Collaborative Cohort of Cohorts for COVID-19 Research (C4R) Study: Study Design.

The Collaborative Cohort of Cohorts for COVID-19 Research (C4R) is a national prospective study of adults comprising 14 established US prospective cohort studies. Starting as early as 1971, investigators in the C4R cohort studies have collected data on clinical and subclinical diseases and their risk factors, including behavior, cognition, biomarkers, and social determinants of health. C4R links this pre-coronavirus disease 2019 (COVID-19) phenotyping to information on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and acute and postacute COVID-related illness. C4R is largely population-based, has an age range of 18-108 years, and reflects the racial, ethnic, socioeconomic, and geographic diversity of the United States. C4R ascertains SARS-CoV-2 infection and COVID-19 illness using standardized questionnaires, ascertainment of COVID-related hospitalizations and deaths, and a SARS-CoV-2 serosurvey conducted via dried blood spots. Master protocols leverage existing robust retention rates for telephone and in-person examinations and high-quality event surveillance. Extensive prepandemic data minimize referral, survival, and recall bias. Data are harmonized with research-quality phenotyping unmatched by clinical and survey-based studies; these data will be pooled and shared widely to expedite collaboration and scientific findings. This resource will allow evaluation of risk and resilience factors for COVID-19 severity and outcomes, including postacute sequelae, and assessment of the social and behavioral impact of the pandemic on long-term health trajectories.

Reducing the Risk of Mortality in Chronic Obstructive Pulmonary Disease With Pharmacotherapy: A Narrative Review.

In 2020, chronic obstructive pulmonary disease (COPD) was the fifth leading cause of death in the United States excluding COVID-19, and its mortality burden has been rising since the 1980s. Smoking cessation, long-term oxygen therapy, noninvasive ventilation, and lung volume reduction surgery have had a beneficial effect on mortality; however, until recently, the effects of pharmacologic therapies on all-cause mortality have been unclear. Inhaled pharmacologic treatments for patients with COPD include combinations of long-acting muscarinic receptor antagonists (LAMAs), long-acting-ß2-agonists (LABAs), and inhaled corticosteroids (ICS). The recent IMPACT and ETHOS clinical trials reported mortality benefits with ICS/LAMA/LABA triple therapy compared with LAMA/LABA dual therapy. In IMPACT, fluticasone furoate/umeclidinium/vilanterol therapy significantly reduced the risk of on-/off-treatment all-cause mortality vs umeclidinium/vilanterol (hazard ratio, 0.72; 95% CI, 0.53 to 0.99; P=.042). The ETHOS trial found a reduction in the risk of on-/off-treatment all-cause mortality in patients treated with budesonide/glycopyrrolate/formoterol vs glycopyrrolate/formoterol (hazard ratio, 0.51 [0.33 to 0.80]; nominal P=.0035). Both trials included populations of patients with symptomatic COPD at high risk of future exacerbations, and a post hoc analysis of the final retrieved vital status data suggested that the observed mortality benefits are conferred by the ICS component. In conclusion, triple therapy reduces the risk of mortality in patients with symptomatic COPD characterized by moderate or severe airflow obstruction and a recent history of moderate or severe exacerbations. This benefit is likely to be driven by reductions in exacerbations. Future research efforts should focus on improving the long-term prognosis of patients living with COPD.

Characteristics and outcomes of ambulatory patients with suspected COVID-19 at a respiratory referral center.

RATIONALE: SARS-CoV-2 continues to cause a global pandemic and management of COVID-19 in outpatient settings remains challenging. OBJECTIVE: We sought to describe characteristics of patients with chronic respiratory disease (CRD) experiencing symptoms consistent with COVID-19, who were seen in a novel Acute Respiratory Clinic, prior to widely available testing, emergence of variants, COVID-19 vaccination, and post-vaccination (breakthrough) SARS-CoV-2 infections. METHODS: Retrospective electronic medical record data were analyzed from 907 adults with presumed COVID-19 seen between March 16, 2020 and January 7, 2021. Data included demographics, comorbidities, medications, vital signs, laboratory tests, pulmonary function tests, patient disposition, and co-infections. The overdispersed data (aod) R package was used to create a logit model using COVID-19 diagnosis by PCR as the dichotomous outcome variable. Univariate, conventional multivariate and elastic net machine learning were used to analyze data. RESULTS: Male gender, elevated baseline temperature, and respiratory rate predicted COVID-19 diagnosis. Eosinopenia, neutrophilia, and lymphocytosis were also associated with COVID-19 diagnosis. However, asthma and COPD diagnoses were not associated with SARS-CoV-2 PCR positive test. Male gender, low oxygen saturation, and lower forced expiratory volume in 1 s (FEV1) were associated with higher hospital referral. CONCLUSIONS: CRD patients with acute respiratory symptoms in the ambulatory setting were more likely to have COVID-19 if male, febrile and tachypneic. Patients with lower pre-morbid FEV1 and lower SPO2 are more likely to be referred to the hospital. A composite of vitals sigs and WBC differential help risk stratify CRD patients seeking care for presumed COVID-19.

Machine Learning and Prediction of All-Cause Mortality in COPD.

BACKGROUND: COPD is a leading cause of mortality. RESEARCH QUESTION: We hypothesized that applying machine learning to clinical and quantitative CT imaging features would improve mortality prediction in COPD. STUDY DESIGN AND METHODS: We selected 30 clinical, spirometric, and imaging features as inputs for a random survival forest. We used top features in a Cox regression to create a machine learning mortality prediction (MLMP) in COPD model and also assessed the performance of other statistical and machine learning models. We trained the models in subjects with moderate to severe COPD from a subset of subjects in Genetic Epidemiology of COPD (COPDGene) and tested prediction performance in the remainder of individuals with moderate to severe COPD in COPDGene and Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE). We compared our model with the BMI, airflow obstruction, dyspnea, exercise capacity (BODE) index; BODE modifications; and the age, dyspnea, and airflow obstruction index. RESULTS: We included 2,632 participants from COPDGene and 1,268 participants from ECLIPSE. The top predictors of mortality were 6-min walk distance, FEV1 % predicted, and age. The top imaging predictor was pulmonary artery-to-aorta ratio. The MLMP-COPD model resulted in a C index ≥ 0.7 in both COPDGene and ECLIPSE (6.4- and 7.2-year median follow-ups, respectively), significantly better than all tested mortality indexes (P < .05). The MLMP-COPD model had fewer predictors but similar performance to that of other models. The group with the highest BODE scores (7-10) had 64% mortality, whereas the highest mortality group defined by the MLMP-COPD model had 77% mortality (P = .012). INTERPRETATION: An MLMP-COPD model outperformed four existing models for predicting all-cause mortality across two COPD cohorts. Performance of machine learning was similar to that of traditional statistical methods. The model is available online at: https://cdnm.shinyapps.io/cgmortalityapp/.