Rashid Nazia, Levy Gerald D, Wu Yi-Lin, Zheng Chengyi, Koblick River, Cheetham T Craig
Drug Information Services, Kaiser Permanente SCAL Region, 12254 Bellflower Blvd, Room 106, 1st Floor, Downey, CA, 90242, USA.
Southern California Permanente Medical Group, Kaiser Permanente Southern California, Downey, CA, USA.
Rheumatol Int. 2015 Nov;35(11):1799-807. doi: 10.1007/s00296-015-3284-3. Epub 2015 May 20.
Gout flares have been challenging to identify in retrospective databases due to gout flares not being well documented by diagnosis codes, making it difficult to conduct accurate database studies. Previous studies have used different algorithms, and in this study, we used a computer-based method to identify gout flares. The objectives of this study were to identify gout flares in gout patients newly initiated on urate-lowering therapy and evaluate factors associated with a patient experiencing gout flares after starting drug treatment. This was a retrospective cohort study identifying gout patients newly initiated on a urate-lowering therapy (ULT) during the study time period of January 1, 2007-December 31, 2010. The index date was the first dispensed ULT prescription during the study time period. Patients had to be ≥18 years of age on index date, have no history of prior ULT prescription during 12 months before index date, and were required to have 12 months of continuous membership with drug benefit during pre-/post-index. Electronic chart notes were reviewed to identify gout flares; these reviews helped create a validated computer-based method to further identify patients with gout flares and were categorized into 0 gout flares, 1-2 gout flares, and ≥3 gout flares during the 12 months post-index period. Multivariable logistic regression was used to examine patient and clinical factors associated with gout flares during the 12-month follow-up period. There were 8905 patients identified as the final cohort and 68 % of these patients had one or more gout flares during the 12-month follow-up: 2797 patients (31 %) had 0 gout flares, 4836 (54 %) had 1-2 gout flares, and 1272 patients (14 %) had ≥3 gout flares. Using a multivariate regression analyses, factors independently associated with 1-2 gout flares and ≥3 gout flares versus no gout flares were similar, however, with slight differences, such as younger patients were more likely to have 1-2 gout flares and patients ≥65 years of age had ≥3 gout flares. Factors such as male gender, not attaining sUA goal, having ≥3 comorbidities, diuretics use, no changes in initial ULT dose, and not adhering to ULT all were associated with gout flares versus no gout flares. Using a new method to identify gout flares, we had the opportunity to compare our findings with the previous studies. Our study findings echo other previous studies where older patients, male, diuretics, having a greater number of comorbidities, and non-adherence are more likely to have more gout flares during the first year of newly initiating ULT. There is an unmet need for patients with gout to be educated and managed more closely, especially during the first year.
由于痛风发作在诊断编码中记录不完善，在回顾性数据库中识别痛风发作具有挑战性，这使得开展准确的数据库研究变得困难。以往的研究使用了不同的算法，在本研究中，我们采用了一种基于计算机的方法来识别痛风发作。本研究的目的是在新开始降尿酸治疗的痛风患者中识别痛风发作，并评估开始药物治疗后患者发生痛风发作的相关因素。这是一项回顾性队列研究，在2007年1月1日至2010年12月31日的研究时间段内，识别新开始降尿酸治疗（ULT）的痛风患者。索引日期是研究时间段内首次开具的ULT处方日期。患者在索引日期时必须年满18岁，在索引日期前12个月内无既往ULT处方史，并且在索引前/后需要有12个月的药物福利连续参保记录。对电子病历进行审查以识别痛风发作；这些审查有助于创建一种经过验证的基于计算机的方法，以进一步识别痛风发作患者，并在索引后12个月期间分为0次痛风发作、1 - 2次痛风发作和≥3次痛风发作。使用多变量逻辑回归来检查在12个月随访期内与痛风发作相关的患者和临床因素。共有8905名患者被确定为最终队列，其中68%的患者在12个月随访期间有一次或多次痛风发作：2797名患者（31%）无痛风发作，4836名患者（54%）有1 - 2次痛风发作，1272名患者（14%）有≥3次痛风发作。通过多变量回归分析，与1 - 2次痛风发作和≥3次痛风发作相对于无痛风发作独立相关的因素相似，但存在细微差异，例如年轻患者更有可能有1 - 2次痛风发作，而65岁及以上患者有≥3次痛风发作。男性性别、未达到血尿酸（sUA）目标、有≥3种合并症、使用利尿剂、初始ULT剂量无变化以及未坚持ULT治疗等因素均与痛风发作相对于无痛风发作相关。使用一种新的方法来识别痛风发作，我们有机会将我们的研究结果与以往的研究进行比较。我们的研究结果与其他以往的研究一致，即在新开始ULT治疗的第一年，老年患者、男性、使用利尿剂、合并症较多以及不依从治疗的患者更有可能有更多的痛风发作。痛风患者有未被满足的需求，需要更密切地接受教育和管理，尤其是在第一年。
