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Objective: To determine which early‐stage variables best predicted the deterioration of coronavirus disease 2019 (COVID‐19) among community‐isolated people infected with severe acute respiratory syndrome coronavirus 2 and to test the performance of prediction using only inexpensive‐to‐measure variables. Methods: Medical records of 3145 people isolated in two Fangcang shelter hospitals (large‐scale community isolation centers) from February to March 2020 were accessed. Two complementary methods—machine learning algorithms and competing risk survival analyses—were used to test potential predictors, including age, gender, severity upon admission, symptoms (general symptoms, respiratory symptoms, and gastrointestinal symptoms), computed tomography (CT) signs, and comorbid chronic diseases. All variables were measured upon (or shortly after) admission. The outcome was deterioration versus recovery of COVID‐19. Results: More than a quarter of the 3145 people did not present any symptoms, while one‐third ended isolation due to deterioration. Machine learning models identified moderate severity upon admission, old age, and CT ground‐glass opacity as the most important predictors of deterioration. Removing CT signs did not degrade the performance of models. Competing risk models identified age ≥ 35 years, male gender, moderate severity upon admission, cough, expectoration, CT patchy opacity, CT consolidation, comorbid diabetes, and comorbid cardiovascular or cerebrovascular diseases as significant predictors of deterioration, while a stuffy or runny nose as a predictor of recovery. Conclusions: Early‐stage prediction of COVID‐19 deterioration can be made with inexpensive‐to‐measure variables, such as demographic characteristics, severity upon admission, observable symptoms, and self‐reported comorbid diseases, among asymptomatic people and mildly to moderately symptomatic patients. [ABSTRACT FROM AUTHOR]

Most studies of coronavirus disease 2019 (COVID-19) progression have focused on the transfer of patients within secondary or tertiary care hospitals from regular wards to intensive care units. Little is known about the risk factors predicting the progression to severe COVID-19 among patients in community isolation, who are either asymptomatic or suffer from only mild to moderate symptoms. Using a multivariable competing risk survival analysis, we identify several important predictors of progression to severe COVID-19—rather than to recovery—among patients in the largest community isolation center in Wuhan, China from 6 February 2020 (when the center opened) to 9 March 2020 (when it closed). All patients in community isolation in Wuhan were either asymptomatic or suffered from mild to moderate COVID-19 symptoms. We performed competing risk survival analysis on time-to-event data from a cohort study of all COVID-19 patients (n = 1753) in the isolation center. The potential predictors we investigated were the routine patient data collected upon admission to the isolation center: age, sex, respiratory symptoms, gastrointestinal symptoms, general symptoms, and computed tomography (CT) scan signs. The main outcomes were time to severe COVID-19 or recovery. The factors predicting progression to severe COVID-19 were: male sex (hazard ratio (HR) = 1.29, 95% confidence interval (CI) 1.04–1.58, p = 0.018), young and old age, dyspnea (HR = 1.58, 95% CI 1.24–2.01, p

Most studies of coronavirus disease 2019 (COVID-19) progression have focused on the transfer of patients within secondary or tertiary care hospitals from regular wards to intensive care units. Little is known about the risk factors predicting the progression to severe COVID-19 among patients in community isolation, who are either asymptomatic or suffer from only mild to moderate symptoms. Using a multivariable competing risk survival analysis, we identify several important predictors of progression to severe COVID-19-rather than to recovery-among patients in the largest community isolation center in Wuhan, China from 6 February 2020 (when the center opened) to 9 March 2020 (when it closed). All patients in community isolation in Wuhan were either asymptomatic or suffered from mild to moderate COVID-19 symptoms. We performed competing risk survival analysis on time-to-event data from a cohort study of all COVID-19 patients ( n  = 1753) in the isolation center. The potential predictors we investigated were the routine patient data collected upon admission to the isolation center: age, sex, respiratory symptoms, gastrointestinal symptoms, general symptoms, and computed tomography (CT) scan signs. The main outcomes were time to severe COVID-19 or recovery. The factors predicting progression to severe COVID-19 were: male sex (hazard ratio (HR) = 1.29, 95% confidence interval (CI) 1.04-1.58, p  = 0.018), young and old age, dyspnea (HR = 1.58, 95% CI 1.24-2.01, p  < 0.001), and CT signs of ground-glass opacity (HR = 1.39, 95% CI 1.04-1.86, p  = 0.024) and infiltrating shadows (HR = 1.84, 95% CI 1.22-2.78, p  = 0.004). The risk of progression was found to be lower among patients with nausea or vomiting (HR = 0.53, 95% CI 0.30-0.96, p  = 0.036) and headaches (HR = 0.54, 95% CI 0.29-0.99, p  = 0.046). Our results suggest that several factors that can be easily measured even in resource-poor settings (dyspnea, sex, and age) can be used to identify mild COVID-19 patients who are at increased risk of disease progression. Looking for CT signs of ground-glass opacity and infiltrating shadows may be an affordable option to support triage decisions in resource-rich settings. Common and unspecific symptoms (headaches, nausea, and vomiting) are likely to have led to the identification and subsequent community isolation of COVID-19 patients who were relatively unlikely to deteriorate. Future public health and clinical guidelines should build on this evidence to improve the screening, triage, and monitoring of COVID-19 patients who are asymtomatic or suffer from mild to moderate symptoms., (© 2022 THE AUTHORS. Published by Elsevier LTD on behalf of Chinese Academy of Engineering and Higher Education Press Limited Company.)