Development and validation of a prospective study to predict the risk of readmission within 365 days of respiratory failure: based on a random survival forest algorithm combined with COX regression modeling
Abstract Background There is a need to develop and validate a widely applicable nomogram for predicting readmission of respiratory failure patients within 365 days. Methods We recruited patients with respiratory failure at the First People’s Hospital of Yancheng and the People’s Hospital of Jiangsu....
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2024-02-01
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| Series: | BMC Pulmonary Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12890-024-02862-9 |
| _version_ | 1797276002695512064 |
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| author | Zhongxiang Liu Zhixiao Sun Hang Hu Yuan Yin Bingqing Zuo |
| author_facet | Zhongxiang Liu Zhixiao Sun Hang Hu Yuan Yin Bingqing Zuo |
| author_sort | Zhongxiang Liu |
| collection | DOAJ |
| description | Abstract Background There is a need to develop and validate a widely applicable nomogram for predicting readmission of respiratory failure patients within 365 days. Methods We recruited patients with respiratory failure at the First People’s Hospital of Yancheng and the People’s Hospital of Jiangsu. We used the least absolute shrinkage and selection operator regression to select significant features for multivariate Cox proportional hazard analysis. The Random Survival Forest algorithm was employed to construct a model for the variables that obtained a coefficient of 0 following LASSO regression, and subsequently determine the prediction score. Independent risk factors and the score were used to develop a multivariate COX regression for creating the line graph. We used the Harrell concordance index to quantify the predictive accuracy and the receiver operating characteristic curve to evaluate model performance. Additionally, we used decision curve analysiso assess clinical usefulness. Results The LASSO regression and multivariate Cox regression were used to screen hemoglobin, diabetes and pneumonia as risk variables combined with Score to develop a column chart model. The C index is 0.927 in the development queue, 0.924 in the internal validation queue, and 0.922 in the external validation queue. At the same time, the predictive model also showed excellent calibration and higher clinical value. Conclusions A nomogram predicting readmission of patients with respiratory failure within 365 days based on three independent risk factors and a jointly developed random survival forest algorithm has been developed and validated. This improves the accuracy of predicting patient readmission and provides practical information for individualized treatment decisions. |
| first_indexed | 2024-03-07T15:22:05Z |
| format | Article |
| id | doaj.art-967a18ce05db41db8b62fb8ea21c90ac |
| institution | Directory Open Access Journal |
| issn | 1471-2466 |
| language | English |
| last_indexed | 2024-03-07T15:22:05Z |
| publishDate | 2024-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Pulmonary Medicine |
| spelling | doaj.art-967a18ce05db41db8b62fb8ea21c90ac2024-03-05T17:34:41ZengBMCBMC Pulmonary Medicine1471-24662024-02-0124111210.1186/s12890-024-02862-9Development and validation of a prospective study to predict the risk of readmission within 365 days of respiratory failure: based on a random survival forest algorithm combined with COX regression modelingZhongxiang Liu0Zhixiao Sun1Hang Hu2Yuan Yin3Bingqing Zuo4Department of Pulmonary and Critical Care Medicine, The Yancheng Clinical College of Xuzhou Medical University, The First People’s Hospital of YanchengDepartment of Pulmonary and Critical Care Medicine, The Yancheng Clinical College of Xuzhou Medical University, The First People’s Hospital of YanchengDepartment of Pulmonary and Critical Care Medicine, The Yancheng Clinical College of Xuzhou Medical University, The First People’s Hospital of YanchengDepartment of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Nanjing Medical UnivesityDepartment of Pulmonary and Critical Care Medicine, The Yancheng Clinical College of Xuzhou Medical University, The First People’s Hospital of YanchengAbstract Background There is a need to develop and validate a widely applicable nomogram for predicting readmission of respiratory failure patients within 365 days. Methods We recruited patients with respiratory failure at the First People’s Hospital of Yancheng and the People’s Hospital of Jiangsu. We used the least absolute shrinkage and selection operator regression to select significant features for multivariate Cox proportional hazard analysis. The Random Survival Forest algorithm was employed to construct a model for the variables that obtained a coefficient of 0 following LASSO regression, and subsequently determine the prediction score. Independent risk factors and the score were used to develop a multivariate COX regression for creating the line graph. We used the Harrell concordance index to quantify the predictive accuracy and the receiver operating characteristic curve to evaluate model performance. Additionally, we used decision curve analysiso assess clinical usefulness. Results The LASSO regression and multivariate Cox regression were used to screen hemoglobin, diabetes and pneumonia as risk variables combined with Score to develop a column chart model. The C index is 0.927 in the development queue, 0.924 in the internal validation queue, and 0.922 in the external validation queue. At the same time, the predictive model also showed excellent calibration and higher clinical value. Conclusions A nomogram predicting readmission of patients with respiratory failure within 365 days based on three independent risk factors and a jointly developed random survival forest algorithm has been developed and validated. This improves the accuracy of predicting patient readmission and provides practical information for individualized treatment decisions.https://doi.org/10.1186/s12890-024-02862-9Respiratory failureNomogramReadmissionRandom survival forest algorithmCOX regression modeling |
| spellingShingle | Zhongxiang Liu Zhixiao Sun Hang Hu Yuan Yin Bingqing Zuo Development and validation of a prospective study to predict the risk of readmission within 365 days of respiratory failure: based on a random survival forest algorithm combined with COX regression modeling BMC Pulmonary Medicine Respiratory failure Nomogram Readmission Random survival forest algorithm COX regression modeling |
| title | Development and validation of a prospective study to predict the risk of readmission within 365 days of respiratory failure: based on a random survival forest algorithm combined with COX regression modeling |
| title_full | Development and validation of a prospective study to predict the risk of readmission within 365 days of respiratory failure: based on a random survival forest algorithm combined with COX regression modeling |
| title_fullStr | Development and validation of a prospective study to predict the risk of readmission within 365 days of respiratory failure: based on a random survival forest algorithm combined with COX regression modeling |
| title_full_unstemmed | Development and validation of a prospective study to predict the risk of readmission within 365 days of respiratory failure: based on a random survival forest algorithm combined with COX regression modeling |
| title_short | Development and validation of a prospective study to predict the risk of readmission within 365 days of respiratory failure: based on a random survival forest algorithm combined with COX regression modeling |
| title_sort | development and validation of a prospective study to predict the risk of readmission within 365 days of respiratory failure based on a random survival forest algorithm combined with cox regression modeling |
| topic | Respiratory failure Nomogram Readmission Random survival forest algorithm COX regression modeling |
| url | https://doi.org/10.1186/s12890-024-02862-9 |
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