Pulmonary sequelae of SARS-CoV-2 infection and factors associated with persistent abnormal lung function at six months after infection: Prospective cohort study.

BACKGROUND: Information on the long-term pulmonary sequelae following SARS-CoV-2 infection is limited. METHODS: Prospective cohort study of hospitalized and non-hospitalized adult patients age >18 with documented SARS-CoV-2 infection by RT-PCR three months prior to enrolment between June and December 2020. Participants underwent full pulmonary function test (PFT), cardiopulmonary exercise testing at 3 months and 6 months. Primary outcome was mean differences of forced vital capacity (FVC), diffuse capacity of lung for carbon monoxide (DLCO), and oxygen consumption (VO2) at 6 vs. 3 months. Secondary outcomes were respiratory outcomes classified into 5 clinical groups-no lung disease, resolved lung disease, persistent lung disease, PFT abnormalities attributable to pre-existing lung disease or other factors, and mild PFT abnormalities of uncertain clinical significance. RESULTS: Fifty-one, 30 hospitalized and 21 non-hospitalized, participants were included. Median age was 51 years; 20 (39.2%) were female. Mean (±SD) percent predicted values of FVC, DLCO and VO2 at 3 vs 6-month-visits were 96.2 ± 15.6 vs. 97.6 ± 15.5, 73.74 ±18 vs. 78.5 ± 15.5, and 75.5 ± 18.9 vs. 76.1 ± 21.5, respectively. Nineteen (37%) patients had physiologic and/or radiographic evidence of lung disease at 3 months with eight (15.7%) continuing to have persistent disease at 6 months. History of diabetes, hypertension, ICU admission and elevated D-Dimer levels were associated with persistent lung disease at 6 months. INTERPRETATION: Persistent lung disease at 6 months post SARS-CoV-2 infection exists. Changes of lung function between 3- and 6-months are not significant. A longer follow-up is required to determine long-term prognosis.

Impact of respiratory viral infections on mortality and critical illness among hospitalized patients with chronic obstructive pulmonary disease.

BACKGROUND: Seasonal respiratory viral infections are associated with exacerbations and morbidity among patients with COPD. The real-world clinical outcomes associated with seasonal viral infections are less well established among hospitalized patients. RESEARCH QUESTION: To estimate the association between seasonal respiratory viral infections, 30-day mortality, and intensive care unit (ICU) admission among hospitalized COPD patients. STUDY DESIGN AND METHODS: We conducted an analysis of a national prospective multicenter cohort of COPD patients hospitalized with acute respiratory illness during winter seasons (2011-2015) in Canada. Nasopharyngeal swabs were performed on all patients at the onset of hospital admission for diagnosis of viral infection. Primary outcomes were 30-day mortality and ICU admissions. Secondary outcomes included invasive/non-invasive ventilation use. RESULTS: Among 3931 hospitalized patients with COPD, 28.5% (1122/3931) were diagnosed with seasonal respiratory viral infection. Viral infection was associated with increased admission to ICU (OR 1.5, 95% CI 1.2-1.9) and need for mechanical ventilation (OR 1.9, 95% CI 1.4-2.5), but was not associated with mortality (OR 1.1, 95% CI 0.8-1.4). Patients with respiratory syncytial virus (RSV) were equally likely to require ICU admission (OR 1.09, 95% CI 0.67-1.78), and more likely to need non-invasive ventilation (OR 3.1; 95% CI 1.8-5.1) compared to patients with influenza. INTERPRETATION: Our results suggest COPD patients requiring hospitalization for respiratory symptoms should routinely receive viral testing at admission, especially for RSV and influenza, to inform prognosis, clinical management, and infection control practices during winter seasons. Patients with COPD will be an important target population for newly developed RSV therapeutics. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov ID: NCT01517191.

Trends in all-cause mortality and inpatient and outpatient visits for ambulatory care sensitive conditions during the first year of the COVID-19 pandemic: A population-based study.

BACKGROUND: The impact of the COVID-19 pandemic on the management of ambulatory care sensitive conditions (ACSCs) remains unknown. OBJECTIVES: To compare observed and expected (projected based on previous years) trends in all-cause mortality and healthcare use for ACSCs in the first year of the pandemic (March 2020 to March 2021). DESIGN, SETTING AND PARTICIPANTS: We conducted a population-based study using provincial health administrative data on general adul population (Ontario, Canada). OUTCOMES AND MEASURES: Monthly all-cause mortality, and hospitalizations, emergency department (ED) and outpatient visit rates (per 100,000 people at-risk) for seven combined ACSCs (asthma, chronic obstructive pulmonary disease, angina, congestive heart failure, hypertension, diabetes, and epilepsy) during the first year were compared with similar periods in previous years (2016-2019) by fitting monthly time series autoregressive integrated moving-average models. RESULTS: Compared to previous years, all-cause mortality rates increased at the beginning of the pandemic (observed rate in March to May 2020 of 79.98 vs. projected of 71.24 [66.35-76.50]) and then returned to expected in June 2020-except among immigrants and people with mental health conditions where they remained elevated. Hospitalization and ED visit rates for ACSCs remained lower than projected throughout the first year: observed hospitalization rate of 37.29 versus projected of 52.07 (47.84-56.68); observed ED visit rate of 92.55 versus projected of 134.72 (124.89-145.33). ACSC outpatient visit rates decreased initially (observed rate of 4299.57 vs. projected of 5060.23 [4712.64-5433.46]) and then returned to expected in June 2020.

Comprehensive compartmental model and calibration algorithm for the study of clinical implications of the population-level spread of COVID-19: a study protocol.

INTRODUCTION: The complex dynamics of the coronavirus disease 2019 (COVID-19) pandemic has made obtaining reliable long-term forecasts of the disease progression difficult. Simple mechanistic models with deterministic parameters are useful for short-term predictions but have ultimately been unsuccessful in extrapolating the trajectory of the pandemic because of unmodelled dynamics and the unrealistic level of certainty that is assumed in the predictions. METHODS AND ANALYSIS: We propose a 22-compartment epidemiological model that includes compartments not previously considered concurrently, to account for the effects of vaccination, asymptomatic individuals, inadequate access to hospital care, post-acute COVID-19 and recovery with long-term health complications. Additionally, new connections between compartments introduce new dynamics to the system and provide a framework to study the sensitivity of model outputs to several concurrent effects, including temporary immunity, vaccination rate and vaccine effectiveness. Subject to data availability for a given region, we discuss a means by which population demographics (age, comorbidity, socioeconomic status, sex and geographical location) and clinically relevant information (different variants, different vaccines) can be incorporated within the 22-compartment framework. Considering a probabilistic interpretation of the parameters allows the model's predictions to reflect the current state of uncertainty about the model parameters and model states. We propose the use of a sparse Bayesian learning algorithm for parameter calibration and model selection. This methodology considers a combination of prescribed parameter prior distributions for parameters that are known to be essential to the modelled dynamics and automatic relevance determination priors for parameters whose relevance is questionable. This is useful as it helps prevent overfitting the available epidemiological data when calibrating the parameters of the proposed model. Population-level administrative health data will serve as partial observations of the model states. ETHICS AND DISSEMINATION: Approved by Carleton University's Research Ethics Board-B (clearance ID: 114596). Results will be made available through future publication.

Cost-effectiveness analysis of 3 months of weekly rifapentine and isoniazid compared to isoniazid monotherapy in a Canadian arctic setting.

OBJECTIVE: To assess the cost effectiveness of once weekly rifapentine and isoniazid for 12 weeks (3HP) to the current standard care for latent tuberculosis (TB) infection (LTBI) in Iqaluit, Nunavut. DESIGN: A cost-effectiveness analysis using a Markov model reflecting local practices for LTBI treatment. SETTING: A remote Canadian arctic community with a high incidence of TB. PARTICIPANTS: Hypothetical patients with LTBI. INTERVENTIONS: The cost effectiveness of 3HP was compared with the existing standard of care in the study region which consists of 9 months of twice weekly isoniazid (9H) given by directly observed therapy. OUTCOME MEASURES: Effectiveness was measured in quality-adjusted life years (QALYs) with model parameters were derived from historical programmatic data, a local implementation study of 3HP and published literature. Costs from the perspective of the Nunavut healthcare system were measured in 2019 US dollars and were obtained primarily from local, empirically collected data. Secondary health outcomes included estimated TB cases and TB deaths averted using 3HP versus 9H. One way and probabilistic sensitivity analyses were performed. RESULTS: The 3HP regimen was dominant over 9H: costs were lower (US$628 vs US$924/person) and health outcomes slightly improved (20.14 vs 20.13 QALYs/person). In comparison to 9H, 3HP treatment resulted in fewer TB cases (27.89 vs 30.16/1000 persons) and TB deaths (2.29 vs 2.48/1000 persons). 3HP completion, initiation and risk of fatal adverse events were the primary drivers of cost effectiveness. CONCLUSION: In a remote Canadian arctic setting, using 3HP instead of 9H for LTBI treatment may result in cost savings and similar or improved health outcomes.