Thangaratinam Shakila, Allotey John, Marlin Nadine, Mol Ben W, Von Dadelszen Peter, Ganzevoort Wessel, Akkermans Joost, Ahmed Asif, Daniels Jane, Deeks Jon, Ismail Khaled, Barnard Ann Marie, Dodds Julie, Kerry Sally, Moons Carl, Riley Richard D, Khan Khalid S
Women's Health Research Unit, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
Multidisciplinary Evidence Synthesis Hub (MESH), Queen Mary University of London, London, UK.
Health Technol Assess. 2017 Apr;21(18):1-100. doi: 10.3310/hta21180.
The prognosis of early-onset pre-eclampsia (before 34 weeks' gestation) is variable. Accurate prediction of complications is required to plan appropriate management in high-risk women.
To develop and validate prediction models for outcomes in early-onset pre-eclampsia.
Prospective cohort for model development, with validation in two external data sets.
Model development: 53 obstetric units in the UK. Model transportability: PIERS (Pre-eclampsia Integrated Estimate of RiSk for mothers) and PETRA (Pre-Eclampsia TRial Amsterdam) studies.
Pregnant women with early-onset pre-eclampsia.
Nine hundred and forty-six women in the model development data set and 850 women (634 in PIERS, 216 in PETRA) in the transportability (external validation) data sets.
The predictors were identified from systematic reviews of tests to predict complications in pre-eclampsia and were prioritised by Delphi survey.
The primary outcome was the composite of adverse maternal outcomes established using Delphi surveys. The secondary outcome was the composite of fetal and neonatal complications.
We developed two prediction models: a logistic regression model (PREP-L) to assess the overall risk of any maternal outcome until postnatal discharge and a survival analysis model (PREP-S) to obtain individual risk estimates at daily intervals from diagnosis until 34 weeks. Shrinkage was used to adjust for overoptimism of predictor effects. For internal validation (of the full models in the development data) and external validation (of the reduced models in the transportability data), we computed the ability of the models to discriminate between those with and without poor outcomes (-statistic), and the agreement between predicted and observed risk (calibration slope).
The PREP-L model included maternal age, gestational age at diagnosis, medical history, systolic blood pressure, urine protein-to-creatinine ratio, platelet count, serum urea concentration, oxygen saturation, baseline treatment with antihypertensive drugs and administration of magnesium sulphate. The PREP-S model additionally included exaggerated tendon reflexes and serum alanine aminotransaminase and creatinine concentration. Both models showed good discrimination for maternal complications, with anoptimism-adjusted -statistic of 0.82 [95% confidence interval (CI) 0.80 to 0.84] for PREP-L and 0.75 (95% CI 0.73 to 0.78) for the PREP-S model in the internal validation. External validation of the reduced PREP-L model showed good performance with a -statistic of 0.81 (95% CI 0.77 to 0.85) in PIERS and 0.75 (95% CI 0.64 to 0.86) in PETRA cohorts for maternal complications, and calibrated well with slopes of 0.93 (95% CI 0.72 to 1.10) and 0.90 (95% CI 0.48 to 1.32), respectively. In the PIERS data set, the reduced PREP-S model had a -statistic of 0.71 (95% CI 0.67 to 0.75) and a calibration slope of 0.67 (95% CI 0.56 to 0.79). Low gestational age at diagnosis, high urine protein-to-creatinine ratio, increased serum urea concentration, treatment with antihypertensive drugs, magnesium sulphate, abnormal uterine artery Doppler scan findings and estimated fetal weight below the 10th centile were associated with fetal complications.
The PREP-L model provided individualised risk estimates in early-onset pre-eclampsia to plan management of high- or low-risk individuals. The PREP-S model has the potential to be used as a triage tool for risk assessment. The impacts of the model use on outcomes need further evaluation.
Current Controlled Trials ISRCTN40384046.
The National Institute for Health Research Health Technology Assessment programme.
早发型子痫前期（妊娠34周前）的预后存在差异。准确预测并发症对于为高危女性制定适当的管理方案至关重要。
开发并验证早发型子痫前期结局的预测模型。
用于模型开发的前瞻性队列研究，并在两个外部数据集进行验证。
模型开发：英国53个产科单位。模型可移植性：PIERS（母亲子痫前期综合风险评估）和PETRA（阿姆斯特丹子痫前期试验）研究。
早发型子痫前期孕妇。
模型开发数据集中有946名女性，可移植性（外部验证）数据集中有850名女性（PIERS中634名，PETRA中216名）。
预测因素通过对预测子痫前期并发症的检测进行系统评价确定，并经德尔菲调查进行优先排序。
主要结局是通过德尔菲调查确定的不良母亲结局的综合指标。次要结局是胎儿和新生儿并发症的综合指标。
我们开发了两个预测模型：一个逻辑回归模型（PREP-L），用于评估直至产后出院时任何母亲结局的总体风险；一个生存分析模型（PREP-S），用于从诊断到34周期间按日间隔获取个体风险估计值。采用收缩法调整预测因素效应的过度乐观估计。对于内部验证（开发数据中的完整模型）和外部验证（可移植性数据中的简化模型），我们计算模型区分有无不良结局者的能力（C统计量）以及预测风险与观察风险之间的一致性（校准斜率）。
PREP-L模型包括母亲年龄、诊断时的孕周、病史、收缩压、尿蛋白与肌酐比值、血小板计数、血清尿素浓度、血氧饱和度、抗高血压药物的基线治疗以及硫酸镁的使用。PREP-S模型还包括腱反射亢进以及血清丙氨酸氨基转移酶和肌酐浓度。两个模型对母亲并发症均显示出良好的区分能力，内部验证中PREP-L模型的乐观调整C统计量为0.82[95%置信区间（CI）0.80至0.84]，PREP-S模型为0.75（95%CI 0.73至0.78）。简化后的PREP-L模型在PIERS队列中外部验证显示出良好性能，母亲并发症的C统计量为0.81（95%CI 0.77至0.85），在PETRA队列中为0.75（95%CI 0.64至0.86），校准斜率分别为0.93（95%CI 0.72至1.10）和0.90（95%CI 0.48至1.32）。在PIERS数据集中，简化后的PREP-S模型的C统计量为0.71（95%CI 0.67至0.75），校准斜率为0.67（95%CI 0.56至0.79）。诊断时孕周小、尿蛋白与肌酐比值高、血清尿素浓度升高、抗高血压药物治疗、硫酸镁使用、子宫动脉多普勒扫描结果异常以及估计胎儿体重低于第10百分位数与胎儿并发症相关。
PREP-L模型为早发型子痫前期提供个体化风险估计，以规划高危或低危个体的管理。PREP-S模型有潜力用作风险评估的分诊工具。模型使用对结局的影响需要进一步评估。
Current Controlled Trials ISRCTN40384046。
英国国家卫生研究院卫生技术评估项目。
