Incubation period, clinical and lung CT features for early prediction of COVID-19 deterioration: development and internal verification of a risk model.

BACKGROUND: Most severe, critical, or mortal COVID-19 cases often had a relatively stable period before their status worsened. We developed a deterioration risk model of COVID-19 (DRM-COVID-19) to predict exacerbation risk and optimize disease management on admission. METHOD: We conducted a multicenter retrospective cohort study with 239 confirmed symptomatic COVID-19 patients. A combination of the least absolute shrinkage and selection operator (LASSO), change-in-estimate (CIE) screened out independent risk factors for the multivariate logistic regression model (DRM-COVID-19) from 44 variables, including epidemiological, demographic, clinical, and lung CT features. The compound study endpoint was progression to severe, critical, or mortal status. Additionally, the model's performance was evaluated for discrimination, accuracy, calibration, and clinical utility, through internal validation using bootstrap resampling (1000 times). We used a nomogram and a network platform for model visualization. RESULTS: In the cohort study, 62 cases reached the compound endpoint, including 42 severe, 18 critical, and two mortal cases. DRM-COVID-19 included six factors: dyspnea [odds ratio (OR) 4.89;confidence interval (95% CI) 1.53-15.80], incubation period (OR 0.83; 95% CI 0.68-0.99), number of comorbidities (OR 1.76; 95% CI 1.03-3.05), D-dimer (OR 7.05; 95% CI, 1.35-45.7), C-reactive protein (OR 1.06; 95% CI 1.02-1.1), and semi-quantitative CT score (OR 1.50; 95% CI 1.27-1.82). The model showed good fitting (Hosmer-Lemeshow goodness, X2(8) = 7.0194, P = 0.53), high discrimination (the area under the receiver operating characteristic curve, AUROC, 0.971; 95% CI, 0.949-0.992), precision (Brier score = 0.051) as well as excellent calibration and clinical benefits. The precision-recall (PR) curve showed excellent classification performance of the model (AUCPR = 0.934). We prepared a nomogram and a freely available online prediction platform ( https://deterioration-risk-model-of-covid-19.shinyapps.io/DRMapp/ ). CONCLUSION: We developed a predictive model, which includes the including incubation period along with clinical and lung CT features. The model presented satisfactory prediction and discrimination performance for COVID-19 patients who might progress from mild or moderate to severe or critical on admission, improving the clinical prognosis and optimizing the medical resources.

Clinical characteristics of COVID-19 infection in chronic obstructive pulmonary disease: a multicenter, retrospective, observational study.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has been a global pandemic disease, with more than 4 million cases and nearly 300,000 deaths. Little is known about COVID-19 in patients with chronic obstructive pulmonary disease (COPD). We aimed to evaluate the influence of preexisting COPD on the progress and outcomes of COVID-19. METHODS: This was a multicenter, retrospective, observational study. We enrolled 1,048 patients aged 40 years and above, including 50 patients with COPD and 998 patients without COPD, and with COVID-19 confirmed via high-throughput sequencing or real-time reverse transcription-polymerase chain reaction, between December 11, 2019 and February 20, 2020. We collected data of demographics, pathologic test results, radiologic imaging, and treatments. The primary outcomes were composite endpoints determined by admission to an intensive care unit, the use of mechanical ventilation, or death. RESULTS: Compared with patients who had COVID-19 but not COPD, those with COPD had higher rates of fatigue (56.0% vs. 40.2%), dyspnea (66.0% vs. 26.3%), diarrhea (16.0% vs. 3.6%), and unconsciousness (8.0% vs. 1.7%) and a significantly higher proportion of increased activated partial thromboplastin time (23.5% vs. 5.2%) and D-dimer (65.9% vs. 29.3%), as well as ground-glass opacities (77.6% vs. 60.3%), local patchy shadowing (61.2% vs. 41.4%), and interstitial abnormalities (51.0% vs. 19.8%) on chest computed tomography. Patients with COPD were more likely to develop bacterial or fungal coinfection (20.0% vs. 5.9%), acute respiratory distress syndrome (ARDS) (20.0% vs. 7.3%), septic shock (14.0% vs. 2.3%), or acute renal failure (12.0% vs. 1.3%). Patients with COPD and COVID-19 had a higher risk of reaching the composite endpoints [hazard ratio (HR): 2.17, 95% confidence interval (CI): 1.40-3.38; P=0.001] or death (HR: 2.28, 95% CI: 1.15-4.51; P=0.019), after adjustment. CONCLUSIONS: In this study, patients with COPD who developed COVID-19 showed a higher risk of admission to the intensive care unit, mechanical ventilation, or death.