RESUMEN
Early prediction of COVID-19 in-hospital mortality relies usually on patients' preexisting comorbidities and is rarely reproducible in independent cohorts. We wanted to compare the role of routinely measured biomarkers of immunity, inflammation, and cellular damage with preexisting comorbidities in eight different machine-learning models to predict mortality, and evaluate their performance in an independent population. We recruited and followed-up consecutive adult patients with SARS-Cov-2 infection in two different Italian hospitals. We predicted 60-day mortality in one cohort (development dataset, n = 299 patients, of which 80% was allocated to the development dataset and 20% to the training set) and retested the models in the second cohort (external validation dataset, n = 402). Demographic, clinical, and laboratory features at admission, treatments and disease outcomes were significantly different between the two cohorts. Notably, significant differences were observed for %lymphocytes (p < 0.05), international-normalized-ratio (p < 0.01), platelets, alanine-aminotransferase, creatinine (all p < 0.001). The primary outcome (60-day mortality) was 29.10% (n = 87) in the development dataset, and 39.55% (n = 159) in the external validation dataset. The performance of the 8 tested models on the external validation dataset were similar to that of the holdout test dataset, indicating that the models capture the key predictors of mortality. The shap analysis in both datasets showed that age, immune features (%lymphocytes, platelets) and LDH substantially impacted on all models' predictions, while creatinine and CRP varied among the different models. The model with the better performance was model 8 (60-day mortality AUROC 0.83 ± 0.06 in holdout test set, 0.79 ± 0.02 in external validation dataset). The features that had the greatest impact on this model's prediction were age, LDH, platelets, and %lymphocytes, more than comorbidities or inflammation markers, and these findings were highly consistent in both datasets, likely reflecting the virus effect at the very beginning of the disease.
RESUMEN
The coronavirus disease 2019 (COVID-19) pandemic, caused by the new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), provoked the most striking international public health crisis of our time. COVID-19 can cause a range of breathing problems, from mild to critical, with potential evolution to respiratory failure and acute respiratory distress syndrome. Elderly adults and those affected with chronic cardiovascular, metabolic, and respiratory conditions carry a higher risk of severe COVID-19. Given the global burden of asthma, there are well-founded concerns that the relationship between COVID-19 and asthma could represent a "dangerous liaison".Here we aim to review the latest evidence on the links between asthma and COVID-19 and provide reasoned answers to current concerns, such as the risk of developing SARS-CoV-2 infection and/or severe COVID-19 stratified by asthmatic patients, the contribution of type-2 vs. non-type-2 asthma and asthma-COPD overlap to the risk of COVID-19 development. We also address the potential role of both standard anti-inflammatory asthma therapies and new biological agents for severe asthma, such as mepolizumab, reslizumab, and benralizumab, on the susceptibility to SARS-CoV-2 infection and severe COVID-19 outcomes.
RESUMEN
OBJECTIVE: We aimed to investigate whether the stratification of outpatients with coronavirus disease 2019 (COVID-19) pneumonia by body mass index (BMI) can help predict hospitalization and other severe outcomes. PATIENTS AND METHODS: We prospectively collected consecutive cases of community-managed COVID-19 pneumonia from March 1 to April 20, 2020, in the province of Bergamo and evaluated the association of overweight (25 kg/m2 ≤ BMI <30 kg/m2) and obesity (≥30 kg/m2) with time to hospitalization (primary end point), low-flow domiciliary oxygen need, noninvasive mechanical ventilation, intubation, and death due to COVID-19 (secondary end points) in this cohort. We analyzed the primary end point using multivariable Cox models. RESULTS: Of 338 patients included, 133 (39.4%) were overweight and 77 (22.8%) were obese. Age at diagnosis was younger in obese patients compared with those overweight or with normal weight (P<.001), whereas diabetes, dyslipidemia, and heart diseases were differently distributed among BMI categories. Azithromycin, hydroxychloroquine, and prednisolone use were similar between BMI categories (P>.05). Overall, 105 (31.1%) patients were hospitalized, and time to hospitalization was significantly shorter for obese vs over- or normal-weight patients (P<.001). In the final multivariable analysis, obese patients were more likely to require hospitalization than nonobese patients (hazard ratio, 5.83; 95% CI, 3.91 to 8.71). Results were similar in multiple sensitivity analyses. Low-flow domiciliary oxygen need, hospitalization with noninvasive mechanical ventilation, intubation, and death were significantly associated with obesity (P<.001). CONCLUSION: In patients with community-managed COVID-19 pneumonia, obesity is associated with a higher hospitalization risk and overall worse outcomes than for nonobese patients.