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Predicting non-compliance to pancreatic enzyme supplementation therapy in chronic pancreatitis: A machine
Anjali Srikanth Mannava1, Misbah Unnisa1, Neha Sree Thuraka2
1Pancreas Clinic, Asian Institute of Gastroenterology, Hyderabad, 500 082, India.
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.
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Pancreatitis is the inflammation of the pancreas, which occurs when the immune system becomes active and causes swelling, pain, and disruptions in organ function. Pancreatitis can manifest as either an acute or chronic condition.
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