Mazouni Chafika, Rouzier Roman, Collette Emmanuelle, Menard Jean-Pierre, Magnin Georges, Gamerre Marc, Deter Russell
Department of Obstetrics and Gynecology, Conception Hospital, Marseille, France.
Acta Obstet Gynecol Scand. 2008;87(5):518-23. doi: 10.1080/00016340802012254.
To develop and validate a nomogram that predicts individual probability of cesarean delivery in cases of macrosomia (>4,000 g).
The nomogram was built based on the data from 246 patients who delivered macrosomic infants at Conception Hospital (Marseille, France), and was validated on an external population of 206 patients. Logistic regression was used to construct a model to predict the probability of cesarean section. The calculations were based on actual birth weight.
The accuracy of the model was evaluated by area under the receiver operator curve.
In the multivariate analysis performed on the training set, maternal age (p=0.002), parity (p=0.003), and maternal height <1.65 m (p=0.01) were found to be significantly associated with the occurrence of cesarean delivery and included in the nomogram. The final variables included in the nomogram were: age (p=0.01), maternal height (p=0.02), parity (p<0.001), and previous cesarean section (p=0.009). Area under the ROCs was 0.80 and 0.78 in the training set before and after bootstrapping, respectively, and 0.88 in the validation set. The calibration of the nomogram was good.
We have developed a nomogram based on actual birth weight that accurately predicts the risk of cesarean delivery in cases of macrosomia. This tool might be useful for decision-making.
开发并验证一种列线图，用于预测巨大儿（>4000g）剖宫产的个体概率。
该列线图基于法国马赛受孕医院246例分娩巨大儿患者的数据构建，并在206例外部患者群体中进行验证。采用逻辑回归构建预测剖宫产概率的模型。计算基于实际出生体重。
通过受试者操作特征曲线下面积评估模型的准确性。
在对训练集进行的多变量分析中，发现产妇年龄（p = 0.002）、产次（p = 0.003）和产妇身高<1.65m（p = 0.01）与剖宫产的发生显著相关，并纳入列线图。列线图最终纳入的变量为：年龄（p = 0.01）、产妇身高（p = 0.02）、产次（p<0.001）和既往剖宫产史（p = 0.009）。训练集在自展前后的ROC曲线下面积分别为0.80和0.78，验证集为0.88。列线图的校准良好。
我们基于实际出生体重开发了一种列线图，可准确预测巨大儿剖宫产风险。该工具可能有助于决策。
