Bedard Nicholas A, Pugely Andrew J, McHugh Michael, Lux Nathan, Otero Jesse E, Bozic Kevin J, Gao Yubo, Callaghan John J
N. A. Bedard, A. J. Pugely, M. McHugh, N. Lux, J. E. Otero, Y. Gao, J. J. Callaghan Department of Orthopaedic Surgery and Rehabilitation, University of Iowa Hospitals and Clinics, Iowa City, IA, USA K. J. Bozic, Department of Surgery & Perioperative Care, Dell Medical School at the University of Texas at Austin, Austin, TX, USA.
Clin Orthop Relat Res. 2018 Jan;476(1):52-63. doi: 10.1007/s11999.0000000000000011.
Use of large clinical and administrative databases for orthopaedic research has increased exponentially. Each database represents unique patient populations and varies in their methodology of data acquisition, which makes it possible that similar research questions posed to different databases might result in answers that differ in important ways.
QUESTIONS/PURPOSES: (1) What are the differences in reported demographics, comorbidities, and complications for patients undergoing primary TKA among four databases commonly used in orthopaedic research? (2) How does the difference in reported complication rates vary depending on whether only inpatient data or 30-day postoperative data are analyzed?
Patients who underwent primary TKA during 2010 to 2012 were identified within the National Surgical Quality Improvement Programs (NSQIP), the Nationwide Inpatient Sample (NIS), the Medicare Standard Analytic Files (MED), and the Humana Administrative Claims database (HAC). NSQIP is a clinical registry that captures both inpatient and outpatient events up to 30 days after surgery using clinical reviewers and strict definitions for each variable. The other databases are administrative claims databases with their comorbidity and adverse event data defined by diagnosis and procedure codes used for reimbursement. NIS is limited to inpatient data only, whereas HAC and MED also have outpatient data. The number of patients undergoing primary TKA from each database was 48,248 in HAC, 783,546 in MED, 393,050 in NIS, and 43,220 in NSQIP. NSQIP definitions for comorbidities and surgical complications were matched to corresponding International Classification of Diseases, 9 Revision/Current Procedural Terminology codes and these coding algorithms were used to query NIS, MED, and HAC. Age, sex, comorbidities, and inpatient versus 30-day postoperative complications were compared across the four databases. Given the large sample sizes, statistical significance was often detected for small, clinically unimportant differences; thus, the focus of comparisons was whether the difference reached an absolute difference of twofold to signify an important clinical difference.
Although there was a higher proportion of males in NIS and NSQIP and patients in NIS were younger, the difference was slight and well below our predefined threshold for a clinically important difference. There was variation in the prevalence of comorbidities and rates of postoperative complications among databases. The prevalence of chronic obstructive pulmonary disease (COPD) and coagulopathy in HAC and MED was more than twice that in NIS and NSQIP (relative risk [RR] for COPD: MED versus NIS 3.1, MED versus NSQIP 4.5, HAC versus NIS 3.6, HAC versus NSQIP 5.3; RR for coagulopathy: MED versus NIS 3.9, MED versus NSQIP 3.1, HAC versus NIS 3.3, HAC versus NSQIP 2.7; p < 0.001 for all comparisons). NSQIP had more than twice the obesity as NIS (RR 0.35). Rates of stroke within 30 days of TKA had more than a twofold difference among all databases (p < 0.001). HAC had more than twice the rates of 30-day complications at all endpoints compared with NSQIP and more than twice the 30-day infections as MED. A comparison of inpatient and 30-day complications rates demonstrated more than twice the amount of wound infections and deep vein thromboses is captured when data are analyzed out to 30 days after TKA (p < 0.001 for all comparisons).
When evaluating research utilizing large databases, one must pay particular attention to the type of database used (administrative claims, clinical registry, or other kinds of databases), time period included, definitions utilized for specific variables, and the population captured to ensure it is best suited for the specific research question. Furthermore, with the advent of bundled payments, policymakers must meticulously consider the data sources used to ensure the data analytics match historical sources.
Level III, therapeutic study.
用于骨科研究的大型临床和管理数据库的使用呈指数级增长。每个数据库代表独特的患者群体,并且在数据采集方法上各不相同,这使得针对不同数据库提出的类似研究问题可能会得出在重要方面存在差异的答案。
问题/目的:(1)在骨科研究中常用的四个数据库中,接受初次全膝关节置换术(TKA)的患者所报告的人口统计学特征、合并症和并发症有哪些差异?(2)根据仅分析住院患者数据还是术后30天数据,报告的并发症发生率差异如何变化?
在国家外科质量改进计划(NSQIP)、全国住院患者样本(NIS)、医疗保险标准分析文件(MED)和Humana管理索赔数据库(HAC)中识别出2010年至2012年期间接受初次TKA的患者。NSQIP是一个临床登记处,通过临床评审人员并对每个变量使用严格定义来记录手术术后30天内的住院和门诊事件。其他数据库是管理索赔数据库,其合并症和不良事件数据由用于报销的诊断和程序代码定义。NIS仅限于住院患者数据,而HAC和MED也有门诊患者数据。每个数据库中接受初次TKA的患者数量分别为:HAC中48,248例,MED中783,546例,NIS中393,050例,NSQIP中43,220例。将NSQIP中合并症和手术并发症的定义与相应的国际疾病分类第9版/当前程序术语代码进行匹配,并使用这些编码算法查询NIS、MED和HAC。在四个数据库之间比较年龄、性别、合并症以及住院患者与术后30天并发症。鉴于样本量较大,对于微小的、临床上无重要意义的差异通常也能检测到统计学显著性;因此,比较的重点是差异是否达到两倍的绝对差异以表明存在重要的临床差异。
尽管NIS和NSQIP中的男性比例较高且NIS中的患者更年轻,但差异很小且远低于我们预先定义的具有临床重要意义差异的阈值。各数据库之间合并症的患病率和术后并发症发生率存在差异。HAC和MED中慢性阻塞性肺疾病(COPD)和凝血病的患病率是NIS和NSQIP中的两倍多(COPD的相对风险[RR]:MED与NIS相比为3.1,MED与NSQIP相比为4.5,HAC与NIS相比为3.6,HAC与NSQIP相比为5.3;凝血病的RR:MED与NIS相比为3.9,MED与NSQIP相比为3.1,HAC与NIS相比为3.3,HAC与NSQIP相比为2.7;所有比较的p < 0.001)。NSQIP中的肥胖症患者数量是NIS中的两倍多(RR为0.35)。TKA术后30天内的中风发生率在所有数据库之间相差两倍多(p < 0.001)。与NSQIP相比,HAC在所有终点的30天并发症发生率是其两倍多,与MED相比,30天感染率是其两倍多。住院患者和术后30天并发症发生率的比较表明,当分析TKA术后30天的数据时,伤口感染和深静脉血栓形成的数量是分析住院患者数据时的两倍多(所有比较的p < 0.001)。
在评估利用大型数据库的研究时,必须特别注意所使用数据库的类型(管理索赔、临床登记或其他类型的数据库)、所包含的时间段、用于特定变量的定义以及所涵盖的人群,以确保其最适合特定的研究问题。此外,随着捆绑支付的出现,政策制定者必须仔细考虑所使用的数据源,以确保数据分析与历史来源相匹配。
III级,治疗性研究。