A predictive model to explore risk factors for severe COVID-19.

With the rapid spread of the novel coronavirus (COVID-19), a sustained global pandemic has emerged. Globally, the cumulative death toll is in the millions. The rising number of COVID-19 infections and deaths has severely impacted the lives of people worldwide, healthcare systems, and economic development. We conducted a retrospective analysis of the characteristics of COVID-19 patients. This analysis includes clinical features upon initial hospital admission, relevant laboratory test results, and imaging findings. We aimed to identify risk factors for severe illness and to construct a predictive model for assessing the risk of severe COVID-19. We collected and analyzed electronic medical records of confirmed COVID-19 patients admitted to the Affiliated Hospital of Jiangsu University (Zhenjiang, China) between December 18, 2022, and February 28, 2023. According to the WHO diagnostic criteria for the novel coronavirus, we divided the patients into two groups: severe and non-severe, and compared their clinical, laboratory, and imaging data. Logistic regression analysis, the least absolute shrinkage and selection operator (LASSO) regression, and receiver operating characteristic (ROC) curve analysis were used to identify the relevant risk factors for severe COVID-19 patients. Patients were divided into a training cohort and a validation cohort. A nomogram model was constructed using the "rms" package in R software. Among the 346 patients, the severe group exhibited significantly higher respiratory rates, breathlessness, altered consciousness, neutrophil-to-lymphocyte ratio (NLR), and lactate dehydrogenase (LDH) levels compared to the non-severe group. Imaging findings indicated that the severe group had a higher proportion of bilateral pulmonary inflammation and ground-glass opacities compared to the non-severe group. NLR and LDH were identified as independent risk factors for severe patients. The diagnostic performance was maximized when NLR, respiratory rate (RR), and LDH were combined. Based on the statistical analysis results, we developed a COVID-19 severity risk prediction model. The total score is calculated by adding up the scores for each of the twelve independent variables. By mapping the total score to the lowest scale, we can estimate the risk of COVID-19 severity. In addition, the calibration plots and DCA analysis showed that the nomogram had better discrimination power for predicting the severity of COVID-19. Our results showed that the development and validation of the predictive nomogram had good predictive value for severe COVID-19.

Biomarker of severity in hospitalised patients with COVID-19: a retrospective study.

BACKGROUND: The novel COVID-19 was rapidly spreading and was highly contagious. COVID-19 caused over 6 million deaths worldwide, with high mortality rates, particularly in severe cases. OBJECTIVE: This study aimed to investigate whether serum albumin-neutrophil count to lymphocyte count ratio (NLR) score (ANS) could be used as a prognostic indicator of COVID-19 severity. DESIGN: A retrospective study. PARTICIPANTS: Based on the WHO diagnostic criteria, patients were classified as either non-severe (n=270) or severe (n=100). PRIMARY AND SECONDARY OUTCOME MEASURES: NLR, serum albumin level and ANS. MAIN RESULTS: The NLR of patients with severe disease was significantly higher than that of those with non-severe disease. Serum albumin levels were significantly lower in patients with severe disease than in those with non-severe disease. The cut-off values representing the maximum potential effectiveness of serum albumin and NLR were 33.5 g/L and 8.25, respectively, according to the Youden index. In patients with severe COVID-19, we observed that the serum albumin level, NLR and ANS were independent prognostic indicators of severe COVID-19 using logistic analysis. The relative risk of severe COVID-19 was 7.65 (95% CI 3.72 to 15.75, p<0.05) in the ANS 2 group compared with that in ANS 0. CONCLUSIONS: ANS could be used as a prognostic indicator of COVID-19 severity.