Construction and validation of a deterioration model for elderly COVID-19 Sub-variant BA.2 patients
RationaleCOVID-19 pandemic has imposed tremendous stress and burden on the economy and society worldwide. There is an urgent demand to find a new model to estimate the deterioration of patients inflicted by Omicron variants.ObjectiveThis study aims to develop a model to predict the deterioration of...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-04-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2023.1137136/full |
| _version_ | 1797847710781407232 |
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| author | Yinyan Wu Benjie Xiao Jingjing Xiao Yudi Han Huazheng Liang Huazheng Liang Zhangwei Yang Yong Bi Yong Bi |
| author_facet | Yinyan Wu Benjie Xiao Jingjing Xiao Yudi Han Huazheng Liang Huazheng Liang Zhangwei Yang Yong Bi Yong Bi |
| author_sort | Yinyan Wu |
| collection | DOAJ |
| description | RationaleCOVID-19 pandemic has imposed tremendous stress and burden on the economy and society worldwide. There is an urgent demand to find a new model to estimate the deterioration of patients inflicted by Omicron variants.ObjectiveThis study aims to develop a model to predict the deterioration of elderly patients inflicted by Omicron Sub-variant BA.2.MethodsCOVID-19 patients were randomly divided into the training and the validation cohorts. Both Lasso and Logistic regression analyses were performed to identify prediction factors, which were then selected to build a deterioration model in the training cohort. This model was validated in the validation cohort.Measurements and main resultsThe deterioration model of COVID-19 was constructed with five indices, including C-reactive protein, neutrophil count/lymphocyte count (NLR), albumin/globulin ratio (A/G), international normalized ratio (INR), and blood urea nitrogen (BUN). The area under the ROC curve (AUC) showed that this model displayed a high accuracy in predicting deterioration, which was 0.85 in the training cohort and 0.85 in the validation cohort. The nomogram provided an easy way to calculate the possibility of deterioration, and the decision curve analysis (DCA) and clinical impact curve analysis (CICA)showed good clinical net profit using this model.ConclusionThe model we constructed can identify and predict the risk of deterioration (requirement for ventilatory support or death) in elderly patients and it is clinically practical, which will facilitate medical decision making and allocating medical resources to those with critical conditions. |
| first_indexed | 2024-04-09T18:15:48Z |
| format | Article |
| id | doaj.art-8925b7630c784ea2911781f8ac625273 |
| institution | Directory Open Access Journal |
| issn | 2296-858X |
| language | English |
| last_indexed | 2024-04-09T18:15:48Z |
| publishDate | 2023-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj.art-8925b7630c784ea2911781f8ac6252732023-04-13T04:35:38ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2023-04-011010.3389/fmed.2023.11371361137136Construction and validation of a deterioration model for elderly COVID-19 Sub-variant BA.2 patientsYinyan Wu0Benjie Xiao1Jingjing Xiao2Yudi Han3Huazheng Liang4Huazheng Liang5Zhangwei Yang6Yong Bi7Yong Bi8Department of Neurology, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People’s Hospital Affiliated to Tongji University School of Medicine, Shanghai, ChinaDepartment of Neurology, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People’s Hospital Affiliated to Tongji University School of Medicine, Shanghai, ChinaDepartment of Neurology, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People’s Hospital Affiliated to Tongji University School of Medicine, Shanghai, ChinaDepartment of Neurology, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People’s Hospital Affiliated to Tongji University School of Medicine, Shanghai, ChinaShanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Shanghai, ChinaMonash Suzhou Research Institute, Suzhou Industrial Park, Suzhou Jiangsu, ChinaMedical Department, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, ChinaAffiliated Zhoupu Hospital, Shanghai University of Medicine and Health Siences, Shanghai, ChinaTranslational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People’s Hospital Affiliated to Tongji University School of Medicine, Shanghai, ChinaRationaleCOVID-19 pandemic has imposed tremendous stress and burden on the economy and society worldwide. There is an urgent demand to find a new model to estimate the deterioration of patients inflicted by Omicron variants.ObjectiveThis study aims to develop a model to predict the deterioration of elderly patients inflicted by Omicron Sub-variant BA.2.MethodsCOVID-19 patients were randomly divided into the training and the validation cohorts. Both Lasso and Logistic regression analyses were performed to identify prediction factors, which were then selected to build a deterioration model in the training cohort. This model was validated in the validation cohort.Measurements and main resultsThe deterioration model of COVID-19 was constructed with five indices, including C-reactive protein, neutrophil count/lymphocyte count (NLR), albumin/globulin ratio (A/G), international normalized ratio (INR), and blood urea nitrogen (BUN). The area under the ROC curve (AUC) showed that this model displayed a high accuracy in predicting deterioration, which was 0.85 in the training cohort and 0.85 in the validation cohort. The nomogram provided an easy way to calculate the possibility of deterioration, and the decision curve analysis (DCA) and clinical impact curve analysis (CICA)showed good clinical net profit using this model.ConclusionThe model we constructed can identify and predict the risk of deterioration (requirement for ventilatory support or death) in elderly patients and it is clinically practical, which will facilitate medical decision making and allocating medical resources to those with critical conditions.https://www.frontiersin.org/articles/10.3389/fmed.2023.1137136/fullcoronavirusCOVID-19deterioration modelprognosisprediction |
| spellingShingle | Yinyan Wu Benjie Xiao Jingjing Xiao Yudi Han Huazheng Liang Huazheng Liang Zhangwei Yang Yong Bi Yong Bi Construction and validation of a deterioration model for elderly COVID-19 Sub-variant BA.2 patients Frontiers in Medicine coronavirus COVID-19 deterioration model prognosis prediction |
| title | Construction and validation of a deterioration model for elderly COVID-19 Sub-variant BA.2 patients |
| title_full | Construction and validation of a deterioration model for elderly COVID-19 Sub-variant BA.2 patients |
| title_fullStr | Construction and validation of a deterioration model for elderly COVID-19 Sub-variant BA.2 patients |
| title_full_unstemmed | Construction and validation of a deterioration model for elderly COVID-19 Sub-variant BA.2 patients |
| title_short | Construction and validation of a deterioration model for elderly COVID-19 Sub-variant BA.2 patients |
| title_sort | construction and validation of a deterioration model for elderly covid 19 sub variant ba 2 patients |
| topic | coronavirus COVID-19 deterioration model prognosis prediction |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2023.1137136/full |
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