Predictive value of machine learning for the severity of acute pancreatitis: A systematic review and meta-analysis

Rui Qian; Jiamei Zhuang; Jianjun Xie; Honghui Cheng; Haiya Ou; Xiang Lu; Zichen Ouyang

doi:10.1016/j.heliyon.2024.e29603

Predictive value of machine learning for the severity of acute pancreatitis: A systematic review and meta-analysis

Heliyon. 2024 Apr 15;10(8):e29603. doi: 10.1016/j.heliyon.2024.e29603. eCollection 2024 Apr 30.

Authors

Rui Qian¹, Jiamei Zhuang², Jianjun Xie¹, Honghui Cheng¹, Haiya Ou¹, Xiang Lu³, Zichen Ouyang⁴

Affiliations

¹ Department of Gastroenterology, Shenzhen Bao'an Chinese Medicine Hospital, Guangzhou University of Chinese Medicine, Shenzhen 518000, China.
² The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, 518033, China.
³ Department of Plumonary and Critical Care Medicine, Shenzhen Bao'an Chinese Medicine Hospital, Guangzhou University of Chinese Medicine, Shenzhen 518000, China.
⁴ Department of Hepatology, Shenzhen Bao'an Chinese Medicine Hospital, Guangzhou University of Chinese Medicine, Shenzhen 518000, China.

Abstract

Background: Predicting the severity of acute pancreatitis (AP) early poses a challenge in clinical practice. While there are well-established clinical scoring tools, their actual predictive performance remains uncertain. Various studies have explored the application of machine-learning methods for early AP prediction. However, a more comprehensive evidence-based assessment is needed to determine their predictive accuracy. Hence, this systematic review and meta-analysis aimed to evaluate the predictive accuracy of machine learning in assessing the severity of AP.

Methods: PubMed, EMBASE, Cochrane Library, and Web of Science were systematically searched until December 5, 2023. The risk of bias in eligible studies was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Subgroup analyses, based on different machine learning types, were performed. Additionally, the predictive accuracy of mainstream scoring tools was summarized.

Results: This systematic review ultimately included 33 original studies. The pooled c-index in both the training and validation sets was 0.87 (95 % CI: 0.84-0.89) and 0.88 (95 % CI: 0.86-0.90), respectively. The sensitivity in the training set was 0.81 (95 % CI: 0.77-0.84), and in the validation set, it was 0.79 (95 % CI: 0.71-0.85). The specificity in the training set was 0.84 (95 % CI: 0.78-0.89), and in the validation set, it was 0.90 (95 % CI: 0.86-0.93). The primary model incorporated was logistic regression; however, its predictive accuracy was found to be inferior to that of neural networks, random forests, and xgboost. The pooled c-index of the APACHE II, BISAP, and Ranson were 0.74 (95 % CI: 0.68-0.80), 0.77 (95 % CI: 0.70-0.85), and 0.74 (95 % CI: 0.68-0.79), respectively.

Conclusions: Machine learning demonstrates excellent accuracy in predicting the severity of AP, providing a reference for updating or developing a straightforward clinical prediction tool.

Keywords: Acute pancreatitis; Machine learning; Predictive value; Severity; Systematic review.