Clinical Data based XGBoost Algorithm for infection risk prediction of patients with decompensated cirrhosis: a 10-year (2012–2021) Multicenter Retrospective Case-control study
Abstract Objectives To appraise effective predictors for infection in patients with decompensated cirrhosis (DC) by using XGBoost algorithm in a retrospective case-control study. Methods Clinical data were retrospectively collected from 6,648 patients with DC admitted to five tertiary hospitals. Ind...
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| Format: | Article |
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BMC
2023-09-01
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| Series: | BMC Gastroenterology |
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| Online Access: | https://doi.org/10.1186/s12876-023-02949-3 |
| _version_ | 1797559354501627904 |
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| author | Jing Zheng Jianjun Li Zhengyu Zhang Yue Yu Juntao Tan Yunyu Liu Jun Gong Tingting Wang Xiaoxin Wu Zihao Guo |
| author_facet | Jing Zheng Jianjun Li Zhengyu Zhang Yue Yu Juntao Tan Yunyu Liu Jun Gong Tingting Wang Xiaoxin Wu Zihao Guo |
| author_sort | Jing Zheng |
| collection | DOAJ |
| description | Abstract Objectives To appraise effective predictors for infection in patients with decompensated cirrhosis (DC) by using XGBoost algorithm in a retrospective case-control study. Methods Clinical data were retrospectively collected from 6,648 patients with DC admitted to five tertiary hospitals. Indicators with significant differences were determined by univariate analysis and least absolute contraction and selection operator (LASSO) regression. Further multi-tree extreme gradient boosting (XGBoost) machine learning-based model was used to rank importance of features selected from LASSO and subsequently constructed infection risk prediction model with simple-tree XGBoost model. Finally, the simple-tree XGBoost model is compared with the traditional logical regression (LR) model. Performances of models were evaluated by area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. Results Six features, including total bilirubin, blood sodium, albumin, prothrombin activity, white blood cell count, and neutrophils to lymphocytes ratio were selected as predictors for infection in patients with DC. Simple-tree XGBoost model conducted by these features can predict infection risk accurately with an AUROC of 0.971, sensitivity of 0.915, and specificity of 0.900 in training set. The performance of simple-tree XGBoost model is better than that of traditional LR model in training set, internal verification set, and external feature set (P < 0.001). Conclusions The simple-tree XGBoost predictive model developed based on a minimal amount of clinical data available to DC patients with restricted medical resources could help primary healthcare practitioners promptly identify potential infection. |
| first_indexed | 2024-03-10T17:44:11Z |
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| id | doaj.art-b4902de597ca41f98c0f345bf8b55dfe |
| institution | Directory Open Access Journal |
| issn | 1471-230X |
| language | English |
| last_indexed | 2024-03-10T17:44:11Z |
| publishDate | 2023-09-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Gastroenterology |
| spelling | doaj.art-b4902de597ca41f98c0f345bf8b55dfe2023-11-20T09:35:49ZengBMCBMC Gastroenterology1471-230X2023-09-0123111010.1186/s12876-023-02949-3Clinical Data based XGBoost Algorithm for infection risk prediction of patients with decompensated cirrhosis: a 10-year (2012–2021) Multicenter Retrospective Case-control studyJing Zheng0Jianjun Li1Zhengyu Zhang2Yue Yu3Juntao Tan4Yunyu Liu5Jun Gong6Tingting Wang7Xiaoxin Wu8Zihao Guo9Operation Management Office, Affiliated Banan Hospital of Chongqing Medical UniversityDepartment of Cardiothoracic Surgery, Affiliated Banan Hospital of Chongqing Medical UniversityMedical Records Department, the First Affiliated Hospital, Zhejiang University School of MedicineSenior Bioinformatician Department of Quantitative Health Sciences, Mayo ClinicOperation Management Office, Affiliated Banan Hospital of Chongqing Medical UniversityMedical Records Department, the Second Affiliated Hospital of Chongqing Medical UniversityDepartment of Information Center, the University Town Hospital of Chongqing Medical UniversityCollege of Medical Informatics, Chongqing Medical UniversityState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Centre for Infectious Diseases, the First Affiliated Hospital, Zhejiang University School of MedicineDepartment of Gastroenterology, Chongqing Banan Cancer HospitalAbstract Objectives To appraise effective predictors for infection in patients with decompensated cirrhosis (DC) by using XGBoost algorithm in a retrospective case-control study. Methods Clinical data were retrospectively collected from 6,648 patients with DC admitted to five tertiary hospitals. Indicators with significant differences were determined by univariate analysis and least absolute contraction and selection operator (LASSO) regression. Further multi-tree extreme gradient boosting (XGBoost) machine learning-based model was used to rank importance of features selected from LASSO and subsequently constructed infection risk prediction model with simple-tree XGBoost model. Finally, the simple-tree XGBoost model is compared with the traditional logical regression (LR) model. Performances of models were evaluated by area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. Results Six features, including total bilirubin, blood sodium, albumin, prothrombin activity, white blood cell count, and neutrophils to lymphocytes ratio were selected as predictors for infection in patients with DC. Simple-tree XGBoost model conducted by these features can predict infection risk accurately with an AUROC of 0.971, sensitivity of 0.915, and specificity of 0.900 in training set. The performance of simple-tree XGBoost model is better than that of traditional LR model in training set, internal verification set, and external feature set (P < 0.001). Conclusions The simple-tree XGBoost predictive model developed based on a minimal amount of clinical data available to DC patients with restricted medical resources could help primary healthcare practitioners promptly identify potential infection.https://doi.org/10.1186/s12876-023-02949-3Decompensated cirrhosisInfectionXGBoost algorithmPrediction modelMulticenter |
| spellingShingle | Jing Zheng Jianjun Li Zhengyu Zhang Yue Yu Juntao Tan Yunyu Liu Jun Gong Tingting Wang Xiaoxin Wu Zihao Guo Clinical Data based XGBoost Algorithm for infection risk prediction of patients with decompensated cirrhosis: a 10-year (2012–2021) Multicenter Retrospective Case-control study BMC Gastroenterology Decompensated cirrhosis Infection XGBoost algorithm Prediction model Multicenter |
| title | Clinical Data based XGBoost Algorithm for infection risk prediction of patients with decompensated cirrhosis: a 10-year (2012–2021) Multicenter Retrospective Case-control study |
| title_full | Clinical Data based XGBoost Algorithm for infection risk prediction of patients with decompensated cirrhosis: a 10-year (2012–2021) Multicenter Retrospective Case-control study |
| title_fullStr | Clinical Data based XGBoost Algorithm for infection risk prediction of patients with decompensated cirrhosis: a 10-year (2012–2021) Multicenter Retrospective Case-control study |
| title_full_unstemmed | Clinical Data based XGBoost Algorithm for infection risk prediction of patients with decompensated cirrhosis: a 10-year (2012–2021) Multicenter Retrospective Case-control study |
| title_short | Clinical Data based XGBoost Algorithm for infection risk prediction of patients with decompensated cirrhosis: a 10-year (2012–2021) Multicenter Retrospective Case-control study |
| title_sort | clinical data based xgboost algorithm for infection risk prediction of patients with decompensated cirrhosis a 10 year 2012 2021 multicenter retrospective case control study |
| topic | Decompensated cirrhosis Infection XGBoost algorithm Prediction model Multicenter |
| url | https://doi.org/10.1186/s12876-023-02949-3 |
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