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Objectives <p>Chronic 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.</p> Methods <p>A 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.</p> Results <p>In 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.</p> Conclusions <p>This 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.</p> Graphical Abstract <p></p>

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

  • 期刊论文

  • 发表日期：

    2025年12月27日

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

摘要

Objectives

Chronic 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.

Methods

A 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.

Results

In 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.

Conclusions

This 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.

Graphical Abstract