Evidence map›Paper›PMID 41454217›Full record

ArticleIndian journal of gastroenterology : official journal of the Indian Society of Gastroenterology2025

Predicting non-compliance to pancreatic enzyme supplementation therapy in chronic pancreatitis: A machine learning-based approach.

Anjali Srikanth Mannava, Misbah Unnisa, Neha Sree Thuraka, Shagufta Farheen, Abdul Rasheed, Rajesh Goud, D Nageshwar Reddy, Rupjyoti Talukdar

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Article in Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Anjali Srikanth MannavaPancreas Clinic, Asian Institute of Gastroenterology, Hyderabad, 500 082, India.
Misbah UnnisaPancreas Clinic, Asian Institute of Gastroenterology, Hyderabad, 500 082, India.
Neha Sree ThurakaDivision of Pancreatology, Asian Institute of Gastroenterology, Hyderabad, 500 082, India.
Shagufta FarheenPancreas Clinic, Asian Institute of Gastroenterology, Hyderabad, 500 082, India.
Abdul RasheedPancreas Clinic, Asian Institute of Gastroenterology, Hyderabad, 500 082, India.
Rajesh GoudPancreas Clinic, Asian Institute of Gastroenterology, Hyderabad, 500 082, India.
D Nageshwar ReddyDepartment of Gastroenterology, Asian Institute of Gastroenterology, Hyderabad, 500 082, India.
Rupjyoti TalukdarPancreas Clinic, Department of Gastroenterology, Asian Institute of Gastroenterology, Mind Space Road, Gachibowli, Hyderabad, 500 032, India. rup_talukdar@yahoo.com.ORCID http://orcid.org/0000-0002-4255-6651

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesChronic pancreatitis results in pancreatic exocrine insufficiency (PEI), which is treated with pancreatic enzyme replacement therapy (PERT). Despite the clinical benefits, non-compliance to PERT is a frequent problem. Our study aimed at (a) identifying predictors of non-compliance to PERT using machine learning (ML) algorithms and (b) analyzing patient-reported reasons for non-compliance to PERT.

methodsA prospective observational study was conducted at a high-volume tertiary care center on two independent cohorts of chronic pancreatitis patients on PERT. In Cohort 1 (1057 patients screened), we used ML algorithms to identify predictors of non-compliance to PERT. The best ML model based on performance metrics was chosen for Shapley Additive explanations (SHAP) analysis. In Cohort 2 (465 patients screened), we conducted detailed interviews to understand patient-reported reasons for non-compliance.

resultsIn Cohort 1 (751 patients analyzed; median age, 37.5 years; males, 73.1%; idiopathic, 61.9%), 166 (22.1%) patients were non-compliant to PERT. Extreme gradient boosting (XGBoost) exhibited the highest accuracy (area under the curve, AUC = 0.91). The strongest predictors of non-compliance based on SHAP were disease duration, age, fat-restricted diet, rural residence and educational status. A non-compliance (NC) score was developed based on SHAP. In Cohort 2 (129 patients analyzed; mean age, 36 years; males, 72.1%; idiopathic, 52.3%), high treatment costs (34.1%), negligence (19.4%) and adverse effects (10.9%) were the most reported reasons for non-compliance.

conclusionsThis study identifies predictors of non-compliance to PERT (disease duration, age, fat-restricted diet, rural habitat, undergraduate education status) in patients with chronic pancreatitis using machine-learning algorithms. The NC score can be a useful tool to identify patients at risk for non-compliance, who can be subjected to targeted counselling. The NC score needs to be validated in large, independent, multi-centre cohorts of patients.

Indexed as

Chronic pancreatitisMachine learningNon-compliancePancreatic enzyme replacement therapyPancreatic exocrine insufficiency

Identifiers

PMID41454217

What Socratic holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.