An early novel prognostic model for predicting 80-day survival of patients with COVID-19.

The outbreak of the novel coronavirus disease 2019 (COVID-19) has had an unprecedented impact worldwide, and it is of great significance to predict the prognosis of patients for guiding clinical management. This study aimed to construct a nomogram to predict the prognosis of COVID-19 patients. Clinical records and laboratory results were retrospectively reviewed for 331 patients with laboratory-confirmed COVID-19 from Huangshi Hospital of Traditional Chinese Medicine (TCM) (Infectious Disease Hospital) and Third Affiliated Hospital of Sun Yat-sen University. All COVID-19 patients were followed up for 80 days, and the primary outcome was defined as patient death. Cases were randomly divided into training (n=199) and validation (n=132) groups. Based on baseline data, we used statistically significant prognostic factors to construct a nomogram and assessed its performance. The patients were divided into Death (n=23) and Survival (n=308) groups. Analysis of clinical characteristics showed that these patients presented with fever (n=271, 81.9%), diarrhea (n=20, 6.0%) and had comorbidities (n=89, 26.9.0%). Multivariate Cox regression analysis showed that age, UREA and LDH were independent risk factors for predicting 80-day survival of COVID-19 patients. We constructed a qualitative nomogram with high C-indexes (0.933 and 0.894 in the training and validation groups, respectively). The calibration curve for 80-day survival showed optimal agreement between the predicted and actual outcomes. Decision curve analysis revealed the high clinical net benefit of the nomogram. Overall, our nomogram could effectively predict the 80-day survival of COVID-19 patients and hence assist in providing optimal treatment and decreasing mortality rates.

U-Shaped Relationship of Low-Density Lipoprotein Cholesterol With Risk of Severe COVID-19 From a Multicenter Pooled Analysis.

Low-density lipoprotein cholesterol (LDL-C) is a well-known risk factor for coronary heart disease but protects against infection and sepsis. We aimed to disclose the exact association between LDL-C and severe 2019 novel coronavirus disease (COVID-19). Baseline data were retrospectively collected for 601 non-severe COVID-19 patients from two centers in Guangzhou and one center in Shenzhen, and patients on admission were medically observed for at least 15 days to determine the final outcome, including the non-severe group (n = 460) and the severe group (severe and critical cases) (n = 141). Among 601 cases, 76 (12.65%) received lipid-lowering therapy; the proportion of patients taking lipid-lowering drugs in the severe group was higher than that in the non-severe group (22.7 vs. 9.6%). We found a U-shaped association between LDL-C level and risk of severe COVID-19 using restricted cubic splines. Using univariate logistic regression analysis, odds ratios for severe COVID-19 for patients with LDL-C ≤1.6 mmol/L (61.9 mg/dL) and above 3.4 mmol/L (131.4 mg/dL) were 2.29 (95% confidence interval 1.12-4.68; p = 0.023) and 2.02 (1.04-3.94; p = 0.039), respectively, compared to those with LDL-C of 2.81-3.40 mmol/L (108.6-131.4 mg/dL); following multifactorial adjustment, odds ratios were 2.61 (1.07-6.37; p = 0.035) and 2.36 (1.09-5.14; p = 0.030). Similar results were yielded using 0.3 and 0.5 mmol/L categories of LDL-C and sensitivity analyses. Both low and high LDL-C levels were significantly associated with higher risk of severe COVID-19. Although our findings do not necessarily imply causality, they suggest that clinicians should pay more attention to lipid-lowering therapy in COVID-19 patients to improve clinical prognosis.

A Tool for Early Prediction of Severe Coronavirus Disease 2019 (COVID-19): A Multicenter Study Using the Risk Nomogram in Wuhan and Guangdong, China.

BACKGROUND: Because there is no reliable risk stratification tool for severe coronavirus disease 2019 (COVID-19) patients at admission, we aimed to construct an effective model for early identification of cases at high risk of progression to severe COVID-19. METHODS: In this retrospective multicenter study, 372 hospitalized patients with nonsevere COVID-19 were followed for > 15 days after admission. Patients who deteriorated to severe or critical COVID-19 and those who maintained a nonsevere state were assigned to the severe and nonsevere groups, respectively. Based on baseline data of the 2 groups, we constructed a risk prediction nomogram for severe COVID-19 and evaluated its performance. RESULTS: The training cohort consisted of 189 patients, and the 2 independent validation cohorts consisted of 165 and 18 patients. Among all cases, 72 (19.4%) patients developed severe COVID-19. Older age; higher serum lactate dehydrogenase, C-reactive protein, coefficient of variation of red blood cell distribution width, blood urea nitrogen, and direct bilirubin; and lower albumin were associated with severe COVID-19. We generated the nomogram for early identifying severe COVID-19 in the training cohort (area under the curve [AUC], 0.912 [95% confidence interval {CI}, .846-.978]; sensitivity 85.7%, specificity 87.6%) and the validation cohort (AUC, 0.853 [95% CI, .790-.916]; sensitivity 77.5%, specificity 78.4%). The calibration curve for probability of severe COVID-19 showed optimal agreement between prediction by nomogram and actual observation. Decision curve and clinical impact curve analyses indicated that nomogram conferred high clinical net benefit. CONCLUSIONS: Our nomogram could help clinicians with early identification of patients who will progress to severe COVID-19, which will enable better centralized management and early treatment of severe disease.