1 Department of Pharmacotherapy, University of Utah College of Pharmacy, Salt Lake City, and SelectHealth, Murray, Utah.
2 Department of Pharmacotherapy, University of Utah College of Pharmacy, Salt Lake City.
J Manag Care Spec Pharm. 2017 Sep;23(9):926-934. doi: 10.18553/jmcp.2017.23.9.926.
While statins are safe and efficacious, some patients may experience statin intolerance or treatment-limiting adverse events. Identifying patients with statin intolerance may allow optimal management of cardiovascular event risk through other strategies. Recently, an administrative claims data (ACD) algorithm was developed to identify patients with statin intolerance and validated against electronic medical records. However, how this algorithm compared with perceptions of statin intolerance by integrated delivery networks remains largely unknown.
To determine the concurrent validity of an algorithm developed by a regional integrated delivery network multidisciplinary panel (MP) and a published ACD algorithm in identifying patients with statin intolerance.
The MP consisted of 3 physicians and 2 pharmacists with expertise in cardiology, internal medicine, and formulary management. The MP algorithm used pharmacy and medical claims to identify patients with statin intolerance, classifying them as having statin intolerance if they met any of the following criteria: (a) medical claim for rhabdomyolysis, (b) medical claim for muscle weakness, (c) an outpatient medical claim for creatinine kinase assay, (d) fills for ≥ 2 different statins excluding dose increases, (e) decrease in statin dose, or (f) discontinuation of a statin with a subsequent fill for a nonstatin lipid-lowering therapy. The validated ACD algorithm identified statin intolerance as absolute intolerance with rhabdomyolysis; absolute intolerance without rhabdomyolysis (i.e., other adverse events); or as dose titration intolerance. Adult patients (aged ≥ 18 years) from the integrated delivery network with at least 1 prescription fill for a statin between January 1, 2011, and December 31, 2012 (first fill defined the index date) were identified. Patients with ≥ 1 year pre- and ≥ 2 years post-index continuous enrollment and no statin prescription fills in the pre-index period were included. The MP and ACD algorithms were applied to the population, and concordance was examined using individual (i.e., sensitivity, specificity, positive predictive value [PPV], and negative predictive value [NPV]) and overall performance measures (i.e., accuracy, Cohen's kappa coefficient, balanced accuracy, F-1 score, and phi coefficient).
After applying the inclusion criteria, 7,490 patients were evaluated for statin intolerance. The mean (SD) age of the population was 51.1 (8.5) years, and 55.7% were male. The MP and ACD algorithms classified 11.3% and 5.4% of patients as having statin intolerance, respectively. The concordance of the MP algorithm was mixed, with negative classification of statin intolerance measures having high concordance (specificity 0.91, NPV 0.97) and positive classification of statin intolerance measures having poor concordance (sensitivity 0.45, PPV 0.21). Overall performance measures showed mixed agreement between the algorithms.
Both algorithms used a mix of pharmacy and medical claims and may be useful for organizations interested in identifying patients with statin intolerance. By identifying patients with statin intolerance, organizations may consider a variety of options, including using nonstatin lipid-lowering therapies, to manage cardiovascular event risk in these patients.
This study was funded by Regeneron Pharmaceuticals and Sanofi US. Boklage is employed by, and owns stock in, Regeneron, and Charland is employed by Sanofi. Bellows has received fees from Avenir for advisory board membership and grants from Myriad Genetics, Biogen, Janssen, and National Institutes of Health. Brixner reports advisory board and consultancy fees and grants from Sanofi. Mitchell reports consultancy fees from Sanofi. Study concept and design were contributed by Bellows, Boklage, Charland, and Brixner. Bellows, Sainski-Nguyen, and Olsen took the lead in data collection, along with Mitchell. Data interpretation was performed by Mitchell, along with the other authors. The manuscript was written by Bellows, Sainski-Nguyen, and Olsen and revised by all the authors.
尽管他汀类药物安全且有效，但有些患者可能会出现他汀不耐受或治疗相关的不良反应。确定他汀不耐受的患者，可通过其他策略来优化心血管事件风险的管理。最近，一项基于行政索赔数据（ACD）的算法被开发出来，用于识别他汀不耐受的患者，并通过电子病历进行验证。然而，该算法与综合医疗服务网络对他汀不耐受的看法相比，其相关性如何，目前还知之甚少。
确定由区域综合医疗服务网络多学科小组（MP）和已发表的 ACD 算法开发的算法，在识别他汀不耐受患者方面的现用性。
MP 由 3 名医生和 2 名药剂师组成，他们在心脏病学、内科和处方管理方面具有专业知识。MP 算法使用药房和医疗记录来识别他汀不耐受的患者，如果他们符合以下任何标准，则将其归类为他汀不耐受：（a）横纹肌溶解症的医疗索赔，（b）肌肉无力的医疗索赔，（c）门诊肌酸激酶检测的医疗索赔，（d）≥ 2 种不同的他汀类药物的处方，不包括剂量增加，（e）他汀类药物剂量减少，或（f）他汀类药物停药，随后开非他汀类降脂治疗药物。经过验证的 ACD 算法将他汀不耐受定义为绝对不耐受伴横纹肌溶解症；绝对不耐受无横纹肌溶解症（即其他不良反应）；或剂量调整不耐受。从综合医疗服务网络中选取至少在 2011 年 1 月 1 日至 2012 年 12 月 31 日期间服用过他汀类药物的成年患者（年龄≥ 18 岁）作为研究对象（第一次开处方的日期定义为索引日期）。患者需要在索引日期前和后至少有 1 年和 2 年的连续参保，且在索引日期前没有他汀类药物的处方记录。将 MP 和 ACD 算法应用于该人群，并通过个体（即敏感性、特异性、阳性预测值 [PPV] 和阴性预测值 [NPV]）和总体性能指标（即准确性、科恩氏 κ 系数、平衡准确性、F-1 分数和 φ 系数）来检查一致性。
在应用纳入标准后，共评估了 7490 例他汀不耐受患者。人群的平均（标准差）年龄为 51.1（8.5）岁，55.7%为男性。MP 和 ACD 算法分别将 11.3%和 5.4%的患者归类为他汀不耐受。MP 算法的一致性混杂，他汀不耐受的阴性分类具有较高的一致性（特异性 0.91，NPV 0.97），他汀不耐受的阳性分类一致性较差（敏感性 0.45，PPV 0.21）。总体性能指标显示算法之间存在混合一致性。
这两种算法都使用了药房和医疗记录的混合数据，对于有兴趣识别他汀不耐受患者的组织可能会有所帮助。通过识别他汀不耐受的患者，组织可以考虑使用非他汀类降脂药物等多种选择，来管理这些患者的心血管事件风险。
本研究由 Regeneron 制药公司和赛诺菲美国公司资助。Boklage 受雇于 Regeneron 并拥有其股票，Charland 受雇于赛诺菲。Bellows 曾因担任顾问委员会成员和接受 Myriad Genetics、Biogen、Janssen 和美国国立卫生研究院的资助而获得费用。Brixner 报告了与 Sanofi 的咨询费和赠款。研究概念和设计由 Bellows、Boklage 和 Charland 贡献。Bellows、Sainski-Nguyen 和 Olsen 带头进行数据收集，Mitchell 也参与了这项工作。数据解释由 Mitchell 完成，其他作者也参与其中。手稿由 Bellows、Sainski-Nguyen 和 Olsen 撰写，并由所有作者修订。
