Identifying multiple myeloma patients using data from the French health insurance databases: Validation using a cancer registry.
This study aimed to assess the performance of several algorithms based on hospital diagnoses and the long-term diseases scheme to identify multiple myeloma patients.Potential multiple myeloma patients in 2010 to 2013 were identified using the presence of hospital records with at least 1 main diagnosis code for multiple myeloma (ICD-10 "C90"). Alternative algorithms also considered related and associated diagnoses, combination with long-term conditions, or at least 2 diagnoses. Incident patients were those with no previous "C90" codes in the past 24 or 12 months. The sensitivity, specificity, and positive and negative predictive values (PPVs and NPVs) were computed, using a French cancer registry for the corresponding area and period as the criterion standard.Long-term conditions data extracted concerned 11,559 patients (21,846 for hospital data). The registry contained 125 cases of multiple myeloma. Sensitivity was 70% when using only main hospital diagnoses (specificity 100%, PPV 79%), 76% when also considering related diagnoses (specificity 100%, PPV 74%), and 90% with associated diagnoses included (100% specificity, 64% PPV).In relation with their good performance, selected algorithms can be used to study the benefit and risk of drugs in treated multiple myeloma patients.
Medical and Clinical Pharmacology Unit, Toulouse University Hospital INSERM 1027, University of Toulouse CIC 1436, Toulouse University Hospital Department of Hematology, Toulouse University Hospital Tarn Cancer Registry, Albi French Network of Cancer Registries (FRANCIM), France.
Aurore
A
Palmaro
Martin
M
Gauthier
Cécile
C
Conte
Pascale
P
Grosclaude
Fabien
F
Despas
Maryse
M
Lapeyre-Mestre
D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D002908:Chronic Disease; D005260:Female; D005602:France; D006760:Hospitalization / Q000706:statistics & numerical data; D006801:Humans; D007345:Insurance Claim Review / Q000706:statistics & numerical data*; D038801:International Classification of Diseases; D008875:Middle Aged; D009101:Multiple Myeloma / Q000453:epidemiology*; D011237:Predictive Value of Tests; D012042:Registries / Q000706:statistics & numerical data*; D012189:Retrospective Studies