Heterogeneity of treatment effect of vilobelimab in COVID-19: a secondary analysis of a randomised controlled trial.

In a phase 3 trial (PANAMO, NCT04333420), vilobelimab, a complement 5a (C5a) inhibitor, reduced 28-day mortality in mechanically ventilated COVID-19 patients. This post hoc analysis of 368 patients aimed to explore treatment heterogeneity through unsupervised learning. All available clinical variables at baseline were used as input. Treatment heterogeneity was assessed using latent class analysis (LCA), Ward's hierarchical clustering (HC) and the adjudication to previously described clinical sepsis phenotypes. The primary outcome was 28-day mortality. For LCA, a 2-class latent model was deemed most suitable. In the LCA model, 82 (22%) patients were assigned to class 1 and 286 (78%) to class 2. Class 1 was defined by more severely ill patients with significantly higher mortality. In an adjusted logistic regression, no heterogeneity of treatment effect (HTE) between classes was observed (p = 0.998). For HC, no significant classes were found (p = 0.669). Using the previously described clinical sepsis subtypes, 41 patients (11%) were adjudicated subtype alpha (α), 17 (5%) beta (ß), 112 (30%) delta (δ) and 198 (54%) gamma (γ). HTE was observed between clinical subtypes (p = 0.001) with improved 28-day mortality after treatment with vilobelimab for the δ subtype (OR = 0.17, 95% CI 0.07-0.40, p < 0.001). No signal for harm of treatment with vilobelimab was observed in any class or clinical subtype. Overall, treatment effect with vilobelimab was consistent across different classes and subtypes, except for the δ subtype, suggesting potential additional benefit for the most severely ill patients.

Automatic Population of the Case Report Forms for an International Multifactorial Adaptive Platform Trial Amid the COVID-19 Pandemic.

OBJECTIVES: To automatically populate the case report forms (CRFs) for an international, pragmatic, multifactorial, response-adaptive, Bayesian COVID-19 platform trial. METHODS: The locations of focus included 27 hospitals and 2 large electronic health record (EHR) instances (1 Cerner Millennium and 1 Epic) that are part of the same health system in the United States. This paper describes our efforts to use EHR data to automatically populate four of the trial's forms: baseline, daily, discharge, and response-adaptive randomization. RESULTS: Between April 2020 and May 2022, 417 patients from the UPMC health system were enrolled in the trial. A MySQL-based extract, transform, and load pipeline automatically populated 499 of 526 CRF variables. The populated forms were statistically and manually reviewed and then reported to the trial's international data coordinating center. CONCLUSIONS: We accomplished automatic population of CRFs in a large platform trial and made recommendations for improving this process for future trials.

Adaptation of the Risk Analysis Index for Frailty Assessment Using Diagnostic Codes.

Importance: Frailty is associated with adverse outcomes after even minor physiologic stressors. The validated Risk Analysis Index (RAI) quantifies frailty; however, existing methods limit application to in-person interview (clinical RAI) and quality improvement datasets (administrative RAI). Objective: To expand the utility of the RAI utility to available International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) administrative data, using the National Inpatient Sample (NIS). Design, Setting, and Participants: RAI parameters were systematically adapted to ICD-10-CM codes (RAI-ICD) and were derived (NIS 2019) and validated (NIS 2020). The primary analysis included survey-weighed discharge data among adults undergoing major surgical procedures. Additional external validation occurred by including all operative and nonoperative hospitalizations in the NIS (2020) and in a multihospital health care system (UPMC, 2021-2022). Data analysis was conducted from January to May 2023. Exposures: RAI parameters and in-hospital mortality. Main Outcomes and Measures: The association of RAI parameters with in-hospital mortality was calculated and weighted using logistic regression, generating an integerized RAI-ICD score. After initial validation, thresholds defining categories of frailty were selected by a full complement of test statistics. Rates of elective admission, length of stay, hospital charges, and in-hospital mortality were compared across frailty categories. C statistics estimated model discrimination. Results: RAI-ICD parameters were weighted in the 9 548 206 patients who were hospitalized (mean [SE] age, 55.4 (0.1) years; 3 742 330 male [weighted percentage, 39.2%] and 5 804 431 female [weighted percentage, 60.8%]), modeling in-hospital mortality (2.1%; 95% CI, 2.1%-2.2%) with excellent derivation discrimination (C statistic, 0.810; 95% CI, 0.808-0.813). The 11 RAI-ICD parameters were adapted to 323 ICD-10-CM codes. The operative validation population of 8 113 950 patients (mean [SE] age, 54.4 (0.1) years; 3 148 273 male [weighted percentage, 38.8%] and 4 965 737 female [weighted percentage, 61.2%]; in-hospital mortality, 2.5% [95% CI, 2.4%-2.5%]) mirrored the derivation population. In validation, the weighted and integerized RAI-ICD yielded good to excellent discrimination in the NIS operative sample (C statistic, 0.784; 95% CI, 0.782-0.786), NIS operative and nonoperative sample (C statistic, 0.778; 95% CI, 0.777-0.779), and the UPMC operative and nonoperative sample (C statistic, 0.860; 95% CI, 0.857-0.862). Thresholds defining robust (RAI-ICD <27), normal (RAI-ICD, 27-35), frail (RAI-ICD, 36-45), and very frail (RAI-ICD >45) strata of frailty maximized precision (F1 = 0.33) and sensitivity and specificity (Matthews correlation coefficient = 0.26). Adverse outcomes increased with increasing frailty. Conclusion and Relevance: In this cohort study of hospitalized adults, the RAI-ICD was rigorously adapted, derived, and validated. These findings suggest that the RAI-ICD can extend the quantification of frailty to inpatient adult ICD-10-CM-coded patient care datasets.

From ICU Syndromes to ICU Subphenotypes: Consensus Report and Recommendations For Developing Precision Medicine in ICU.

Critical care uses syndromic definitions to describe patient groups for clinical practice and research. There is growing recognition that a "precision medicine" approach is required and that integrated biologic and physiologic data identify reproducible subpopulations that may respond differently to treatment. This article reviews the current state of the field and considers how to successfully transition to a precision medicine approach. In order to impact clinical care, identified subpopulations must do more than differentiate prognosis. They must differentiate response to treatment, ideally by defining subgroups with distinct functional or pathobiological mechanisms (endotypes). There are now multiple examples of reproducible subpopulations of sepsis, acute respiratory distress syndrome, and acute kidney or brain injury described using clinical, physiological, and/or biological data. Many of these subpopulations have demonstrated the potential to define differential treatment response, largely in retrospective studies, and that the same treatment-responsive subpopulations may cross multiple clinical syndromes (treatable traits). To bring about a change in clinical practice, a precision medicine approach must be evaluated in prospective clinical studies requiring novel adaptive trial designs. Several such studies are underway but there are multiple challenges to be tackled. Such subpopulations must be readily identifiable and be applicable to all critically ill populations around the world. Subdividing clinical syndromes into subpopulations will require large patient numbers. Global collaboration of investigators, clinicians, industry and patients over many years will therefore be required to transition to a precision medicine approach and ultimately realize treatment advances seen in other medical fields. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Association Between Medicare's Sepsis Reporting Policy (SEP-1) and the Documentation of a Sepsis Diagnosis in the Clinical Record.

STUDY DESIGN: Interrupted time series analysis of a retrospective, electronic health record cohort. OBJECTIVE: To determine the association between the implementation of Medicare's sepsis reporting measure (SEP-1) and sepsis diagnosis rates as assessed in clinical documentation. BACKGROUND: The role of health policy in the effort to improve sepsis diagnosis remains unclear. PATIENTS AND METHODS: Adult patients hospitalized with suspected infection and organ dysfunction within 6 hours of presentation to the emergency department, admitted to one of 11 hospitals in a multi-hospital health system from January 2013 to December 2017. Clinician-diagnosed sepsis, as reflected by the inclusion of the terms "sepsis" or "septic" in the text of clinical notes in the first two calendar days following presentation. RESULTS: Among 44,074 adult patients with sepsis admitted to 11 hospitals over 5 years, the proportion with sepsis documentation was 32.2% just before the implementation of SEP-1 in the third quarter of 2015 and increased to 37.3% by the fourth quarter of 2017. Of the 9 post-SEP-1 quarters, 8 had odds ratios for a sepsis diagnosis >1 (overall range: 0.98-1.26; P value for a joint test of statistical significance = 0.005). The effects were clinically modest, with a maximum effect of an absolute increase of 4.2% (95% CI: 0.9-7.8) at the end of the study period. The effect was greater in patients who did not require vasopressors compared with patients who required vasopressors ( P value for test of interaction = 0.02). CONCLUSIONS: SEP-1 implementation was associated with modest increases in sepsis diagnosis rates, primarily among patients who did not require vasoactive medications.

Cardiac Function Before Sepsis and Clinical Outcomes.

This cohort study characterizes heterogeneity in cardiac function prior to sepsis and describes associations with hospitalization outcomes and mortality.

Identification of a hyperinflammatory sepsis phenotype using protein biomarker and clinical data in the ProCESS randomized trial.

Sepsis is a heterogeneous syndrome and phenotypes have been proposed using clinical data. Less is known about the contribution of protein biomarkers to clinical sepsis phenotypes and their importance for treatment effects in randomized trials of resuscitation. The objective is to use both clinical and biomarker data in the Protocol-Based Care for Early Septic Shock (ProCESS) randomized trial to determine sepsis phenotypes and to test for heterogeneity of treatment effect by phenotype comparing usual care to protocolized early, goal-directed therapy(EGDT). In this secondary analysis of a subset of patients with biomarker sampling in the ProCESS trial (n = 543), we identified sepsis phenotypes prior to randomization using latent class analysis of 20 clinical and biomarker variables. Logistic regression was used to test for interaction between phenotype and treatment arm for 60-day inpatient mortality. Among 543 patients with severe sepsis or septic shock in the ProCESS trial, a 2-class model best fit the data (p = 0.01). Phenotype 1 (n = 66, 12%) had increased IL-6, ICAM, and total bilirubin and decreased platelets compared to phenotype 2 (n = 477, 88%, p < 0.01 for all). Phenotype 1 had greater 60-day inpatient mortality compared to Phenotype 2 (41% vs 16%; p < 0.01). Treatment with EGDT was associated with worse 60-day inpatient mortality compared to usual care (58% vs. 23%) in Phenotype 1 only (p-value for interaction = 0.05). The 60-day inpatient mortality was similar comparing EGDT to usual care in Phenotype 2 (16% vs. 17%). We identified 2 sepsis phenotypes using latent class analysis of clinical and protein biomarker data at randomization in the ProCESS trial. Phenotype 1 had increased inflammation, organ dysfunction and worse clinical outcomes compared to phenotype 2. Response to EGDT versus usual care differed by phenotype.

Distribution of Acute and Chronic Kidney Disease Across Clinical Phenotypes for Sepsis.

BACKGROUND: Sepsis is the most common cause of acute kidney injury (AKI) in critically ill patients. Four phenotypes (α, ß, γ, δ) for sepsis, which have different outcomes and responses to treatment, were described using routine clinical data in the electronic health record. RESEARCH QUESTION: Do the frequencies of AKI, acute kidney disease (AKD), chronic kidney disease (CKD), and AKI on CKD differ by sepsis phenotype? STUDY DESIGN AND METHODS: This was a secondary analysis of a randomized clinical trial of early resuscitation, including patients with septic shock at 31 sites. After excluding patients with end-stage kidney disease and missing data, we determined frequencies of the following clinical outcomes: AKI (defined within 24 h as Kidney Disease: Improving Global Outcomes stages 2 or 3 or stage 1 with tissue inhibitor of metalloproteinases-2 × insulin-like growth factor binding protein 7 value of > 2.0), CKD, and AKD (persistence of AKI at any stage on day 7 after enrollment) across four phenotypes. We performed multivariable logistic regression to assess the risk-adjusted association between development of AKI and AKD and phenotype. RESULTS: Among 1,090 eligible patients, 543 patients (50%) had AKI. Across phenotypes, the frequencies of AKI varied, being highest in the δ and ß phenotypes (78% and 71%, respectively) and the lowest in the α phenotype (26%; P < .001). AKD occurred most often in the δ phenotype (41%) and least often in the α phenotype (8%; P < .001). The highest frequencies of CKD and of AKI on CKD were found in the ß phenotype (53% and 38% respectively; P < .001 for both). In the multivariable logistic regression models (α phenotype as reference), δ phenotype showed the strongest association with AKI (OR, 12.33; 95% CI, 7.81-19.47; P < .001) and AKD (OR, 9.18; 95% CI, 5.44-15.51; P < .001). INTERPRETATION: The rates of AKI and AKD differed across clinical sepsis phenotypes and are more common among patients with phenotypes ß and δ. Phenotype ß showed a higher level of underlying CKD that predisposed patients to new AKI. The α and γ phenotypes showed lower frequencies of AKI and less progression to AKD.

Reframing sepsis immunobiology for translation: towards informative subtyping and targeted immunomodulatory therapies.

Sepsis is a common and deadly condition. Within the current model of sepsis immunobiology, the framing of dysregulated host immune responses into proinflammatory and immunosuppressive responses for the testing of novel treatments has not resulted in successful immunomodulatory therapies. Thus, the recent focus has been to parse observable heterogeneity into subtypes of sepsis to enable personalised immunomodulation. In this Personal View, we highlight that many fundamental immunological concepts such as resistance, disease tolerance, resilience, resolution, and repair are not incorporated into the current sepsis immunobiology model. The focus for addressing heterogeneity in sepsis should be broadened beyond subtyping to encompass the identification of deterministic molecular networks or dominant mechanisms. We explicitly reframe the dysregulated host immune responses in sepsis as altered homoeostasis with pathological disruption of immune-driven resistance, disease tolerance, resilience, and resolution mechanisms. Our proposal highlights opportunities to identify novel treatment targets and could enable successful immunomodulation in the future.

Data-derived subtypes of delirium during critical illness.

BACKGROUND: To understand delirium heterogeneity, prior work relied on psychomotor symptoms or risk factors to identify subtypes. Data-driven approaches have used machine learning to identify biologically plausible, treatment-responsive subtypes of other acute illnesses but have not been used to examine delirium. METHODS: We conducted a secondary analysis of a large, multicenter prospective cohort study involving adults in medical or surgical ICUs with respiratory failure or shock who experienced delirium per the Confusion Assessment Method for the ICU. We used data collected before delirium diagnosis in an unsupervised latent class model to identify delirium subtypes and then compared demographics, clinical characteristics, and outcomes between subtypes in the final model. FINDINGS: The 731 patients who developed delirium during critical illness had a median age of 63 [IQR, 54-72] years, a median Sequential Organ Failure Assessment score of 8.0 [6.0-11.0] and 613 [83.4%] were mechanically ventilated at delirium identification. A four-class model best fit the data with 50% of patients in subtype (ST) 1, 18% in subtype 2, 17% in subtype 3, and 14% in subtype 4. Subtype 2-which had more shock and kidney impairment-had the highest mortality (33% [ST2] vs. 17% [ST1], 25% [ST3], and 17% [ST4], p = 0.003). Subtype 4-which received more benzodiazepines and opioids-had the longest duration of delirium (6 days [ST4] vs. 3 [ST1], 4 [ST2], and 3 days [ST3], p < 0.001) and coma (4 days [ST4] vs. 2 [ST1], 1 [ST2], and 2 days [ST3], p < 0.001). Each of the four data-derived delirium subtypes was observed within previously identified psychomotor and risk factor-based delirium subtypes. Clinically significant cognitive impairment affected all subtypes at follow-up, but its severity did not differ by subtype (3-month, p = 0.26; 12-month, p = 0.80). INTERPRETATION: The four data-derived delirium subtypes identified in this study should now be validated in independent cohorts, examined for differential treatment effects in trials, and inform mechanistic work evaluating treatment targets. FUNDING: National Institutes of Health (T32HL007820, R01AG027472).

Uncovering heterogeneity in sepsis: a comparative analysis of subphenotypes.

PURPOSE: The heterogeneity in sepsis is held responsible, in part, for the lack of precision treatment. Many attempts to identify subtypes of sepsis patients identify those with shared underlying biology or outcomes. To date, though, there has been limited effort to determine overlap across these previously identified subtypes. We aimed to determine the concordance of critically ill patients with sepsis classified by four previously described subtype strategies. METHODS: This secondary analysis of a multicenter prospective observational study included 522 critically ill patients with sepsis assigned to four previously established subtype strategies, primarily based on: (i) clinical data in the electronic health record (α, ß, γ, and δ), (ii) biomarker data (hyper- and hypoinflammatory), and (iii-iv) transcriptomic data (Mars1-Mars4 and SRS1-SRS2). Concordance was studied between different subtype labels, clinical characteristics, biological host response aberrations, as well as combinations of subtypes by sepsis ensembles. RESULTS: All four subtype labels could be adjudicated in this cohort, with the distribution of the clinical subtype varying most from the original cohort. The most common subtypes in each of the four strategies were γ (61%), which is higher compared to the original classification, hypoinflammatory (60%), Mars2 (35%), and SRS2 (54%). There was no clear relationship between any of the subtyping approaches (Cramer's V = 0.086-0.456). Mars2 and SRS1 were most alike in terms of host response biomarkers (p = 0.079-0.424), while other subtype strategies showed no clear relationship. Patients enriched for multiple subtypes revealed that characteristics and outcomes differ dependent on the combination of subtypes made. CONCLUSION: Among critically ill patients with sepsis, subtype strategies using clinical, biomarker, and transcriptomic data do not identify comparable patient populations and are likely to reflect disparate clinical characteristics and underlying biology.

Time to treatment and mortality for clinical sepsis subtypes.

BACKGROUND: Sepsis is common, deadly, and heterogenous. Prior analyses of patients with sepsis and septic shock in New York State showed a risk-adjusted association between more rapid antibiotic administration and bundled care completion, but not an intravenous fluid bolus, with reduced in-hospital mortality. However, it is unknown if clinically identifiable sepsis subtypes modify these associations. METHODS: Secondary analysis of patients with sepsis and septic shock enrolled in the New York State Department of Health cohort from January 1, 2015 to December 31, 2016. Patients were classified as clinical sepsis subtypes (α, ß, γ, δ-types) using the Sepsis ENdotyping in Emergency CAre (SENECA) approach. Exposure variables included time to 3-h sepsis bundle completion, antibiotic administration, and intravenous fluid bolus completion. Then logistic regression models evaluated the interaction between exposures, clinical sepsis subtypes, and in-hospital mortality. RESULTS: 55,169 hospitalizations from 155 hospitals were included (34% α, 30% ß, 19% γ, 17% δ). The α-subtype had the lowest (N = 1,905, 10%) and δ-subtype had the highest (N = 3,776, 41%) in-hospital mortality. Each hour to completion of the 3-h bundle (aOR, 1.04 [95%CI, 1.02-1.05]) and antibiotic initiation (aOR, 1.03 [95%CI, 1.02-1.04]) was associated with increased risk-adjusted in-hospital mortality. The association differed across subtypes (p-interactions < 0.05). For example, the outcome association for the time to completion of the 3-h bundle was greater in the δ-subtype (aOR, 1.07 [95%CI, 1.05-1.10]) compared to α-subtype (aOR, 1.02 [95%CI, 0.99-1.04]). Time to intravenous fluid bolus completion was not associated with risk-adjusted in-hospital mortality (aOR, 0.99 [95%CI, 0.97-1.01]) and did not differ among subtypes (p-interaction = 0.41). CONCLUSION: Timely completion of a 3-h sepsis bundle and antibiotic initiation was associated with reduced risk-adjusted in-hospital mortality, an association modified by clinically identifiable sepsis subtype.

Caring for Patients With Acute Respiratory Distress Syndrome: Summary of the 2023 ESICM Practice Guidelines.

This article summarizes the 2023 updated ARDS guidelines from the European Society of Intensive Care Medicine, including the guidelines' methods, findings, and implications, along with reflections on next steps.

Evolving Real-World Effectiveness of Monoclonal Antibodies for Treatment of COVID-19 : A Cohort Study.

BACKGROUND: Treatment guidelines and U.S. Food and Drug Administration emergency use authorizations (EUAs) of monoclonal antibodies (mAbs) for treatment of high-risk outpatients with mild to moderate COVID-19 changed frequently as different SARS-CoV-2 variants emerged. OBJECTIVE: To evaluate whether early outpatient treatment with mAbs, overall and by mAb product, presumed SARS-CoV-2 variant, and immunocompromised status, is associated with reduced risk for hospitalization or death at 28 days. DESIGN: Hypothetical pragmatic randomized trial from observational data comparing mAb-treated patients with a propensity score-matched, nontreated control group. SETTING: Large U.S. health care system. PARTICIPANTS: High-risk outpatients eligible for mAb treatment under any EUA with a positive SARS-CoV-2 test result from 8 December 2020 to 31 August 2022. INTERVENTION: Single-dose intravenous mAb treatment with bamlanivimab, bamlanivimab-etesevimab, sotrovimab, bebtelovimab, or intravenous or subcutaneous casirivimab-imdevimab administered within 2 days of a positive SARS-CoV-2 test result. MEASUREMENTS: The primary outcome was hospitalization or death at 28 days among treated patients versus a nontreated control group (no treatment or treatment ≥3 days after SARS-CoV-2 test date). RESULTS: The risk for hospitalization or death at 28 days was 4.6% in 2571 treated patients and 7.6% in 5135 nontreated control patients (risk ratio [RR], 0.61 [95% CI, 0.50 to 0.74]). In sensitivity analyses, the corresponding RRs for 1- and 3-day treatment grace periods were 0.59 and 0.49, respectively. In subgroup analyses, those receiving mAbs when the Alpha and Delta variants were presumed to be predominant had estimated RRs of 0.55 and 0.53, respectively, compared with 0.71 for the Omicron variant period. Relative risk estimates for individual mAb products all suggested lower risk for hospitalization or death. Among immunocompromised patients, the RR was 0.45 (CI, 0.28 to 0.71). LIMITATIONS: Observational study design, SARS-CoV-2 variant presumed by date rather than genotyping, no data on symptom severity, and partial data on vaccination status. CONCLUSION: Early mAb treatment among outpatients with COVID-19 is associated with lower risk for hospitalization or death for various mAb products and SARS-CoV-2 variants. PRIMARY FUNDING SOURCE: None.

A Prospective Cohort Protocol for the Remnant Investigation in Sepsis Study.

BACKGROUND: Sepsis is a common and deadly syndrome, accounting for more than 11 million deaths annually. To mature a deeper understanding of the host and pathogen mechanisms contributing to poor outcomes in sepsis, and thereby possibly inform new therapeutic targets, sophisticated, and expensive biorepositories are typically required. We propose that remnant biospecimens are an alternative for mechanistic sepsis research, although the viability and scientific value of such remnants are unknown. METHODS AND RESULTS: The Remnant Biospecimen Investigation in Sepsis study is a prospective cohort study of 225 adults (age ≥ 18 yr) presenting to the emergency department with community sepsis, defined as sepsis-3 criteria within 6 hours of arrival. The primary objective was to determine the scientific value of a remnant biospecimen repository in sepsis linked to clinical phenotyping in the electronic health record. We will study candidate multiomic readouts of sepsis biology, governed by a conceptual model, and determine the precision, accuracy, integrity, and comparability of proteins, small molecules, lipids, and pathogen sequencing in remnant biospecimens compared with paired biospecimens obtained according to research protocols. Paired biospecimens will include plasma from sodium-heparin, EDTA, sodium fluoride, and citrate tubes. CONCLUSIONS: The study has received approval from the University of Pittsburgh Human Research Protection Office (Study 21120013). Recruitment began on October 25, 2022, with planned release of primary results anticipated in 2024. Results will be made available to the public, the funders, critical care societies, laboratory medicine scientists, and other researchers.

Sepsis biomarkers and diagnostic tools with a focus on machine learning.

Over the last years, there have been advances in the use of data-driven techniques to improve the definition, early recognition, subtypes characterisation, prognostication and treatment personalisation of sepsis. Some of those involve the discovery or evaluation of biomarkers or digital signatures of sepsis or sepsis sub-phenotypes. It is hoped that their identification may improve timeliness and accuracy of diagnosis, suggest physiological pathways and therapeutic targets, inform targeted recruitment into clinical trials, and optimise clinical management. Given the complexities of the sepsis response, panels of biomarkers or models combining biomarkers and clinical data are necessary, as well as specific data analysis methods, which broadly fall under the scope of machine learning. This narrative review gives a brief overview of the main machine learning techniques (mainly in the realms of supervised and unsupervised methods) and published applications that have been used to create sepsis diagnostic tools and identify biomarkers.

Monoclonal Antibodies for Treatment of SARS-CoV-2 Infection During Pregnancy : A Cohort Study.

BACKGROUND: Monoclonal antibody (mAb) treatment decreases hospitalization and death in high-risk outpatients with mild to moderate COVID-19. However, no studies have evaluated adverse events and effectiveness of mAbs in pregnant persons compared with no mAb treatment. OBJECTIVE: To determine the frequency of drug-related adverse events and obstetric-associated safety outcomes after treatment with mAb compared with no mAb treatment of pregnant persons, and the association between mAb treatment and a composite of 28-day COVID-19-related hospital admission or emergency department (ED) visit, COVID-19-associated delivery, or mortality. DESIGN: Retrospective, propensity score-matched, cohort study. SETTING: UPMC Health System from 30 April 2021 to 21 January 2022. PARTICIPANTS: Persons aged 12 years or older with a pregnancy episode and any documented positive SARS-CoV-2 test (polymerase chain reaction or antigen test). INTERVENTION: Bamlanivimab and etesevimab, casirivimab and imdevimab, or sotrovimab treatment compared with no mAb treatment. MEASUREMENTS: Drug-related adverse events, obstetric-associated safety outcomes among persons who delivered, and a risk-adjusted composite of 28-day COVID-19-related hospital admission or ED visit, COVID-19-associated delivery, or mortality. RESULTS: Among 944 pregnant persons (median age [interquartile range (IQR)], 30 years [26 to 33 years]; White (79.5%; n = 750); median Charlson Comorbidity Index score [IQR], 0 [0 to 0]), 552 received mAb treatment (58%). Median gestational age at COVID-19 diagnosis or treatment was 179 days (IQR, 123 to 227), and most persons received sotrovimab (69%; n = 382). Of those with known vaccination status, 392 (62%) were fully vaccinated. Drug-related adverse events were uncommon (n = 8; 1.4%), and there were no differences in any obstetric-associated outcome among 778 persons who delivered. In the total population, the risk ratio for mAb treatment of the composite 28-day COVID-19-associated outcome was 0.71 (95% CI, 0.37 to 1.4). The propensity score-matched risk ratio was 0.61 (95% CI, 0.34 to 1.1). There were no deaths among mAb-treated patients compared with 1 death in the nontreated control patients. There were more non-COVID-19-related hospital admissions in the mAb-treated persons in the unmatched cohort (14 [2.5%] vs. 2 [0.5%]; risk ratio, 5.0; 95% CI, 1.1 to 21.7); however, there was no difference in the propensity score-matched rates, which were 2.5% mAb-treated vs. 2% untreated (risk ratio, 1.3; 95% CI, 0.58% to 2.8%). LIMITATIONS: Drug-related adverse events were patient and provider reported and potentially underrepresented. Symptom severity at the time of SARS-CoV-2 testing was not available for nontreated patients. CONCLUSION: In pregnant persons with mild to moderate COVID-19, adverse events after mAb treatment were mild and rare. There was no difference in obstetric-associated safety outcomes between mAb treatment and no treatment among persons who delivered. There was no difference in 28-day COVID-19-associated outcomes and non-COVID-19-related hospital admissions for mAb treatment compared with no mAb treatment in a propensity score-matched cohort. PRIMARY FUNDING SOURCE: No funding was received for this study.