Clinical Data based XGBoost Algorithm for infection risk prediction of patients with decompensated cirrhosis: a 10-year (2012-2021) Multicenter Retrospective Case-control study

作者全名："Zheng, Jing; Li, Jianjun; Zhang, Zhengyu; Yu, Yue; Tan, Juntao; Liu, Yunyu; Gong, Jun; Wang, Tingting; Wu, Xiaoxin; Guo, Zihao"

作者地址："[Zheng, Jing; Tan, Juntao] Chongqing Med Univ, Banan Hosp, Operat Management Off, Chongqing 401320, Peoples R China; [Li, Jianjun] Chongqing Med Univ, Banan Hosp, Dept Cardiothorac Surg, Chongqing 401320, Peoples R China; [Zhang, Zhengyu] Zhejiang Univ, Affiliated Hosp 1, Sch Med, Med Records Dept, Hangzhou 310003, Peoples R China; [Yu, Yue] Mayo Clin, Sr Bioinformatician Dept Quantitat Hlth Sci, Rochester, MN 55905 USA; [Liu, Yunyu] Chongqing Med Univ, Affiliated Hosp 2, Med Records Dept, Chongqing 400010, Peoples R China; [Gong, Jun] Chongqing Med Univ, Univ Town Hosp, Dept Informat Ctr, Chongqing 401331, Peoples R China; [Wang, Tingting] Chongqing Med Univ, Coll Med Informat, Chongqing 400016, Peoples R China; [Wu, Xiaoxin] Zhejiang Univ, Sch Med, Affiliated Hosp 1, Natl Clin Res Ctr Infect Dis,State Key Lab Diag &, 79 Qing Chun Rd, Hangzhou 310003, Zhejiang, Peoples R China; [Guo, Zihao] Chongqing Banan Canc Hosp, Dept Gastroenterol, Chongqing 400054, Peoples R China"

通信作者："Wu, XX (通讯作者)，Zhejiang Univ, Sch Med, Affiliated Hosp 1, Natl Clin Res Ctr Infect Dis,State Key Lab Diag &, 79 Qing Chun Rd, Hangzhou 310003, Zhejiang, Peoples R China.; Guo, ZH (通讯作者)，Chongqing Banan Canc Hosp, Dept Gastroenterol, Chongqing 400054, Peoples R China."

来源：BMC GASTROENTEROLOGY

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:001147578600001

JCR分区：Q2

影响因子：2.5

年份：2023

卷号：23

期号：1

开始页：　

结束页：　

文献类型：Article

关键词：Decompensated cirrhosis; Infection; XGBoost algorithm; Prediction model; Multicenter

摘要："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."

基金机构：Natural Science Foundation of Zhejiang Province [LQ21H190004]

基金资助正文：This work was supported by grants from the Natural Science Foundation of Zhejiang Province [grant number LQ21H190004].