Development and Validation of a Nomogram for the Failed Conversion of Labor Analgesia to Cesarean Section Anesthesia

Abstract

Purpose

The conversion of epidural labor analgesia (ELA) to epidural surgical anesthesia (ESA) for intrapartum cesarean section (CS) often encounters failures. This study aimed to develop a nomogram for predicting the failure rate of this conversion.

Patients and Methods

A retrospective analysis was conducted on data from the Fujian Maternity and Child Health Hospital. Pregnant women (n=214) who underwent cesarean section after receiving labor analgesia. We performed correlation heat map and Lasso regression in terms of exclusion confounding factors and screening independent variables. A nomogram was developed to predict the occurrence.

Results

The developed nomogram incorporated variables such as pregnant history, weight, premature rupture of membranes (PROM), dural puncture epidural (DPE), anesthesiologist level of cesarean section (ALOCS), and Anesthesiologist level of labor analgesia (ALOLA). The model demonstrated good predictive performance, providing a practical tool for assessing the risk of failure in converting labor analgesia to cesarean section anesthesia.

Conclusion

The nomogram can aid anesthesiologists in making informed decisions and optimizing patient care. By utilizing the nomogram, clinicians can estimate the probability of conversion failure based on individual patient characteristics and clinical factors.

Keywords:

• labor analgesia
• cesarean section anesthesia
• conversion failure
• nomogram
• predictive model

Acknowledgments

We would like to extend our deepest gratitude to the staff and management of the Fujian Maternity and Child Health Hospital for their unwavering support and for granting us access to the invaluable data that made this research possible. We are particularly thankful to the anesthesiologists and medical staff who provided insights and expertise that greatly assisted the research. Our appreciation also goes to the pregnant women who participated in this study, without whom this research would not have been feasible. We are indebted to our colleagues who provided constructive feedback during the various stages of this study, and to those who assisted in the statistical analysis, ensuring the robustness of our findings. Lastly, we would like to thank our families and friends for their continuous encouragement and understanding throughout the duration of this project. Their belief in the significance of our work has been a constant source of motivation.

Disclosure

The authors declare that there are no conflicts of interest regarding the publication of this paper. No financial or non-financial benefits have been received or will be received from any party related directly or indirectly to the subject of this article. All data and materials used in this research are publicly available and are referenced accordingly. The research was conducted with the highest standards of integrity, and all findings and conclusions are drawn from the data without any external influence.