Algorithms to Identify Preeclampsia in Routinely Collected Healthcare Data: A Systematic Review.
12 affiliations
- 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.
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 in population-based healthcare databases, summarize validation metrics, and describe the range of reported prevalence estimates.Evidence review
Following PRISMA guidelines of 2021, PubMed was searched for studies published from January 1, 2010, to July 1, 2025. Observational studies using reproducible code-based algorithms to identify preeclampsia in routinely collected healthcare data were included. Screening and full-text 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.Findings
Across included studies, 43.6% used Nordic nationwide registries, 30.1% used claims databases, and 12.1% used electronic health records. ICD-10 O14.x was used in nearly all studies; inclusion of O11.x and O15.x varied. Validation studies consistently demonstrated high specificity (> 90%) but low sensitivity (≤ 80%), reflecting under-ascertainment 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.Conclusions and relevance
Algorithms for identifying preeclampsia in real-world 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-specificity algorithms may be particularly useful for comparative safety studies, whereas potential under-ascertainment should be considered when estimating incidence or prevalence.
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BibTeX
@article{scotto2026algorithms,
title = {{Algorithms to Identify Preeclampsia in Routinely Collected Healthcare Data: A Systematic Review.}},
author = {Julie Scotto and Anna Beckmeyer-Borowko and Sonia Guleria and Eman A Alghamdi and Susan E Andrade and Sonia M Grandi and Ditte Mølgaard-Nielsen and Maryline Le Noan-Lainé and Elena Rivero-Ferrer and Meritxell Sabidó Espin and Sandra Lopez-Leon},
journal = {Pharmacoepidemiology and drug safety},
year = {2026},
month = oct,
doi = {10.1002/pds.70490},
url = {https://europepmc.org/article/MED/42853001},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - Algorithms to Identify Preeclampsia in Routinely Collected Healthcare Data: A Systematic Review.
AU - Julie Scotto
AU - Anna Beckmeyer-Borowko
AU - Sonia Guleria
AU - Eman A Alghamdi
AU - Susan E Andrade
AU - Sonia M Grandi
AU - Ditte Mølgaard-Nielsen
AU - Maryline Le Noan-Lainé
AU - Elena Rivero-Ferrer
AU - Meritxell Sabidó Espin
AU - Sandra Lopez-Leon
PY - 2026
DA - 2026/10/01
JO - Pharmacoepidemiology and drug safety
DO - 10.1002/pds.70490
UR - https://europepmc.org/article/MED/42853001
AB - 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 in population-based healthcare databases, summarize validation metrics, and describe the range of reported prevalence estimates.Evidence review Following PRISMA guidelines of 2021, PubMed was searched for studies published from January 1, 2010, to July 1, 2025. Observational studies using reproducible code-based algorithms to identify preeclampsia in routinely collected healthcare data were included. Screening and full-text 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.Findings Across included studies, 43.6% used Nordic nationwide registries, 30.1% used claims databases, and 12.1% used electronic health records. ICD-10 O14.x was used in nearly all studies; inclusion of O11.x and O15.x varied. Validation studies consistently demonstrated high specificity (> 90%) but low sensitivity (≤ 80%), reflecting under-ascertainment 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.Conclusions and relevance Algorithms for identifying preeclampsia in real-world 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-specificity algorithms may be particularly useful for comparative safety studies, whereas potential under-ascertainment should be considered when estimating incidence or prevalence.
SN - 1053-8569
SN - 1099-1557
ER -