PMID- 36653317
OWN - NLM
STAT- MEDLINE
DCOM- 20230314
LR  - 20230314
IS  - 1440-1746 (Electronic)
IS  - 0815-9319 (Linking)
VI  - 38
IP  - 3
DP  - 2023 Mar
TI  - Development and evaluation of machine learning models and nomogram for the 
      prediction of severe acute pancreatitis.
PG  - 468-475
LID - 10.1111/jgh.16125 [doi]
AB  - BACKGROUND AND AIM: Severe acute pancreatitis (SAP) in patients progresses 
      rapidly and can cause multiple organ failures associated with high mortality. We 
      aimed to train a machine learning (ML) model and establish a nomogram that could 
      identify SAP, early in the course of acute pancreatitis (AP). METHODS: In this 
      retrospective study, 631 patients with AP were enrolled in the training cohort. 
      For predicting SAP early, five supervised ML models were employed, such as random 
      forest (RF), K-nearest neighbors (KNN), and naive Bayes (NB), which were 
      evaluated by accuracy (ACC) and the areas under the receiver operating 
      characteristic curve (AUC). The nomogram was established, and the predictive 
      ability was assessed by the calibration curve and AUC. They were externally 
      validated by an independent cohort of 109 patients with AP. RESULTS: In the 
      training cohort, the AUC of RF, KNN, and NB models were 0.969, 0.954, and 0.951, 
      respectively, while the AUC of the Bedside Index for Severity in Acute 
      Pancreatitis (BISAP), Ranson and Glasgow scores were only 0.796, 0.847, and 
      0.837, respectively. In the validation cohort, the RF model also showed the 
      highest AUC, which was 0.961. The AUC for the nomogram was 0.888 and 0.955 in the 
      training and validation cohort, respectively. CONCLUSIONS: Our findings suggested 
      that the RF model exhibited the best predictive performance, and the nomogram 
      provided a visual scoring model for clinical practice. Our models may serve as 
      practical tools for facilitating personalized treatment options and improving 
      clinical outcomes through pre-treatment stratification of patients with AP.
CI  - (c) 2023 Journal of Gastroenterology and Hepatology Foundation and John Wiley & 
      Sons Australia, Ltd.
FAU - Luo, Zhu
AU  - Luo Z
AD  - Department of Clinical Laboratory, Second Affiliated Hospital of Wenzhou Medical 
      University, Wenzhou, China.
FAU - Shi, Jialin
AU  - Shi J
AD  - Key Laboratory of Intelligent Medical Imaging of Wenzhou, First Affiliated 
      Hospital of Wenzhou Medical University, Wenzhou, China.
FAU - Fang, Yangyang
AU  - Fang Y
AD  - School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, 
      Wenzhou, China.
FAU - Pei, Shunjie
AU  - Pei S
AD  - School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, 
      Wenzhou, China.
FAU - Lu, Yutian
AU  - Lu Y
AD  - Department of Clinical Laboratory, Affiliated Central Hospital of Taizhou 
      University, Taizhou, China.
FAU - Zhang, Ruxia
AU  - Zhang R
AD  - Department of Clinical Laboratory, Second Affiliated Hospital of Wenzhou Medical 
      University, Wenzhou, China.
FAU - Ye, Xin
AU  - Ye X
AD  - Department of Clinical Laboratory, Second Affiliated Hospital of Wenzhou Medical 
      University, Wenzhou, China.
FAU - Wang, Wenxing
AU  - Wang W
AD  - Department of Gastroenterology and Hepatology, Second Affiliated Hospital of 
      Wenzhou Medical University, Wenzhou, China.
FAU - Li, Mengtian
AU  - Li M
AD  - Department of Clinical Laboratory, Second Affiliated Hospital of Wenzhou Medical 
      University, Wenzhou, China.
FAU - Li, Xiangjun
AU  - Li X
AD  - Department of Clinical Laboratory, Second Affiliated Hospital of Wenzhou Medical 
      University, Wenzhou, China.
FAU - Zhang, Mengyue
AU  - Zhang M
AD  - Department of Clinical Laboratory, Second Affiliated Hospital of Wenzhou Medical 
      University, Wenzhou, China.
FAU - Xiang, Guangxin
AU  - Xiang G
AD  - School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, 
      Wenzhou, China.
FAU - Pan, Zhifang
AU  - Pan Z
AD  - Key Laboratory of Intelligent Medical Imaging of Wenzhou, First Affiliated 
      Hospital of Wenzhou Medical University, Wenzhou, China.
FAU - Zheng, Xiaoqun
AU  - Zheng X
AUID- ORCID: 0000-0002-9816-4833
AD  - Department of Clinical Laboratory, Second Affiliated Hospital of Wenzhou Medical 
      University, Wenzhou, China.
AD  - School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, 
      Wenzhou, China.
AD  - Key Laboratory of Laboratory Medicine, Ministry of Education of China, Wenzhou, 
      China.
LA  - eng
GR  - 2020Y0775/Wenzhou Basic Scientific Research Project/
PT  - Journal Article
DEP - 20230127
PL  - Australia
TA  - J Gastroenterol Hepatol
JT  - Journal of gastroenterology and hepatology
JID - 8607909
SB  - IM
MH  - Humans
MH  - *Pancreatitis
MH  - Retrospective Studies
MH  - Nomograms
MH  - Severity of Illness Index
MH  - Acute Disease
MH  - Bayes Theorem
MH  - Prognosis
MH  - Machine Learning
OTO - NOTNLM
OT  - Machine learning
OT  - Nomogram
OT  - Prediction
OT  - Random forest model
OT  - Severe acute pancreatitis
EDAT- 2023/01/19 06:00
MHDA- 2023/03/15 06:00
CRDT- 2023/01/18 22:52
PHST- 2022/12/27 00:00 [revised]
PHST- 2022/11/08 00:00 [received]
PHST- 2023/01/16 00:00 [accepted]
PHST- 2023/01/19 06:00 [pubmed]
PHST- 2023/03/15 06:00 [medline]
PHST- 2023/01/18 22:52 [entrez]
AID - 10.1111/jgh.16125 [doi]
PST - ppublish
SO  - J Gastroenterol Hepatol. 2023 Mar;38(3):468-475. doi: 10.1111/jgh.16125. Epub 
      2023 Jan 27.