标题： 基于规则的算法用于产科护理中的时间事件检测(TED-PC)：从有和没有新冠肺炎的孕妇电子健康记录中推断妊娠年龄和分娩日期 显示英文标题

标题： Temporal Events Detector for Pregnancy Care (TED-PC): A Rule-based Algorithm to Infer Gestational Age and Delivery Date from Electronic Health Records of Pregnant Women with and without COVID-19

摘要： 目标：开发一个基于规则的算法，通过从国家冠状病毒队列协作（N3C）的电子健康记录（EHR）中推断妊娠周数和分娩日期，检测患有新冠肺炎的女性的临床事件的时间信息。 材料和方法： EHR 由观察性医疗结果伙伴关系（OMOP）临床数据模型（CDM）标准化。 EHR 表型分析得到了 270,897 名孕妇（2018-06-01 至 2021-05-31）。 我们开发了一个基于规则的算法，并进行了多级评估以测试算法的内容效度和临床效度；并对妊娠天数小于 150 或大于 300 天的个体进行了极端值分析。 结果：该算法在 270,897 名孕妇中识别出 296,194 次妊娠（16,659 例新冠肺炎病例，174 例和 744 例无新冠肺炎的疫情期病例）。 对于推断妊娠年龄，95% 的案例（n=40）具有中等至高准确性（Cohen Kappa = 0.62）；100% 的案例（n=40）具有中等至高时间信息粒度（Cohen Kappa = 1）。 对于推断分娩日期，准确性为 100%（Cohen Kappa = 1）。 极端妊娠长度的妊娠年龄检测准确性为 93.3%（Cohen Kappa = 1）。 感染新冠肺炎的母亲在肥胖（35.1% vs. 29.5%）、糖尿病（17.8% vs. 17.0%）、慢性阻塞性肺病（COPD）（0.2% vs. 0.1%）、呼吸窘迫综合征（ARDS）（1.8% vs. 0.2%）方面的患病率更高。 讨论：我们通过算法探索了不同时间点感染新冠肺炎的孕妇特征：首次从完整的产前护理中推断时间信息，并使用 N3C 检测孕妇感染 SARS-CoV-2 的时间。 结论：该算法在推断妊娠年龄和分娩日期方面表现出优异的有效性，这支持了 N3C 上的国家 EHR 队列研究新冠肺炎对妊娠的影响。 显示英文摘要

摘要： Objective: To develop a rule-based algorithm that detects temporal information of clinical events during pregnancy for women with COVID-19 by inferring gestational weeks and delivery dates from Electronic Health Records (EHR) from the National COVID Cohort Collaborate (N3C). Materials and Methods: The EHR are normalized by the Observational Medical Outcomes Partnership (OMOP) Clinical Data Model (CDM). EHR phenotyping resulted in 270,897 pregnant women (2018-06-01 to 2021-05-31). We developed a rule-based algorithm and performed a multi-level evaluation to test content validity and clinical validity of the algorithm; and extreme value analysis for individuals with <150 or >300 days of gestation. Results: The algorithm identified 296,194 pregnancies (16,659 COVID-19 174 and 744 without COVID-19 peri-pandemic) in 270,897 pregnant women. For inferring gestational age, 95% cases (n=40) have moderate-high accuracy (Cohen Kappa = 0.62); 100% cases (n=40) have moderate-high granularity of temporal information (Cohen Kappa = 1). For inferring delivery dates, the accuracy is 100% (Cohen Kappa = 1). Accuracy of gestational age detection for extreme length of gestation is 93.3% (Cohen Kappa = 1). Mothers with COVID-19 showed higher prevalence in obesity (35.1% vs. 29.5%), diabetes (17.8% vs. 17.0%), chronic obstructive pulmonary disease (COPD) (0.2% vs. 0.1%), respiratory distress syndrome (ARDS) (1.8% vs. 0.2%). Discussion: We explored the characteristics of pregnant women by different timing of COVID-19 with our algorithm: the first to infer temporal information from complete antenatal care and detect the timing of SARS-CoV-2 infection for pregnant women using N3C. Conclusion: The algorithm shows excellent validity in inferring gestational age and delivery dates, which supports national EHR cohorts on N3C studying the impact of COVID-19 on pregnancy.