Adverse outcomes of hypertensive disorders of pregnancy: development and validation of a predictive model
10.3760/cma.j.cn113903-20210623-00575
- VernacularTitle:妊娠期高血压疾病不良结局预测模型的构建及验证
- Author:
Fangcan SUN
1
;
Bing HAN
;
Youguo CHEN
;
Yan GAO
;
Minhong SHEN
;
Wen ZHONG
Author Information
1. 苏州大学附属第一医院妇产科，苏州 215006
- Keywords:
Hypertension, pregnancy-induced;
Pregnancy outcome;
Logistic models;
Forecasting;
Nomograms
- From:
Chinese Journal of Perinatal Medicine
2022;25(3):169-178
- CountryChina
- Language:Chinese
-
Abstract:
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 2 978 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.
