PMID- 35309227
OWN - NLM
STAT- MEDLINE
DCOM- 20220428
LR  - 20220428
IS  - 2296-2565 (Electronic)
IS  - 2296-2565 (Linking)
VI  - 10
DP  - 2022
TI  - Prediction of Atrial Fibrillation in Hospitalized Elderly Patients With Coronary 
      Heart Disease and Type 2 Diabetes Mellitus Using Machine Learning: A Multicenter 
      Retrospective Study.
PG  - 842104
LID - 10.3389/fpubh.2022.842104 [doi]
LID - 842104
AB  - BACKGROUND: The objective of this study was to use machine learning algorithms to 
      construct predictive models for atrial fibrillation (AF) in elderly patients with 
      coronary heart disease (CHD) and type 2 diabetes mellitus (T2DM). METHODS: The 
      diagnosis and treatment data of elderly patients with CHD and T2DM, who were 
      treated in four tertiary hospitals in Chongqing, China from 2015 to 2021, were 
      collected. Five machine learning algorithms: logistic regression, logistic 
      regression+least absolute shrinkage and selection operator, classified regression 
      tree (CART), random forest (RF) and extreme gradient lifting (XGBoost) were used 
      to construct the prediction models. The area under the receiver operating 
      characteristic curve (AUC), sensitivity, specificity, and accuracy were used as 
      the comparison measures between different models. RESULTS: A total of 3,858 
      elderly patients with CHD and T2DM were included. In the internal validation 
      cohort, XGBoost had the highest AUC (0.743) and sensitivity (0.833), and RF had 
      the highest specificity (0.753) and accuracy (0.735). In the external 
      verification, RF had the highest AUC (0.726) and sensitivity (0.686), and CART 
      had the highest specificity (0.925) and accuracy (0.841). Total bilirubin, 
      triglycerides and uric acid were the three most important predictors of AF. 
      CONCLUSION: The risk prediction models of AF in elderly patients with CHD and 
      T2DM based on machine learning algorithms had high diagnostic value. The 
      prediction models constructed by RF and XGBoost were more effective. The results 
      of this study can provide reference for the clinical prevention and treatment of 
      AF.
CI  - Copyright (c) 2022 Xu, Peng, Tan, Zhao, Yang and Tian.
FAU - Xu, Qian
AU  - Xu Q
AD  - College of Medical Informatics, Chongqing Medical University, Chongqing, China.
AD  - Medical Data Science Academy, Chongqing Medical University, Chongqing, China.
AD  - Collection Development Department of Library, Chongqing Medical University, 
      Chongqing, China.
FAU - Peng, Yan
AU  - Peng Y
AD  - Department of Cardiology, University-Town Hospital of Chongqing Medical 
      University, Chongqing, China.
FAU - Tan, Juntao
AU  - Tan J
AD  - Operation Management Office, Affiliated Banan Hospital of Chongqing Medical 
      University, Chongqing, China.
FAU - Zhao, Wenlong
AU  - Zhao W
AD  - College of Medical Informatics, Chongqing Medical University, Chongqing, China.
AD  - Medical Data Science Academy, Chongqing Medical University, Chongqing, China.
FAU - Yang, Meijie
AU  - Yang M
AD  - College of Medical Informatics, Chongqing Medical University, Chongqing, China.
FAU - Tian, Jie
AU  - Tian J
AD  - Medical Data Science Academy, Chongqing Medical University, Chongqing, China.
AD  - Department of Cardiology, Ministry of Education Key Laboratory of Child 
      Development and Disorders, National Clinical Research Center for Child Health and 
      Disorders, China International Science and Technology Cooperation Base of Child 
      Development and Critical Disorders, Children's Hospital of Chongqing Medical 
      University, Chongqing, China.
AD  - Chongqing Key Laboratory of Pediatrics, Chongqing, China.
LA  - eng
PT  - Journal Article
PT  - Multicenter Study
PT  - Research Support, Non-U.S. Gov't
DEP - 20220304
PL  - Switzerland
TA  - Front Public Health
JT  - Frontiers in public health
JID - 101616579
SB  - IM
MH  - Aged
MH  - *Atrial Fibrillation/diagnosis
MH  - *Coronary Disease/epidemiology
MH  - *Diabetes Mellitus, Type 2/complications
MH  - Humans
MH  - Machine Learning
MH  - Retrospective Studies
PMC - PMC8931193
OTO - NOTNLM
OT  - atrial fibrillation
OT  - coronary heart disease
OT  - machine learning
OT  - prediction models
OT  - type 2 diabetes mellitus
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- 2022/03/22 06:00
MHDA- 2022/04/29 06:00
PMCR- 2022/03/04
CRDT- 2022/03/21 08:48
PHST- 2021/12/23 00:00 [received]
PHST- 2022/02/09 00:00 [accepted]
PHST- 2022/03/21 08:48 [entrez]
PHST- 2022/03/22 06:00 [pubmed]
PHST- 2022/04/29 06:00 [medline]
PHST- 2022/03/04 00:00 [pmc-release]
AID - 10.3389/fpubh.2022.842104 [doi]
PST - epublish
SO  - Front Public Health. 2022 Mar 4;10:842104. doi: 10.3389/fpubh.2022.842104. 
      eCollection 2022.