Pancreas · 2024
Predictive Insights Into Exocrine Pancreatic Insufficiency in Chronic Pancreatitis and Autoimmune Pancreatitis: A Decision Tree Approach.
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Tanaka Tomoyuki, Kimura Takefumi, Wakabayashi Shun-Ichi, Okamura Takuma, Shigeto Shohei, Tanaka Naoki, Kondo Shohei, Horiuchi Ichitaro, Kuraishi Yasuhiro, Nakamura Akira, Ashihara Norihiro, Kanai Keita, Nagaya Tadanobu, Watanabe Takayuki, Umemura Takeji
Аннотация
OBJECTIVE: 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.
MATERIALS AND METHODS: 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.
RESULTS: Thirty-nine patients (68%) exhibited EPI in the training cohort. The decision tree algorithm revealed body mass index (≤21.378 kg/m 2 ) 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.
CONCLUSIONS: 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.
MATERIALS AND METHODS: 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.
RESULTS: Thirty-nine patients (68%) exhibited EPI in the training cohort. The decision tree algorithm revealed body mass index (≤21.378 kg/m 2 ) 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.
CONCLUSIONS: 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.