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

Abstract

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

Methods: Using 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.

Results: We 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).

Conclusion: The 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.