Health AI Central
Peer-Reviewed Publication
Pharmacoepidemiol Drug Saf2026;35(10):e70490.October 1, 2026Journal Article

Algorithms to Identify Preeclampsia in Routinely Collected Healthcare Data: A Systematic Review.

Julie Scotto1, Anna Beckmeyer-Borowko2, Sonia Guleria3, Eman A Alghamdi4, Susan E Andrade5, Sonia M Grandi6, Ditte Mølgaard-Nielsen7, Maryline Le Noan-Lainé8, Elena Rivero-Ferrer9, Meritxell Sabidó Espin10, Sandra Lopez-Leon11,12
1Bristol Myers Squibb, Princeton, New Jersey, USA.
2Novartis Pharmaceuticals, Basel, Switzerland.
3Real-World Research, Parexel International, Gothenburg, Sweden.
4Saudi Food and Drug Authority, Riyadh, Saudi Arabia.
5Department of Population Medicine, Harvard Medical School & Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA.
6Child Health Evaluative Sciences, the Hospital for Sick Children and Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada.
7Novo Nordisk A/S, Global Patient Safety, Søborg, Denmark.
8Eli Lilly and Company, Global Patient Safety, Cork, Ireland.
9RTI Health Solutions, Pharmacoepidemiology and Regulatory Grade RWE, Barcelona, Spain.
10Department of Epidemiology, Merck Healthcare KGaA, Darmstadt, Germany.
11Novartis Pharmaceuticals, East Hanover, New Jersey, USA.
12Rutgers Center for Pharmacoepidemiology and Treatment Science, Rutgers University, New Brunswick, New Jersey, USA.
PubMed PMC DOI PDF

Abstract

IMPORTANCE: Preeclampsia is a leading cause of maternal morbidity and mortality globally. As real-world 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. OBJECTIVE: To identify and characterize algorithms used to ascertain preeclampsia…

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