Construction of a nomogram model for predicting infectious intrapartum fever.

Objectives: To investigate influencing factors of intrapartum fever during vaginal delivery and to construct a prediction model for infectious intrapartum fever.

Methods: A total of 444 patients with intrapartum fever admitted in Ningbo Women and Children's Hospital from January 2020 to December 2021 were enrolled. The clinical data and laboratory findings were compared between patients with infectious intrapartum fever and non-infectious intrapartum fever, and the factors associated with intrapartum fever were analyzed with a multivariate logistic regression model. A prediction nomogram model was constructed based on the factors of intrapartum fever and its predictive efficiency was evaluated by correction curve and receiver operator characteristic curve.

Results: In the 444 cases, 182 (41.0%) had definite intrauterine infection and 262 (59.0%) had no infectious intrapartum fever. Univariate analysis showed that the length of hospital stay before induced labor, the time of induced abortion, misoprostol administration, autoimmune diseases, white blood cell count (WBC) and hypersensitive C-reactive protein (hs-CRP) levels were significantly different between the two groups (all <0.05). Multivariate analysis showed that misoprostol administration and autoimmune diseases were protective factors (=0.31 and 0.36, both <0.05) for infectious intrapartum fever, while high WBC and hs-CRP were risk factors (=1.20 and 1.09, both <0.05). The area under the curve of nomogram model for predicting infectious intrapartum fever was 0.823, and the calibration curve validation showed that the predicted and measured values were in general agreement.

Conclusions: Multiple factors cause intrapartum fever. The nomogram model constructed in this study has good predictive accuracy for infectious intrapartum fever.