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The Best Use of the Charlson Comorbidity Index With Electronic Health Care Database to Predict Mortality

Bannay, Aurélie MD*; Chaignot, Christophe MSc; Blotière, Pierre-Olivier MSc; Basson, Mickaël MD, MSc; Weill, Alain MD; Ricordeau, Philippe MD; Alla, François MD, PhD

doi: 10.1097/MLR.0000000000000471
Original Articles

Background: The most used score to measure comorbidity is the Charlson index. Its application to a health care administrative database including International Classification of Diseases, 10th edition (ICD-10) codes, medical procedures, and medication required studying its properties on survival. Our objectives were to adapt the Charlson comorbidity index to the French National Health Insurance database to predict 1-year mortality of discharged patients and to compare discrimination and calibration of different versions of the Charlson index.

Methods: Our cohort included all adults discharged from a hospital stay in France in 2010 registered in the French National Health Insurance general scheme. The pathologies of the Charlson index were identified through ICD-10 codes of discharge diagnoses and long-term disease, specific medical procedures, and reimbursement of specific medications in the past 12 months before inclusion.

Results: We included 6,602,641 subjects at the date of their first discharge from medical, surgical, or obstetrical department in 2010. One-year survival was 94.88%, decreasing from 98.41% for Charlson index of 0–71.64% for Charlson index of ≥5. With a discrimination of 0.91 and an appropriate calibration curve, we retained the crude Cox model including the age-adjusted Charlson index as a 4-level score.

Conclusions: Our study is the first to adapt the Charlson index to a large health care database including >6 million of inpatients. When mortality is the outcome, we recommended using the age-adjusted Charlson index as 4-level score to take into account comorbidities.

*Faculté de médecine de Nancy, Université de Lorraine, Vandoeuvre les Nancy

CNAMTS, Paris, France

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Presented at 3 national French congresses: (i) 2èmes Journées scientifiques de l’assurance maladie, October 9–10, 2012, Paris, France; (ii) XXVIème Congrès national EMOIS, March 21–22, 2013, Nancy, France; (iii) 8ème Conférence Francophone d’Epidémiologie Clinique, May 14–16, 2014, Bordeaux, France.

The authors declare no conflict of interest.

Reprints: Aurélie Bannay, MD, Faculté de médecine de Nancy, Université de Lorraine, 9 avenue de la forêt de Haye, Vandoeuvre les Nancy 54500, France. E-mail: aurelie.bannay@univ-lorraine.fr.

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