Katherine L. Hoffman

Longitudinal modified treatment policies (LMTP) have been recently developed as a novel method to define and estimate causal parameters that depend on the natural value of treatment. LMTPs represent an important advancement in causal inference for longitudinal studies as they allow the non-parametric definition and estimation of the joint effect of multiple categorical, numerical, or continuous exposures measured at several time points. We extend the LMTP methodology to problems in which the outcome is a time-to-event variable subject to right-censoring and competing risks. We present identification results and non-parametric locally efficient estimators that use flexible data-adaptive regression techniques to alleviate model misspecification bias, while retaining important asymptotic properties such as $\sqrt{n}$-consistency. We present an application to the estimation of the effect of the time-to-intubation on acute kidney injury amongst COVID-19 hospitalized patients, where death by other causes is taken to be the competing event.

Observational databases are invaluable resources when randomized clinical trials (RCTs) are infeasible or unavailable. However, the correctness of the conclusions gleaned from analyses of observational data hinges on the careful consideration of study design principles and choice of estimation methods.^1-4

Importance: Communication and adoption of modern study design and analytical techniques is of high importance for the improvement of clinical research from observational data. Objective: To 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 Organization’s (WHO) meta-analysis of corticosteroid randomized controlled trials (RCTs) as a benchmark. Design: Retrospective cohort study using longitudinal electronic health record data for 28 days from time of hospitalization. Settings: Multi-center New York City hospital system. Participants: Adult patients hospitalized between March 1-May 15, 2020 with COVID-19 and not on corticosteroids for chronic use. Intervention: Corticosteroid 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 outcome: 28-day mortality from time of hospitalization. Results: 3,298 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 WHO’s 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 Relevance: Clinical 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 Points: Question: How 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? Findings: In 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. Meaning: Clinical 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.

Rationale: COVID-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. Objectives: To identify and characterize distinct subphenotypes of COVID-19 critical illness defined by the post-intubation trajectory of Sequential Organ Failure Assessment (SOFA) score. Methods: Intubated 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 Results: Distinct 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. Conclusions: There 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 Commentary: Scientific Knowledge on the Subject: COVID-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 Field: We 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.

BACKGROUND: Mitochondrial dysfunction, a proposed mechanism of chronic obstructive pulmonary disease (COPD) pathogenesis, is associated with the leakage of mitochondrial DNA (mtDNA), which may be detected extracellularly in various bodily fluids. Despite evidence for the increased prevalence of chronic kidney disease in COPD subjects and for mitochondrial dysfunction in the kidneys of murine COPD models, whether urine mtDNA (u-mtDNA) associates with measures of disease severity in COPD is unknown. METHODS: Cell-free u-mtDNA, defined as copy number of mitochondrially encoded NADH dehydrogenase-1 ( MTND1 ) gene, was measured by quantitative PCR and normalized to urine creatinine in cell-free urine samples from participants in the Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS) cohort. Urine albumin/creatinine ratios (UACR) were measured in the same samples. Associations between u-mtDNA, UACR, and clinical disease parameters — including FEV ₁ % predicted, clinical measures of exercise tolerance, respiratory symptom burden, and chest CT measures of lung structure — were examined. RESULTS: U-mtDNA and UACR levels were measured in never smokers ( n = 63), smokers without airflow obstruction ( n = 107), participants with mild/moderate COPD ( n = 139), and participants with severe COPD ( n = 166). U-mtDNA was associated with increased respiratory symptom burden, especially among smokers without COPD. Significant sex differences in u-mtDNA levels were observed, with females having higher u-mtDNA levels across all study subgroups. U-mtDNA associated with worse spirometry and CT emphysema in males only and with worse respiratory symptoms in females only. Similar associations were not found with UACR. CONCLUSION: U-mtDNA levels may help to identify distinct clinical phenotypes and underlying pathobiological differences in males versus females with COPD. TRIAL REGISTRATION: This study has been registered at ClinicalTrials.gov ( NCT01969344). FUNDING: US NIH, National Heart, Lung and Blood Institute, supplemented by contributions made through the Foundation for the NIH and the COPD Foundation from AstraZeneca/MedImmune, Bayer, Bellerophon Therapeutics, Boehringer-Ingelheim Pharmaceuticals Inc., Chiesi Farmaceutici S.p.A., Forest Research Institute Inc., GlaxoSmithKline, Grifols Therapeutics Inc., Ikaria Inc., Novartis Pharmaceuticals Corporation, Nycomed GmbH, ProterixBio, Regeneron Pharmaceuticals Inc., Sanofi, Sunovion, Takeda Pharmaceutical Company, and Theravance Biopharma and Mylan.