Highlights
- •Predictive models for breakthrough pain were developed.
- •Retrospective data of neuraxial labour analgesia from a single centre were used.
- •Machine learning (random forest and XGBoost) and logistic regression were used.
- •The models performed similarly, with area-under-the-curve 0.763–0.772.
- •The model derived using logistic regression required the fewest variables.
Abstract
Introduction
Risk-prediction models for breakthrough pain facilitate interventions to forestall
inadequate labour analgesia, but limited work has used machine learning to identify
predictive factors. We compared the performance of machine learning and regression
techniques in identifying parturients at increased risk of breakthrough pain during
labour epidural analgesia.
Methods
A single-centre retrospective study involved parturients receiving patient-controlled
epidural analgesia. The primary outcome was breakthrough pain. We randomly selected
80% of the cohort (training cohort) to develop three prediction models using random
forest, XGBoost, and logistic regression, followed by validation against the remaining
20% of the cohort (validation cohort). Area-under-the-receiver operating characteristic
curve (AUC), sensitivity, specificity, and positive and negative predictive values
(PPV and NPV) were used to assess model performance.
Results
Data from 20 716 parturients were analysed. The incidence of breakthrough pain was
14.2%. Of 31 candidate variables, random forest, XGBoost and logistic regression models
included 30, 23, and 15 variables, respectively. Unintended venous puncture, post-neuraxial
analgesia highest pain score, number of dinoprostone suppositories, neuraxial technique,
number of neuraxial attempts, depth to epidural space, body mass index, pre-neuraxial
analgesia oxytocin infusion rate, maternal age, pre-neuraxial analgesia cervical dilation,
anaesthesiologist rank, and multiparity, were identified in all three models. All
three models performed similarly, with AUC 0.763–0.772, sensitivity 67.0–69.4%, specificity
70.9–76.2%, PPV 28.3–31.8%, and NPV 93.3–93.5%.
Conclusions
Machine learning did not improve the prediction of breakthrough pain compared with
multivariable regression. Larger population-wide studies are needed to improve predictive
ability.
Keywords
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Article info
Publication history
Published online: August 25, 2020
Accepted:
August 17,
2020
Identification
Copyright
© 2020 Elsevier Ltd. All rights reserved.
