ABSTRACT
The aim of this research was to develop a quantitative method for clinicians to predict the probability of improved prognosis in patients with coronavirus disease 2019 (COVID-19). Data on 104 patients admitted to hospital with laboratory-confirmed COVID-19 infection from 10 January 2020 to 26 February 2020 were collected. Clinical information and laboratory findings were collected and compared between the outcomes of improved patients and non-improved patients. The least absolute shrinkage and selection operator (LASSO) logistics regression model and two-way stepwise strategy in the multivariate logistics regression model were used to select prognostic factors for predicting clinical outcomes in COVID-19 patients. The concordance index (C-index) was used to assess the discrimination of the model, and internal validation was performed through bootstrap resampling. A novel predictive nomogram was constructed by incorporating these features. Of the 104 patients included in the study (median age 55 years), 75 (72.1%) had improved short-term outcomes, while 29 (27.9%) showed no signs of improvement. There were numerous differences in clinical characteristics and laboratory findings between patients with improved outcomes and patients without improved outcomes. After a multi-step screening process, prognostic factors were selected and incorporated into the nomogram construction, including immunoglobulin A (IgA), C-reactive protein (CRP), creatine kinase (CK), acute physiology and chronic health evaluation II (APACHE II), and interaction between CK and APACHE II. The C-index of our model was 0.962 (95% confidence interval (CI), 0.931-0.993) and still reached a high value of 0.948 through bootstrapping validation. A predictive nomogram we further established showed close performance compared with the ideal model on the calibration plot and was clinically practical according to the decision curve and clinical impact curve. The nomogram we constructed is useful for clinicians to predict improved clinical outcome probability for each COVID-19 patient, which may facilitate personalized counselling and treatment.
ABSTRACT
BACKGROUND: Despite the ongoing spread of coronavirus disease 2019 (COVID-19), knowledge about factors affecting prolonged viral excretion is limited. METHODS: In this study, we retrospectively collected data from 99 hospitalized patients with coronavirus disease 2019 (COVID-19) between 19 January and 17 February 2020 in Zhejiang Province, China. We classified them into 2 groups based on whether the virus test results eventually became negative. Cox proportional hazards regression was used to evaluate factors associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) shedding. RESULTS: Among 99 patients, 61 patients had SARS-CoV-2 clearance (virus-negative group), but 38 patients had sustained positive results (virus-positive group). The median duration of SARS-CoV-2 excretion was 15 (interquartile range, 12-19) days among the virus-negative patients. The shedding time was significantly increased if the fecal SARS-CoV-2 RNA test result was positive. Male sex (hazard ratio [HR], 0.58 [95% confidence interval {CI}, .35-.98]), immunoglobulin use (HR, 0.42 [95% CI, .24-.76]), APACHE II score (HR, 0.89 [95% CI, .84-.96]), and lymphocyte count (HR, 1.81 [95% CI, 1.05-3.1]) were independent factors associated with a prolonged duration of SARS-CoV-2 shedding. Antiviral therapy and corticosteroid treatment were not independent factors. CONCLUSIONS: SARS-CoV-2 RNA clearance time was associated with sex, disease severity, and lymphocyte function. The current antiviral protocol and low-to-moderate dosage of corticosteroid had little effect on the duration of viral excretion.