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.

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.

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.

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.

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.

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.

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.

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.

Effects of pulsatile intravenous follicle-stimulating hormone treatment on ovarian function in women with obesity.

OBJECTIVE: To establish conditions for effective hypothalamic suppression in women with normal and high body mass index (BMI) and test the hypothesis that intravenous (IV) administration of pulsatile recombinant follicle-stimulating hormone (rFSH) can overcome the clinically evident dysfunctional pituitary-ovarian axis in women with obesity. DESIGN: Prospective interventional study. SETTING: Academic medical center. PATIENT(S): Twenty-seven normal-weight women and 27 women with obesity, who were eumenorrheic and aged 21-39 years. INTERVENTION(S): Two-day frequent blood sampling study, in early follicular phase, before and after cetrorelix suppression of gonadotropins and exogenous pulsatile IV rFSH administration. MAIN OUTCOME MEASURE(S): Serum inhibin B and estradiol (E2) levels (basal and rFSH stimulated). RESULT(S): A modified gonadotropin-releasing hormone antagonism protocol effectively suppressed production of endogenous gonadotropins in women with normal and high BMIs, providing a model to address the functional role of FSH in the hypothalamic-pituitary-ovarian axis. The IV rFSH treatment resulted in equivalent serum levels and pharmacodynamics in normal-weight women and those with obesity. However, women with obesity exhibited reduced basal levels of inhibin B and E2 and a significantly decreased response to FSH stimulation. The BMI was inversely correlated with serum inhibin B and E2. In spite of this observed deficit in ovarian function, pulsatile IV rFSH treatment in women with obesity resulted in E2 and inhibin B levels comparable with those in normal-weight women, in the absence of exogenous FSH stimulation. CONCLUSION(S): Despite normalization of FSH levels and pulsatility by exogenous IV administration, women with obesity demonstrate ovarian dysfunction with respect to E2 and inhibin B secretion. Pulsatile FSH can partially correct the relative hypogonadotropic hypogonadism of obesity, thereby providing a potential treatment strategy to mitigate some of the adverse effects of high BMI on fertility, assisted reproduction, and pregnancy outcomes. CLINICAL TRIAL REGISTRATION NUMBER: ClinicalTrials.gov #NCT02478775.

Real-world evaluation of bebtelovimab effectiveness during the period of COVID-19 Omicron variants, including BA.4/BA.5.

OBJECTIVES: Bebtelovimab is an anti-SARS-CoV-2 monoclonal antibody active against Omicron lineage variants authorized to treat high-risk outpatients with COVID-19. We sought to determine the real-world effectiveness of bebtelovimab during the Omicron phases BA.2/BA2.12.1/BA4/BA5. METHODS: We conducted a retrospective cohort study of adults with SARS-CoV-2 infection between April 6 and October 11, 2022, using health records linked to vaccine and mortality data. We used propensity scores to match of bebtelovimab-treated with untreated outpatients. The primary outcome was 28-day all-cause hospitalization. The secondary outcomes were 28-day COVID-19-related hospitalization, 28-day all-cause mortality, 28-day emergency department visits, maximum respiratory support level, intensive care unit admission, and in-hospital mortality among hospitalized patients. We used logistic regression to determine bebtelovimab treatment effectiveness. RESULTS: Among 22,720 patients with SARS-COV-2 infection, 3739 bebtelovimab-treated patients were matched to 5423 untreated patients. Compared with no treatment, bebtelovimab was associated with lower odds of 28-day all-cause hospitalization (1.3% vs 2.1%, adjusted odds ratio: 0.53; 95% confidence interval: 0.37-0.74, P <0.001), as well as COVID-19-related hospitalization (1.0% vs 2.0%, adjusted odds ratio: 0.44 [95% confidence interval: 0.30-0.64], P <0.001). Bebtelovimab appeared to be more beneficial in lowering the odds of hospitalization among patients with two or more comorbidities (interaction P = 0.03). CONCLUSION: During the Omicron BA.2/BA.2.12.1/BA.4/BA.5 variant phase, bebtelovimab was associated with lower hospitalization.

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.

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

BACKGROUND: Neutralizing monoclonal antibodies (mAbs) were authorized for the treatment of COVID-19 outpatients based on clinical trials completed early in the pandemic, which were underpowered for mortality and subgroup analyses. Real-world data studies are promising for further assessing rapidly deployed therapeutics. RESEARCH QUESTION: Did mAb treatment prevent progression to severe disease and death across pandemic phases and based on risk factors, including prior vaccination status? STUDY DESIGN AND METHODS: This observational cohort study included nonhospitalized adult patients with SARS-CoV-2 infection from November 2020 to October 2021 using electronic health records from a statewide health system plus state-level vaccine and mortality data. Using propensity matching, we selected approximately 2.5 patients not receiving mAbs for each patient who received mAb treatment under emergency use authorization. The primary outcome was 28-day hospitalization; secondary outcomes included mortality and hospitalization severity. RESULTS: Of 36,077 patients with SARS-CoV-2 infection, 2,675 receiving mAbs were matched to 6,677 patients not receiving mAbs. Compared with mAb-untreated patients, mAb-treated patients had lower all-cause hospitalization (4.0% vs 7.7%; adjusted OR, 0.48; 95% CI, 0.38-0.60) and all-cause mortality (0.1% vs 0.9%; adjusted OR, 0.11; 95% CI, 0.03-0.29) to day 28; differences persisted to day 90. Among hospitalized patients, mAb-treated patients had shorter hospital length of stay (5.8 vs 8.5 days) and lower risk of mechanical ventilation (4.6% vs 16.6%). Results were similar for preventing hospitalizations during the Delta variant phase (adjusted OR, 0.35; 95% CI, 0.25-0.50) and across subgroups. Number-needed-to-treat (NNT) to prevent hospitalization was lower for subgroups with higher baseline risk of hospitalization; for example, multiple comorbidities (NNT = 17) and not fully vaccinated (NNT = 24) vs no comorbidities (NNT = 88) and fully vaccinated (NNT = 81). INTERPRETATION: Real-world data revealed a strong association between receipt of mAbs and reduced hospitalization and deaths among COVID-19 outpatients across pandemic phases. Real-world data studies should be used to guide practice and policy decisions, including allocation of scarce resources.

Change in effectiveness of sotrovimab for preventing hospitalization and mortality for at-risk COVID-19 outpatients during an Omicron BA.1 and BA.1.1-predominant phase.

OBJECTIVES: Sotrovimab effectively prevented progression to severe disease and mortality following infection with pre-Omicron SARS-CoV-2 variants. We sought to determine whether sotrovimab is similarly effective against SARS-CoV-2 Omicron variant infection. METHODS: Observational cohort study of non-hospitalized adult patients with SARS-CoV-2 infection from December 26, 2021, to March 10, 2022, using electronic health records from a statewide health system. We propensity-matched patients not receiving authorized treatment for each patient treated with sotrovimab. The primary outcome was 28-day hospitalization; secondary outcomes included mortality. We also propensity-matched sotrovimab-treated patients from the Omicron and Delta phases. Logistic regression was used to determine sotrovimab effectiveness during Omicron and between variant phases. RESULTS: Of 30,247 SARS-CoV-2 Omicron variant infected outpatients, we matched 1542 receiving sotrovimab to 3663 not receiving treatment. Sotrovimab treatment was not associated with reduced odds of 28-day hospitalization (2.5% vs 3.2%; adjusted odds ratio [OR] 0.82, 95% CI 0.55, 1.19) or mortality (0.1% vs 0.2%; adjusted OR 0.62, 95% CI 0.07, 2.78). Between phases, the observed treatment OR was higher during Omicron than during Delta (OR 0.85 vs 0.39, respectively; interaction P-value = 0.053). CONCLUSION: Real-world evidence demonstrated that sotrovimab was not associated with reduced 28-day hospitalization or mortality among COVID-19 outpatients during the Omicron BA.1 phase.

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.

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.

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.

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 COVID-19 patients, is effective against the SARS-CoV-2 Delta variant to prevent progression to severe disease and mortality. Methods: Observational cohort study of non-hospitalized adult patients with SARS-CoV-2 infection from October 1 st 2021 - December 11 th 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 1,563 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% versus 5.7%; adjusted OR 0.37, 95% CI 0.19-0.66) and an 89% decrease in the odds of all-cause 28-day mortality (raw rate 0% versus 1.0%; adjusted OR 0.11, 95% CI 0.0-0.79), and may reduce respiratory disease severity among those hospitalized. Conclusion: 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.

Cluster activation mapping with application to computed tomography scans of the lung.

Purpose: An open question in deep clustering is how to explain what in the image is driving the cluster assignments. This is especially important for applications in medical imaging when the derived cluster assignments may inform decision-making or create new disease subtypes. We develop cluster activation mapping (CLAM), which is methodology to create localization maps highlighting the image regions important for cluster assignment. Approach: Our approach uses a linear combination of the activation channels from the last layer of the encoder within a pretrained autoencoder. The activation channels are weighted by a channelwise confidence measure, which is a modification of score-CAM. Results: Our approach performs well under medical imaging-based simulation experiments, when the image clusters differ based on size, location, and intensity of abnormalities. Under simulation, the cluster assignments were predicted with 100% accuracy when the number of clusters was set at the true value. In addition, applied to computed tomography scans from a sarcoidosis population, CLAM identified two subtypes of sarcoidosis based purely on CT scan presentation, which were significantly associated with pulmonary function tests and visual assessment scores, such as ground-glass, fibrosis, and honeycombing. Conclusions: CLAM is a transparent methodology for identifying explainable groupings of medical imaging data. As deep learning networks are often criticized and not trusted due to their lack of interpretability, our contribution of CLAM to deep clustering architectures is critical to our understanding of cluster assignments, which can ultimately lead to new subtypes of diseases.

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.