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.
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.
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DOI：
10.1371/journal.pone.0276923
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发表时间：
2022
期刊：
影响因子：
3.7
通讯作者：
中科院分区：
文献类型：
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确定SARS-CoV-2病毒感染相对于特定孕周的时间对于描述病毒感染时间在不良妊娠结局中的作用至关重要。然而，当涉及到电子健康记录（EHR）时，这项任务很困难。为应对COVID-19疫情对孕产妇健康的影响，我们寻求开发并验证临床信息提取算法，以检测临床事件相对于孕周的时间。我们使用了来自国家COVID队列协作组织（N3 C）的EHR，其中EHR通过观察性医学成果伙伴关系（OMOP）公共数据模型（CDM）进行标准化。我们对270，897名孕妇进行了EHR表型分析（2018年6月1日至2021年5月31日）。我们开发了一种基于规则的算法，并进行了多层次的评估，以测试内容有效性和临床有效性，以及极端的妊娠时间（<150 or >300）。该算法在270，897名孕妇中确定了296，194例妊娠（16，659例COVID-19，174，744例无COVID-19）。对于推断胎龄，95%的病例（n = 40）具有中-高准确性（Cohen's Kappa = 0.62），100%的病例（n = 40）具有中-高粒度的时间信息（Cohen's Kappa = 1）。对于推断交货日期，准确度为100%（Cohen's Kappa = 1）。胎龄检测的准确性为93.3%（科恩的Kappa = 1）的极端长度的妊娠。患有COVID-19的母亲在肥胖或超重（35.1%对29.5%）、糖尿病（17.8%对17.0%）、慢性阻塞性肺疾病（0.2%对0.1%）、呼吸窘迫综合征或急性呼吸衰竭（1.8%对0.2%）方面的患病率较高。我们探索了不同孕周的SARS-CoV-2感染的孕妇的特征与我们的算法。TED-PC是第一个从EHR中推断出与每个临床事件相关的确切孕周，并检测出孕妇感染SARS-CoV-2的时间。该算法在推断胎龄和分娩日期方面表现出出色的临床有效性，这支持N3 C上的多个EHR队列研究COVID-19对妊娠的影响。
Identifying the time of SARS-CoV-2 viral infection relative to specific gestational weeks is critical for delineating the role of viral infection timing in adverse pregnancy outcomes. However, this task is difficult when it comes to Electronic Health Records (EHR). In combating the COVID-19 pandemic for maternal health, we sought to develop and validate a clinical information extraction algorithm to detect the time of clinical events relative to gestational weeks. We used EHR from the National COVID Cohort Collaborative (N3C), in which the EHR are normalized by the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). We performed EHR phenotyping, resulting in 270,897 pregnant women (June 1st, 2018 to May 31st, 2021). We developed a rule-based algorithm and performed a multi-level evaluation to test content validity and clinical validity, and extreme length of gestation (<150 or >300). The algorithm identified 296,194 pregnancies (16,659 COVID-19, 174,744 without COVID-19) in 270,897 pregnant women. For inferring gestational age, 95% cases (n = 40) have moderate-high accuracy (Cohen’s Kappa = 0.62); 100% cases (n = 40) have moderate-high granularity of temporal information (Cohen’s Kappa = 1). For inferring delivery dates, the accuracy is 100% (Cohen’s Kappa = 1). The accuracy of gestational age detection for the extreme length of gestation is 93.3% (Cohen’s Kappa = 1). Mothers with COVID-19 showed higher prevalence in obesity or overweight (35.1% vs. 29.5%), diabetes (17.8% vs. 17.0%), chronic obstructive pulmonary disease (0.2% vs. 0.1%), respiratory distress syndrome or acute respiratory failure (1.8% vs. 0.2%). We explored the characteristics of pregnant women by different gestational weeks of SARS-CoV-2 infection with our algorithm. TED-PC is the first to infer the exact gestational week linked with every clinical event from EHR and detect the timing of SARS-CoV-2 infection in pregnant women. The algorithm shows excellent clinical validity in inferring gestational age and delivery dates, which supports multiple EHR cohorts on N3C studying the impact of COVID-19 on pregnancy.
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影响因子：
3.3
作者：
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通讯作者：
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DOI：
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发表时间：
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DOI：
10.1146/annurev-biodatasci-080917-013315
发表时间：
2018-01-01
期刊：
ANNUAL REVIEW OF BIOMEDICAL DATA SCIENCE, VOL 1
影响因子：
--
作者：
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