BACKGROUND Muscular dystrophies (MDs) are a group of inherited conditions characterized by progressive muscle degeneration and weakness. The rarity and heterogeneity of the population with MD have hindered therapeutic developments as well as epidemiological and health outcomes research. The objective of the study was to develop and validate a case-finding algorithm utilizing administrative claims data to identify and characterize patients with MD. METHODS This retrospective cohort study used medical chart validation to evaluate an ICD-9/10 coding algorithm in a large commercial claims database. Patients were identified who had ≥2 office visits with a diagnosis of hereditary progressive MDs from January 1, 2013 through December 31, 2016, were male, and younger than 18 years at the time of first MD diagnosis. Cases who met the algorithm were then validated against medical charts. Diagnoses of MD and specific type (Duchenne, Becker, or other MD) were confirmed by medical chart review by trained reviewers. Positive predictive value (PPV) and 95% confidence intervals (CI) were calculated using a 2 × 2 contingence table. Patient demographic, clinical, and health utilization characteristics were summarized using basic descriptive statistics. RESULTS Charts were obtained and reviewed for 109 patients who met the algorithm. The PPV of the case-identifying algorithm for MD was 95% (95% CI 88-98%). Of the 103 confirmed MD cases, 87 patients (85%, 95% CI 76-91%) had Duchenne or Becker MD; 76 patients (74%, 95% CI 64-82%) had Duchenne MD, and 11 patients (11%, 95% CI 5-18%) had Becker MD. A total of 74 (67.9%) patients had ≥1 pediatric complex chronic condition (other than neurologic/neuromuscular disease); 54 (49.5%) had cardiovascular conditions; 14 (12.8%) had respiratory conditions; 50 (45.9%) had bone-related issues; 11 (10.1%) had impaired growth; and 6 (5.5%) had puberty delay. CONCLUSIONS The results of this study demonstrate that the case-finding algorithm accurately identified patients with MD, primarily Duchenne MD, within a large administrative database. The algorithm, which was constructed using a few items easily accessible from claims, can be used to facilitate epidemiological and health outcomes research in the Duchenne patient population.

中文翻译：

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在医疗保健索赔中用于识别遗传性进行性肌营养不良的算法的图表验证。

背景技术肌营养不良症（MDs）是一组以进行性肌肉变性和无力为特征的遗传病。MD人群的稀有性和异质性阻碍了治疗的发展以及流行病学和健康结果的研究。该研究的目的是利用行政理赔数据来开发和验证一种病例查找算法，以识别和表征患有MD的患者。方法这项回顾性队列研究使用医学图表验证来评估大型商业索赔数据库中的ICD-9 / 10编码算法。从2013年1月1日至2016年12月31日，诊断出患有遗传性进行性MD的≥2次办公室就诊的患者为男性，首次诊断MD时年龄小于18岁。然后，将符合该算法的病例对照病历进行验证。MD的诊断和特定类型（Duchenne，Becker或其他MD）的诊断已由经过培训的审阅者进行了病历审查。使用2×2列联表计算阳性预测值（PPV）和95％置信区间（CI）。使用基本描述性统计数据总结了患者的人口统计，临床和健康利用特征。结果获得了109例符合该算法的患者的病历表并进行了回顾。MD的案例识别算法的PPV为95％（95％CI 88-98％）。在103例确诊的MD病例中，有87名患者（85％，95％CI为76-91％）患有Duchenne或Becker MD。76例患者（74％，95％CI 64-82％）患有Duchenne MD，11例患者（11％，95％CI 5-18％）患有Becker MD。总共74（67。9％）患者患有≥1例小儿复杂的慢性病（神经系统/神经肌肉疾病除外）；54（49.5％）人患有心血管疾病；14人（占12.8％）患有呼吸道疾病; 50位（占45.9％）有与骨骼有关的问题；11（10.1％）增长受到损害；其中6个（5.5％）的青春期延迟。结论这项研究的结果表明，病例查找算法可在大型管理数据库中准确识别出患有MD的患者，主要是Duchenne MD。该算法使用从索赔中容易获得的一些项目构造而成，可用于促进Duchenne患者人群的流行病学和健康结果研究。其中6个（5.5％）的青春期延迟。结论这项研究的结果表明，病例查找算法可在大型管理数据库中准确识别出患有MD的患者，主要是Duchenne MD。该算法使用从索赔中容易获得的一些项目构造而成，可用于促进Duchenne患者人群的流行病学和健康结果研究。其中6个（5.5％）的青春期延迟。结论这项研究的结果表明，病例查找算法可在大型管理数据库中准确识别出患有MD的患者，主要是Duchenne MD。该算法使用从索赔中容易获得的一些项目构造而成，可用于促进Duchenne患者人群的流行病学和健康结果研究。