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Updated: Jun 19, 2026
An Experimental Paradigm for the Prediction of Post-Operative Pain PPOP
Published on: January 27, 2010
A machine learning-based framework for predicting postpartum chronic pain: a retrospective study
Fan Liu1, Ting Li1, Dongxu Zhou1
1Institution of Neuroscience and Brain Disease, Department of Anesthesiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, No 136, Jingzhou Street, Xiangcheng District, Xiangyang, Hubei, 441000, China.
Background:
Postpartum chronic pain is prevalent, affecting many women after delivery. Machine learning algorithms have been widely used in predicting postoperative conditions. We investigated the prevalence of and risk factors for postpartum chronic pain, and aimed to develop a machine learning model for its prediction.
Methods:
Pregnant women in our tertiary hospital were screened from July 2021 to June 2022. Postoperative pain intensity was assessed using the numerical rating scale at 1, 3, and 6 months after delivery. Six machine learning algorithms were benchmarked using the nested resampling method, and their performance was evaluated based on classification error (CE). The algorithm with the best performance evaluation was used to establish the model for predicting chronic pain 6 months after delivery. Shapley additive explanations analysis was used to assess the contribution of each variable to the model.
Results:
A total of 1,398 postpartum women were included for analysis, among whom 383 developed chronic pain 6 months after delivery. The least absolute shrinkage selection operator identified five relevant factors: numerical rating scale at 3 days after delivery, body mass index before delivery, newborn weight, multiparous delivery, and back pain during gestation. The CEs for the algorithms were as follows: K-nearest neighbor, 0.212; logistic regression, 0.342; linear discriminant analysis, 0.343; naive Bayes, 0.346; ranger, 0.219; and extreme gradient boosting model, 0.147. The extreme gradient boosting model exhibited the best performance (CE = 0.147, F1 = 0.851) and was selected for model establishment. Visualization using Shapley additive explanations facilitated the interpretation of the influence of the five variables in the model.
Conclusions:
The extreme gradient boosting algorithm, which incorporates five risk factors, demonstrated strong performance in predicting postpartum chronic pain.
Trial Registration:
https//www.chictr.org.cn/ (ChiCTR2300070514).
