Predicting the Need for Therapeutic Intervention and Mortality in Acute Pancreatitis: A Two-Center International Study Using Machine Learning

Authors

Na Shi
Lan Lan
Jiawei Luo
Ping Zhu
Thomas R W Ward
Peter Szatmary
Robert Sutton
Wei Huang
John A Windsor
Xiaobo Zhou
Qing Xia

Publication Date

4-11-2022

Journal

Journal of Personalized Medicine

Abstract

BACKGROUND: Current approaches to predicting intervention needs and mortality have reached 65-85% accuracy, which falls below clinical decision-making requirements in patients with acute pancreatitis (AP). We aimed to accurately predict therapeutic intervention needs and mortality on admission, in AP patients, using machine learning (ML).

METHODS: Data were obtained from three databases of patients admitted with AP: one retrospective (Chengdu) and two prospective (Liverpool and Chengdu) databases. Intervention and mortality differences, as well as potential predictors, were investigated. Univariate analysis was conducted, followed by a random forest ML algorithm used in multivariate analysis, to identify predictors. The ML performance matrix was applied to evaluate the model's performance.

RESULTS: Three datasets of 2846 patients included 25 potential clinical predictors in the univariate analysis. The top ten identified predictors were obtained by ML models, for predicting interventions and mortality, from the training dataset. The prediction of interventions includes death in non-intervention patients, validated with high accuracy (96%/98%), the area under the receiver-operating-characteristic curve (0.90/0.98), and positive likelihood ratios (22.3/69.8), respectively. The post-test probabilities in the test set were 55.4% and 71.6%, respectively, which were considerably superior to existing prognostic scores. The ML model, for predicting mortality in intervention patients, performed better or equally with prognostic scores.

CONCLUSIONS: ML, using admission clinical predictors, can accurately predict therapeutic interventions and mortality in patients with AP.

Keywords

acute pancreatitis, machine learning, predictor, interventions, mortality

DOI

10.3390/jpm12040616

PMID

35455733

PMCID

PMC9031087

PubMedCentral® Posted Date

4-11-2022

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes