Parkland Center for Clinical Innovation, 8435 Stemmons Fwy, Ste 1150, Dallas, TX 75247. Email:
Am J Manag Care. 2021 May 1;27(5):e145-e151. doi: 10.37765/ajmc.2021.88636.
To develop and prospectively validate a novel model incorporating claims and community-level socioeconomic data to predict preterm birth at scale among pregnant Medicaid women with no history of preterm birth (PTB).
A longitudinal Texas Medicaid cohort study, with 2-year retrospective model building (October 2015-October 2017) and a 1-year prospective model validation phase (January 2018-December 2018).
Inclusion criteria were females aged 11 to 55 years with at least 1 live singleton birth and no history of PTB. The primary outcome was live singleton birth earlier than 35 weeks. Covariates were medical/mental/behavioral comorbidities, obstetric history, sociodemographic characteristics, and health services utilization. Of multiple models built, the most parsimonious was selected to classify pregnancies as very high, high, medium, and low risk. Model performance was evaluated using positive predictive value (PPV), sensitivity, case identification ratio (1 / PPV), and timing of prediction.
The model was built on 6689 pregnancies and validated on 7855 pregnancies. PTB rate earlier than 35 weeks was approximately 3.3%. Significant risk predictors included prenatal visit attendance, insurance gap days, and medical/obstetrical comorbidities. Model PPV was approximately 4-fold higher for very high-risk women (14.7%) vs cohort (3.3%) and so was the case identification ratio (1:7 vs 1:30, respectively). Sensitivity was good, with 57% of PTBs classified as medium risk or higher. Timing of prediction was clinically relevant, with more than 80% of PTBs risk stratified before 24 weeks.
We report a novel PTB prediction model among pregnant Medicaid women without PTB history, which is timely, accurate, practical, and scalable. We leverage Medicaid and community data readily accessible by Medicaid plans to support population-level interventions to prevent PTBs.
开发并前瞻性验证一种新模型，该模型结合了索赔和社区层面的社会经济数据，以预测在没有早产史的怀孕 Medicaid 女性中大规模早产的情况。
这是一项德克萨斯州 Medicaid 队列的纵向研究，有 2 年的回顾性模型构建（2015 年 10 月至 2017 年 10 月）和 1 年的前瞻性模型验证阶段（2018 年 1 月至 2018 年 12 月）。
纳入标准为年龄在 11 至 55 岁之间、至少有 1 次活产单胎且无早产史的女性。主要结局是早于 35 周的活产单胎分娩。协变量包括医疗/精神/行为合并症、产科史、社会人口统计学特征和卫生服务利用情况。在构建的多个模型中，选择最简约的模型来对妊娠进行分类，分为极高、高、中、低风险。使用阳性预测值（PPV）、灵敏度、病例识别率（1/PPV）和预测时间来评估模型性能。
该模型构建于 6689 例妊娠中，并在 7855 例妊娠中进行了验证。早于 35 周的早产率约为 3.3%。显著的风险预测因素包括产前检查就诊情况、保险缺口天数和医疗/产科合并症。极高风险女性的模型 PPV 约为 4 倍（14.7%）高于队列（3.3%），病例识别率也相应提高（分别为 1:7 和 1:30）。灵敏度较好，57%的早产被归类为中高危。预测时间具有临床意义，80%以上的早产风险在 24 周前分层。
我们报告了一项针对没有早产史的怀孕 Medicaid 女性的新型早产预测模型，该模型及时、准确、实用且可扩展。我们利用 Medicaid 和社区数据，这些数据很容易被 Medicaid 计划获取，以支持针对预防早产的人群干预措施。
