摘要
目的建立及验证妊娠期高血压疾病(hypertensive disorders of pregnancy, HDP)不良结局的预测模型。方法回顾性分析2011年5月1日至2019年4月30日于苏州大学附属第一医院和四川省妇幼保健院分娩的HDP患者资料。根据入院48 h内是否发生不良结局, 对HDP患者进行分组(不良结局组与非不良结局组)。应用单因素分析、套索算法(least absolute shrinkage and selection operator, LASSO)和多因素logistic回归分析筛选不良结局的影响因素, 并建立预测模型。采用受试者工作特征(receiver operating characteristic, ROC)曲线下面积(area under the curve, AUC)及校准图等评估预测效能。利用bootstrap重复取样法进行内部验证。建立模型后, 回顾性收集2019年5月1日至2020年4月30日在苏州大学附属第一医院分娩的HDP患者相关资料, 对模型进行外部验证。利用R语言构建列线图。结果 (1)纳入2 978例HDP患者进行建模, 其中不良结局组356例(12.0%);纳入233例HDP患者进行外部验证, 其中40例(17.2%)在入院48 h内发生不良结局。(2)根据LASSO及多因素logistic回归分析, 筛选出预测HDP患者发生不良结局的最优预测因素, 包括入院孕周、是否正规产前检查、症状数目、平均动脉压、血小板计数、纤维蛋白原、血清白蛋白、尿素及肌酐指标。据此建立logistic预测模型。(3)该模型的AUC为0.878(95%CI:0.858~0.897), 界值为0.136, 此时灵敏度为0.778(95%CI:0.731~0.820), 特异度为0.848(95%CI:0.834~0.862)。Hosmer-Lemeshow检验显示P>0.05, 校准图的校准斜率为1, 截距为0。(4)内部验证结果表明模型有较好的一致性。外部验证的AUC为0.872(95%CI:0.807~0.937)。Hosmer-Lemeshow检验的P>0.05, 绘制的校准曲线显示斜率为1.001。(5)同时用R语言构建了该模型的列线图。结论初步建立了预测HDP患者发生不良结局的预测模型。该模型具有一定准确度, 可以作为评估HDP相关并发症的量化工具。
Objective:To develop and validate a predictive model for adverse outcomes in women with hypertensive disorders of pregnancy(HDP).Methods:We retrospectively analyzed the clinical data of patients diagnosed with HDP and delivered at the First Affiliated Hospital of Soochow University or Sichuan Provincial Maternity and Child Health Care Hospital between May 1,2011,and April 30,2019.These patients were categorized as the adverse outcome group or the control group with adverse outcomes within 48 h after admission.Univariate analysis,least absolute shrinkage,selection operator(LASSO),and multivariable logistic regression were employed to analyze factors influencing the adverse outcomes and develop a predictive model.The receiver operating characteristic(ROC)curve and calibration plot was used to assess the predictive performance.Bootstrapping was used for the internal validation and the retrospective dataset of patients with HDP from the First Affiliated Hospital of Soochow University from May 1,2019,to April 30,2020,for the external validation.A graphic nomogram was created through R software based on the model.Results:(1)Of the 2978 HDP patients who were included in the development set,356 were in the adverse outcome group,accounting for 12.0%;of the 233 patients who were included in the external validation set,40 presented with adverse outcomes within 48 h after admission,accounting for 17.2%.(2)Nine optimal predictors were identified based on the LASSO regression analysis and multivariable logistic regression,consisting of gestational age on admission,routine prenatal care,number of symptoms,mean arterial pressure,platelet count,fibrinogen,albumin,serum urea,and serum creatinine,based on which the logistic predictive model was established.(3)The ROC curve for this predictive model achieved an area under the curve(AUC)of 0.878(95%CI:0.858-0.897),and the ideal cut-off value for predicted probability was 0.136,with a sensitivity of 0.778(95%CI:0.731-0.820)and specificity of 0.848(95%CI:0.834-0.862).The model was well-calibrated as the Hosmer-Lemeshow test showed that P>0.05.The calibration plot of the model had a slope of 1 and an intercept of 0.(4)The model showed good consistency in the internal validation and had an AUC of 0.872(95%CI:0.807-0.937)in the external validation.The Hosmer-Lemeshow test showed that the P value was>0.05,and the calibration slope was 1.001.(5)A nomogram was constructed for convenient clinical use.Conclusion:A relatively accurate prediction model for adverse outcomes in HDP patients was established,which could be used as a valuable quantitative tool for assessing HDP-related complications.
作者
孙芳璨
韩冰
陈友国
高岩
沈敏红
钟文
Sun Fangcan;Han Bing;Chen Youguo;Gao Yan;Shen Minhong;Zhong Wen(Department of Obstetrics and Gynecology,the First Affiliated Hospital of Soochow University,Suzhou 215006,China;Department of Obstetrics,Sichuan Provincial Maternity and Child Health Care Hospital,Chengdu 610045,China)
出处
《中华围产医学杂志》
CAS
CSCD
北大核心
2022年第3期169-178,共10页
Chinese Journal of Perinatal Medicine
基金
江苏省卫生健康委科研项目(H2019010)。
作者简介
通信作者:韩冰,Email:hanbing@suda.edu.cn,电话:0512-6797229;通信作者:高岩,Email:290475126@qq.com,电话:028-65978579。
