ABSTRACT
Background: The medium- to long-term pulmonary consequences of coronavirus disease 2019 (COVID-19) after recovery from acute infection remain unclear. Several studies have examined this issue and reported heterogeneous results. Methods: : We conducted a systematic review and meta-analysis using a random-effects model to estimate the pooled prevalence of pulmonary sequelae after COVID-19, namely impaired diffusion capacity and pulmonary fibrosis, at least 1 month after the initial infection. PubMed, Embase, and Cochrane Library were searched from January 1, 2020, to February 15, 2021 to identify related studies. We also assessed whether the initial severity of infection was associated with impaired diffusion capacity in the recovery phase. Results: : Of the 8159 studies identified, 29 met our eligibility criteria. Among these studies, 25 and 20 had data on follow-up pulmonary function testing and chest computed tomography (CT), respectively. Impaired diffusion capacity (<80% of predicted values or lower limit of normal) was the most common pulmonary abnormality (pooled prevalence 34%, 95% CI 27%–41%). When classified according to the severity of index infection, patients with severe COVID-19 were more likely to display impaired diffusion capacity than those with non-severe COVID-19 (pooled odds ratio 2.97, 95% CI 2.10–4.20). On follow-up chest CT, pulmonary fibrosis was found in 26% (95% CI 17%–36%) of the patients. Conclusions: : A substantial number of patients recovering from COVID-19 displayed impaired diffusion capacity and pulmonary fibrosis. The severity of index COVID-19 was associated with impaired diffusion capacity, highlighting the importance of respiratory follow-up in patients recovering from severe COVID-19. Systematic review registration number: PROSPERO CRD42021234357
Subject(s)
COVID-19ABSTRACT
Background: This study aimed to evaluate incidence risk and severe clinical outcomes in COVID-19 disease among short-term users of acid-suppressants in South Korea. Methods: : This retrospective cohort study, conducted using a nationwide claims database for South Korea, used data from patients with COVID-19 tested between January 1 and May 15, 2020. Patients aged over 18 years and prescribed proton pump inhibitors (PPI) or histamine-2 receptor antagonist (H 2 RA) for more than 7 days were identified. Primary outcome was COVID-19 while secondary outcomes were all-cause mortality, hospitalization with respiratory disease, or intensive respiratory intervention. Large-scale propensity scores were used to match patients, while the Cox proportional hazard model was utilized to evaluate any association between exposure and outcome(s). The risk estimates were calibrated by using 123 falsification endpoints. Results: We identified 26,166 PPI users and 62,117 H 2 RA users. After propensity score matching, compared to H 2 RA use, PPI use was not significantly associated with lower risk of COVID-19 (calibrated hazard ratio [HR], 0.81 [95% confidence interval (CI), 0.30–2.19]); moreover, PPI use was not associated with adverse clinical outcomes in COVID-19, namely, hospitalization with respiratory disease (calibrated HR, 0.88 [95% CI, 0.72–1.08]), intensive respiratory interventions (calibrated HR, 0.92 [95% CI, 0.46–1.82]), except for all-cause mortality (calibrated HR, 0.54 [95% CI, 0.31–0.95]). Conclusions: In this study, we found that the PPI user was not associated with risk of COVID-19 compared to H 2 RA users. There was no significant relationship between severe clinical outcomes of COVID-19 and exposure to PPI compared with H 2 RA, except for all-cause mortality.
Subject(s)
COVID-19 , Respiratory Tract InfectionsABSTRACT
Background: Prone positioning is recommended for patients with moderate-to-severe acute respiratory distress syndrome (ARDS) receiving mechanical ventilation. While the debate continues as to whether COVID-19 ARDS is clinically different from non-COVID ARDS, there is little data on whether the physiological effects of prone positioning differ between the two conditions. We aimed to compare the physiological effect of prone positioning between patients with COVID-19 ARDS and those with non-COVID ARDS. Methods: We retrospectively compared 23 patients with COVID-19 ARDS and 145 patients with non-COVID ARDS treated using prone positioning while on mechanical ventilation. Changes in PaO2/FiO2 ratio and static respiratory system compliance (Crs) after the first session of prone positioning were compared between the two groups: first, using all patients with non-COVID ARDS, and second, using subgroups of patients with non-COVID ARDS matched 1:1 with patients with COVID-19 ARDS for baseline PaO2/FiO2 ratio and static Crs. We also evaluated whether the response to the first prone positioning session was associated with the clinical outcome. Results: When compared with the entire group of patients with non-COVID ARDS, patients with COVID-19 ARDS showed more pronounced improvement in the PaO2/FiO2 ratio (adjusted difference 39.3 [95% CI 5.2–73.5] mmHg) and static Crs (adjusted difference 3.4 [95% CI 1.1–5.6] mL/cmH2O). However, these between-group differences were not significant when the matched samples (either PaO2/FiO2-matched or compliance-matched) were analyzed. The improvements in PaO2/FiO2 ratio (subdistribution hazard ratio 1.19, 95% CI 1.08–1.30) and static Crs (subdistribution hazard ratio 1.57, 95% CI 1.29–1.91) after the first prone positioning session were associated with successful discontinuation of mechanical ventilation in patients with COVID-19 ARDS. Conclusions: In patients with COVID-19 ARDS, prone positioning was as effective in improving respiratory physiology as in patients with non-COVID ARDS. Thus, it should be actively considered as a therapeutic option. The physiological response to the first session of prone positioning was predictive of the clinical outcome of patients with COVID-19 ARDS.
Subject(s)
COVID-19ABSTRACT
Background and Objective As a response to the ongoing COVID-19 pandemic, several prediction models have been rapidly developed, with the aim of providing evidence-based guidance. However, no COVID-19 prediction model in the existing literature has been found to be reliable. Models are commonly assessed to have a risk of bias, often due to insufficient reporting, use of non-representative data, and lack of large-scale external validation. In this paper, we present the Observational Health Data Sciences and Informatics (OHDSI) analytics pipeline for patient-level prediction as a standardized approach for rapid yet reliable development and validation of prediction models. We demonstrate how our analytics pipeline and open-source software can be used to answer important prediction questions while limiting potential causes of bias (e.g., by validating phenotypes, specifying the target population, performing large-scale external validation and publicly providing all analytical source code). Methods We show step-by-step how to implement the pipeline for the question: ‘In patients hospitalized with COVID-19, what is the risk of death 0 to 30 days after hospitalization’. We develop models using six different machine learning methods in a US claims database containing over 20,000 COVID-19 hospitalizations and externally validate the models using data containing over 45,000 COVID-19 hospitalizations from South Korea, Spain, and the US. Results Our open-source tools enabled us to efficiently go end-to-end from problem design to reliable model development and evaluation. When predicting death in patients hospitalized for COVID-19 adaBoost, random forest, gradient boosting machine, and decision tree yielded similar or lower internal and external validation discrimination performance compared to L1-regularized logistic regression, whereas the MLP neural network consistently resulted in lower discrimination. L1-regularized logistic regression models were well calibrated. Conclusion Our results show that following the OHDSI analytics pipeline for patient-level prediction can enable the rapid development towards reliable prediction models. The OHDSI tools and pipeline are open source and available to researchers around the world.
Subject(s)
COVID-19ABSTRACT
Background: Routinely collected real world data (RWD) have great utility in aiding the novel coronavirus disease (COVID-19) pandemic response [1,2]. Here we present the international Observational Health Data Sciences and Informatics (OHDSI) [3] Characterizing Health Associated Risks, and Your Baseline Disease In SARS-COV-2 (CHARYBDIS) framework for standardisation and analysis of COVID-19 RWD.Methods: We conducted a descriptive cohort study using a federated network of data partners in the United States, Europe (the Netherlands, Spain, the UK, Germany, France and Italy) and Asia (South Korea and China). The study protocol and analytical package were released on 11th June 2020 and are iteratively updated via GitHub [4]. Findings: We identified three non-mutually exclusive cohorts of 4,537,153 individuals with a clinical COVID-19 diagnosis or positive test, 886,193 hospitalized with COVID-19, and 113,627 hospitalized with COVID-19 requiring intensive services. All comorbidities, symptoms, medications, and outcomes are described by cohort in aggregate counts, and are available in an interactive website: https://data.ohdsi.org/Covid19CharacterizationCharybdis/. Interpretation: CHARYBDIS findings provide benchmarks that contribute to our understanding of COVID-19 progression, management and evolution over time. This can enable timely assessment of real-world outcomes of preventative and therapeutic options as they are introduced in clinical practice.
Subject(s)
COVID-19 , Coronavirus Infections , Leishmaniasis, CutaneousABSTRACT
Introduction: Despite the proven benefits of dexamethasone in hospitalized COVID-19 patients, the optimum time for the administration of dexamethasone is unknown. We investigated the progression of COVID-19 pneumonia based on the timing of dexamethasone administration.Methods: A single-center, retrospective cohort study based on medical record reviews was conducted between June 10 and September 21, 2020. We compared the risk of severe COVID-19, defined as the use of a high-flow nasal cannula or a mechanical ventilator, between groups that received dexamethasone either within 24 hours of hypoxemia (early dexamethasone group) or 24 hours after hypoxemia (late dexamethasone group). Hypoxemia was defined as room-air SpO2 <90%. Results: Among 59 patients treated with dexamethasone for COVID-19 pneumonia, 30 were in the early dexamethasone group and 29 were in the late dexamethasone group. There was no significant difference in baseline characteristics, the time interval from symptom onset to diagnosis or hospitalization, or the use of antiviral or antibacterial agents between the two groups. The early dexamethasone group showed a significantly lower rate of severe COVID-19 compared to the control group (75.9% vs 40.0%, P-value=0.012). Further, the early dexamethasone group showed a significantly shorter total duration of oxygen supplementation (10.45 d vs. 21.61 d, P-value=0.003) and length of stay in the hospital (19.76 d vs. 27.21 d, P-value=0.013). However, extracorporeal membrane oxygenation and in-hospital mortality rates were not significantly different between the two groups.Conclusions: Early administration of dexamethasone may prevent the progression of COVID-19 to a severe disease, without increased mortality.
Subject(s)
COVID-19 , Hypoxia , PneumoniaABSTRACT
Background In this study we phenotyped individuals hospitalised with coronavirus disease 2019 (COVID-19) in depth, summarising entire medical histories, including medications, as captured in routinely collected data drawn from databases across three continents. We then compared individuals hospitalised with COVID-19 to those previously hospitalised with influenza. Methods We report demographics, previously recorded conditions and medication use of patients hospitalised with COVID-19 in the US (Columbia University Irving Medical Center [CUIMC], Premier Healthcare Database [PHD], UCHealth System Health Data Compass Database [UC HDC], and the Department of Veterans Affairs [VA OMOP]), in South Korea (Health Insurance Review & Assessment [HIRA]), and Spain (The Information System for Research in Primary Care [SIDIAP] and HM Hospitales [HM]). These patients were then compared with patients hospitalised with influenza in 2014-19. Results 34,128 (US: 8,362, South Korea: 7,341, Spain: 18,425) individuals hospitalised with COVID-19 were included. Between 4,811 (HM) and 11,643 (CUIMC) unique aggregate characteristics were extracted per patient, with all summarised in an accompanying interactive website (http://evidence.ohdsi.org/Covid19CharacterizationHospitalization/). Patients were majority male in the US (CUIMC: 52%, PHD: 52%, UC HDC: 54%, VA OMOP: 94%,) and Spain (SIDIAP: 54%, HM: 60%), but were predominantly female in South Korea (HIRA: 60%). Age profiles varied across data sources. Prevalence of asthma ranged from 4% to 15%, diabetes from 13% to 43%, and hypertensive disorder from 24% to 70% across data sources. Between 14% and 33% were taking drugs acting on the renin-angiotensin system in the 30 days prior to hospitalisation. Compared to 81,596 individuals hospitalised with influenza in 2014-19, patients admitted with COVID-19 were more typically male, younger, and healthier, with fewer comorbidities and lower medication use. Conclusions We provide a detailed characterisation of patients hospitalised with COVID-19. Protecting groups known to be vulnerable to influenza is a useful starting point to minimize the number of hospital admissions needed for COVID-19. However, such strategies will also likely need to be broadened so as to reflect the particular characteristics of individuals hospitalised with COVID-19.