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Zhang N, Fan K, Ji H, Ma X, Wu J, Huang Y, Wang X, Gui R, Chen B, Zhang H, Zhang Z, Zhang X, Gong Z, Wang Y. Identification of risk factors for infection after mitral valve surgery through machine learning approaches. Front Cardiovasc Med 2023; 10:1050698. [PMID: 37383697 PMCID: PMC10294678 DOI: 10.3389/fcvm.2023.1050698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 05/31/2023] [Indexed: 06/30/2023] Open
Abstract
Background Selecting features related to postoperative infection following cardiac surgery was highly valuable for effective intervention. We used machine learning methods to identify critical perioperative infection-related variables after mitral valve surgery and construct a prediction model. Methods Participants comprised 1223 patients who underwent cardiac valvular surgery at eight large centers in China. The ninety-one demographic and perioperative parameters were collected. Random forest (RF) and least absolute shrinkage and selection operator (LASSO) techniques were used to identify postoperative infection-related variables; the Venn diagram determined overlapping variables. The following ML methods: random forest (RF), extreme gradient boosting (XGBoost), Support Vector Machine (SVM), Gradient Boosting Decision Tree (GBDT), AdaBoost, Naive Bayesian (NB), Logistic Regression (LogicR), Neural Networks (nnet) and artificial neural network (ANN) were developed to construct the models. We constructed receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) was calculated to evaluate model performance. Results We identified 47 and 35 variables with RF and LASSO, respectively. Twenty-one overlapping variables were finally selected for model construction: age, weight, hospital stay, total red blood cell (RBC) and total fresh frozen plasma (FFP) transfusions, New York Heart Association (NYHA) class, preoperative creatinine, left ventricular ejection fraction (LVEF), RBC count, platelet (PLT) count, prothrombin time, intraoperative autologous blood, total output, total input, aortic cross-clamp (ACC) time, postoperative white blood cell (WBC) count, aspartate aminotransferase (AST), alanine aminotransferase (ALT), PLT count, hemoglobin (Hb), and LVEF. The prediction models for infection after mitral valve surgery were established based on these variables, and they all showed excellent discrimination performance in the test set (AUC > 0.79). Conclusions Key features selected by machine learning methods can accurately predict infection after mitral valve surgery, guiding physicians in taking appropriate preventive measures and diminishing the infection risk.
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Affiliation(s)
- Ningjie Zhang
- Department of Blood Transfusion, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Kexin Fan
- Department of Laboratory Medicine, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Hongwen Ji
- Department of Anesthesiology, Fuwai Hospital National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Xianjun Ma
- Department of Blood Transfusion, Qilu Hospital of Shandong University, Jinan, China
| | - Jingyi Wu
- Department of Transfusion, Xiamen Cardiovascular Hospital Xiamen University, Xiamen, China
| | - Yuanshuai Huang
- Department of Transfusion, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Xinhua Wang
- Department of Transfusion, Beijing Aerospace General Hospital, Beijing, China
| | - Rong Gui
- Department of Transfusion, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Bingyu Chen
- Department of Transfusion, Zhejiang Provincial People's Hospital, Hangzhou, China
| | - Hui Zhang
- Department of Basic Medical Sciences, Changsha Medical University, Changsha, China
| | - Zugui Zhang
- Institute for Research on Equity and Community Health, Christiana Care Health System, Newark, DE, United States
| | - Xiufeng Zhang
- Department of Respiratory Medicine, Second Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Zheng Gong
- Sino-Cellbiomed Institutes of Medical Cell & Pharmaceutical Proteins Qingdao University, Qingdao, Shandong, China
- Department of Basic Medicine, Xiangnan University, Chenzhou, China
| | - Yongjun Wang
- Department of Blood Transfusion, The Second Xiangya Hospital, Central South University, Changsha, China
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Jiang H, Liu L, Wang Y, Ji H, Ma X, Wu J, Huang Y, Wang X, Gui R, Zhao Q, Chen B. Machine Learning for the Prediction of Complications in Patients After Mitral Valve Surgery. Front Cardiovasc Med 2021; 8:771246. [PMID: 34977184 PMCID: PMC8716451 DOI: 10.3389/fcvm.2021.771246] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 11/02/2021] [Indexed: 12/24/2022] Open
Abstract
Background: This study intended to use a machine learning model to identify critical preoperative and intraoperative variables and predict the risk of several severe complications (myocardial infarction, stroke, renal failure, and hospital mortality) after cardiac valvular surgery.Study Design and Methods: A total of 1,488 patients undergoing cardiac valvular surgery in eight large tertiary hospitals in China were examined. Fifty-four perioperative variables, such as essential demographic characteristics, concomitant disease, preoperative laboratory indicators, operation type, and intraoperative information, were collected. Machine learning models were developed and validated by 10-fold cross-validation. In each fold, Recursive Feature Elimination was used to select key variables. Ten machine learning models and logistic regression were developed. The area under the receiver operating characteristic (AUROC), accuracy (ACC), Youden index, sensitivity, specificity, F1-score, positive predictive value (PPV), and negative predictive value (NPV) were used to compare the prediction performance of different models. The SHapley Additive ex Planations package was applied to interpret the best machine learning model. Finally, a model was trained on the whole dataset with the merged key variables, and a web tool was created for clinicians to use.Results: In this study, 14 vital variables, namely, intraoperative total input, intraoperative blood loss, intraoperative colloid bolus, Classification of New York Heart Association (NYHA) heart function, preoperative hemoglobin (Hb), preoperative platelet (PLT), age, preoperative fibrinogen (FIB), intraoperative minimum red blood cell volume (Hct), body mass index (BMI), creatinine, preoperative Hct, intraoperative minimum Hb, and intraoperative autologous blood, were finally selected. The eXtreme Gradient Boosting algorithms (XGBOOST) algorithm model presented a significantly better predictive performance (AUROC: 0.90) than the other models (ACC: 81%, Youden index: 70%, sensitivity: 89%, specificity: 81%, F1-score:0.26, PPV: 15%, and NPV: 99%).Conclusion: A model for predicting several severe complications after cardiac valvular surgery was successfully developed using a machine learning algorithm based on 14 perioperative variables, which could guide clinical physicians to take appropriate preventive measures and diminish the complications for patients at high risk.
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Affiliation(s)
- Haiye Jiang
- Clinical Laboratory, The Third Xiangya Hospital, Central South University, Changsha, China
- Hunan Engineering Technology Research Center of Optoelectronic Health Detection, Changsha, China
| | - Leping Liu
- Department of Transfusion, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Yongjun Wang
- Department of Blood Transfusion, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Hongwen Ji
- Department of Anesthesiology, Fuwai Hospital National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Xianjun Ma
- Department of Blood Transfusion, Qilu Hospital of Shandong University, Jinan, China
| | - Jingyi Wu
- Department of Transfusion, Xiamen Cardiovascular Hospital Xiamen University, Xiamen, China
| | - Yuanshuai Huang
- Department of Transfusion, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Xinhua Wang
- Department of Transfusion, Beijing Aerospace General Hospital, Beijing, China
| | - Rong Gui
- Department of Transfusion, The Third Xiangya Hospital, Central South University, Changsha, China
- *Correspondence: Rong Gui
| | - Qinyu Zhao
- College of Engineering & Computer Science, Australian National University, Canberra, ACT, Australia
- Qinyu Zhao
| | - Bingyu Chen
- Department of Transfusion, Zhejiang Provincial People's Hospital, Hangzhou, China
- Bingyu Chen
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