Development and performance assessment of novel machine learning models to predict pneumonia after liver transplantation

Respiratory Research

Liubing Chen¹  Ziqing Hei¹  Chaojin Chen¹  Shaoli Zhou¹  Yihan Zhang¹  Yang Yang²  Shilong Gao³  Zihan Mo⁴  Bohan Wang⁴  Dong Yang⁴ 

[1] Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-Sen University, No. 600 Tianhe Road, 10630, Guangzhou, Guangdong, People’s Republic of China;Department of Hepatic Surgery and Liver Transplantation Center, The Third Affiliated Hospital of Sun Yat-Sen University, No. 600 Tianhe Road, 510630, Guangzhou, Guangdong, People’s Republic of China;Department of Information, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People’s Republic of China;Guangzhou AID Cloud Technology Co., LTD, Guangzhou, People’s Republic of China;

关键词: Liver transplantation;    Postoperative pneumonia;    Machine learning;    Postoperative pulmonary complications;    Disease prediction;    Risk factors;    Early intervention;    Deep learning;    ML algorithm;    Extreme gradient boosting;   

DOI  :  10.1186/s12931-021-01690-3

来源: Springer

【 摘 要 】

BackgroundPneumonia is the most frequently encountered postoperative pulmonary complications (PPC) after orthotopic liver transplantation (OLT), which cause high morbidity and mortality rates. We aimed to develop a model to predict postoperative pneumonia in OLT patients using machine learning (ML) methods.MethodsData of 786 adult patients underwent OLT at the Third Affiliated Hospital of Sun Yat-sen University from January 2015 to September 2019 was retrospectively extracted from electronic medical records and randomly subdivided into a training set and a testing set. With the training set, six ML models including logistic regression (LR), support vector machine (SVM), random forest (RF), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost) and gradient boosting machine (GBM) were developed. These models were assessed by the area under curve (AUC) of receiver operating characteristic on the testing set. The related risk factors and outcomes of pneumonia were also probed based on the chosen model.Results591 OLT patients were eventually included and 253 (42.81%) were diagnosed with postoperative pneumonia, which was associated with increased postoperative hospitalization and mortality (P < 0.05). Among the six ML models, XGBoost model performed best. The AUC of XGBoost model on the testing set was 0.734 (sensitivity: 52.6%; specificity: 77.5%). Pneumonia was notably associated with 14 items features: INR, HCT, PLT, ALB, ALT, FIB, WBC, PT, serum Na+, TBIL, anesthesia time, preoperative length of stay, total fluid transfusion and operation time.ConclusionOur study firstly demonstrated that the XGBoost model with 14 common variables might predict postoperative pneumonia in OLT patients.

【 授权许可】

CC BY