Safi Zeineb, Venugopal Neethu, Ali Haytham, Makhlouf Michel, Farooq Faisal, Boughorbel Sabri
Research Department, Sidra Medicine, Doha, Qatar.
Division of Neonatalogy, Sidra Medicine, Doha, Qatar.
BioData Min. 2022 Aug 17;15(1):17. doi: 10.1186/s13040-022-00298-7.
Preterm deliveries have many negative health implications on both mother and child. Identifying the population level factors that increase the risk of preterm deliveries is an important step in the direction of mitigating the impact and reducing the frequency of occurrence of preterm deliveries. The purpose of this work is to identify preterm delivery risk factors and their progression throughout the pregnancy from a large collection of Electronic Health Records (EHR).
The study cohort includes about 60,000 deliveries in the USA with the complete medical history from EHR for diagnoses, medications and procedures. We propose a temporal analysis of risk factors by estimating and comparing risk ratios and variable importance at different time points prior to the delivery event. We selected the following time points before delivery: 0, 12 and 24 week(s) of gestation. We did so by conducting a retrospective cohort study of patient history for a selected set of mothers who delivered preterm and a control group of mothers that delivered full-term. We analyzed the extracted data using logistic regression and random forests models. The results of our analyses showed that the highest risk ratio and variable importance corresponds to history of previous preterm delivery. Other risk factors were identified, some of which are consistent with those that are reported in the literature, others need further investigation.
The comparative analysis of the risk factors at different time points showed that risk factors in the early pregnancy related to patient history and chronic condition, while the risk factors in late pregnancy are specific to the current pregnancy. Our analysis unifies several previously reported studies on preterm risk factors. It also gives important insights on the changes of risk factors in the course of pregnancy. The code used for data analysis will be made available on github.
早产对母亲和孩子都有许多负面健康影响。识别增加早产风险的人群层面因素是减轻早产影响和降低其发生频率的重要一步。这项工作的目的是从大量电子健康记录（EHR）中识别早产风险因素及其在整个孕期的发展情况。
该研究队列包括美国约60000例分娩，这些分娩都有来自EHR的完整病史，包括诊断、用药和治疗程序。我们通过估计和比较分娩事件前不同时间点的风险比和变量重要性，对风险因素进行了时间分析。我们选择了分娩前的以下时间点：妊娠0周、12周和24周。我们通过对一组早产母亲和一组足月分娩母亲的病史进行回顾性队列研究来做到这一点。我们使用逻辑回归和随机森林模型分析提取的数据。我们的分析结果表明，最高风险比和变量重要性对应于既往早产史。还识别出了其他风险因素，其中一些与文献报道的一致，另一些则需要进一步研究。
对不同时间点风险因素的比较分析表明，孕早期的风险因素与患者病史和慢性病有关，而孕晚期的风险因素则特定于当前妊娠。我们的分析统一了此前几项关于早产风险因素的研究。它还对孕期风险因素的变化提供了重要见解。用于数据分析的代码将在github上提供。
