Machine learning-based decision tool for selecting patients with idiopathic acute pancreatitis for endosonography to exclude a biliary aetiology

Abstract

Background: Biliary microlithiasis/sludge is detected in approximately 30% of patients with idiopathic acute pancreatitis (IAP). As recurrent biliary pancreatitis can be prevented, the underlying aetiology of IAP should be established.

Aim: To develop a machine learning (ML) based decision tool for the use of endosonography (EUS) in pancreatitis patients to detect sludge and microlithiasis.

Methods: We retrospectively used routinely recorded clinical and laboratory parameters of 218 consecutive patients with confirmed AP admitted to our tertiary care hospital between 2015 and 2020. Patients who did not receive EUS as part of the diagnostic work-up and whose pancreatitis episode could be adequately explained by other causes than biliary sludge and microlithiasis were excluded. We trained supervised ML classifiers using H₂O.ai automatically selecting the best suitable predictor model to predict microlithiasis/sludge. The predictor model was further validated in two independent retrospective cohorts from two tertiary care centers (117 patients).

Results: Twenty-eight categorized patients' variables recorded at admission were identified to compute the predictor model with an accuracy of 0.84 [95% confidence interval (CI): 0.791-0.9185], positive predictive value of 0.84, and negative predictive value of 0.80 in the identification cohort (218 patients). In the validation cohort, the robustness of the prediction model was confirmed with an accuracy of 0.76 (95%CI: 0.673-0.8347), positive predictive value of 0.76, and negative predictive value of 0.78 (117 patients).

Conclusion: We present a robust and validated ML-based predictor model consisting of routinely recorded parameters at admission that can predict biliary sludge and microlithiasis as the cause of AP.

Keywords: Acute pancreatitis; Biliary pancreatitis; Endosonography; Idiopathic acute pancreatitis; Microlithiasis; Sludge.

Figure 1

Flow chart for development and external independent validation of microlithiasis prediction score. In the Ludwig-Maximilians-Universität in Munich identification cohort, 218 acute pancreatitis patients treated as inpatients between 2015-2020 were included in the final machine learning-based score survey. The validation cohort, consisting of 117 pancreatitis cases, was composed of patient data from the University Hospital of Göttingen and Technical University Munich. The microlithiasis predictive model was trained using data from both biliary sludge and biliary microlithiasis patients to cover the entirety of biliary microconcrements and to reflect the current lack of uniform definitions of biliary sludge and biliary microlithiasis in clinical practice. EUS: Endosonography; AP: Acute pancreatitis.

Figure 2

Machine-learning based model for the prediction of biliary sludge and microlithiasis in the context of acute (presumed) idiopathic acute pancreatitis. Of the initial 192 variables analysed, 154 were included in the categorisation step after excluding those variables without evidence of variable variance. Using an auto-machine learning approach, the final (iterative) predictive model was developed via the base model step. ML: Machine learning.

Figure 3

Graphical representation of the prediction model variables according to importance of scale. A: Variables of the final (iterated) auto-machine learning prediction model are ordered by scale of importance; B and C: Precoat diagram showing robust positive and negative prediction (3/81 patient cases were misclassified as microlithiasis and not other-acute pancreatitis). Gamma-GT: Gamma-glutamyl transpeptidase; AST: Aspartate aminotransferase; GOT: Glutamic oxalacetic transaminases; ALT: Alanine transaminase; GPT: Glutamic pyruvic transaminase; LDH: Lactate dehydrogenase; RDW: Red blood cell distribution width.