Background: The Centers for Disease Control and Prevention advises that patients with moderate to severe asthma belong to a high-risk group that is susceptible to severe coronavirus disease 2019 (COVID-19). However, the association between asthma and COVID-19 has not been well-established. Objective: The primary objective was to determine the prevalence of asthma among patients with COVID-19 in a major US health system. We assessed the clinical characteristics and comorbidities in asthmatic and nonasthmatic patients with COVID-19. We also determined the risk of hospitalization associated with asthma and/or inhaled corticosteroid use. Methods: Medical records of patients with COVID-19 were searched by a computer algorithm (March 1 to April 15, 2020), and chart review was used to validate the diagnosis of asthma and medications prescribed for asthma. All patients had PCRconfirmed COVID-19. Demographic and clinical features were characterized. Regression models were used to assess the associations between asthma and corticosteroid use and the risk of COVID-19-related hospitalization. Results: Of 1526 patients identified with COVID-19, 220 (14%) were classified as having asthma. Asthma was not associated with an increased risk of hospitalization (relative risk, 0.96; 95% CI, 0.77-1.19) after adjusting for age, sex, and comorbidities. The ongoing use of inhaled corticosteroids did not increase the risk of hospitalization in a similar adjusted model (relative risk, 1.39; 95% CI, 0.90-2.15). Conclusions: Despite a substantial prevalence of asthma in our COVID-19 cohort, asthma was not associated with an increased risk of hospitalization. Similarly, the use of inhaled corticosteroids with or without systemic corticosteroids was not associated with COVID-19-related hospitalization. (J Allergy Clin Immunol 2020;146:307-14.)

Pulmonary fibrosis is a hallmark of diseases such as systemic sclerosis (SSc, scleroderma) and idiopathic pulmonary fibrosis (IPF). To date, the therapeutic options for patients with pulmonary fibrosis are limited, and organ transplantation remains the most effective option. Insulin-like growth factor-binding protein 5 (IGFBP-5) is a conserved member of the IGFBP family of proteins that is overexpressed in SSc and IPF. In this study, we demonstrate that both exogenous and adenovirally expressed IGFBP-5 promote fibrosis by increasing the production of extracellular matrix (ECM) genes and the expression of pro-fibrotic genes in primary human lung fibroblasts. IGFBP-5 increased expression of the pro-fibrotic growth factor CTGF and levels of the matrix crosslinking enzyme lysyl oxidase (LOX). Silencing of IGFBP-5 had different effects in lung fibroblasts from normal donors and patients with SSc or IPF. Moreover, we show that IGFBP-5 increases expression of ECM genes, CTGF, and LOX in human lung tissues maintained in organ culture. Together, our data extend our previous findings and demonstrate that IGFBP-5 exerts its pro-fibrotic activity by directly inducing expression of ECM and pro-fibrotic genes. Further, IGFBP-5 promotes its own expression, generating a positive feedback loop. This suggests that IGFBP-5 likely acts in concert with other growth factors to drive fibrosis and tissue remodeling.

Objective. To examine the effects of a smartphone application (app) to monitor longitudinal electronic patientreported outcomes (ePROs) on patient satisfaction and disease activity in patients with rheumatoid arthritis (RA).Methods. We conducted a 6-month randomized controlled trial of care coordination along with an app (intervention) versus care coordination alone (control) in 191 RA patients. Participants in the intervention group were prompted to provide information daily using ePROs. In both the intervention and control groups, a care coordinator contacted participants at 6 and 18 weeks to assess for flares. The main outcome measures were the global satisfaction score from the Treatment Satisfaction Questionnaire for Medication (TSQM), the score from the Perceived Efficacy in Patient-Physician Interactions (PEPPI) Questionnaire, and the Clinical Disease Activity Index (CDAI) score.Results. Groups were similar at baseline. The median TSQM score at 6 months was 83.3 in both groups, and the median PEPPI score at 6 months was 50 in both groups. The median CDAI score at 6 months was 8 in the intervention group versus 10 in the control group. No statistically significant group differences in the medians of TSQM, PEPPI, or CDAI scores at 6 months were detected. Of the 67 intervention participants who completed the exit survey, 90% rated their likelihood of recommending the app as ≥7 of 10. Of the 11 physicians who completed the exit survey, 73% agreed/strongly agreed that they wanted to continue offering the app to patients. Conclusion.A mobile app designed to collect ePRO data on RA symptoms did not significantly improve patient satisfaction or disease activity compared to care coordination alone. However, both patients and physicians reported positive experiences with the app.ClinicalTrials.gov identifier: NCT02822521.

Objective Patients with diffuse cutaneous systemic sclerosis (dcSSc) display a complex clinical phenotype. Transcriptional profiling of whole blood or tissue from patients are affected by changes in cellular composition that drive gene expression and an inability to detect minority cell populations. We undertook this study to focus on the 2 main subtypes of circulating monocytes, classical monocytes (CMs) and nonclassical monocytes (NCMs) as a biomarker of SSc disease severity. Methods SSc patients were recruited from the Prospective Registry for Early Systemic Sclerosis. Clinical data were collected, as well as peripheral blood for isolation of CMs and NCMs. Age‐, sex‐, and race‐matched healthy volunteers were recruited as controls. Bulk macrophages were isolated from the skin in a separate cohort. All samples were assayed by RNA sequencing (RNA‐seq). Results We used an unbiased approach to cluster patients into 3 groups (groups A–C) based on the transcriptional signatures of CMs relative to controls. Each group maintained their characteristic transcriptional signature in NCMs. Genes up‐regulated in group C demonstrated the highest expression compared to the other groups in SSc skin macrophages, relative to controls. Patients from groups B and C exhibited worse lung function than group A, although there was no difference in SSc skin disease at baseline, relative to controls. We validated our approach by applying our group classifications to published bulk monocyte RNA‐seq data from SSc patients, and we found that patients without skin disease were most likely to be classified as group A. Conclusion We are the first to show that transcriptional signatures of CMs and NCMs can be used to unbiasedly stratify SSc patients and correlate with disease activity outcome measures.

Background Severity of illness scores—Acute Physiology and Chronic Health Evaluation, Simplified Acute Physiology Score, and Sequential Organ Failure Assessment—are current risk stratification and mortality prediction tools used in intensive care units (ICUs) worldwide. Developers of artificial intelligence or machine learning (ML) models predictive of ICU mortality use the severity of illness scores as a reference point when reporting the performance of these computational constructs. Objective This study aimed to perform a literature review and meta-analysis of articles that compared binary classification ML models with the severity of illness scores that predict ICU mortality and determine which models have superior performance. This review intends to provide actionable guidance to clinicians on the performance and validity of ML models in supporting clinical decision-making compared with the severity of illness score models. Methods Between December 15 and 18, 2020, we conducted a systematic search of PubMed, Scopus, Embase, and IEEE databases and reviewed studies published between 2000 and 2020 that compared the performance of binary ML models predictive of ICU mortality with the performance of severity of illness score models on the same data sets. We assessed the studies' characteristics, synthesized the results, meta-analyzed the discriminative performance of the ML and severity of illness score models, and performed tests of heterogeneity within and among studies. Results We screened 461 abstracts, of which we assessed the full text of 66 (14.3%) articles. We included in the review 20 (4.3%) studies that developed 47 ML models based on 7 types of algorithms and compared them with 3 types of the severity of illness score models. Of the 20 studies, 4 (20%) were found to have a low risk of bias and applicability in model development, 7 (35%) performed external validation, 9 (45%) reported on calibration, 12 (60%) reported on classification measures, and 4 (20%) addressed explainability. The discriminative performance of the ML-based models, which was reported as AUROC, ranged between 0.728 and 0.99 and between 0.58 and 0.86 for the severity of illness score–based models. We noted substantial heterogeneity among the reported models and considerable variation among the AUROC estimates for both ML and severity of illness score model types. Conclusions ML-based models can accurately predict ICU mortality as an alternative to traditional scoring models. Although the range of performance of the ML models is superior to that of the severity of illness score models, the results cannot be generalized due to the high degree of heterogeneity. When presented with the option of choosing between severity of illness score or ML models for decision support, clinicians should select models that have been externally validated, tested in the practice environment, and updated to the patient population and practice environment. Trial Registration PROSPERO CRD42021203871; https://tinyurl.com/28v2nch8