post-acute sequelae of SARS-CoV-2 infection (PASC) using real-world patient data such as electronic health records (EHR). Prior studies have typically been conducted on patient cohorts with small sample sizes1 or specific patient populations2,3 limiting generalizability. This study aims to characterize PASC using the EHR data warehouses from two large national patient-centered clinical research networks (PCORnet), INSIGHT and OneFlorida+, which include 11 million patients in New York City (NYC) and 16.8 million patients in Florida respectively. With a high-throughput causal inference pipeline using high-dimensional inverse propensity score adjustment, we identified a broad list of diagnoses and medications with significantly higher incidence 30-180 days after the laboratory-confirmed SARS-CoV-2 infection compared to non-infected patients. We found more PASC diagnoses and a higher risk of PASC in NYC than in Florida, which highlights the heterogeneity of PASC in different populations.

Auto-Immunoproteomics Analysis of COVID-19 ICU Patients Revealed Increased Levels of Autoantibodies Related to Male Reproductive System

The role of autoantibodies in coronavirus disease (COVID-19) complications is not yet fully understood. The current investigation screened two independent cohorts of 97 COVID-19 patients (Discovery (Disc) cohort from Qatar (n = 49) and Replication (Rep) cohort from New York (n = 48)) utilizing high-throughput KoRectly Expressed (KREX) immunome protein-array technology. Autoantibody responses to 57 proteins were significantly altered in the COVID-19 Disc cohort compared to healthy controls (P [≤] 0.05). The Rep cohort had altered autoantibody responses against 26 proteins compared to non-COVID-19 ICU patients that served as controls. Both cohorts showed substantial similarities (r2 = 0.73) and exhibited higher autoantibodies responses to numerous transcription factors, immunomodulatory proteins, and human disease markers. Analysis of the combined cohorts revealed elevated autoantibody responses against SPANXN4, STK25, ATF4, PRKD2, and CHMP3 proteins in COVID-19 patients. KREX analysis of the specific IgG autoantibody responses indicates that the targeted host proteins are supposedly increased in COVID-19 patients. The autoantigen-autoantibody response was cross-validated for SPANXN4 and STK25 proteins using Uniprot BLASTP and sequence alignment tools. SPANXN4 is essential for spermiogenesis and male fertility, which may predict a potential role for this protein in COVID-19 associated male reproductive tract complications and warrants further research. Significance StatementCoronavirus disease (COVID-19), caused by the SARS-CoV-2 virus, has emerged as a global pandemic with a high morbidity rate and multiorgan complications. It is observed that the host immune system contributes to the varied responses to COVID-19 pathogenesis. Autoantibodies, immune system proteins that mistakenly target the bodys own tissue, may underlie some of this variation. We screened total IgG autoantibody responses against 1,318 human proteins in two COVID-19 patient cohorts. We observed several novel markers in COVID-19 patients that are associated with male fertility, such as sperm protein SPANXN4, STK25, and the apoptotic factor ATF4. Particularly, elevated levels of autoantibodies against the testicular tissue-specific protein SPANXN4 offer significant evidence of anticipating the protein role in COVID-19 associated male reproductive complications.

The maladaptive vascular response in COVID-19 acute respiratory distress syndrome and recovery

Vascular injury is a menacing element of acute respiratory distress syndrome (ARDS) pathogenesis. To better understand the role of vascular injury in COVID-19 ARDS, we used lung autopsy immunohistochemistry and blood proteomics from COVID-19 subjects at distinct timepoints in disease pathogenesis, including a hospitalized cohort at risk of ARDS development ("at risk", N=59), an intensive care unit cohort with ARDS ("ARDS", N=31), and a cohort recovering from ARDS ("recovery", N=12). COVID-19 ARDS lung autopsy tissue revealed an association between vascular injury and platelet-rich microthrombi. This link guided the derivation of a protein signature in the at risk cohort characterized by lower expression of vascular proteins in subjects who died, an early signal of vascular limitation termed the maladaptive vascular response. These findings were replicated in COVID-19 ARDS subjects, as well as when bacterial and influenza ARDS patients (N=29) were considered, hinting at a common final pathway of vascular injury that is more disease (ARDS) then cause (COVID-19) specific, and may be related to vascular cell death. Among recovery subjects, our vascular signature identified patients with good functional recovery one year later. This vascular injury signature could be used to identify ARDS patients most likely to benefit from vascular targeted therapies.

Hyperglycemia in Acute COVID-19 is Characterized by Adipose Tissue Dysfunction and Insulin Resistance

COVID-19 has proven to be a metabolic disease resulting in adverse outcomes in individuals with diabetes or obesity. Patients infected with SARS-CoV-2 and hyperglycemia suffer from longer hospital stays, higher risk of developing acute respiratory distress syndrome (ARDS), and increased mortality compared to those who do not develop hyperglycemia. Nevertheless, the pathophysiological mechanism(s) of hyperglycemia in COVID-19 remains poorly characterized. Here we show that insulin resistance rather than pancreatic beta cell failure is the prevalent cause of hyperglycemia in COVID-19 patients with ARDS, independent of glucocorticoid treatment. A screen of protein hormones that regulate glucose homeostasis reveals that the insulin sensitizing adipokine adiponectin is reduced in hyperglycemic COVID-19 patients. Hamsters infected with SARS-CoV-2 also have diminished expression of adiponectin. Together these data suggest that adipose tissue dysfunction may be a driver of insulin resistance and adverse outcomes in acute COVID-19.

Evaluation of albumin kinetics in mechanically ventilated patients with COVID-19 compared to those with sepsis-induced ARDS

COVID-19 outcomes like mortality have been associated with albumin alteration. However, it is unclear whether albumin changes in COVID-19 are pathogen specific or not. To this end, we characterized the kinetics of serum albumin in mechanically ventilated patients with COVID-19 compared to mechanically ventilated patients with sepsis-induced Acute Respiratory Distress Syndrome (ARDS). We discovered two phases of alterations in albumin levels during the course of Covid-19 critical illness, but not for the sepsis-induced ARDS. Our findings suggest the metabolic effects of COVID-19 are pathogen-specific and albumin recovery may signal the cessation of a deleterious immune response in this disease.

Novel clinical subphenotypes in COVID-19: derivation, validation, prediction, temporal patterns, and interaction with social determinants of health

The coronavirus disease 2019 (COVID-19) is heterogeneous and our understanding of the biological mechanisms of host response to the novel viral infection remains limited. Identification of meaningful clinical subphenotypes may benefit pathophysiological study, clinical practice, and clinical trials. Here, our aim was to derive and validate COVID-19 subphenotypes using machine learning and routinely collected clinical data, assess temporal patterns of these subphenotypes during the pandemic course, and examine their interaction with social determinants of health (SDoH). We retrospectively analyzed 14418 COVID-19 patients in five major medical centers in New York City (NYC), between March 1 and June 12, 2020. Using clustering analysis, four biologically distinct subphenotypes were derived in the development cohort (N = 8199). Importantly, the identified subphenotypes were highly predictive of clinical outcomes (especially 60-day mortality). Sensitivity analyses in the development cohort, and re-derivation and prediction in the internal (N = 3519) and external (N = 3519) validation cohorts confirmed the reproducibility and usability of the subphenotypes. Further analyses showed varying subphenotype prevalence across the peak of the outbreak in NYC. We also found that SDoH specifically influenced mortality outcome in Subphenotype IV, which is associated with older age, worse clinical manifestation, and high comorbidity burden. Our findings may lead to a better understanding of how COVID-19 causes disease in different populations and potentially benefit clinical trial development. The temporal patterns and SDoH implications of the subphenotypes may add new insights to health policy to reduce social disparity in the pandemic.

Identifying organ dysfunction trajectory-based subphenotypes in critically ill patients with COVID-19

RationaleCOVID-19-associated respiratory failure offers the unprecedented opportunity to evaluate the differential host response to a uniform pathogenic insult. Prior studies of Acute Respiratory Distress Syndrome (ARDS) have identified subphenotypes with differential outcomes. Understanding whether there are distinct subphenotypes of severe COVID-19 may offer insight into its pathophysiology. ObjectivesTo identify and characterize distinct subphenotypes of COVID-19 critical illness defined by the post-intubation trajectory of Sequential Organ Failure Assessment (SOFA) score. MethodsIntubated COVID-19 patients at two hospitals in New York city were leveraged as development and validation cohorts. Patients were grouped into mild, intermediate, and severe strata by their baseline post-intubation SOFA. Hierarchical agglomerative clustering was performed within each stratum to detect subphenotypes based on similarities amongst SOFA score trajectories evaluated by Dynamic Time Warping. Statistical tests defined trajectory subphenotype predictive markers. Measurements and Main ResultsDistinct worsening and recovering subphenotypes were identified within each stratum, which had distinct 7-day post-intubation SOFA progression trends. Patients in the worsening suphenotypes had a higher mortality than those in the recovering subphenotypes within each stratum (mild stratum, 29.7% vs. 10.3%, p=0.033; intermediate stratum, 29.3% vs. 8.0%, p=0.002; severe stratum, 53.7% vs. 22.2%, p<0.001). Worsening and recovering subphenotypes were replicated in the validation cohort. Routine laboratory tests, vital signs, and respiratory variables rather than demographics and comorbidities were predictive of the worsening and recovering subphenotypes. ConclusionsThere are clear worsening and recovering subphenotypes of COVID-19 respiratory failure after intubation, which are more predictive of outcomes than baseline severity of illness. Organ dysfunction trajectory may be well suited as a surrogate for research in COVID-19 respiratory failure. At a Glance CommentaryO_ST_ABSScientific Knowledge on the SubjectC_ST_ABSCOVID-19 associated respiratory failure leads to a significant risk of morbidity and mortality. It is clear that there is heterogeneity in the viral-induced host response leading to differential outcomes, even amongst those treated with mechanical ventilation. There are many studies of COVID-19 disease which use intubation status as an outcome or an inclusion criterion. However, there is less understanding of the post intubation course in COVID-19. What This Study Adds to the FieldWe have developed and validated a novel subphenotyping model based on post-intubation organ dysfunction trajectory in COVID-19 patients. Specifically, we identified clear worsening and recovering organ dysfunction trajectory subphenotypes, which are more predictive of outcomes than illness severity at baseline. Dynamic inflammatory markers and ventilator variables rather than baseline severity of illness, demographics and comorbidities differentiate the worsening and recovering subphenotypes. Trajectory subphenotypes offer a potential road map for understanding the evolution of critical illness in COVID-19.

Risk of Ischemic Stroke in Patients with Covid-19 versus Patients with Influenza

Key PointsO_ST_ABSQuestionC_ST_ABSHow does the risk of acute ischemic stroke compare between patients with Covid-19 and patients with influenza (a respiratory virus previously linked to stroke)? FindingsIn this large retrospective cohort study conducted at two academic hospitals in New York City, patients with emergency department visits and hospitalizations with Covid-19 were approximately seven times as likely to have an acute ischemic stroke as compared to patients with emergency department visits or hospitalizations with influenza. MeaningPatients with Covid-19 are at heightened risk for acute ischemic stroke as compared to patients with influenza. ImportanceCase series without control groups suggest that Covid-19 may cause ischemic stroke, but whether Covid-19 is associated with a higher risk of ischemic stroke than would be expected from a viral respiratory infection is uncertain. ObjectiveTo compare the rate of ischemic stroke between patients with Covid-19 and patients with influenza, a respiratory viral illness previously linked to stroke. DesignA retrospective cohort study. SettingTwo academic hospitals in New York City. ParticipantsWe included adult patients with emergency department visits or hospitalizations with Covid-19 from March 4, 2020 through May 2, 2020. Our comparison cohort included adult patients with emergency department visits or hospitalizations with influenza A or B from January 1, 2016 through May 31, 2018 (calendar years spanning moderate and severe influenza seasons). ExposuresCovid-19 infection confirmed by evidence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the nasopharynx by polymerase chain reaction, and laboratory-confirmed influenza A or B. Main Outcomes and MeasuresA panel of neurologists adjudicated the primary outcome of acute ischemic stroke and its clinical characteristics, etiological mechanisms, and outcomes. We used logistic regression to compare the proportion of Covid-19 patients with ischemic stroke versus the proportion among patients with influenza. ResultsAmong 2,132 patients with emergency department visits or hospitalizations with Covid-19, 31 patients (1.5%; 95% confidence interval [CI], 1.0%-2.1%) had an acute ischemic stroke. The median age of patients with stroke was 69 years (interquartile range, 66-78) and 58% were men. Stroke was the reason for hospital presentation in 8 (26%) cases. For our comparison cohort, we identified 1,516 patients with influenza, of whom 0.2% (95% CI, 0.0-0.6%) had an acute ischemic stroke. After adjustment for age, sex, and race, the likelihood of stroke was significantly higher with Covid-19 than with influenza infection (odds ratio, 7.5; 95% CI, 2.3-24.9). Conclusions and RelevanceApproximately 1.5% of patients with emergency department visits or hospitalizations with Covid-19 experienced ischemic stroke, a rate 7.5-fold higher than in patients with influenza. Future studies should investigate the thrombotic mechanisms in Covid-19 in order to determine optimal strategies to prevent disabling complications like ischemic stroke.