Predictive Insights Into Exocrine Pancreatic Insufficiency in Chronic Pancreatitis and Autoimmune Pancreatitis: A Decision Tree Approach.
Journal
Pancreas
ISSN: 1536-4828
Titre abrégé: Pancreas
Pays: United States
ID NLM: 8608542
Informations de publication
Date de publication:
25 Jan 2024
25 Jan 2024
Historique:
medline:
24
1
2024
pubmed:
24
1
2024
entrez:
24
1
2024
Statut:
aheadofprint
Résumé
Exocrine pancreatic insufficiency (EPI) is a common manifestation of chronic pancreatitis (CP) and autoimmune pancreatitis (AIP). This study aimed to estimate the presence of EPI in patients with CP or AIP using alternative clinical markers. A machine learning analysis employing a decision tree model was conducted on a retrospective training cohort comprising 57 patients with CP or AIP to identify EPI, defined as fecal elastase-1 levels less than 200 μg/g. The outcomes were then confirmed in a validation cohort of 26 patients. Thirty-nine patients (68%) exhibited EPI in the training cohort. The decision tree algorithm revealed body mass index (≤21.378 kg/m2) and total protein level (≤7.15 g/dL) as key variables for identifying EPI. The algorithm's performance was assessed using 5-fold cross-validation, yielding area under the receiver operating characteristic curve values of 0.890, 0.875, 0.750, 0.625, and 0.771, respectively. The results from the validation cohort closely replicated those in the training cohort. Decision tree analysis revealed that EPI in patients with CP or AIP can be identified based on body mass index and total protein. These findings may help guide the implementation of appropriate treatments for EPI.
Identifiants
pubmed: 38266223
doi: 10.1097/MPA.0000000000002290
pii: 00006676-990000000-00107
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.
Déclaration de conflit d'intérêts
The authors declare no conflict of interest.
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