Prognostic factors for mortality, intensive care unit and hospital admission due to SARS-CoV-2: a systematic review and meta-analysis of cohort studies in Europe.

BACKGROUND: As mortality from coronavirus disease 2019 (COVID-19) is strongly age-dependent, we aimed to identify population subgroups at an elevated risk for adverse outcomes from COVID-19 using age-/gender-adjusted data from European cohort studies with the aim to identify populations that could potentially benefit from booster vaccinations. METHODS: We performed a systematic literature review and meta-analysis to investigate the role of underlying medical conditions as prognostic factors for adverse outcomes due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), including death, hospitalisation, intensive care unit (ICU) admission and mechanical ventilation within three separate settings (community, hospital and ICU). Cohort studies that reported at least age and gender-adjusted data from Europe were identified through a search of peer-reviewed articles published until 11 June 2021 in Ovid Medline and Embase. Results are presented as odds ratios with 95% confidence intervals and absolute risk differences in deaths per 1000 COVID-19 patients. FINDINGS: We included 88 cohort studies with age-/gender-adjusted data from 6 653 207 SARS-CoV-2 patients from Europe. Hospital-based mortality was associated with high and moderate certainty evidence for solid organ tumours, diabetes mellitus, renal disease, arrhythmia, ischemic heart disease, liver disease and obesity, while a higher risk, albeit with low certainty, was noted for chronic obstructive pulmonary disease and heart failure. Community-based mortality was associated with a history of heart failure, stroke, diabetes and end-stage renal disease. Evidence of high/moderate certainty revealed a strong association between hospitalisation for COVID-19 and solid organ transplant recipients, sleep apnoea, diabetes, stroke and liver disease. INTERPRETATION: The results confirmed the strong association between specific prognostic factors and mortality and hospital admission. Prioritisation of booster vaccinations and the implementation of nonpharmaceutical protective measures for these populations may contribute to a reduction in COVID-19 mortality, ICU and hospital admissions.

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/.

Outcomes Evaluated in Controlled Clinical Trials on the Management of COVID-19: A Methodological Systematic Review.

It is crucial that randomized controlled trials (RCTs) on the management of coronavirus disease 2019 (COVID-19) evaluate the outcomes that are critical to patients and clinicians, to facilitate relevance, interpretability, and comparability. This methodological systematic review describes the outcomes evaluated in 415 RCTs on the management of COVID-19, that were registered with ClinicalTrials.gov, by 5 May 2020, and the instruments used to measure these outcomes. Significant heterogeneity was observed in the selection of outcomes and instruments. Mortality, adverse events and treatment success or failure are only evaluated in 64.4%, 48.4% and 43% of the included studies, respectively, while other outcomes are selected less often. Studies focusing on more severe presentations (hospitalized patients or requiring intensive care) most frequently evaluate mortality (72.5%) and adverse events (55.6%), while hospital admission (50.8%) and viral detection/load (55.6%) are most frequently assessed in the community setting. Outcome measurement instruments are poorly reported and heterogeneous. Follow-up does not exceed one month in 64.3% of these earlier trials, and long-term COVID-19 burden is rarely assessed. The methodological issues identified could delay the introduction of potentially life-saving treatments in clinical practice. Our findings demonstrate the need for greater consistency, to enable decision makers to compare and contrast studies.

COVID-19 and COPD: a narrative review of the basic science and clinical outcomes.

The 2019 coronavirus disease (COVID-19) pandemic is caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). Clinical outcomes, including mortality, are worse in males, older individuals and patients with comorbidities. COPD patients are included in shielding strategies due to their susceptibility to virus-induced exacerbations, compromised pulmonary function and high prevalence of associated comorbidities. Using evidence from basic science and cohort studies, this review addresses key questions concerning COVID-19 and COPD. First, are there mechanisms by which COPD patients are more susceptible to SARS-CoV-2 infection? Secondly, do inhaled corticosteroids offer protection against COVID-19? And, thirdly, what is the evidence regarding clinical outcomes from COVID-19 in COPD patients? This up-to-date review tackles some of the key issues which have significant impact on the long-term outlook for COPD patients in the context of COVID-19.

Automatic oxygen titration with O2matic® to patients admitted with COVID-19 and hypoxemic respiratory failure.

INTRODUCTION: Patients with coronavirus disease (COVID-19) and pneumonitis often have hypoxemic respiratory failure and a need of supplementary oxygen. Guidelines recommend controlled oxygen, for most patients with a recommended interval of SpO2 between 92 and 96%. We aimed to determine if closed-loop control of oxygen was feasible in patients with COVID-19 and could maintain SpO2 in the specified interval. METHODS: Patients were prospectively enrolled in an observational study on a medical ward dedicated to patients with COVID-19. Closed-loop controlled oxygen was delivered by O2matic® which can deliver 0-15 liters/min and adjusts flow every second based on 15 seconds averaging of SpO2 measured by pulse oximetry. Lung function parameters were measured at admission. RESULTS: Fifteen patients (six women, nine men) participated in the study. Average age was 72 years. Lung function was severely impaired with FEV1, FVC and PEF reduced to approximately 50%. The average stay on the ward was 3.2 days and O2matic was used on average for 66 hours, providing 987 hours of observation. O2matic maintained SpO2 in the desired interval for 82.9% of the time. Time with SpO2 > 2% below interval was 5.1% and time with SpO2 > 2% above interval was 0.6%. CONCLUSION: Closed-loop control of oxygen to patients with COVID-19 is feasible and can maintain SpO2 in the specified interval in the majority of time. Closed-loop automated control could be of particular benefit for patients in isolation with decreased visibility, surveillance and monitoring. Further studies must examine the clinical benefits.