tetano
Editor, Senior Moderator
BMJ Open
. 2024 Oct 8;14(10):e082616.
doi: 10.1136/bmjopen-2023-082616. Development and validation of predictive models for mortality of cases with COVID-19 (Omicron BA.5.2.48 and B.7.14): a retrospective study
Peifang Li[SUP] #[/SUP][SUP] 1 2 [/SUP], Huiliang Yang[SUP] #[/SUP][SUP] 3 [/SUP], Jinyu Wu[SUP] 1 2 [/SUP], Yanfei Ma[SUP] 1 [/SUP], Ailin Hou[SUP] 1 [/SUP], Jiali Chen[SUP] 4 [/SUP], Ning Ning[SUP] 4 [/SUP]
Affiliations
Objectives: With the emergence of new COVID-19 variants (Omicron BA.5.2.48 and B.7.14), predicting the mortality of infected patients has become increasingly challenging due to the continuous mutation of the virus. Existing models have shown poor performance and limited clinical utility. This study aims to identify the independent risk factors and develop practical predictive models for mortality among patients infected with new COVID-19 variants.
Design: A retrospective study.
Setting and participants: We extracted data from 1029 COVID-19 patients in the respiratory disease wards of a general hospital in China between 22 December 2022 and 15 February 2023.
Outcome measures: Mortality within 15 days after hospital discharge.
Results: A total of 987 cases with new COVID-19 variants (Omicron BA.5.2.48 and B.7.14) were eventually included, among them, 153 (15.5%) died. Non-invasive ventilation, intubation, myoglobin, international normalised ratio, age, number of diagnoses, respiratory rate, pulse, neutrophil count and albumin were the most important predictors of mortality among new COVID-19 variants. The area under the curve of logistic regression (LR), decision tree (DT) and Extreme Gradient Boosting (XGBoost) models were 0.959, 0.883 and 0.993, respectively. The diagnostic accuracy was 0.926 for LR, 0.918 for DT and 0.977 for XGBoost. XGBoost model had the highest sensitivity (0.908) and specificity (0.989).
Conclusion: Our study developed and validated three practical models for predicting mortality in patients with new COVID-19 variants. All models performed well, and XGBoost was the best-performing model.
Keywords: COVID-19; mortality; prognosis.
. 2024 Oct 8;14(10):e082616.
doi: 10.1136/bmjopen-2023-082616. Development and validation of predictive models for mortality of cases with COVID-19 (Omicron BA.5.2.48 and B.7.14): a retrospective study
Peifang Li[SUP] #[/SUP][SUP] 1 2 [/SUP], Huiliang Yang[SUP] #[/SUP][SUP] 3 [/SUP], Jinyu Wu[SUP] 1 2 [/SUP], Yanfei Ma[SUP] 1 [/SUP], Ailin Hou[SUP] 1 [/SUP], Jiali Chen[SUP] 4 [/SUP], Ning Ning[SUP] 4 [/SUP]
Affiliations
- PMID: 39384246
- DOI: 10.1136/bmjopen-2023-082616
Objectives: With the emergence of new COVID-19 variants (Omicron BA.5.2.48 and B.7.14), predicting the mortality of infected patients has become increasingly challenging due to the continuous mutation of the virus. Existing models have shown poor performance and limited clinical utility. This study aims to identify the independent risk factors and develop practical predictive models for mortality among patients infected with new COVID-19 variants.
Design: A retrospective study.
Setting and participants: We extracted data from 1029 COVID-19 patients in the respiratory disease wards of a general hospital in China between 22 December 2022 and 15 February 2023.
Outcome measures: Mortality within 15 days after hospital discharge.
Results: A total of 987 cases with new COVID-19 variants (Omicron BA.5.2.48 and B.7.14) were eventually included, among them, 153 (15.5%) died. Non-invasive ventilation, intubation, myoglobin, international normalised ratio, age, number of diagnoses, respiratory rate, pulse, neutrophil count and albumin were the most important predictors of mortality among new COVID-19 variants. The area under the curve of logistic regression (LR), decision tree (DT) and Extreme Gradient Boosting (XGBoost) models were 0.959, 0.883 and 0.993, respectively. The diagnostic accuracy was 0.926 for LR, 0.918 for DT and 0.977 for XGBoost. XGBoost model had the highest sensitivity (0.908) and specificity (0.989).
Conclusion: Our study developed and validated three practical models for predicting mortality in patients with new COVID-19 variants. All models performed well, and XGBoost was the best-performing model.
Keywords: COVID-19; mortality; prognosis.