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Open Access Highly Accessed Research article

Automated comparison of last hospital main diagnosis and underlying cause of death ICD10 codes, France, 2008–2009

Agathe Lamarche-Vadel123*, Gérard Pavillon1, Albertine Aouba1, Lars Age Johansson4, Laurence Meyer235, Eric Jougla1 and Grégoire Rey1

Author Affiliations

1 Inserm, CépiDc (Epidemiology center on medical causes of death), CHU Bicêtre, 80 rue du Général Leclerc, Kremlin Bicêtre, CEDEX 94270, France

2 Inserm, UMRS 1018, Kremlin-Bicêtre, France

3 Université Paris Sud, Kremlin-Bicêtre, France

4 Swedish National Board of Health and Welfare, Center for Epidemiology, Stockholm, Sweden

5 AP-HP, CHU Bicêtre, Service de Santé Publique et d’Epidémiologie, Kremlin-Bicêtre, France

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BMC Medical Informatics and Decision Making 2014, 14:44  doi:10.1186/1472-6947-14-44

Published: 5 June 2014



In the age of big data in healthcare, automated comparison of medical diagnoses in large scale databases is a key issue. Our objectives were: 1) to formally define and identify cases of independence between last hospitalization main diagnosis (MD) and death registry underlying cause of death (UCD) for deceased subjects hospitalized in their last year of life; 2) to study their distribution according to socio-demographic and medico-administrative variables; 3) to discuss the interest of this method in the specific context of hospital quality of care assessment.


1) Elaboration of an algorithm comparing MD and UCD, relying on Iris, a coding system based on international standards. 2) Application to 421,460 beneficiaries of the general health insurance regime (which covers 70% of French population) hospitalized and deceased in 2008–2009.


1) Independence, was defined as MD and UCD belonging to different trains of events leading to death 2) Among the deaths analyzed automatically (91.7%), 8.5% of in-hospital deaths and 19.5% of out-of-hospital deaths were classified as independent. Independence was more frequent in elder patients, as well as when the discharge-death time interval grew (14.3% when death occurred within 30 days after discharge and 27.7% within 6 to 12 months) and for UCDs other than neoplasms.


Our algorithm can identify cases where death can be considered independent from the pathology treated in hospital. Excluding these deaths from the ones allocated to the hospitalization process could contribute to improve post-hospital mortality indicators. More generally, this method has the potential of being developed and used for other diagnoses comparisons across time periods or databases.

Cause of death; Death certificate; Medical coding; Hospital mortality; Quality indicators; Health care; Medical record linkage