Vaccine Safety Datalink infrastructure enhancements for evaluating the safety of maternal vaccination

Abstract

Background: 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. Methods: 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. Results: 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. Conclusion: 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. Plain Language Summary Improving identification of pregnancies in the Vaccine Safety Datalink electronic medical record databases to allow for better and faster monitoring of vaccination safety during pregnancy Introduction: 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. Methods: 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. Results: 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. Conclusion: 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.

Receipt of COVID-19 Vaccine During Pregnancy and Preterm or Small-for-Gestational-Age at Birth — Eight Integrated Health Care Organizations, United States, December 15, 2020–July 22, 2021

“…Pregnancies ending in live birth were identified from standardized VSD files using a validated pregnancy algorithm. The algorithm uses International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis codes, Current Procedural Terminology codes, birth records, and electronic health record data (last menstrual period and expected delivery date) to identify the date and gestational age for live births (5). The algorithm then estimates the pregnancy start date, equivalent to the last menstrual period.…”

Identification of pregnancies and infants within a US commercial healthcare administrative claims database

“…As expected, the number of pregnancies identified in 2020 was lower given that this was a partial year: the study period ended on 30 September 2020 and with a data lag of 6 months, we expected have incorporated ICD-10-CM codes into their pregnancy identification process as well. 17 While we used a similar approach to Hornbrook et al 2 to identify pregnancy outcomes and to estimate LMP when Z3A codes were absent, we did not replicate the Hornbrook algorithms exactly as we did not have the additional data sources available including gestational age from hospital discharge summaries and an EMR-based preterm birth prevention database. Like Wentzell et al 18 that ranked outcomes as more reliable if the outcome date was from an inpatient stay, we ranked outcomes as more reliable if both a procedure code and diagnosis code were observed on the same date.…”

“…This study builds on previous work 7,9,15–17 identifying pregnancy episodes and outcomes in North American claims databases. Compared to previous work, our algorithms incorporate a key element: we used the additional granularity provided by ICD‐10‐CM Z3A codes to estimate LMP.…”

“…Compared to previous work, our algorithms incorporate a key element: we used the additional granularity provided by ICD‐10‐CM Z3A codes to estimate LMP. Although we developed ICD‐10‐CM algorithms to build on previous work which used International Classification of Diseases, 9th revision Clinical Modification (ICD‐9‐CM) codes, others have incorporated ICD‐10‐CM codes into their pregnancy identification process as well 17 . While we used a similar approach to Hornbrook et al 2 to identify pregnancy outcomes and to estimate LMP when Z3A codes were absent, we did not replicate the Hornbrook algorithms exactly as we did not have the additional data sources available including gestational age from hospital discharge summaries and an EMR‐based preterm birth prevention database.…”

Who is pregnant? defining real-world data-based pregnancy episodes in the National COVID Cohort Collaborative (N3C)

“…To overcome these limitations, EHR-based investigations commonly utilize algorithms for determining gestational age and pregnancy (or delivery) episodes using diagnostic codes and delivery dates [11][12][13][14][15].…”