Advera Health Analytics, 3663 N. Laughlin Rd., Ste. 102, Santa Rosa, CA 95403.
J Manag Care Spec Pharm. 2015 Dec;21(12):1134-43. doi: 10.18553/jmcp.2015.21.12.1134.
Given the multiple limitations associated with relatively homogeneous preapproval clinical trials, inadequate data disclosures, slow reaction times from regulatory bodies, and deep-rooted bias against disclosing and publishing negative results, there is an acute need for the development of analytics that reflect drug safety in heterogeneous, real-world populations.
To develop a drug safety statistic that estimates downstream medical costs associated with serious adverse events (AEs) and unfavorable patient outcomes associated with the use of 706 FDA-approved drugs.
All primary suspect case reports for each drug were collected from the FDA's Adverse Event Reporting System database (FAERS) from 2010-2014. The Medical Dictionary for Regulatory Activities (MedDRA) was used to code serious AEs and outcomes, which were tallied for each case report. Medical costs associated with AEs and poor patient outcomes were derived from Agency for Healthcare Research and Quality (AHRQ) survey data, and their corresponding ICD-9-CM codes were mapped to MedDRA terms. Nonserious AEs and outcomes were not included. For each case report, either the highest AE cost or, if no eligible AE was listed, the highest outcome cost was used. All costed cases were aggregated for each drug and divided by the number of patients exposed to obtain a downstream estimated direct medical cost burden per exposure. Each drug was assigned a corresponding 1-100 point total.
The 706 drugs showed an exponential distribution of downstream costs, and the data were transformed using the natural log to approximate a normal distribution. The minimum score was 8.29, and the maximum score was 99.25, with a mean of 44.32. Drugs with the highest individual scores tended to be kinase inhibitors, thalidomide analogs, and endothelin receptor antagonists. When scores were analyzed across Established Pharmacologic Class (EPC), the kinase inhibitor and endothelin receptor antagonist classes had the highest total. However, other EPCs with median scores of 75 and above included hepatitis C virus NS3/4A protease inhibitor, recombinant human interferon beta, vascular endothelial growth factor-directed antibody, and tumor necrosis factor blocker. When Anatomical Therapeutic Chemical classifications were analyzed, antineoplastic drugs were outliers with approximately 80% of their individual scores 60 and above, while approximately 20%-30% of blood and anti-infective drugs had scores of 60 and above. Within-drug class results served to differentiate similar drugs. For example, 6 serotonin reuptake inhibitors had a score range of 35 to 53.
This scoring system is based on estimated direct medical costs associated with postmarketing AEs and poor patient outcomes and thereby helps fill a large information gap regarding drug safety in real-world patient populations.
鉴于相对同质的预批准临床试验、数据披露不足、监管机构反应缓慢以及对披露和发表负面结果根深蒂固的偏见等多种限制,因此迫切需要开发反映异质真实世界人群中药物安全性的分析方法。
开发一种药物安全性统计方法,用于估计与严重不良事件(AE)相关的下游医疗成本以及与使用 706 种 FDA 批准药物相关的不良患者结局。
从 2010 年至 2014 年,从 FDA 的不良事件报告系统数据库(FAERS)中收集了每种药物的所有主要怀疑病例报告。使用监管活动医学词典(MedDRA)对严重 AE 和结局进行编码,对每个病例报告进行汇总。与 AE 和不良患者结局相关的医疗费用源自医疗保健研究和质量局(AHRQ)调查数据,并将其相应的 ICD-9-CM 代码映射到 MedDRA 术语。未包括非严重 AE 和结局。对于每个病例报告,使用最高 AE 成本,或者如果没有列出合格的 AE,则使用最高结局成本。对每种药物的所有计费病例进行汇总,并除以暴露的患者人数,以获得每例暴露的下游估计直接医疗费用负担。为每种药物分配相应的 1-100 分。
706 种药物的下游成本呈指数分布,使用自然对数对数据进行转换以近似正态分布。最低得分为 8.29,最高得分为 99.25,平均得分为 44.32。得分最高的药物往往是激酶抑制剂、沙利度胺类似物和内皮素受体拮抗剂。当按既定药理分类(EPC)分析得分时,激酶抑制剂和内皮素受体拮抗剂类别的得分最高。然而,其他 EPC 的中位数得分在 75 分及以上,包括丙型肝炎病毒 NS3/4A 蛋白酶抑制剂、重组人干扰素β、血管内皮生长因子导向抗体和肿瘤坏死因子阻滞剂。当分析解剖治疗化学分类时,抗肿瘤药物是异常值,其单独得分在 60 分及以上的约占 80%,而血液和抗感染药物的约 20%-30%的得分在 60 分及以上。同类药物的内部结果有助于区分类似药物。例如,6 种 5-羟色胺再摄取抑制剂的得分范围为 35 至 53。
该评分系统基于与上市后 AE 和不良患者结局相关的估计直接医疗成本,从而有助于填补真实世界患者人群中药物安全性的大量信息空白。