ArticleMedicine2017
Identifying multiple myeloma patients using data from the French health insurance databases: Validation using a cancer registry.
Article in Medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Article
- Article
- Use of a hospital administrative database to identify and characterize community-acquired, hospital-acquired and drug-induced acute kidney injury.Journal of nephrology · 2022Article
- Intra-database validation of case-identifying algorithms using reconstituted electronic health records from healthcare claims data.BMC medical research methodology · 2021Article
- Vaccination during the First Diagnosis of Multiple Myeloma: A Cohort Study of the French National Health Insurance Database.Vaccines · 2020Article
- Article
- A novel approach for medical research on lymphomas: A study validation of claims-based algorithms to identify incident cases.Medicine · 2018Article
- Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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