Development and validation of machine learning models for predicting post-cesarean pain and individualized pain management strategies: a multicenter study
Abstract Background Effective management of postoperative pain remains a significant challenge in obstetric care due to the variability in pain perception and response influenced by physical, medical, and psychosocial factors. Current standardized pain management protocols often fail to accommodate...
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| Format: | Article |
| Language: | English |
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BMC
2025-04-01
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| Series: | BMC Anesthesiology |
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| Online Access: | https://doi.org/10.1186/s12871-025-03034-w |
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| author | Shenjuan Lv Ning Sun Chunhui Hao Junqing Li Yun Li |
| author_facet | Shenjuan Lv Ning Sun Chunhui Hao Junqing Li Yun Li |
| author_sort | Shenjuan Lv |
| collection | DOAJ |
| description | Abstract Background Effective management of postoperative pain remains a significant challenge in obstetric care due to the variability in pain perception and response influenced by physical, medical, and psychosocial factors. Current standardized pain management protocols often fail to accommodate this variability, necessitating more tailored approaches. Objective This study aims to improve postoperative pain management following cesarean sections by developing personalized protocols using machine learning (ML) models. Method The study analyzed the efficacy of eight ML models, including XGBoost, Random Forest, and Neural Networks, using data from two distinct hospital cohorts. Performance metrics such as Root Mean Squared Error (RMSE) and Coefficient of Determination (R²) were evaluated through internal and external validations. SHAP value analysis was used to identify key predictors influencing pain management outcomes. Results The XGBoost model demonstrated superior performance, achieving the lowest RMSE and highest R². Key factors impacting pain management included esketamine use, anesthesia method, and anesthetic drug type, with esketamine significantly delaying the first activation of patient-controlled intravenous analgesia (PCIA). Conclusions The study highlights the potential of machine learning to refine postoperative pain management strategies in obstetric care, suggesting that personalized approaches, particularly incorporating esketamine and specific anesthesia methods, could enhance patient outcomes. Trial registration Not applicable. |
| format | Article |
| id | doaj-art-19eb56a4df464badb35df00ccab81427 |
| institution | OA Journals |
| issn | 1471-2253 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Anesthesiology |
| spelling | doaj-art-19eb56a4df464badb35df00ccab814272025-08-20T02:17:02ZengBMCBMC Anesthesiology1471-22532025-04-012511910.1186/s12871-025-03034-wDevelopment and validation of machine learning models for predicting post-cesarean pain and individualized pain management strategies: a multicenter studyShenjuan Lv0Ning Sun1Chunhui Hao2Junqing Li3Yun Li4Department of Anesthesiology, Jinan Second Maternal and Child Health HospitalUltrasound Department, Jinan Second Maternal and Child Health HospitalDepartment of Anesthesiology, Jinan Second Maternal and Child Health HospitalUltrasound Department, Jinan Second Maternal and Child Health HospitalDepartment of Pain Management, Provincial Hospital Affiliated to Shandong First Medical UniversityAbstract Background Effective management of postoperative pain remains a significant challenge in obstetric care due to the variability in pain perception and response influenced by physical, medical, and psychosocial factors. Current standardized pain management protocols often fail to accommodate this variability, necessitating more tailored approaches. Objective This study aims to improve postoperative pain management following cesarean sections by developing personalized protocols using machine learning (ML) models. Method The study analyzed the efficacy of eight ML models, including XGBoost, Random Forest, and Neural Networks, using data from two distinct hospital cohorts. Performance metrics such as Root Mean Squared Error (RMSE) and Coefficient of Determination (R²) were evaluated through internal and external validations. SHAP value analysis was used to identify key predictors influencing pain management outcomes. Results The XGBoost model demonstrated superior performance, achieving the lowest RMSE and highest R². Key factors impacting pain management included esketamine use, anesthesia method, and anesthetic drug type, with esketamine significantly delaying the first activation of patient-controlled intravenous analgesia (PCIA). Conclusions The study highlights the potential of machine learning to refine postoperative pain management strategies in obstetric care, suggesting that personalized approaches, particularly incorporating esketamine and specific anesthesia methods, could enhance patient outcomes. Trial registration Not applicable.https://doi.org/10.1186/s12871-025-03034-wMachine learningPain managementPost-cesarean painSHAP valuesEsketamine |
| spellingShingle | Shenjuan Lv Ning Sun Chunhui Hao Junqing Li Yun Li Development and validation of machine learning models for predicting post-cesarean pain and individualized pain management strategies: a multicenter study BMC Anesthesiology Machine learning Pain management Post-cesarean pain SHAP values Esketamine |
| title | Development and validation of machine learning models for predicting post-cesarean pain and individualized pain management strategies: a multicenter study |
| title_full | Development and validation of machine learning models for predicting post-cesarean pain and individualized pain management strategies: a multicenter study |
| title_fullStr | Development and validation of machine learning models for predicting post-cesarean pain and individualized pain management strategies: a multicenter study |
| title_full_unstemmed | Development and validation of machine learning models for predicting post-cesarean pain and individualized pain management strategies: a multicenter study |
| title_short | Development and validation of machine learning models for predicting post-cesarean pain and individualized pain management strategies: a multicenter study |
| title_sort | development and validation of machine learning models for predicting post cesarean pain and individualized pain management strategies a multicenter study |
| topic | Machine learning Pain management Post-cesarean pain SHAP values Esketamine |
| url | https://doi.org/10.1186/s12871-025-03034-w |
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