Cross-validation of comorbidity items in two national databases in a sample of patients with end-stage kidney disease

• Main Authors: Isabella Vanorio-Vega (Author), Panayotis Constantinou (Author), Assia Hami (Author), Eric Cellarier (Author), Antoine Rachas (Author), Philippe Tuppin (Author), Cécile Couchoud (Author)
• Format: Book
• Published: BMC, 2023-10-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 10.1186/s12913-023-10145-y; 1472-6963
• Keywords: Medico-administrative databases; Algorithm validation; Comorbidities; Public aspects of medicine; RA1-1270
• Source: BMC Health Services Research, Vol 23, Iss 1, Pp 1-10 (2023)

Abstract Background The use of national medico-administrative databases for epidemiological studies has increased in the last decades. In France, the Healthcare Expenditures and Conditions Mapping (HECM) algorithm has been developed to analyse and monitor the morbidity and economic burden of 58 diseases. We aimed to assess the performance of the HECM in identifying different conditions in patients with end-stage kidney disease (ESKD) using data from the REIN registry (the French National Registry for patients with ESKD). Methods We included all patients over 18 years of age who started renal replacement therapy in France in 2018. Five conditions with a similar definition in both databases were included (ESKD, diabetes, human immunodeficiency virus [HIV], coronary insufficiency, and cancer). The performance of each SNDS algorithm was assessed using sensitivity, specificity, positive predictive values (PPVs), negative predictive values (NPVs), and Cohen's kappa coefficient. Results In total 5,971 patients were included. Among them, 81% were identified as having ESKD in both databases. Diabetes was the condition with the best performance, with a sensitivity, specificity, PPV, NPV, and Kappa coefficient all over 80%. Cancer had the lowest level of agreement with a Kappa coefficient of 51% and a high specificity and high NPV (94% and 95%). The conditions for which the definition in the HECM included disease-specific medications performed better in our study. Conclusion The HECM showed good to very good concordance with the REIN database information overall, with the exception of cancer. Further validation of the HECM tool in other populations should be performed.