PMID- 36826583
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230301
IS  - 2308-3425 (Electronic)
IS  - 2308-3425 (Linking)
VI  - 10
IP  - 2
DP  - 2023 Feb 17
TI  - An In-Hospital Mortality Risk Model for Elderly Patients Undergoing Cardiac 
      Valvular Surgery Based on LASSO-Logistic Regression and Machine Learning.
LID - 10.3390/jcdd10020087 [doi]
LID - 87
AB  - BACKGROUND: To preferably evaluate and predict the risk for in-hospital mortality 
      in elderly patients receiving cardiac valvular surgery, we developed a new 
      prediction model using least absolute shrinkage and selection operator 
      (LASSO)-logistic regression and machine learning (ML) algorithms. METHODS: 
      Clinical data including baseline characteristics and peri-operative data of 7163 
      elderly patients undergoing cardiac valvular surgery from January 2016 to 
      December 2018 were collected at 87 hospitals in the Chinese Cardiac Surgery 
      Registry (CCSR). Patients were divided into training (N = 5774 [80%]) and testing 
      samples (N = 1389 [20%]) according to their date of operation. LASSO-logistic 
      regression models and ML models were used to analyze risk factors and develop the 
      prediction model. We compared the discrimination and calibration of each model 
      and EuroSCORE II. RESULTS: A total of 7163 patients were included in this study, 
      with a mean age of 69.8 (SD 4.5) years, and 45.0% were women. Overall, 
      in-hospital mortality was 4.05%. The final model included seven risk factors: 
      age, prior cardiac surgery, cardiopulmonary bypass duration time (CPB time), left 
      ventricular ejection fraction (LVEF), creatinine clearance rate (CCr), combined 
      coronary artery bypass grafting (CABG) and New York Heart Association (NYHA) 
      class. LASSO-logistic regression, linear discriminant analysis (LDA), support 
      vector classification (SVC) and logistic regression (LR) models had the best 
      discrimination and calibration in both training and testing cohorts, which were 
      superior to the EuroSCORE II. CONCLUSIONS: The mortality rate for elderly 
      patients undergoing cardiac valvular surgery was relatively high. LASSO-logistic 
      regression, LDA, SVC and LR can predict the risk for in-hospital mortality in 
      elderly patients receiving cardiac valvular surgery well.
FAU - Zhu, Kun
AU  - Zhu K
AUID- ORCID: 0000-0002-5494-2760
AD  - Cardiac Surgery Centre, Fuwai Hospital, Chinese Academy of Medical Sciences and 
      Peking Union Medical College, Beijing 100037, China.
FAU - Lin, Hongyuan
AU  - Lin H
AD  - Cardiac Surgery Centre, Fuwai Hospital, Chinese Academy of Medical Sciences and 
      Peking Union Medical College, Beijing 100037, China.
FAU - Yang, Xichun
AU  - Yang X
AD  - Department of Anesthesiology, Beijing Cancer Hospital, Peking University, Beijing 
      100142, China.
FAU - Gong, Jiamiao
AU  - Gong J
AD  - Cardiac Surgery Centre, Fuwai Hospital, Chinese Academy of Medical Sciences and 
      Peking Union Medical College, Beijing 100037, China.
FAU - An, Kang
AU  - An K
AD  - Cardiac Surgery Centre, Fuwai Hospital, Chinese Academy of Medical Sciences and 
      Peking Union Medical College, Beijing 100037, China.
FAU - Zheng, Zhe
AU  - Zheng Z
AD  - Cardiac Surgery Centre, Fuwai Hospital, Chinese Academy of Medical Sciences and 
      Peking Union Medical College, Beijing 100037, China.
FAU - Hou, Jianfeng
AU  - Hou J
AD  - Cardiac Surgery Centre, Fuwai Hospital, Chinese Academy of Medical Sciences and 
      Peking Union Medical College, Beijing 100037, China.
LA  - eng
GR  - 2020YFC2008100/Ministry of Science and Technology of the People's Republic of 
      China/
PT  - Journal Article
DEP - 20230217
PL  - Switzerland
TA  - J Cardiovasc Dev Dis
JT  - Journal of cardiovascular development and disease
JID - 101651414
PMC - PMC9963974
OTO - NOTNLM
OT  - LASSO-logistic regression
OT  - machine learning
OT  - mortality risk
OT  - prediction models
OT  - valvular heart disease
COIS- The authors declare no conflict of interest.
EDAT- 2023/02/25 06:00
MHDA- 2023/02/25 06:01
PMCR- 2023/02/17
CRDT- 2023/02/24 11:21
PHST- 2022/12/06 00:00 [received]
PHST- 2023/02/11 00:00 [revised]
PHST- 2023/02/14 00:00 [accepted]
PHST- 2023/02/24 11:21 [entrez]
PHST- 2023/02/25 06:00 [pubmed]
PHST- 2023/02/25 06:01 [medline]
PHST- 2023/02/17 00:00 [pmc-release]
AID - jcdd10020087 [pii]
AID - jcdd-10-00087 [pii]
AID - 10.3390/jcdd10020087 [doi]
PST - epublish
SO  - J Cardiovasc Dev Dis. 2023 Feb 17;10(2):87. doi: 10.3390/jcdd10020087.