Frontiers in medicine · 2026
Development and validation of a risk nomogram for failed conversion from labor epidural analgesia to cesarean epidural anesthesia.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
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Ошибки и ограничения
История · было → стало
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Su Guangyuan, Cheng Xue, Jin Yanheng, Jing Yibing, Yu Lizhong, Sun Hong, Han Chao
Аннотация
PURPOSE: This study aimed to develop and validate a risk prediction nomogram for failed conversion from epidural labor analgesia to cesarean epidural anesthesia.
PATIENTS AND METHODS: Parturients undergoing non-immediate cesarean section after epidural labor analgesia between January 2022 and December 2024 across two centers were retrospectively analyzed (model-development cohort: 389; external validation cohort: 129). Potential risk factors were screened by univariate analysis in the training cohort, and six independent predictors were identified using multivariable logistic regression. A predictive model was then developed based on these predictors and validated using external data. Model performance was evaluated by discrimination (AUROC), calibration, and internal/external validation.
RESULTS: The conversion failure rate was 18.5% in the model-development cohort and 18.6% in the external validation cohort. Six predictors were incorporated into the nomogram: obstetric anesthesiologist involvement, 10-min post-analgesia VAS score, breakthrough pain, frequent rescue epidural top-ups, asymmetric sensory blockade, and programmed intermittent epidural bolus (PIEB) pump mode. The model showed good discrimination in both the training (AUROC 0.89, 95% CI 0.85-0.94) and external validation (AUROC 0.89, 95% CI 0.83-0.96) cohorts, with acceptable calibration.
CONCLUSION: We developed and externally validated a clinically interpretable nomogram for predicting failed labor-to-cesarean epidural conversion. Incorporating six routinely available variables, this tool may help convert early clinical signs of inadequate epidural function into a quantitative decision aid for anesthetic planning.
PATIENTS AND METHODS: Parturients undergoing non-immediate cesarean section after epidural labor analgesia between January 2022 and December 2024 across two centers were retrospectively analyzed (model-development cohort: 389; external validation cohort: 129). Potential risk factors were screened by univariate analysis in the training cohort, and six independent predictors were identified using multivariable logistic regression. A predictive model was then developed based on these predictors and validated using external data. Model performance was evaluated by discrimination (AUROC), calibration, and internal/external validation.
RESULTS: The conversion failure rate was 18.5% in the model-development cohort and 18.6% in the external validation cohort. Six predictors were incorporated into the nomogram: obstetric anesthesiologist involvement, 10-min post-analgesia VAS score, breakthrough pain, frequent rescue epidural top-ups, asymmetric sensory blockade, and programmed intermittent epidural bolus (PIEB) pump mode. The model showed good discrimination in both the training (AUROC 0.89, 95% CI 0.85-0.94) and external validation (AUROC 0.89, 95% CI 0.83-0.96) cohorts, with acceptable calibration.
CONCLUSION: We developed and externally validated a clinically interpretable nomogram for predicting failed labor-to-cesarean epidural conversion. Incorporating six routinely available variables, this tool may help convert early clinical signs of inadequate epidural function into a quantitative decision aid for anesthetic planning.