{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T06:08:29Z","timestamp":1779170909802,"version":"3.51.4"},"reference-count":19,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T00:00:00Z","timestamp":1779148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Effective labor analgesia is paramount for maternal well-being and positive birth experiences. However, the efficacy of intrapartum labor analgesia exhibits considerable inter-individual variability. Identifying reliable preoperative predictors for labor analgesia efficacy is crucial for optimizing pain management strategies and enhancing patient outcomes. This study aimed to develop and validate a prediction model for intrapartum labor analgesia efficacy utilizing preoperative multidimensional indicators.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective study enrolled 137 parturients who received labor analgesia. Preoperative multidimensional indicators, encompassing demographic data, clinical characteristics, and psychological assessments, were systematically collected. Logistic Regression (LR), Naive Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGB) models were developed and comparatively analyzed. Feature selection was conducted using LASSO regression. Model performance was rigorously evaluated using metrics such as Area Under the Curve (AUC), sensitivity, specificity, F1 score, positive predictive value, negative predictive value, calibration curves, and decision curve analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Initial logistic regression analysis revealed several significant preoperative predictors, including pregnancy-induced hypertension (PIH), binary ultrasound cervical length, and continuous ultrasound cervical length. LASSO regression further refined the feature set, illustrating the dynamic changes in coefficients across varying lambda values. Among the developed models, the XGB model consistently demonstrated superior discriminative ability, achieving an AUC of 0.875 (95% CI: 0.802 to 0.948) in the training set and 0.759 (95% CI: 0.606 to 0.912) in the validation set, thereby indicating robust performance. Calibration curves provided insights into the agreement between predicted and observed probabilities across models. Decision curve analysis elucidated the clinical utility of these models, underscoring the potential net benefit of employing these predictive tools.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study successfully developed and validated a prediction model for intrapartum labor analgesia efficacy grounded in preoperative multidimensional indicators. The XGB model exhibited promising performance, suggesting its potential for clinical application in identifying parturients who could benefit from individualized pain management strategies. Further prospective validation in diverse cohorts is warranted to confirm these findings and facilitate clinical implementation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1794753","type":"journal-article","created":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T05:39:16Z","timestamp":1779169156000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Prediction model for intrapartum labor analgesia efficacy based on preoperative multidimensional indicators"],"prefix":"10.3389","volume":"13","author":[{"given":"Biyao","family":"Wang","sequence":"first","affiliation":[{"name":"Wannan Medical College","place":["Wuhu, China"]},{"name":"Department of Anesthesiology, The Second People\u2019s Hospital of Hefei","place":["Hefei, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wensheng","family":"He","sequence":"additional","affiliation":[{"name":"Wannan Medical College","place":["Wuhu, China"]},{"name":"Department of Anesthesiology, The Second People\u2019s Hospital of Hefei","place":["Hefei, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,5,19]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1080\/01443610400023072","article-title":"Pain and stress hormones during childbirth","volume":"26","author":"Alehagen","year":"2005","journal-title":"J Psychosom Obstet Gynaecol"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"CD000331","DOI":"10.1002\/14651858.CD000331.pub4","article-title":"Epidural versus non-epidural or no analgesia in labour","volume":"5","author":"Anim-Somuah","year":"2018","journal-title":"Cochrane Database Syst Rev"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/j.ijoa.2020.08.010","article-title":"Prediction of breakthrough pain during labour neuraxial analgesia: comparison of machine learning and multivariable regression approaches","volume":"45","author":"Tan","year":"2021","journal-title":"Int J Obstet Anesth"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1186\/s12905-020-00948-0","article-title":"The association between labor epidural analgesia and postpartum depression: a systematic review and meta-analysis","volume":"20","author":"Almeida","year":"2020","journal-title":"BMC Womens Health"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"632","DOI":"10.1213\/ANE.0000000000000357","article-title":"A prospective observational study evaluating the ability of prelabor psychological tests to predict labor pain, epidural analgesic consumption, and maternal satisfaction","volume":"119","author":"Carvalho","year":"2014","journal-title":"Anesth Analg"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12871-018-0514-8","article-title":"Investigating determinants for patient satisfaction in women receiving epidural analgesia for labour pain: a retrospective cohort study","volume":"18","author":"Tan","year":"2018","journal-title":"BMC Anesthesiol"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12884-025-07203-0","article-title":"Development and validation of a machine learning model for predicting intrapartum fever using pre-labor analgesia clinical indicators: a multicenter retrospective study","volume":"25","author":"Liu","year":"2025","journal-title":"BMC Pregnancy Childbirth"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"82722-Y","DOI":"10.1038\/s41598-024-82722-y","article-title":"Development and validation of a prediction model for intrapartum fever related to chorioamnionitis (IFTC) in parturients undergoing epidural analgesia","volume":"14","author":"Ling","year":"2024","journal-title":"Sci Rep"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"5439","DOI":"10.2147\/IJWH.S560693","article-title":"Development of a machine learning model to predict epidural-related maternal fever during labor analgesia: a multi-algorithm comparative study with prospective implementation framework","volume":"17","author":"Zhang","year":"2025","journal-title":"Int J Women's Health"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.ijoa.2007.10.008","article-title":"Inadequate pain relief with labor epidurals: a multivariate analysis","volume":"18","author":"Agaram","year":"2009","journal-title":"Int J Obstet Anesth"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"e2129778","DOI":"10.1001\/jamanetworkopen.2021.31683","article-title":"Association of Epidural Analgesia in women in labor with maternal and neonatal outcomes","volume":"4","author":"Kearns","year":"2021","journal-title":"JAMA Netw Open"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"655","DOI":"10.1056\/NEJMoa042573","article-title":"The risk of Cesarean delivery with neuraxial analgesia","volume":"352","author":"Wong","year":"2005","journal-title":"N Engl J Med"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"481","DOI":"10.4097\/kjae.2013.65.5.379","article-title":"Neuraxial analgesia: a review of its effects on the outcome of labor","volume":"65","author":"Jung","year":"2013","journal-title":"Korean J Anesthesiol"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1186\/s44158-025-00224-3","article-title":"Technical aspects of neuraxial analgesia during labor and maternity care: an updated overview","volume":"5","author":"Coviello","year":"2025","journal-title":"J Anesth Analg Crit Care"},{"key":"ref15","doi-asserted-by":"publisher","first-page":"e40550039","DOI":"10.1097\/MD.0000000000042939","article-title":"Predictive models for intrapartum maternal fever: development and validation of pre-analgesia and labor process indicators","volume":"104","author":"Liu","year":"2025","journal-title":"Medicine"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"111582","DOI":"10.1016\/j.jclinane.2024.111582","article-title":"The evaluation of the performance of ChatGPT in the management of labor analgesia","volume":"98","author":"Ismaiel","year":"2024","journal-title":"J Clin Anesth"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"2525","DOI":"10.1038\/s41598-025-86715-3","article-title":"A machine learning-based model for predicting the risk of cognitive frailty in elderly patients on maintenance hemodialysis","volume":"15","author":"Cao","year":"2025","journal-title":"Sci Rep"},{"key":"ref18","doi-asserted-by":"publisher","first-page":"100554","DOI":"10.1016\/j.xagr.2025.100554","article-title":"Relation between epidural analgesia and perinatal outcomes on Robson class 2a","volume":"6","author":"Lecis","year":"2025","journal-title":"AJOG Glob Rep"},{"key":"ref19","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1186\/s12871-018-0638-x","article-title":"Development and validation of a predictive risk factor model for epidural re-siting in women undergoing labour epidural analgesia: a retrospective cohort study","volume":"18","author":"Lee","year":"2018","journal-title":"BMC Anesthesiol"}],"container-title":["Frontiers in 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