Construction and application evaluation of risk prediction model and nomogram for shivering during cesarean sectio

DOI: 10.12201/bmr.202501.00053

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  Abstract: Abstract: Objective To develop and validate a predictive model for the risk of shivering during cesarean section surgery. Methods This study selected women who underwent cesarean sections at our hospital from January 2023 to April 2023 as research subjects. The study compared the relevant influencing factors between patients who experienced intraoperative shivering (n=101) and those who did not (n=124). Various indicators were incorporated into the model construction, and the model was evaluated using metrics such as the Area Under the Curve (AUC) and ten-fold cross-validation. The receiver operating characteristic (ROC) curve and nomogram of the prediction model were also generated. Results Five factors were included in the final prediction model: history of diabetes mellitus, preoperative Simplified Acute Physiology Score (SAPS), anesthesia method, post-anesthesia hypothermia, and intraoperative warming measures. The area under the ROC curve for this model was 0.831 (P < 0.001), with an internal ten-fold cross-validation AUC of up to 0.934, indicating that the model exhibits excellent fitting and discrimination performance. Conclusion The developed model can effectively predict the risk of shivering during cesarean sections, providing valuable guidance for healthcare professionals to implement timely preventive measures for high-risk patients.

  Key words: Cesarean section; shivering; Prediction model; S-AI score

  Submit time: 19 January 2025

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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