{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T02:44:50Z","timestamp":1791945890168,"version":"4.3.4"},"reference-count":45,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T00:00:00Z","timestamp":1791504000000},"content-version":"vor","delay-in-days":8,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Pharmacoepidemiology and Drug"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Importance<\/jats:title>\n                    <jats:p>Preeclampsia is a leading cause of maternal morbidity and mortality globally. As real\u2010world data sources are increasingly used for maternal health surveillance and pharmacoepidemiologic studies, valid algorithms are essential for accurately identifying preeclampsia in routinely collected healthcare data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To identify and characterize algorithms used to ascertain preeclampsia in population\u2010based healthcare databases, summarize validation metrics, and describe the range of reported prevalence estimates.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Evidence Review<\/jats:title>\n                    <jats:p>Following PRISMA guidelines of 2021, PubMed was searched for studies published from January 1, 2010, to July 1, 2025. Observational studies using reproducible code\u2010based algorithms to identify preeclampsia in routinely collected healthcare data were included. Screening and full\u2010text review were performed in duplicate, with discrepancies resolved by a third reviewer. Data extraction focused on algorithm definitions, coding systems, validation methods, and epidemiologic estimates. A total of 289 studies met inclusion criteria, including 13 validation studies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Findings<\/jats:title>\n                    <jats:p>Across included studies, 43.6% used Nordic nationwide registries, 30.1% used claims databases, and 12.1% used electronic health records. ICD\u201010 O14.x was used in nearly all studies; inclusion of O11.x and O15.x varied. Validation studies consistently demonstrated high specificity (&gt;\u200990%) but low sensitivity (\u2264\u200980%), reflecting under\u2010ascertainment when relying solely on diagnosis codes. More than 70% of studies did not report start or end points for the time window of ascertainment. Reported prevalence ranged from 0.6% to 6.6% across case definitions and settings. The prevalence of severe preeclampsia ranged from 0.6% to 2.2%. Algorithms including O11.x, O14.x, and O15.x showed improved sensitivity in some settings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions and Relevance<\/jats:title>\n                    <jats:p>Algorithms for identifying preeclampsia in real\u2010world data are highly specific but variably sensitive, with substantial heterogeneity in coding definitions and ascertainment windows. Standardized reporting and consistent inclusion of O11.x, O14.x, and O15.x may improve comparability, reproducibility, and the accuracy of epidemiologic estimates across data sources. High\u2010specificity algorithms may be particularly useful for comparative safety studies, whereas potential under\u2010ascertainment should be considered when estimating incidence or prevalence.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/pds.70490","type":"journal-article","created":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T13:23:32Z","timestamp":1791552212000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Algorithms to Identify Preeclampsia in Routinely Collected Healthcare Data: A Systematic Review"],"prefix":"10.1002","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-7910-1668","authenticated-orcid":false,"given":"Julie","family":"Scotto","sequence":"first","affiliation":[{"name":"Bristol Myers Squibb  Princeton New Jersey 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