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
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 a...
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| Format: | Article |
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BMJ Publishing Group
2024-10-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/14/10/e082616.full |
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| author | Jiali Chen Ning Ning Jinyu Wu Huiliang Yang Peifang Li Yanfei Ma Ailin Hou |
| author_facet | Jiali Chen Ning Ning Jinyu Wu Huiliang Yang Peifang Li Yanfei Ma Ailin Hou |
| author_sort | Jiali Chen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-bcdb7dc8e4d340048ef822d2fe273299 |
| institution | Kabale University |
| issn | 2044-6055 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-bcdb7dc8e4d340048ef822d2fe2732992025-08-20T03:48:31ZengBMJ Publishing GroupBMJ Open2044-60552024-10-01141010.1136/bmjopen-2023-082616Development and validation of predictive models for mortality of cases with COVID-19 (Omicron BA.5.2.48 and B.7.14): a retrospective studyJiali Chen0Ning Ning1Jinyu Wu2Huiliang Yang3Peifang Li4Yanfei Ma5Ailin Hou6Department of Health Policy, Nanjing Medical University, Nanjing, Jiangsu, ChinaOpen Health, Bethesda, Maryland, USADepartment of Orthopedic Surgery, West China Hospital, Sichuan University / West China School of Nursing, Sichuan University, Chengdu, People`s Republic of ChinaDepartment of Orthopedic Surgery, Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, People`s Republic of ChinaDepartment of Orthopedic Surgery, West China Hospital, Sichuan University / West China School of Nursing, Sichuan University, Chengdu, People`s Republic of ChinaDepartment of Orthopedic Surgery, West China Hospital, Sichuan University / West China School of Nursing, Sichuan University, Chengdu, People`s Republic of ChinaDepartment of Orthopedic Surgery, West China Hospital, Sichuan University / West China School of Nursing, Sichuan University, Chengdu, People`s Republic of ChinaObjectives 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.https://bmjopen.bmj.com/content/14/10/e082616.full |
| spellingShingle | Jiali Chen Ning Ning Jinyu Wu Huiliang Yang Peifang Li Yanfei Ma Ailin Hou 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 BMJ Open |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| url | https://bmjopen.bmj.com/content/14/10/e082616.full |
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