Evidence map›Paper›PMID 39794688›Full record

ArticleBMC medical research methodology2025

Development and validation of a model to identify polycystic ovary syndrome in the French national administrative health database.

Eugénie Micolon, Sandrine Loubiere, Appoline Zimmermann, Julie Berbis, Pascal Auquier, Blandine Courbiere

Abstract readValidation Study
In one paragraph

Article in BMC medical research methodology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Living with PCOS: A Narrative of its Biology, Diagnosis, and Evolving Treatment.Endocrine, metabolic & immune disorders drug targets · 2026
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Eugénie MicolonDepartment of Gynecology-Obstetric and Reproductive Medicine, AP-HM, La Conception University teaching Hospital, 147 Boulevard Baille, Marseille, 13005, France. e.micolon@hotmail.fr.
Sandrine LoubiereDepartment of Clinical Research and Innovation, Support Unit for Clinical Research and Economic Evaluation, Assistance Publique - Hôpitaux de Marseille, Marseille, France.
Appoline ZimmermannDepartment of Gynecology-Obstetric and Reproductive Medicine, AP-HM, La Conception University teaching Hospital, 147 Boulevard Baille, Marseille, 13005, France.
Julie BerbisDepartment of Clinical Research and Innovation, Support Unit for Clinical Research and Economic Evaluation, Assistance Publique - Hôpitaux de Marseille, Marseille, France.
Pascal AuquierDepartment of Clinical Research and Innovation, Support Unit for Clinical Research and Economic Evaluation, Assistance Publique - Hôpitaux de Marseille, Marseille, France.
Blandine CourbiereDepartment of Gynecology-Obstetric and Reproductive Medicine, AP-HM, La Conception University teaching Hospital, 147 Boulevard Baille, Marseille, 13005, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWe aimed to develop and validate an algorithm for identifying women with polycystic ovary syndrome (PCOS) in the French national health data system.

methodsUsing data from the French national health data system, we applied the International Classification of Diseases (ICD-10) related diagnoses E28.2 for PCOS among women aged 18 to 43 years in 2021. Then, we developed an algorithm to identify PCOS using combinations of clinical criteria related to specific drugs claims, biological exams, international classification of Diseases (ICD-10) related diagnoses during hospitalization, and/or registration for long-term conditions. The sensitivity, specificity and positive predictive value (PPV) of different combinations of algorithm criteria were estimated by reviewing the medical records of the Department of Reproductive Medicine at a university hospital for the year 2022, comparing potential women identified as experiencing PCOS by the algorithms with a list of clinically registered women with or without PCOS.

resultsWe identified 2,807 (0.01%) women aged 18 to 43 who received PCOS-related care in 2021 using the ICD-10 code for PCOS in the French National health database. By applying the PCOS algorithm to 349 women, the positive and negative predictive values were 0.90 (95%CI (83-95) and 0.93 (95%CI 0.90-0.96) respectively. The sensitivity of the PCOS algorithm was estimated at 0.85 (95%CI 0.77-0.91) and the specificity at 0.96 (95%CI 0.92-0.98).

conclusionThe validity of the PCOS diagnostic algorithm in women undergoing reproductive health care was acceptable. Our findings may be useful for future studies on PCOS using administrative data on a national scale, or even on an international scale given the similarity of coding in this field.

Indexed as

AlgorithmsDatabases, FactualPolycystic Ovary SyndromeAdolescentAdultFemaleFranceHumansInternational Classification of DiseasesSensitivity and SpecificityYoung AdultCase-finding algorithmNational administrative health data basesPolycystic ovary syndromeValidation study

Identifiers

PMID39794688
PMCPMC11721591

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.