PMID- 34977184
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220411
IS  - 2297-055X (Print)
IS  - 2297-055X (Electronic)
IS  - 2297-055X (Linking)
VI  - 8
DP  - 2021
TI  - Machine Learning for the Prediction of Complications in Patients After Mitral 
      Valve Surgery.
PG  - 771246
LID - 10.3389/fcvm.2021.771246 [doi]
LID - 771246
AB  - 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.
CI  - Copyright (c) 2021 Jiang, Liu, Wang, Ji, Ma, Wu, Huang, Wang, Gui, Zhao and Chen.
FAU - Jiang, Haiye
AU  - Jiang H
AD  - Clinical Laboratory, The Third Xiangya Hospital, Central South University, 
      Changsha, China.
FAU - Liu, Leping
AU  - Liu L
AD  - Department of Transfusion, The Third Xiangya Hospital, Central South University, 
      Changsha, China.
FAU - Wang, Yongjun
AU  - Wang Y
AD  - Department of Blood Transfusion, The Second Xiangya Hospital, Central South 
      University, Changsha, China.
FAU - Ji, Hongwen
AU  - Ji H
AD  - Department of Anesthesiology, Fuwai Hospital National Center for Cardiovascular 
      Diseases, Chinese Academy of Medical Sciences, Peking Union Medical College, 
      Beijing, China.
FAU - Ma, Xianjun
AU  - Ma X
AD  - Department of Blood Transfusion, Qilu Hospital of Shandong University, Jinan, 
      China.
FAU - Wu, Jingyi
AU  - Wu J
AD  - Department of Transfusion, Xiamen Cardiovascular Hospital Xiamen University, 
      Xiamen, China.
FAU - Huang, Yuanshuai
AU  - Huang Y
AD  - Department of Transfusion, The Affiliated Hospital of Southwest Medical 
      University, Luzhou, China.
FAU - Wang, Xinhua
AU  - Wang X
AD  - Department of Transfusion, The Affiliated Hospital of Southwest Medical 
      University, Luzhou, China.
FAU - Gui, Rong
AU  - Gui R
AD  - Department of Transfusion, Beijing Aerospace General Hospital, Beijing, China.
FAU - Zhao, Qinyu
AU  - Zhao Q
AD  - Department of Transfusion, The Third Xiangya Hospital, Central South University, 
      Changsha, China.
AD  - College of Engineering & Computer Science, Australian National University, 
      Canberra, ACT, Australia.
FAU - Chen, Bingyu
AU  - Chen B
AD  - Department of Transfusion, Zhejiang Provincial People's Hospital, Hangzhou, 
      China.
LA  - eng
PT  - Journal Article
DEP - 20211216
PL  - Switzerland
TA  - Front Cardiovasc Med
JT  - Frontiers in cardiovascular medicine
JID - 101653388
EIN - Front Cardiovasc Med. 2022 Feb 08;9:854588. PMID: 35211531
EIN - Front Cardiovasc Med. 2022 Mar 24;9:890752. PMID: 35402546
PMC - PMC8716451
OTO - NOTNLM
OT  - cardiac valvular surgery
OT  - complications
OT  - machine learning
OT  - model
OT  - predict
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2022/01/04 06:00
MHDA- 2022/01/04 06:01
PMCR- 2021/01/01
CRDT- 2022/01/03 05:49
PHST- 2021/09/06 00:00 [received]
PHST- 2021/11/02 00:00 [accepted]
PHST- 2022/01/03 05:49 [entrez]
PHST- 2022/01/04 06:00 [pubmed]
PHST- 2022/01/04 06:01 [medline]
PHST- 2021/01/01 00:00 [pmc-release]
AID - 10.3389/fcvm.2021.771246 [doi]
PST - epublish
SO  - Front Cardiovasc Med. 2021 Dec 16;8:771246. doi: 10.3389/fcvm.2021.771246. 
      eCollection 2021.