Abstract：Objective: To establish the individual nomograms for predicting the risk of GBS infection model by analysis of risk factors related to the infection of Streptococcus lactis (GBS) in pregnant women in late pregnancy. Methods: From Jan 2017 to June 2017, 694 late pregnancy women were enrolled in this study. Their clinical data were analyzed and logistic regression model was used to determine the risk factors related to GBS infection. Then the R software was used to establish a predictive nomogram model of GBS infection. Bootstrap method was used to validate the nomogram model and ROC curve was used to explore the predictive efficacy of the model in predicting the GBS infection. Results: A total of 68 cases out of 694 cases had GBS infection. The prevalence rate was 9.80%. Age (OR=2.79,95CI%:1.45-5.39), overweight before pregnancy/obesity (R=2.50,95CI%:1.11-5.66), vaginitis (OR=3.71, 95CI%:1.88-7.32), urinary tract infection (OR=2.68, 95CI%:1.25-5.78), and pregnancy with diabetes mellitus (OR=3.14, 95CI%:1.39-7.09) were all independent risk factors for GBS infection (P<0.05). The nomogram model had a C-index of 0.816 with good discrimination and accuracy, and ROC curve showed that the area under the curve (AUC) for the nomogram model predicting GBS infection was 0.832 (95%CI: 0.757-0.899). Conclusion: The nomogram built based on age, overweight before pregnancy/obesity, vaginitis, urinary tract infection, and pregnancy with diabetes mellitus has a good discrimination and accuracy which could be helpful for screening the pregnancy women with high risk and setting the interventional plans.
曾宪琴, 王丽俊, 鲁炳怀. 个体化预测妊娠晚期孕妇无乳链球菌感染风险的列线图模型的建立[J]. 河北医学, 2018, 24(2): 296-301.
ZENG Xianqin, WANG Lijun, LU Binghuai. The Establishment of a Nomogram Model of no Streptococcus Infection Risk of Individual Prediction of Pregnant Women in Late Pregnancy. HeBei Med, 2018, 24(2): 296-301.
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2018, Vol. 24 
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