基于人口学及临床特征的妊娠期糖尿病预测模型的研究
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摘要
目的:探讨基于孕妇人口学及临床特征的预测模型对妊娠期糖尿病(GDM)发病风险的评估。方法:收集并筛选2017年1月1日至2017年12月31日定期产前检查并分娩的14307例孕妇。按一定的比例采用随机抽样的方式确立观察组和验证组,观察组包含了约80%的总样本量,验证组包含了约20%的总样本量。利用人口学特征、临床孕产史等数据,通过Logistic回归统计方法对观察组建立GDM预测模型,并用验证组进行模型验证,得到受试者工作特征曲线(ROC),计算曲线下面积(AUC),评估预测模型在临床的预测价值。结果:观察组共有11498例孕妇,其中1779例(15.5%)患有GDM。统计分析表明:既往GDM病史、年龄、糖尿病家族史、孕前体质量指数(BMI)、既往巨大儿分娩史和孕产次与GDM发生显著相关(P<0.05);而文化程度、职业、居住地等与GDM无明显关联(P>0.05);把显著相关的临床风险因素纳入预测模型,得到临床评分系统。验证组共有2809例妊娠妇女,404例(14.4%)患有GDM,通过评分系统后得到AUC为0.659。提示临床评分系统对GDM具有一定的预测价值。结论:调查备孕期或早孕期妇女的既往GDM病史、年龄、糖尿病家族史、孕前BMI、既往巨大儿分娩史和孕产次可以预测GDM的发病风险,并在尽早开始生活方式的干预,达到预防或减少GDM发生。
Objective:To create an early predictive model for GDM based on demographic and clinical characteristics.Methods:14307 cases of women who went through regular prenatal visit and delivery were recruited in a retrospective study from January 2017 to December 2017.These women were divided into the observation group(80%) and the validation group(20%) by random sampling at a certain ratio.Multivariable Logistic regression analysis was performed to develop a risk predictive model in the observation group using the data of demographic characteristics and clinical maternal history.The model was evaluated in the validation group with the receiver operating characteristic(ROC) to calculate the value of area under the curve(AUC).The predictive value of the model was assessed.Results:11498 women were involved in the observation group.1779 pregnant women(15.5%) were diagnosed with GDM.The occurrence of GDM was significantly correlated with previous GDM history,age,family history of diabetes mellitus,pre-pregnancy BMI,previous macrosomia history and maternal history.There was no correlation between the occurrence of GDM and education,profession and residence.The clinical scoring system was obtained by incorporating relevant clinical risk factors into the predictive model.2809 women were involved in the validation group,in which 404 cases(14.4%) were diagnosed with GDM.The AUC was calculated as 0.659 with the scoring system.It was suggested that the clinical scoring system had certain predictive value for GDM.Conclusions:GDM could be predicted by investigating the history of women before pregnancy or in the first trimester including previous GDM history,age,family history of diabetes mellitus,pre-pregnancy BMI,previous macrosomia history and maternal history.Interventions of lifestyle should be provided early to prevent or reduce the occurrence of GDM.
Objective:To create an early predictive model for GDM based on demographic and clinical characteristics.Methods:14307 cases of women who went through regular prenatal visit and delivery were recruited in a retrospective study from January 2017 to December 2017.These women were divided into the observation group(80%) and the validation group(20%) by random sampling at a certain ratio.Multivariable Logistic regression analysis was performed to develop a risk predictive model in the observation group using the data of demographic characteristics and clinical maternal history.The model was evaluated in the validation group with the receiver operating characteristic(ROC) to calculate the value of area under the curve(AUC).The predictive value of the model was assessed.Results:11498 women were involved in the observation group.1779 pregnant women(15.5%) were diagnosed with GDM.The occurrence of GDM was significantly correlated with previous GDM history,age,family history of diabetes mellitus,pre-pregnancy BMI,previous macrosomia history and maternal history.There was no correlation between the occurrence of GDM and education,profession and residence.The clinical scoring system was obtained by incorporating relevant clinical risk factors into the predictive model.2809 women were involved in the validation group,in which 404 cases(14.4%) were diagnosed with GDM.The AUC was calculated as 0.659 with the scoring system.It was suggested that the clinical scoring system had certain predictive value for GDM.Conclusions:GDM could be predicted by investigating the history of women before pregnancy or in the first trimester including previous GDM history,age,family history of diabetes mellitus,pre-pregnancy BMI,previous macrosomia history and maternal history.Interventions of lifestyle should be provided early to prevent or reduce the occurrence of GDM.
引文
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[14] 张眉花,杨慧霞,孙伟杰.糖尿病家族史与妊娠期糖代谢异常发病相关性的前瞻性对照研究[J].中国糖尿病杂志,2005,13(3):163-166.
[15] Jesmin S,Akter S,Akashi H,et al.Screening for gestational diabetes mellitus and its prevalence in Bangladesh[J].Diabetes Research and Clinical Practice,2014,103(1):57-62.
[16] Pintaudi B,Vieste GD,Corrado F,et al.Improvement of selective screening strategy for gestational diabetes through a more accurate definition of high-risk groups[J].European Journal of Endocrinology,2014,170(1):87-93.
[17] Nielsen KK,Damm P,Kapur A,et al.Risk factors for hyperglycaemia in pregnancy in tamil nadu,India[J].PLoS One,2016,11(3):1-18.
[18] Caliskan E,Kayikcioglu F,Ozturk N,et al.A population-based risk factor scoring will decrease unnecessary testing for the diagnosis of gestational diabetes mellitus[J].Acta Obstetricia et Gynecologica Scandinavica,2004,83(6):524-530.
[2] American Diabetes Association.Standards of medical care in diabetes-2014[J].Diabetes Care,2014,37(6):s14-s80.
[3] Yang H,Wei Y,Gao X,et al.Risk factors for gestational diabetes mellitus in Chinese women-a prospective study of 16 286 pregnant women in China[J].Diabetic Medicine,2009,26(11):1099-1104.
[4] Tutino GE,Tam WH,Yang X,et al.Diabetes and pregnancy:perspectives from Asia[J].Diabetic Medicine,2014,31(3):302-318.
[5] Syngelaki A,Bredaki FE,Vaikousi E,et al.Body mass index at 11-13 weeks′gestation and pregnancy complications[J].Fetal Diagnosis and Therapy,2011,30(4):250-265.
[6] Sweeting AN,Appelblom H,Ross GP,et al.First trimester prediction of gestational diabetes mellitus:a clinical model based on maternal demographic parameters[J].Diabetes Research and Clinical Practice,2017,127:44-50.
[7] Lamainde RM,Kwee A,Naaktgeboren CA,et al.External validation of prognostic models to predict risk of gestational diabetes mellitus in one Dutch cohort:prospective multicentre cohort study[J].BMJ,2016,354:i4338.
[8] 宋耕,薛聪颖,杨慧霞.妊娠期糖尿病再妊娠复发的预测与预防[J].中国实用内科杂志,2018,38(6):538-540.
[9] 姜艳,李光辉,刘晓巍.孕妇年龄及孕早期空腹血糖水平在预测妊娠期糖尿病发病中的临床价值研究[J].中国实用妇科与产科,2018,34(2):176-180.
[10] Shin D,Song WO.Prepregnancy body mass index is an independent risk factor for gestational hypertension,gestational diabetes,preterm labor,and small-and large-for-gestational-age infants[J].Journal of Maternal-Fetal and Neonatal Medicine,2015,28(14):1679-1686.
[11] Catalano PM,Norman JE.Obesity,insulin resistance,and pregnancy outcome[J].Reproduction,2010,140(3):365-371.
[12] 梁朝霞,杜蒙恺,陈丹青.高龄妇女再生育的产科合并症风险[J].中国计划生育和妇产科,2014,6(7):10-13.
[13] Getahun D,Fassett MJ,Jacobsen SJ.Gestational diabetes:risk of recurrence in subsequent pregnancies[J].American Journal of Obstetrics and Gynecology,Elsevier Inc,2010,203(5):467.e1-e6.
[14] 张眉花,杨慧霞,孙伟杰.糖尿病家族史与妊娠期糖代谢异常发病相关性的前瞻性对照研究[J].中国糖尿病杂志,2005,13(3):163-166.
[15] Jesmin S,Akter S,Akashi H,et al.Screening for gestational diabetes mellitus and its prevalence in Bangladesh[J].Diabetes Research and Clinical Practice,2014,103(1):57-62.
[16] Pintaudi B,Vieste GD,Corrado F,et al.Improvement of selective screening strategy for gestational diabetes through a more accurate definition of high-risk groups[J].European Journal of Endocrinology,2014,170(1):87-93.
[17] Nielsen KK,Damm P,Kapur A,et al.Risk factors for hyperglycaemia in pregnancy in tamil nadu,India[J].PLoS One,2016,11(3):1-18.
[18] Caliskan E,Kayikcioglu F,Ozturk N,et al.A population-based risk factor scoring will decrease unnecessary testing for the diagnosis of gestational diabetes mellitus[J].Acta Obstetricia et Gynecologica Scandinavica,2004,83(6):524-530.
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