Association between monoclonal antibody therapy, vaccination, and longer-term symptom resolution after acute COVID-19.

Effective therapies for reducing post-acute sequelae of COVID-19 (PASC) symptoms are lacking. Evaluate the association between monoclonal antibody (mAb) treatment or COVID-19 vaccination with symptom recovery in COVID-19 participants. The longitudinal survey-based cohort study was conducted from April 2021 to January 2022 across a multihospital Colorado health system. Adults ≥18 years with a positive SARS-CoV-2 test were included. Primary exposures were mAb treatment and COVID-19 vaccination. The primary outcome was time to symptom resolution after SARS-CoV-2 positive test date. The secondary outcome was hospitalization within 28 days of a positive SARS-CoV-2 test. Analysis included 1612 participants, 539 mAb treated, and 486 with ≥2 vaccinations. Time to symptom resolution was similar between mAb treated versus untreated patients (adjusted hazard ratio (aHR): 0.90, 95% CI: 0.77-1.04). Time to symptom resolution was shorter for patients who received ≥2 vaccinations compared to those unvaccinated (aHR: 1.56, 95% CI: 1.31-1.88). 28-day hospitalization risk was lower for patients receiving mAb therapy (adjusted odds ratio [aOR]: 0.31, 95% CI: 0.19-0.50) and ≥2 vaccinations (aOR: 0.33, 95% CI: 0.20-0.55), compared with untreated or unvaccinated status. Analysis included 1612 participants, 539 mAb treated, and 486 with ≥2 vaccinations. Time to symptom resolution was similar between mAb treated versus untreated patients (adjusted hazard ratio (aHR): 0.90, 95% CI: 0.77-1.04). Time to symptom resolution was shorter for patients who received ≥2 vaccinations compared to those unvaccinated (aHR: 1.56, 95% CI: 1.31-1.88). 28-day hospitalization risk was lower for patients receiving mAb therapy (adjusted odds ratio [aOR]: 0.31, 95% CI: 0.19-0.50) and ≥2 vaccinations (aOR: 0.33, 95% CI: 0.20-0.55), compared with untreated or unvaccinated status. COVID-19 vaccination, but not mAb therapy, was associated with a shorter time to symptom resolution. Both were associated with lower 28-day hospitalization.

Astrogliosis, neuritic microstructure, and sex effects: GFAP is an indicator of neuritic orientation in women.

BACKGROUND: Data from human studies suggest that immune dysregulation is associated with Alzheimer's disease (AD) pathology and cognitive decline and that neurites may be affected early in the disease trajectory. Data from animal studies further indicate that dysfunction in astrocytes and inflammation may have a pivotal role in facilitating dendritic damage, which has been linked with negative cognitive outcomes. To elucidate these relationships further, we have examined the relationship between astrocyte and immune dysregulation, AD-related pathology, and neuritic microstructure in AD-vulnerable regions in late life. METHODS: We evaluated panels of immune, vascular, and AD-related protein markers in blood and conducted in vivo multi-shell neuroimaging using Neurite Orientation Dispersion and Density Imaging (NODDI) to assess indices of neuritic density (NDI) and dispersion (ODI) in brain regions vulnerable to AD in a cohort of older adults (n = 109). RESULTS: When examining all markers in tandem, higher plasma GFAP levels were strongly related to lower neurite dispersion (ODI) in grey matter. No biomarker associations were found with higher neuritic density. Associations between GFAP and neuritic microstructure were not significantly impacted by symptom status, APOE status, or plasma Aß42/40 ratio; however, there was a large sex effect observed for neurite dispersion, wherein negative associations between GFAP and ODI were only observed in females. DISCUSSION: This study provides a comprehensive, concurrent appraisal of immune, vascular, and AD-related biomarkers in the context of advanced grey matter neurite orientation and dispersion methodology. Sex may be an important modifier of the complex associations between astrogliosis, immune dysregulation, and brain microstructure in older adults.

Real-World Evidence of the Neutralizing Monoclonal Antibody Sotrovimab for Preventing Hospitalization and Mortality in COVID-19 Outpatients.

BACKGROUND: It is not known whether sotrovimab, a neutralizing monoclonal antibody (mAb) treatment authorized for early symptomatic coronavirus disease 2019 (COVID-19) patients, is also effective in preventing the progression of severe disease and mortality following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Delta variant infection. METHODS: In an observational cohort study of nonhospitalized adult patients with SARS-CoV-2 infection, 1 October 2021-11 December 2021, using electronic health records from a statewide health system plus state-level vaccine and mortality data, we used propensity matching to select 3 patients not receiving mAbs for each patient who received outpatient sotrovimab treatment. The primary outcome was 28-day hospitalization; secondary outcomes included mortality and severity of hospitalization. RESULTS: Of 10 036 patients with SARS-CoV-2 infection, 522 receiving sotrovimab were matched to 1563 not receiving mAbs. Compared to mAb-untreated patients, sotrovimab treatment was associated with a 63% decrease in the odds of all-cause hospitalization (raw rate 2.1% vs 5.7%; adjusted odds ratio [aOR], 0.37; 95% confidence interval [CI], .19-.66) and an 89% decrease in the odds of all-cause 28-day mortality (raw rate 0% vs 1.0%; aOR, 0.11; 95% CI, .0-.79), and may reduce respiratory disease severity among those hospitalized. CONCLUSIONS: Real-world evidence demonstrated sotrovimab effectiveness in reducing hospitalization and all-cause 28-day mortality among COVID-19 outpatients during the Delta variant phase.

Clinical phenotyping in sarcoidosis using cluster analysis.

BACKGROUND: Most phenotyping paradigms in sarcoidosis are based on expert opinion; however, no paradigm has been widely adopted because of the subjectivity in classification. We hypothesized that cluster analysis could be performed on common clinical variables to define more objective sarcoidosis phenotypes. METHODS: We performed a retrospective cohort study of 554 sarcoidosis cases to identify distinct phenotypes of sarcoidosis based on 29 clinical features. Model-based clustering was performed using the VarSelLCM R package and the Integrated Completed Likelihood (ICL) criteria were used to estimate number of clusters. To identify features associated with cluster membership, features were ranked based on variable importance scores from the VarSelLCM model, and additional univariate tests (Fisher's exact test and one-way ANOVA) were performed using q-values correcting for multiple testing. The Wasfi severity score was also compared between clusters. RESULTS: Cluster analysis resulted in 6 sarcoidosis phenotypes. Salient characteristics for each cluster are as follows: Phenotype (1) supranormal lung function and majority Scadding stage 2/3; phenotype (2) supranormal lung function and majority Scadding stage 0/1; phenotype (3) normal lung function and split Scadding stages between 0/1 and 2/3; phenotype (4) obstructive lung function and majority Scadding stage 2/3; phenotype (5) restrictive lung function and majority Scadding stage 2/3; phenotype (6) mixed obstructive and restrictive lung function and mostly Scadding stage 4. Although there were differences in the percentages, all Scadding stages were encompassed by all of the phenotypes, except for phenotype 1, in which none were Scadding stage 4. Clusters 4, 5, 6 were significantly more likely to have ever been on immunosuppressive treatment and had higher Wasfi disease severity scores. CONCLUSIONS: Cluster analysis produced 6 sarcoidosis phenotypes that demonstrated less severe and severe phenotypes. Phenotypes 1, 2, 3 have less lung function abnormalities, a lower percentage on immunosuppressive treatment and lower Wasfi severity scores. Phenotypes 4, 5, 6 were characterized by lung function abnormalities, more parenchymal abnormalities, an increased percentage on immunosuppressive treatment and higher Wasfi severity scores. These data support using cluster analysis as an objective and clinically useful way to phenotype sarcoidosis subjects and to empower clinicians to identify those with more severe disease versus those who have less severe disease, independent of Scadding stage.

Weight gain trajectories and obesity rates in intensive and conventional treatments of type 1 diabetes from the DCCT compared with a control population without diabetes.

AIM: Obesity is a significant health issue for participants with type 1 diabetes undergoing intensive diabetes management. The temporal pattern and factors associated with weight gain after treatment initiation remain poorly understood including how weight gain in participants with and without type I diabetes compare. Our aim was to compare weight gain in those receiving intensive (INT) and conventional (CONV) type 1 diabetes treatment to a population without diabetes. METHODS: Participants included men and women of 18 years and older in the Diabetes Control and Complications Trial (DCCT) randomized to INT (n = 562) or CONV (n = 568) and a prospective, observational cohort without diabetes from the Coronary Artery Development in Young Adults (CARDIA, controls) study (n = 2446). Body mass index (BMI) trajectories and obesity prevalence were compared between groups and candidate metabolic and therapeutic moderators investigated. RESULTS: Annual weight gain with INT peaked 1.3 years after initiation and was greater than both CONV and controls before and after this peak. Obesity prevalence with INT was lower than controls at baseline, was similar to controls at 2 years and surpassed controls by 5 years. Obesity rates with CONV remained below controls at all time points. Greater annual weight gain in the DCCT was associated with lower haemoglobin A1c , higher insulin dose and family history of type 2 diabetes. CONCLUSIONS: Greater weight gain accompanying INT therapy occurs in two stages, leads to similar or greater obesity rates than controls after 2 years and is primarily modified by glucose control and family history, supportive of a therapeutic-genetic influence on weight trajectories.

Association between treatment failure and hospitalization after receipt of neutralizing monoclonal antibody treatment for COVID-19 outpatients.

BACKGROUND: Neutralizing monoclonal antibodies (mAbs) are highly effective in reducing hospitalization and mortality among early symptomatic COVID-19 patients in clinical trials and real-world data. While resistance to some mAbs has since emerged among new variants, characteristics associated with treatment failure of mAbs remain unknown. METHODS: This multicenter, observational cohort study included patients with COVID-19 who received mAb treatment between November 20, 2020, and December 9, 2021. We utilized electronic health records from a statewide health system plus state-level vaccine and mortality data. The primary outcome was mAb treatment failure, defined as hospitalization or death within 28 days of a positive SARS-CoV-2 test. RESULTS: COVID-19 mAb was administered to 7406 patients. Hospitalization within 28 days of positive SARS-CoV-2 test occurred in 258 (3.5%) of all patients who received mAb treatment. Ten patients (0.1%) died within 28 days, and all but one were hospitalized prior to death. Characteristics associated with treatment failure included having two or more comorbidities excluding obesity and immunocompromised status (adjusted odds ratio [OR] 3.71, 95% confidence interval [CI] 2.52-5.56), lack of SARS-CoV-2 vaccination (OR 2.73, 95% CI 2.01-3.77), non-Hispanic black race/ethnicity (OR 2.21, 95% CI 1.20-3.82), obesity (OR 1.79, 95% CI 1.36-2.34), one comorbidity (OR 1.68, 95% CI 1.11-2.57), age ≥ 65 years (OR 1.62, 95% CI 1.13-2.35), and male sex (OR 1.56, 95% CI 1.21-2.02). Immunocompromised status (none, mild, or moderate/severe), pandemic phase, and type of mAb received were not associated with treatment failure (all p > 0.05). CONCLUSIONS: Comorbidities, lack of prior SARS-CoV-2 vaccination, non-Hispanic black race/ethnicity, obesity, age ≥ 65 years, and male sex are associated with treatment failure of mAbs.

A flexible mixed-data model applied to claims data for post-market surveillance of prescription drug safety behavior.

We develop a new modeling framework for jointly modeling first prescription times and the presence of risk-mitigating behavior for prescription drugs using real-world data. We are interested in active surveillance of clinical quality improvement programs, especially for drugs which enter the market under an FDA-mandated Risk Evaluation and Mitigation Strategy (REMS). Our modeling framework attempts to jointly model two important aspects of prescribing, the time between a drug's initial marketing and a patient's first prescription of that drug, and the presence of risk-mitigating behavior at the first prescription. First prescription times can be flexibly modeled as a mixture of component distributions to accommodate different subpopulations and allow the proportion of prescriptions that exhibit risk-mitigating behavior to change for each component. Risk-mitigating behavior is defined in the context of each drug. We develop a joint model using a mixture of positive unimodal distributions to model first prescription times, and a logistic regression model conditioned on component membership to model the presence of risk-mitigating behavior. We apply our model to two recently approved extended release/long-acting (ER/LA) opioids, which have an FDA-approved blueprint for best prescribing practices to inform our definition of risk-mitigating behavior. We also apply our methods to simulated data to evaluate their performance under various conditions such as clustering.

Heterogeneity and longevity of antibody memory to viruses and vaccines.

Determining the duration of protective immunity requires quantifying the magnitude and rate of loss of antibodies to different virus and vaccine antigens. A key complication is heterogeneity in both the magnitude and decay rate of responses of different individuals to a given vaccine, as well as of a given individual to different vaccines. We analyzed longitudinal data on antibody titers in 45 individuals to characterize the extent of this heterogeneity and used models to determine how it affected the longevity of protective immunity to measles, rubella, vaccinia, tetanus, and diphtheria. Our analysis showed that the magnitude of responses in different individuals varied between 12- and 200-fold (95% coverage) depending on the antigen. Heterogeneity in the magnitude and decay rate contribute comparably to variation in the longevity of protective immunity between different individuals. We found that some individuals have, on average, slightly longer-lasting memory than others-on average, they have higher antibody levels with slower decay rates. We identified different patterns for the loss of protective levels of antibodies to different vaccine and virus antigens. Specifically, we found that for the first 25 to 50 years, virtually all individuals have protective antibody titers against diphtheria and tetanus, respectively, but about 10% of the population subsequently lose protective immunity per decade. In contrast, at the outset, not all individuals had protective titers against measles, rubella, and vaccinia. However, these antibody titers wane much more slowly, with a loss of protective immunity in only 1% to 3% of the population per decade. Our results highlight the importance of long-term longitudinal studies for estimating the duration of protective immunity and suggest both how vaccines might be improved and how boosting schedules might be reevaluated.

Birth outcomes are superior after transfer of fresh versus frozen embryos for donor oocyte recipients.

STUDY QUESTION: For donor oocyte recipients, are birth outcomes superior for fresh versus frozen embryos? SUMMARY ANSWER: Among fresh donor oocyte recipients, fresh embryos are associated with better birth outcomes when compared with frozen embryos. WHAT IS KNOWN ALREADY: Frozen embryo transfer (ET) with vitrification has been associated with improved pregnancy rates, but also increased rates of large for gestational age infants. Donor oocyte recipients represent an attractive biological model to attempt to isolate the impact of embryo cryopreservation on IVF outcomes, yet there is a paucity of studies in this population. STUDY DESIGN, SIZE, DURATION: A retrospective cohort of the US national registry, the Society for Assisted Reproductive Technology Clinic Outcome Reporting System, of IVF cycles of women using fresh donor oocytes resulting in ET between 2013 and 2015. Thawed oocytes were excluded. PARTICIPANTS/MATERIALS, SETTINGS, METHODS: Good obstetric outcome (GBO), defined as a singleton, term, live birth with appropriate for gestational age birth weight, was the primary outcome measure. Secondary outcomes included live birth, clinical pregnancy, spontaneous abortion, preterm birth, multiple births and gestational age-adjusted weight. Outcomes were modeled using the generalized estimating equation approach. MAIN RESULTS AND THE ROLE OF CHANCE: Data are from 25 387 donor oocyte cycles, in which 14 289 were fresh and 11 098 were frozen ETs. A GBO was 27% more likely in fresh ETs (26.3%) compared to frozen (20.9%) (adjusted risk ratio 1.27; 95% confidence interval (CI) 1.21-1.35; P < 0.001). Overall, fresh transfer was more likely to result in a live birth (55.7% versus 39.5%; adjusted risk ratio 1.21; 95% CI 1.18-1.26; P < 0.001). Among singleton births, there was no difference in gestational age-adjusted birth weight between groups. LIMITATION, REASONS FOR CAUTION: Our cohort findings contrast with data from autologous oocytes. Prospective studies with this population are warranted. WIDER IMPLICATIONS OF THE FINDINGS: Among donor oocyte recipients, fresh ETs may be associated with better birth outcomes. Reassuringly, given its prevalent use, modern embryo cryopreservation does not appear to result in phenotypically larger infants. STUDY FUNDING/COMPETING INTEREST(S): None. TRIAL REGISTRATION NUMBER: N/A.

A Bayesian natural cubic B-spline varying coefficient method for non-ignorable dropout.

BACKGROUND: Dropout is a common problem in longitudinal clinical trials and cohort studies, and is of particular concern when dropout occurs for reasons that may be related to the outcome of interest. This paper reviews common parametric models to account for dropout and introduces a Bayesian semi-parametric varying coefficient model for exponential family longitudinal data with non-ignorable dropout. METHODS: To demonstrate these methods, we present results from a simulation study and estimate the impact of drug use on longitudinal CD4 + T cell count and viral load suppression in the Women's Interagency HIV Study. Sensitivity analyses are performed to consider the impact of model assumptions on inference. We compare results between our semi-parametric method and parametric models to account for dropout, including the conditional linear model and a parametric frailty model. We also compare results to analyses that fail to account for dropout. RESULTS: In simulation studies, we show that semi-parametric methods reduce bias and mean squared error when parametric model assumptions are violated. In analyses of the Women's Interagency HIV Study data, we find important differences in estimates of changes in CD4 + T cell count over time in untreated subjects that report drug use between different models used to account for dropout. We find steeper declines over time using our semi-parametric model, which makes fewer assumptions, compared to parametric models. Failing to account for dropout or to meet parametric assumptions of models to account for dropout could lead to underestimation of the impact of hard drug use on CD4 + cell count decline in untreated subjects. In analyses of subjects that initiated highly active anti-retroviral treatment, we find that the estimated probability of viral load suppression is lower in models that account for dropout. CONCLUSIONS: Non-ignorable dropout is an important consideration when analyzing data from longitudinal clinical trials and cohort studies. While methods that account for non-ignorable dropout must make some unavoidable assumptions that cannot be verified from the observed data, many methods make additional parametric assumptions. If these assumptions are not met, inferences can be biased, making more flexible methods with minimal assumptions important.

Radiomic measures from chest high-resolution computed tomography associated with lung function in sarcoidosis.

INTRODUCTION: Pulmonary sarcoidosis is a rare heterogeneous lung disease of unknown aetiology, with limited treatment options. Phenotyping relies on clinical testing including visual scoring of chest radiographs. Objective radiomic measures from high-resolution computed tomography (HRCT) may provide additional information to assess disease status. As the first radiomics analysis in sarcoidosis, we investigate the potential of radiomic measures as biomarkers for sarcoidosis, by assessing 1) differences in HRCT between sarcoidosis subjects and healthy controls, 2) associations between radiomic measures and spirometry, and 3) trends between Scadding stages. METHODS: Radiomic features were computed on HRCT in three anatomical planes. Linear regression compared global radiomic features between sarcoidosis subjects (n=73) and healthy controls (n=78), and identified associations with spirometry. Spatial differences in associations across the lung were investigated using functional data analysis. A subanalysis compared radiomic features between Scadding stages. RESULTS: Global radiomic measures differed significantly between sarcoidosis subjects and controls (p<0.001 for skewness, kurtosis, fractal dimension and Geary's C), with differences in spatial radiomics most apparent in superior and lateral regions. In sarcoidosis subjects, there were significant associations between radiomic measures and spirometry, with a large association found between Geary's C and forced vital capacity (FVC) (p=0.008). Global radiomic measures differed significantly between Scadding stages (p<0.032), albeit nonlinearly, with stage IV having more extreme radiomic values. Radiomics explained 71.1% of the variability in FVC compared with 51.4% by Scadding staging alone. CONCLUSIONS: Radiomic HRCT measures objectively differentiate disease abnormalities, associate with lung function and identify trends in Scadding stage, showing promise as quantitative biomarkers for pulmonary sarcoidosis.

Modeling associations between latent event processes governing time series of pulsing hormones.

This work is motivated by a desire to quantify relationships between two time series of pulsing hormone concentrations. The locations of pulses are not directly observed and may be considered latent event processes. The latent event processes of pulsing hormones are often associated. It is this joint relationship we model. Current approaches to jointly modeling pulsing hormone data generally assume that a pulse in one hormone is coupled with a pulse in another hormone (one-to-one association). However, pulse coupling is often imperfect. Existing joint models are not flexible enough for imperfect systems. In this article, we develop a more flexible class of pulse association models that incorporate parameters quantifying imperfect pulse associations. We propose a novel use of the Cox process model as a model of how pulse events co-occur in time. We embed the Cox process model into a hormone concentration model. Hormone concentration is the observed data. Spatial birth and death Markov chain Monte Carlo is used for estimation. Simulations show the joint model works well for quantifying both perfect and imperfect associations and offers estimation improvements over single hormone analyses. We apply this model to luteinizing hormone (LH) and follicle stimulating hormone (FSH), two reproductive hormones. Use of our joint model results in an ability to investigate novel hypotheses regarding associations between LH and FSH secretion in obese and non-obese women.

Using Cox cluster processes to model latent pulse location patterns in hormone concentration data.

Many hormones, including stress hormones, are intermittently secreted as pulses. The pulsatile location process, describing times when pulses occur, is a regulator of the entire stress system. Characterizing the pulse location process is particularly difficult because the pulse locations are latent; only hormone concentration at sampled times is observed. In addition, for stress hormones the process may change both over the day and relative to common external stimuli. This potentially results in clustering in pulse locations across subjects. Current approaches to characterizing the pulse location process do not capture subject-to-subject clustering in locations. Here we show how a Bayesian Cox cluster process may be adapted as a model of the pulse location process. We show that this novel model of pulse locations is capable of detecting circadian rhythms in pulse locations, clustering of pulse locations between subjects, and identifying exogenous controllers of pulse events. We integrate our pulse location process into a model of hormone concentration, the observed data. A spatial birth-and-death Markov chain Monte Carlo algorithm is used for estimation. We exhibit the strengths of this model on simulated data and adrenocorticotropic and cortisol data collected to study the stress axis in depressed and non-depressed women.

A practical guide to adopting Bayesian analyses in clinical research.

Background: Bayesian statistical approaches are extensively used in new statistical methods but have not been adopted at the same rate in clinical and translational (C&T) research. The goal of this paper is to accelerate the transition of new methods into practice by improving the C&T researcher's ability to gain confidence in interpreting and implementing Bayesian analyses. Methods: We developed a Bayesian data analysis plan and implemented that plan for a two-arm clinical trial comparing the effectiveness of a new opioid in reducing time to discharge from the post-operative anesthesia unit and nerve block usage in surgery. Through this application, we offer a brief tutorial on Bayesian methods and exhibit how to apply four Bayesian statistical packages from STATA, SAS, and RStan to conduct linear and logistic regression analyses in clinical research. Results: The analysis results in our application were robust to statistical package and consistent across a wide range of prior distributions. STATA was the most approachable package for linear regression but was more limited in the models that could be fitted and easily summarized. SAS and R offered more straightforward documentation and data management for the posteriors. They also offered direct programming of the likelihood making them more easily extendable to complex problems. Conclusion: Bayesian analysis is now accessible to a broad range of data analysts and should be considered in more C&T research analyses. This will allow C&T research teams the ability to adopt and interpret Bayesian methodology in more complex problems where Bayesian approaches are often needed.

The textures of sarcoidosis: quantifying lung disease through variograms.

Objective: Sarcoidosis is a granulomatous disease affecting the lungs in over 90% of patients. Qualitative assessment of chest CT by radiologists is standard clinical practice and reliable quantification of disease from CT would support ongoing efforts to identify sarcoidosis phenotypes. Standard imaging feature engineering techniques such as radiomics suffer from extreme sensitivity to image acquisition and processing, potentially impeding generalizability of research to clinical populations. In this work, we instead investigate approaches to engineering variogram-based features with the intent to identify a robust, generalizable pipeline for image quantification in the study of sarcoidosis. Approach: For a cohort of more than 300 individuals with sarcoidosis, we investigated 24 feature engineering pipelines differing by decisions for image registration to a template lung, empirical and model variogram estimation methods, and feature harmonization for CT scanner model, and subsequently 48 sets of phenotypes produced through unsupervised clustering. We then assessed sensitivity of engineered features, phenotypes produced through unsupervised clustering, and sarcoidosis disease signal strength to pipeline. Main results: We found that variogram features had low to mild association with scanner model and associations were reduced by image registration. For each feature type, features were also typically robust to all pipeline decisions except image registration. Strength of disease signal as measured by association with pulmonary function testing and some radiologist visual assessments was strong (optimistic AUC ≈ 0.9, p ≪ 0.0001 in models for architectural distortion, conglomerate mass, fibrotic abnormality, and traction bronchiectasis) and fairly consistent across engineering approaches regardless of registration and harmonization for CT scanner. Significance: Variogram-based features appear to be a suitable approach to image quantification in support of generalizable research in pulmonary sarcoidosis.

Effectiveness of subcutaneous monoclonal antibody treatment in emergency department outpatients with COVID-19.

Objectives: To evaluate whether subcutaneous neutralizing monoclonal antibody (mAb) treatment given in the emergency department (ED) setting was associated with reduced hospitalizations, mortality, and severity of disease when compared to nontreatment among mAb-eligible patients with coronavirus disease 2019 (COVID-19). Methods: This retrospective observational cohort study of ED patients utilized a propensity score-matched analysis to compare patients who received subcutaneous casirivimab and imdevimab mAb to nontreated COVID-19 control patients in November-December 2021. The primary outcome was all-cause hospitalization within 28 days, and secondary outcomes were 90-day hospitalization, 28- and 90-day mortality, and ED length of stay (LOS). Results: Of 1340 patients included in the analysis, 490 received subcutaneous casirivimab and imdevimab, and 850 did not received them. There was no difference observed for 28-day hospitalization (8.4% vs. 10.6%; adjusted odds ratio [aOR] 0.79, 95% confidence intervals [CI] 0.53-1.17) or 90-day hospitalization (11.6% vs. 12.5%; aOR 0.93, 95% CI 0.65-1.31). However, mortality at both the 28-day and 90-day timepoints was substantially lower in the treated group (28-day 0.6% vs. 3.1%; aOR 0.18, 95% CI 0.08-0.41; 90-day 0.6% vs. 3.9%; aOR 0.14, 95% CI 0.06-0.36). Among hospitalized patients, treated patients had shorter hospital LOS (5.7 vs. 11.4 days; adjusted rate ratio [aRR] 0.47, 95% CI 0.33-0.69), shorter intensive care unit LOS (3.8 vs. 10.2 days; aRR 0.22, 95% CI 0.14-0.35), and the severity of hospitalization was lower (aOR 0.45, 95% CI 0.21-0.97) compared to untreated. Conclusions: Among ED patients who presented for symptomatic COVID-19 during the Delta variant phase, ED subcutaneous casirivimab/imdevimab treatment was not associated with a decrease in hospitalizations. However, treatment was associated with lower mortality at 28 and 90 days, hospital LOS, and overall severity of illness.

Real-world use of nirmatrelvir-ritonavir in COVID-19 outpatients during BQ.1, BQ.1.1., and XBB.1.5 predominant omicron variants in three U.S. health systems: a retrospective cohort study.

Background: Ritonavir-boosted Nirmatrelvir (NMV-r), a protease inhibitor with in vitro activity against SARS-CoV-2, can reduce risk of progression to severe COVID-19 among high-risk individuals infected with earlier variants, but less is known about its effectiveness against omicron variants BQ.1/BQ.1.1/XBB.1.5. We sought to evaluate effectiveness of NMV-r in BQ.1/BQ.1.1/XBB.1.5 omicron variants by comparing hospitalisation rates to NMV-r treated patients during a previous omicron phase and to contemporaneous untreated patients. Methods: We conducted a retrospective observational cohort study of non-hospitalised adult patients with SARS-CoV-2 infection using real-world data from three health systems in Colorado and Utah, and compared hospitalisation rates in NMV-r-treated patients in a BA.2/BA.2.12.1/BA.4/BA.5 variant-predominant (first) phase (April 3, 2022-November 12, 2022), with a BQ.1/BQ.1.1/XBB.1.5 variant-predominant (second) phase (November 13, 2022-March 7, 2023). In the primary analysis, we used Firth logistic regression with a two-segment (phase) linear time model, and pre-specified non-inferiority bounds for the mean change between segments. In a pre-specified secondary analysis, we inferred NMV-r effectiveness in a cohort of treated and untreated patients infected during the second phase. For both analyses, the primary outcome was 28-day all-cause hospitalisation. Subgroup analyses assessed treatment effect heterogeneity. Findings: In the primary analysis, 28-day all-cause hospitalisation rates in NMV-r treated patients in the second phase (n = 12,061) were non-inferior compared to the first phase (n = 25,075) (198 [1.6%] vs. 345 [1.4%], adjusted odds ratio (aOR): 0.76 [95% CI 0.54-1.06]), with consistent results among secondary endpoints and key subgroups. Secondary cohort analyses revealed additional evidence for NMV-r effectiveness, with reduced 28-day hospitalisation rates among treated patients compared to untreated patients during a BQ.1/BQ.1.1/XBB.1.5 predominant phase (198/12,061 [1.6%] vs. 376/10,031 [3.7%], aOR 0.34 [95% CI 0.30-0.38), findings robust to additional sensitivity analyses. Interpretation: Real-world evidence from major US healthcare systems suggests ongoing NMV-r effectiveness in preventing hospitalisation during a BQ.1/BQ.1.1/XBB.1.5-predominant phase in the U.S, supporting its continued use in similar patient populations. Funding: U.S. National Institutes of Health.

Chest CT Imaging Provides More Information Than Chest Radiography Alone in Determining Extent of Physiologic Impairment in Pulmonary Sarcoidosis.

BACKGROUND: Sarcoidosis staging primarily has relied on the Scadding chest radiographic system, although chest CT imaging is finding increased clinical use. RESEARCH QUESTION: Whether standardized chest CT scan assessment provides additional understanding of lung function beyond Scadding stage and demographics is unknown and the focus of this study. STUDY DESIGN AND METHODS: We used the National Heart, Lung, and Blood Institute study Genomics Research in Alpha-1 Antitrypsin Deficiency and Sarcoidosis cases of sarcoidosis (n = 351) with Scadding stage and chest CT scans obtained in a standardized manner. One chest radiologist scored all CT scans with a visual scoring system, with a subset read by another chest radiologist. We compared demographic features, Scadding stage, and CT scan findings and the correlation between these measures. Associations between spirometry results and Dlco, CT scan findings, and Scadding stage were determined using regression analysis (n = 318). Agreement between readers was evaluated using Cohen's κ value. RESULTS: CT scan features were inconsistent with Scadding stage in approximately 40% of cases. Most CT scan features assessed on visual scoring were associated negatively with lung function. Associations persisted for FEV1 and Dlco when adjusting for Scadding stage, although some CT scan feature associations with FVC became insignificant. Scadding stage was associated primarily with FEV1, and inclusion of CT scan features reduced significance in association between Scadding stage and lung function. Multivariable regression modeling to identify radiologic measures explaining lung function included Scadding stage for FEV1 and FEV1 to FVC ratio (P < .05) and marginally for Dlco (P < .15). Combinations of CT scan measures accounted for Scadding stage for FVC. Correlations among Scadding stage and CT scan features were noted. Agreement between readers was poor to moderate for presence or absence of CT scan features and poor for degree and location of abnormality. INTERPRETATION: CT scan features explained additional variability in lung function beyond Scadding stage, with some CT scan features obviating the associations between lung function and Scadding stage. Whether CT scan features, phenotypes, or endotypes could be useful for managing patients with sarcoidosis needs more study.

Quantifying the spatial clustering characteristics of radiographic emphysema explains variability in pulmonary function.

Quantitative assessment of emphysema in CT scans has mostly focused on calculating the percentage of lung tissue that is deemed abnormal based on a density thresholding strategy. However, this overall measure of disease burden discards virtually all the spatial information encoded in the scan that is implicitly utilized in a visual assessment. This simplification is likely grouping heterogenous disease patterns and is potentially obscuring clinical phenotypes and variable disease outcomes. To overcome this, several methods that attempt to quantify heterogeneity in emphysema distribution have been proposed. Here, we compare three of those: one based on estimating a power law for the size distribution of contiguous emphysema clusters, a second that looks at the number of emphysema-to-emphysema voxel adjacencies, and a third that applies a parametric spatial point process model to the emphysema voxel locations. This was done using data from 587 individuals from Phase 1 of COPDGene that had an inspiratory CT scan and plasma protein abundance measurements. The associations between these imaging metrics and visual assessment with clinical measures (FEV[Formula: see text], FEV[Formula: see text]-FVC ratio, etc.) and plasma protein biomarker levels were evaluated using a variety of regression models. Our results showed that a selection of spatial measures had the ability to discern heterogeneous patterns among CTs that had similar emphysema burdens. The most informative quantitative measure, average cluster size from the point process model, showed much stronger associations with nearly every clinical outcome examined than existing CT-derived emphysema metrics and visual assessment. Moreover, approximately 75% more plasma biomarkers were found to be associated with an emphysema heterogeneity phenotype when accounting for spatial clustering measures than when they were excluded.

Real-world use of nirmatrelvir-ritonavir in outpatients with COVID-19 during the era of omicron variants including BA.4 and BA.5 in Colorado, USA: a retrospective cohort study.

BACKGROUND: Nirmatrelvir is a protease inhibitor with in-vitro activity against SARS-CoV-2, and ritonavir-boosted nirmatrelvir can reduce the risk of progression to severe COVID-19 among individuals at high risk infected with delta and early omicron variants. However, less is known about the effectiveness of nirmatrelvir-ritonavir during more recent BA.2, BA2.12.1, BA.4, and BA.5 omicron variant surges. We used our real-world data platform to evaluate the effect of nirmatrelvir-ritonavir treatment on 28-day hospitalisation, mortality, and emergency department visits among outpatients with early symptomatic COVID-19 during a SARS-CoV-2 omicron (BA.2, BA2.12.1, BA.4, and BA.5) predominant period in Colorado, USA. METHODS: We did a propensity-matched, retrospective, observational cohort study of non-hospitalised adult patients infected with SARS-CoV-2 between March 26 and Aug 25, 2022, using records from a statewide health system in Colorado. We obtained data from the electronic health records of University of Colorado Health, the largest health system in Colorado, with 13 hospitals and 141 000 annual hospital admissions, and with numerous ambulatory sites and affiliated pharmacies around the state. Included patients had a positive SARS-CoV-2 test or nirmatrelvir-ritonavir medication order. Exclusion criteria were an order for or administration of other SARS-CoV-2 treatments within 10 days of a positive SARS-CoV-2 test, hospitalisation at the time of positive SARS-CoV-2 test, and positive SARS-CoV-2 test more than 10 days before a nirmatrelvir-ritonavir order. We propensity score matched patients treated with nirmatrelvir-ritonavir with untreated patients. The primary outcome was 28-day all-cause hospitalisation. FINDINGS: Among 28 167 patients infected with SARS-CoV-2 between March 26 and Aug 25, 2022, 21 493 met the study inclusion criteria. 9881 patients received treatment with nirmatrelvir-ritonavir and 11 612 were untreated. Nirmatrelvir-ritonavir treatment was associated with reduced 28-day all-cause hospitalisation compared with no antiviral treatment (61 [0·9%] of 7168 patients vs 135 [1·4%] of 9361 patients, adjusted odds ratio (OR) 0·45 [95% CI 0·33-0·62]; p<0·0001). Nirmatrelvir-ritonavir treatment was also associated with reduced 28-day all-cause mortality (two [<0·1%] of 7168 patients vs 15 [0·2%] of 9361 patients; adjusted OR 0·15 [95% CI 0·03-0·50]; p=0·0010). Using subsequent emergency department visits as a surrogate for clinically significant relapse, we observed a decrease after nirmatrelvir-ritonavir treatment (283 [3·9%] of 7168 patients vs 437 [4·7%] of 9361 patients; adjusted OR 0·74 [95% CI 0·63-0·87]; p=0·0002). INTERPRETATION: Real-world evidence reported during a BA.2, BA2.12.1, BA.4, and BA.5 omicron surge showed an association between nirmatrelvir-ritonavir treatment and reduced 28-day all-cause hospitalisation, all-cause mortality, and visits to the emergency department. With results that are among the first to suggest effectiveness of nirmatrelvir-ritonavir for non-hospitalised patients during an omicron period inclusive of BA.4 and BA.5 subvariants, these data support nirmatrelvir-ritonavir as an ongoing first-line treatment for adults acutely infected with SARS-CoV-2. FUNDING: US National Institutes of Health.