Jiangsu Province Key Laboratory of Anesthesiology, Xuzhou Medical University, Xuzhou, Jiangsu, China.
Department of Anesthesiology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221000, Jiangsu, China.
BMC Pregnancy Childbirth. 2021 Jun 25;21(1):445. doi: 10.1186/s12884-021-03891-6.
To explore the risk factors for intrapartum fever and to develop a nomogram to predict the incidence of intrapartum fever.
The general demographic characteristics and perinatal factors of 696 parturients who underwent vaginal birth at the Affiliated Hospital of Xuzhou Medical University from May 2019 to April 2020 were retrospectively analysed. Data was collected from May 2019 to October 2019 on 487 pregnant women who formed a training cohort. A multivariate logistic regression model was used to identify the independent risk factors associated with intrapartum fever during vaginal birth, and a nomogram was developed to predict the occurrence. To verify the nomogram, data was collected from January 2020 to April in 2020 from 209 pregnant women who formed a validation cohort.
The incidence of intrapartum fever in the training cohort was found in 72 of the 487 parturients (14.8%), and the incidence of intrapartum fever in the validation cohort was 31 of the 209 parturients (14.8%). Multivariate logistic regression analysis showed that the following factors were significantly related to intrapartum fever: primiparas (odds ratio [OR] 2.43; 95% confidence interval [CI] 1.15-5.15), epidural labour analgesia (OR 2.89; 95% CI 1.23-6.82), premature rupture of membranes (OR 2.37; 95% CI 1.13-4.95), second stage of labour ≥ 120 min (OR 4.36; 95% CI 1.42-13.41), amniotic fluid pollution degree III (OR 10.39; 95% CI 3.30-32.73), and foetal weight ≥ 4000 g (OR 7.49; 95% CI 2.12-26.54). Based on clinical experience and previous studies, the duration of epidural labour analgesia also appeared to be a meaningful factor for intrapartum fever; therefore, these seven variables were used to develop a nomogram to predict intrapartum fever in parturients. The nomogram achieved a good area under the ROC curve of 0.86 and 0.81 in the training and in the validation cohorts, respectively. Additionally, the nomogram had a well-fitted calibration curve, which also showed excellent diagnostic performance.
We constructed a model to predict the occurrence of fever during childbirth and developed an accessible nomogram to help doctors assess the risk of fever during childbirth. Such assessment may be helpful in implementing reasonable treatment measures.
Clinical Trial Registration: ( www.chictr.org.cn ChiCTR2000035593 ).
探讨分娩期发热的危险因素，并建立预测分娩期发热发生率的列线图。
回顾性分析 2019 年 5 月至 2020 年 4 月在徐州医科大学附属医院行阴道分娩的 696 例产妇的一般人口统计学特征和围产因素。数据于 2019 年 5 月至 2019 年 10 月收集自 487 名形成训练队列的孕妇。采用多变量 logistic 回归模型确定与阴道分娩期间发热相关的独立危险因素，并建立预测模型以预测其发生。为了验证预测模型，2020 年 1 月至 4 月收集了 209 名形成验证队列的孕妇的数据。
训练队列中 487 名产妇中有 72 名（14.8%）发生分娩期发热，验证队列中 209 名产妇中有 31 名（14.8%）发生分娩期发热。多变量 logistic 回归分析显示，以下因素与分娩期发热显著相关：初产妇（比值比[OR]2.43；95%置信区间[CI]1.15-5.15）、硬膜外分娩镇痛（OR 2.89；95%CI 1.23-6.82）、胎膜早破（OR 2.37；95%CI 1.13-4.95）、第二产程≥120 分钟（OR 4.36；95%CI 1.42-13.41）、羊水污染程度Ⅲ级（OR 10.39；95%CI 3.30-32.73）和胎儿体重≥4000 g（OR 7.49；95%CI 2.12-26.54）。基于临床经验和既往研究，硬膜外分娩镇痛的持续时间似乎也是分娩期发热的一个有意义的因素；因此，将这七个变量用于开发预测产妇分娩期发热的列线图。该列线图在训练组和验证组的 ROC 曲线下面积分别达到 0.86 和 0.81，表现出良好的预测效果。此外，该列线图的校准曲线拟合良好，也表现出良好的诊断性能。
我们构建了一个预测分娩期发热发生的模型，并开发了一个易于使用的列线图，以帮助医生评估分娩期发热的风险。这种评估可能有助于实施合理的治疗措施。
临床试验注册：（www.chictr.org.cn ChiCTR2000035593）。
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