Development and validation of a prediction model for intrapartum fever related to chorioamnionitis in parturients undergoing epidural analgesia

Abstract

Intrapartum fever is a common complication in parturients undergoing epidural analgesia (EA), significantly increasing the incidence of maternal and infant complications. This study aims to develop and validate a prediction model for intrapartum fever related to chorioamnionitis (IFTC) in parturients undergoing epidural analgesia. A total of 596 parturients with fever (axillary temperature ≥ 38℃) who received EA from January 2020 to December 2023 were included and randomly assigned to the training set (N = 417) and the validation set (N = 179) according to the ratio of 7:3. The independent risk factors were screened by univariate and multivariate logistic regression analysis to develop a nomogram model. Decision curve analysis (DCA) was used to evaluate the clinical effectiveness and discrimination of the model; calibration curve was used to assess the accuracy of the model. Maximum temperature, meconium-stained amniotic fluid, C-reactive protein (CRP), gestational age and BMI were independent risk factors for predicting IFTC, and the area under receiver operating characteristic curve (AUC) of the training set and the validation set were 0.744 (0.691-0.796) and 0.793 (0.714-0.872), respectively. The calibration curve showed good consistency between predicted and actual results. DCA curve showed that the model had clinical value throughout a broad threshold probability range. The nomogram prediction model based on CRP, meconium-stained amniotic fluid, maximum temperature, gestational age and BMI has good predictive performance for the risk of IFTC in EA parturients.

Keywords: Analgesia; Chorioamnionitis; Epidural; Fever; Labor pain; Prediction.

Fig. 1

Participant recruitment flowchart.

Fig. 2

Nomogram prediction model for intrapartum fever associated with chorioamnionitis during epidural labor analgesia; Each patient’s risk factor is located on a variable axis, and a vertical line is drawn upward to determine the number of points each risk factor has received. By summing the scores of multiple selected variables, a vertical line is drawn downward to determine their corresponding risk probability.

Fig. 3

ROC curve of the prediction model. (A) indicates the ROC curve of the training set, and (B) indicates the ROC curve of the validation set. The x-axis represents 1-specificity, and the y-axis represents sensitivity. The part below the curve is the AUC of the model. Based on the ROC curve, the following thresholds are selected to define increased indicators for identifying IFTC: BMI ≥ 27.82 kg/m2, Maximum body temperature  ≥ 38.45 °C, Gestational age ≥ 39.93 w, CRP ≥ 5.54 mg/L.

Fig. 4

Decision curve of the nomogram in the training set (A) and the validation set (B). The Y-axis represents a net benefit, and the X-axis represents threshold probability.

Fig. 5

Calibration curve of the nomogram in the training set (A) and validation set (B).The horizontal axis is the predicted value in the nomogram, and the vertical axis is the observed value.