论文标题
妊娠护理的时间事件检测器（TED-PC）：一种基于规则的算法，可从有或没有COVID-19
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
论文作者
论文摘要
目的：开发一种基于规则的算法，该算法通过从国家Covid Cohort合作公司（N3C）的电子健康记录（EHR）中推断出妊娠周的妇女（EHR）来检测COVID-19怀孕期间临床事件的时间信息。材料和方法：通过观察医学结果伙伴关系（OMOP）临床数据模型（CDM）将EHR标准化。 EHR表型导致270,897名孕妇（2018-06-01至2021-05-31）。我们开发了一种基于规则的算法，并进行了多层评估，以测试算法的内容有效性和临床有效性。妊娠<150或> 300天的个体的极值分析。结果：该算法在270,897名孕妇中鉴定出296,194例怀孕（16,659 Covid-19174和744，没有Covid-19-19）。对于推断胎龄，95％的病例（n = 40）具有中等最高的精度（Cohen Kappa = 0.62）； 100％病例（n = 40）具有时间信息中等高度粒度（Cohen Kappa = 1）。对于推断交付日期，精度为100％（Cohen Kappa = 1）。妊娠期妊娠期的妊娠年龄检测准确性为93.3％（Cohen Kappa = 1）。与肥胖症患者（35.1％vs. 29.5％），糖尿病（17.8％vs. 17.0％），慢性阻塞性肺疾病（COPD）（0.2％vs. 0.1％），呼吸窘迫综合征（ARDS）（ARDS）（1.8％vs。0.2％），患有肥胖症患病率更高（35.1％vs.29.5％），糖尿病（17.8％vs.17.0％），肥胖症患病率更高（17.8％vs. 17.0％）（1.8％vs.0.2％），患病率较高，患病率较高。讨论：我们通过我们的算法：第一个从完整的产前护理中推断时间信息的算法来探索孕妇的特征，并检测使用N3C的孕妇SARS-COV-2感染的时机。结论：该算法在推断胎龄和递送日期方面具有良好的有效性，该算法支持国家EHR队列对N3C研究Covid-19对妊娠的影响。
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.
