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在法国医疗保险数据库中识别癌症药物治疗方案:多发性骨髓瘤患者的应用。

Identifying cancer drug regimens in French health insurance database: An application in multiple myeloma patients.

机构信息

Medical and Clinical Pharmacology Unit, CHU Toulouse University Hospital, Toulouse, France.

Pharmacoepidemiology Research Unit, INSERM 1027, University of Toulouse, Toulouse, France.

出版信息

Pharmacoepidemiol Drug Saf. 2017 Dec;26(12):1492-1499. doi: 10.1002/pds.4266. Epub 2017 Jul 25.

Abstract

PURPOSE

There is no consensus on how to handle complex drug combinations of cancer drugs through medico-administrative databases. Our objective was to develop an algorithm for identifying the nature and patterns of treatment lines in a cohort of newly treated multiple myeloma patients.

METHODS

A cohort of multiple myeloma patients starting a first treatment line was built using both ambulatory and hospital data from regional data of the French national healthcare system database (SNIIRAM). Patients were identified from January 2011 to September 2013 using ICD-10 codes for multiple myeloma ('C90') within long-term conditions or diagnosis from hospital data. Drugs of interest for cycle identification included bortezomib, imids (thalidomide, lenalidomide), alkylating drugs (cyclophosphamide, melphalan, bendamustine, doxorubicin) and dexamethasone. An algorithm was applied to define combinations of treatment received in the first 6 months of treatment.

RESULTS

Among the 236 patients included, 45% received bortezomib-melphalan-prednisone (VMP: n = 107), 22% bortezomib-thalidomide-dexamethasone (VTD/VTD-PACE: n = 52) and 21% melphalan-prednisone-thalidomide (MPT: n = 49). Other drug regimens consisted in melphalan-prednisone (MP: 7%, n = 17), lenalidomide-dexamethasone (RD) (4%, n = 9), bortezomib-cyclophosphamide-dexamethasone (VCD: n = 1) and bortezomib-bendamustine-dexamethasone (VBD: n = 1). Type of drug regimens and allocation by age class (±65 years) were in accordance with current recommendations.

CONCLUSIONS

This study demonstrates the feasibility of identifying complex drug regimens in onco-haematology, using both outpatient and inpatient drug records in French health insurance databases.

摘要

目的

在医疗管理数据库中,对于癌症药物的复杂药物组合,尚无共识。我们的目的是开发一种算法,用于识别新治疗多发性骨髓瘤患者队列中治疗线的性质和模式。

方法

使用来自法国国家医疗保健系统数据库(SNIIRAM)的区域数据中的门诊和住院数据,构建了开始一线治疗的多发性骨髓瘤患者队列。通过医院数据中的长期病症或诊断的 ICD-10 代码(多发性骨髓瘤的“C90”),于 2011 年 1 月至 2013 年 9 月间识别出患者。用于周期识别的药物包括硼替佐米、免疫调节剂(沙利度胺、来那度胺)、烷化剂(环磷酰胺、美法仑、苯达莫司汀、阿霉素)和地塞米松。应用算法定义治疗开始后 6 个月内接受的治疗组合。

结果

在纳入的 236 名患者中,45%接受硼替佐米-美法仑-泼尼松(VMP:n=107),22%接受硼替佐米-沙利度胺-地塞米松(VTD/VTD-PACE:n=52),21%接受美法仑-泼尼松-沙利度胺(MPT:n=49)。其他药物方案包括美法仑-泼尼松(MP:7%,n=17)、来那度胺-地塞米松(RD)(4%,n=9)、硼替佐米-环磷酰胺-地塞米松(VCD:n=1)和硼替佐米-苯达莫司汀-地塞米松(VBD:n=1)。药物方案的类型和按年龄组(±65 岁)分配与当前建议一致。

结论

这项研究表明,在法国医疗保险数据库中,使用门诊和住院药物记录,识别肿瘤血液学中的复杂药物方案是可行的。

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