Palmaro Aurore, Gauthier Martin, Conte Cécile, Grosclaude Pascale, Despas Fabien, Lapeyre-Mestre Maryse
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.
Medicine (Baltimore). 2017 Mar;96(12):e6189. doi: 10.1097/MD.0000000000006189.
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.
本研究旨在评估基于医院诊断和长期疾病方案的几种算法识别多发性骨髓瘤患者的性能。利用2010年至2013年期间存在至少1个多发性骨髓瘤主要诊断代码(ICD-10“C90”)的医院记录来识别潜在的多发性骨髓瘤患者。替代算法还考虑了相关诊断、与长期病症的组合或至少2种诊断。发病患者是指在过去24个月或12个月内没有先前“C90”代码的患者。以相应地区和时期的法国癌症登记处作为标准对照,计算敏感性、特异性、阳性和阴性预测值(PPV和NPV)。提取的长期病症数据涉及11559名患者(医院数据为21846名)。该登记处包含125例多发性骨髓瘤病例。仅使用医院主要诊断时敏感性为70%(特异性100%,PPV 79%),同时考虑相关诊断时为76%(特异性100%,PPV 74%), 纳入相关诊断时为90%(特异性100%,PPV 64%)。鉴于其良好的性能,所选算法可用于研究治疗多发性骨髓瘤患者的药物的益处和风险。