Naleway Allison L, Crane Bradley, Irving Stephanie A, Bachman Don, Vesco Kimberly K, Daley Matthew F, Getahun Darios, Glenn Sungching C, Hambidge Simon J, Jackson Lisa A, Klein Nicola P, McCarthy Natalie L, McClure David L, Panagiotakopoulos Lakshmi, Panozzo Catherine A, Vazquez-Benitez Gabriela, Weintraub Eric S, Zerbo Ousseny, Kharbanda Elyse O
Center for Health Research, Kaiser Permanente Northwest, 3800 N. Interstate Ave, Portland, OR 97227, USA.
Center for Health Research, Kaiser Permanente Northwest, Portland, OR, USA.
Ther Adv Drug Saf. 2021 Jun 14;12:20420986211021233. doi: 10.1177/20420986211021233. eCollection 2021.
Identifying pregnancy episodes and accurately estimating their beginning and end dates are imperative for observational maternal vaccine safety studies using electronic health record (EHR) data.
We modified the Vaccine Safety Datalink (VSD) Pregnancy Episode Algorithm (PEA) to include both the International Classification of Disease, ninth revision (ICD-9 system) and ICD-10 diagnosis codes, incorporated additional gestational age data, and validated this enhanced algorithm with manual medical record review. We also developed the new Dynamic Pregnancy Algorithm (DPA) to identify pregnancy episodes in real time.
Around 75% of the pregnancy episodes identified by the enhanced VSD PEA were live births, 12% were spontaneous abortions (SABs), 10% were induced abortions (IABs), and 0.4% were stillbirths (SBs). Gestational age was identified for 99% of live births, 89% of SBs, 69% of SABs, and 42% of IABs. Agreement between the PEA-assigned and abstractor-identified pregnancy outcome and outcome date was 100% for live births, but was lower for pregnancy losses. When gestational age was available in the medical record, the agreement was higher for live births (97%), but lower for pregnancy losses (75%). The DPA demonstrated strong concordance with the PEA and identified pregnancy episodes ⩾6 months prior to the outcome date for 89% of live births.
The enhanced VSD PEA is a useful tool for identifying pregnancy episodes in EHR databases. The DPA improves the timeliness of pregnancy identification and can be used for near real-time maternal vaccine safety studies.
It is important to monitor of the safety of vaccines after they have been approved and licensed by the Food and Drug Administration, especially among women vaccinated during pregnancy. The Vaccine Safety Datalink (VSD) monitors vaccine safety through observational studies within large databases of electronic medical records. Since 2012, VSD researchers have used an algorithm called the Pregnancy Episode Algorithm (PEA) to identify the medical records of women who have been pregnant. Researchers then use these medical records to study whether receiving a particular vaccine is linked to any negative outcomes for the woman or her child. The goal of this study was to update and enhance the PEA to include the full set of medical record diagnostic codes [both from the older International Classification of Disease, ninth revision (ICD-9 system) and the newer ICD-10 system] and to incorporate additional sources of data about gestational age. To ensure the validity of the PEA following these enhancements, we manually reviewed medical records and compared the results with the algorithm. We also developed a new algorithm, the Dynamic Pregnancy Algorithm (DPA), to identify women earlier in pregnancy, allowing us to conduct more timely vaccine safety assessments. The new version of the PEA identified 2,485,410 pregnancies in the VSD database. The enhanced algorithm more precisely estimated the beginning of pregnancies, especially those that did not result in live births, due to the new sources of gestational age data. Our new algorithm, the DPA, was successful at identifying pregnancies earlier in gestation than the PEA. The enhanced PEA and the new DPA will allow us to better evaluate the safety of current and future vaccinations administered during or around the time of pregnancy.
对于利用电子健康记录（EHR）数据开展的观察性孕产妇疫苗安全性研究而言，识别妊娠事件并准确估计其开始和结束日期至关重要。
我们对疫苗安全数据链（VSD）妊娠事件算法（PEA）进行了修改，纳入了国际疾病分类第九版（ICD - 9系统）和ICD - 10诊断代码，整合了更多的孕周数据，并通过人工病历审查对这一增强算法进行了验证。我们还开发了新的动态妊娠算法（DPA）以实时识别妊娠事件。
增强后的VSD PEA识别出的妊娠事件中，约75%为活产，12%为自然流产（SAB），10%为人工流产（IAB），0.4%为死产（SB）。99%的活产、89%的死产、69%的自然流产和42%的人工流产确定了孕周。PEA指定的与提取人员确定的妊娠结局及结局日期之间的一致性，活产为100%，但妊娠丢失的一致性较低。当病历中有孕周信息时，活产的一致性更高（97%），但妊娠丢失的一致性较低（75%）。DPA与PEA显示出高度一致性，对于89%的活产，在结局日期前≥6个月识别出妊娠事件。
增强后的VSD PEA是在EHR数据库中识别妊娠事件的有用工具。DPA提高了妊娠识别的及时性，可用于近乎实时的孕产妇疫苗安全性研究。
在疫苗获得美国食品药品监督管理局批准和许可后，监测其安全性非常重要，尤其是在孕期接种疫苗的女性中。疫苗安全数据链（VSD）通过在大型电子病历数据库中开展观察性研究来监测疫苗安全性。自2012年以来，VSD研究人员使用一种名为妊娠事件算法（PEA）的算法来识别怀孕女性的病历。研究人员随后利用这些病历研究接种特定疫苗是否与女性或其孩子的任何不良结局相关。本研究的目的是更新和增强PEA，纳入全套病历诊断代码[既有旧的国际疾病分类第九版（ICD - 9系统），也有更新的ICD - 10系统]，并纳入有关孕周的其他数据来源。为确保这些增强措施后PEA的有效性，我们人工审查了病历并将结果与算法进行了比较。我们还开发了一种新算法，动态妊娠算法（DPA），以在妊娠早期识别女性，使我们能够进行更及时的疫苗安全性评估。PEA的新版本在VSD数据库中识别出2485410例妊娠。由于新的孕周数据来源，增强后的算法更精确地估计了妊娠开始时间，尤其是那些未导致活产的妊娠。我们的新算法DPA成功地比PEA更早地识别出妊娠期的妊娠。增强后的PEA和新的DPA将使我们能够更好地评估当前和未来在孕期或孕期前后接种疫苗的安全性。
