Machine learning and nomogram for predicting severity of acute cholangitis: development and internal validation of a dual-framework model in a retrospective cohort study.
- 1Department of Hepatobiliary Surgery, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, China.
Abstract
Background
Accurate severity grading of acute cholangitis (AC) according to the Tokyo Guidelines 2018 (TG18) is essential for guiding biliary drainage decisions and intensive care allocation, yet bedside application of the multi-parameter grading system remains challenging. Existing prediction models are predominantly binary and lack interpretable clinical tools.Methods
This retrospective cohort study enrolled 1,918 consecutive patients with acute cholangitis graded by TG18 at a single tertiary hepatobiliary centre (2021-2025). Random forest (RF), XGBoost, and support vector machine (SVM) were trained on seven LASSO-selected variables for three-class Grade I/II/III prediction. A separate eight-variable logistic regression nomogram distinguished Grade I from Grade II/III. Models were developed on a 70% training set (n = 1,342) and evaluated on a 30% held-out internal test set (n = 576) using bootstrap confidence intervals.Results
RF achieved macro-AUC 0.84 (95% CI: 0.81-0.87), overall accuracy 72.2% and balanced accuracy 64.9%; XGBoost performed comparably (macro-AUC 0.84, accuracy 71.9%). Per-class recall for RF was 0.83 (Grade I), 0.56 (Grade II) and 0.56 (Grade III), indicating that almost half of Grade II cases were not correctly identified. The nomogram yielded AUC 0.85 (0.82-0.88), sensitivity 0.72, specificity 0.82, and negative predictive value 0.82 at the Youden reference threshold of 0.47; operating characteristics at other thresholds, including a sensitivity-weighted rule-out threshold, are reported in the Supplementary Material. Decision curve analysis suggested potential net benefit across the evaluated range of threshold probabilities. SHAP analysis identified platelet count, age, and serum albumin as the top predictors.Conclusions
A dual-framework combining ML-based three-class severity grading and a nomogram for binary triage may provide complementary support for standardising TG18 severity assessment. Both frameworks predict the TG18 grade itself rather than downstream clinical outcomes, and Grade II recognition remains limited; they should therefore be regarded as adjuncts to-not replacements for-TG18 assessment. Prospective multicentre validation against clinical outcomes is required before any clinical use.
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BibTeX
@article{lyu2026machine,
title = {{Machine learning and nomogram for predicting severity of acute cholangitis: development and internal validation of a dual-framework model in a retrospective cohort study.}},
author = {Yun-Xiao Lyu and Bin Wang and Zhong Lyu},
journal = {Frontiers in medicine},
year = {2026},
month = oct,
doi = {10.3389/fmed.2026.1936260},
url = {https://europepmc.org/article/MED/42921458},
note = {CC BY},
}
RIS
TY - JOUR
TI - Machine learning and nomogram for predicting severity of acute cholangitis: development and internal validation of a dual-framework model in a retrospective cohort study.
AU - Yun-Xiao Lyu
AU - Bin Wang
AU - Zhong Lyu
PY - 2026
DA - 2026/10/06
JO - Frontiers in medicine
DO - 10.3389/fmed.2026.1936260
UR - https://europepmc.org/article/MED/42921458
AB - Background Accurate severity grading of acute cholangitis (AC) according to the Tokyo Guidelines 2018 (TG18) is essential for guiding biliary drainage decisions and intensive care allocation, yet bedside application of the multi-parameter grading system remains challenging. Existing prediction models are predominantly binary and lack interpretable clinical tools.Methods This retrospective cohort study enrolled 1,918 consecutive patients with acute cholangitis graded by TG18 at a single tertiary hepatobiliary centre (2021-2025). Random forest (RF), XGBoost, and support vector machine (SVM) were trained on seven LASSO-selected variables for three-class Grade I/II/III prediction. A separate eight-variable logistic regression nomogram distinguished Grade I from Grade II/III. Models were developed on a 70% training set (n = 1,342) and evaluated on a 30% held-out internal test set (n = 576) using bootstrap confidence intervals.Results RF achieved macro-AUC 0.84 (95% CI: 0.81-0.87), overall accuracy 72.2% and balanced accuracy 64.9%; XGBoost performed comparably (macro-AUC 0.84, accuracy 71.9%). Per-class recall for RF was 0.83 (Grade I), 0.56 (Grade II) and 0.56 (Grade III), indicating that almost half of Grade II cases were not correctly identified. The nomogram yielded AUC 0.85 (0.82-0.88), sensitivity 0.72, specificity 0.82, and negative predictive value 0.82 at the Youden reference threshold of 0.47; operating characteristics at other thresholds, including a sensitivity-weighted rule-out threshold, are reported in the Supplementary Material. Decision curve analysis suggested potential net benefit across the evaluated range of threshold probabilities. SHAP analysis identified platelet count, age, and serum albumin as the top predictors.Conclusions A dual-framework combining ML-based three-class severity grading and a nomogram for binary triage may provide complementary support for standardising TG18 severity assessment. Both frameworks predict the TG18 grade itself rather than downstream clinical outcomes, and Grade II recognition remains limited; they should therefore be regarded as adjuncts to-not replacements for-TG18 assessment. Prospective multicentre validation against clinical outcomes is required before any clinical use.
SN - 2296-858X
ER -