Construction of a Nomogram Prediction Model for Intraoperative Shivering During Caesarean Section.

Abstract

Objective: To explore the risk factors of intraoperative shivering in cesarean section patients, construct a prediction model and evaluate its performance.

Methods: Clinical data of 260 patients undergoing cesarean section from March 2024 to January 2025 were collected, with intraoperative shivering as the primary outcome. Univariate and multivariable logistic regression analyses were performed to identify statistically significant independent risk factors. A risk prediction model was subsequently developed and visualized as a nomogram. The model's discriminative ability, calibration, and clinical utility were evaluated.

Results: The incidence of intraoperative shivering was 32.69%. Multivariable logistic regression analysis revealed that body mass index (BMI), baseline body temperature, American Society of Anesthesiologists (ASA) classification, intraoperative fluid infusion volume, and intraoperative blood loss were independent risk factors for intraoperative shivering (P < 0.05). The area under the curve (AUC) was 0.914, with a sensitivity of 0.894, specificity of 0.823, and Youden index of 0.717, indicating good discriminative ability. The Hosmer-Lemeshow test demonstrated good calibration (χ² = 3.061, P = 0.930). Decision Curve Analysis (DCA) indicated favorable clinical applicability.

Conclusion: The nomogram model demonstrates good predictive performance, assisting clinicians in identifying high-risk parturients prone to intraoperative shivering during cesarean section. Early identification based on risk factors enables implementation of targeted interventions, thereby potentially reducing the incidence and adverse impacts of shivering. This improves maternal intraoperative comfort and perioperative outcomes.