Schor Jonathan S, Kadambi Adesh, Fulcher Isabel, Venkatesh Kartik K, Clapp Mark A, Ebrahim Senan, Ebrahim Ali, Wen Timothy
Delfina Care Inc, San Francisco, CA, USA (Schor, Kadambi, Fulcher, Venkatesh, Clapp, Ebrahim, Ebrahim and Wen).
University of California, San Francisco (UCSF) Medical Scientist Training Program, San Francisco, CA, USA (Schor).
AJOG Glob Rep. 2024 Aug 22;4(4):100386. doi: 10.1016/j.xagr.2024.100386. eCollection 2024 Nov.
Hypertensive disorders of pregnancy (HDP) are significant drivers of maternal and neonatal morbidity and mortality. Current management strategies include early identification and initiation of risk mitigating interventions facilitated by a rules-based checklist. Advanced analytic techniques, such as machine learning, can potentially offer improved and refined predictive capabilities.
To develop and internally validate a machine learning prediction model for hypertensive disorders of pregnancy (HDP) when initiating prenatal care.
We developed a prediction model using data from the prospective multisite cohort Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be (nuMoM2b) among low-risk individuals without a prior history of aspirin utilization for preeclampsia prevention. The primary outcome was the development of HDP. Random forest modeling was utilized to develop predictive models. Recursive feature elimination (RFE) was employed to create a reduced model for each outcome. Area under the curve (AUC), 95% confidence intervals (CI), and calibration curves were utilized to assess discrimination and accuracy. Sensitivity analyses were conducted to compare the sensitivity and specificity of the reduced model compared to existing risk factor-based algorithms.
Of 9,124 assessed low risk nulliparous individuals, 21% (n=1,927) developed HDP. The prediction model for HDP had satisfactory discrimination with an AUC of 0.73 (95% CI: 0.70, 0.75). After RFE, a parsimonious reduced model with 30 features was created with an AUC of 0.71 (95% CI: 0.68, 0.74). Variables included in the model after RFE included body mass index at the first study visit, pre-pregnancy weight, first trimester complete blood count results, and maximum systolic blood pressure at the first visit. Calibration curves for all models revealed relatively stable agreement between predicted and observed probabilities. Sensitivity analysis noted superior sensitivity (AUC 0.80 vs 0.65) and specificity (0.65 vs 0.53) of the model compared to traditional risk factor-based algorithms.
In cohort of low-risk nulliparous pregnant individuals, a prediction model may accurately predict HDP diagnosis at the time of initiating prenatal care and aid employment of close interval monitoring and prophylactic measures earlier in pregnancy.
妊娠期高血压疾病（HDP）是孕产妇和新生儿发病及死亡的重要原因。当前的管理策略包括通过基于规则的检查表促进早期识别并启动风险缓解干预措施。先进的分析技术，如机器学习，可能会提供改进和精细的预测能力。
开发并内部验证一种在开始产前护理时用于预测妊娠期高血压疾病（HDP）的机器学习预测模型。
我们使用来自前瞻性多中心队列未生育妊娠结局研究：监测准妈妈（nuMoM2b）的数据，针对没有先兆子痫预防阿司匹林使用史的低风险个体开发了一个预测模型。主要结局是HDP的发生。采用随机森林建模来开发预测模型。使用递归特征消除（RFE）为每个结局创建一个简化模型。利用曲线下面积（AUC）、95%置信区间（CI）和校准曲线来评估区分度和准确性。进行敏感性分析以比较简化模型与现有基于风险因素的算法的敏感性和特异性。
在9124名评估的低风险未生育个体中，21%（n = 1927）发生了HDP。HDP的预测模型具有令人满意的区分度，AUC为0.73（95%CI：0.70，0.75）。经过RFE后，创建了一个具有30个特征的简约简化模型，AUC为0.71（95%CI：0.68，0.74）。RFE后模型中包含的变量包括首次研究访视时的体重指数、孕前体重、孕早期全血细胞计数结果以及首次访视时的最高收缩压。所有模型的校准曲线显示预测概率和观察概率之间具有相对稳定的一致性。敏感性分析指出，与传统的基于风险因素的算法相比，该模型具有更高的敏感性（AUC 0.80对0.65）和特异性（0.65对0.53）。
在低风险未生育孕妇队列中，一个预测模型可以在开始产前护理时准确预测HDP诊断，并有助于在妊娠早期更早地进行密切间隔监测和采取预防措施。
