• Nicholas A Bedard, Andrew J Pugely, Michael McHugh, Nathan Lux, Jesse E Otero, Kevin J Bozic, Yubo Gao, and John J Callaghan.
• 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 1; 476 (1): 52-63.

BackgroundUse 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?MethodsPatients 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.ResultsAlthough 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).ConclusionsWhen 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 Of EvidenceLevel III, therapeutic study.

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