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• W3041173225 abstract "Where Are We Now? Studies using large databases have contributed greatly to our understanding of medical practice, including orthopaedics. The pioneering work of Wennberg [5] revealed the extent of unwarranted and expensive variation in medical care in the United States. Wennberg’s complex methodology was most commonly applied to Medicare claims data, which contained detailed information on inpatient and outpatient care for nearly all patients older than 65 years. Applying this approach to orthopaedics, Skinner et al. [3] demonstrated wide geographic variation and racial and ethnic disparities in the use of surgical procedures such as TKA. In the current study, Tenget al. [4] reviewed 133 articles published in orthopaedic journals in 2016 and 2017 that used data from the National Inpatient Sample (NIS), a database that records information gathered from a 20% sample of inpatient hospital admissions each year. They found that 93% of these studies did not adhere to at least one of the research design recommendations issued by the Healthcare Cost and Utilization Project (HCUP), which oversees data collection in conjunction with state data collection agencies [2]. The authors suggest that orthopaedic journals should provide a more-rigorous methodologic review of these studies and require adherence to the HCUP recommendations for study design. They also raise the possibility that erroneous conclusions may result if this is not done. Their work is important because it calls into question the soundness of the methods used in a large and growing field of orthopaedic research. Based on their findings, readers should interpret the results of studies using the NIS with caution. Where Do We Need To Go? There has been great interest in using large datasets for research, but there is no single definitive source of data on inpatient and outpatient care for patients of all ages in the United States. There are, however, a number of sources of high-quality data that are now available at low cost, and orthopaedic studies driven by large databases are now extremely common in leading journals. The question is how well these data have been used, and whether we can trust the results. Research using claims data is often criticized for the following reasons: the data are collected for billing purposes, which may introduce bias; the data may be inaccurate or insufficiently detailed for research purposes; and the data do not include physical examination findings or patient-oriented outcomes measures, radiographs, or many other factors of interest to orthopaedic surgeons. Determining the incidence, prevalence, or surgical rates is difficult when the appropriate population denominator is unknown. The results of an analysis of a large claims database such as the NIS may be different from the results of a smaller but more-detailed data collection effort such as the National Surgical Quality Improvement Program [1]. Does this mean that one of these is “wrong”? Perhaps it is a matter of using each tool properly and recognizing its limitations. How Do We Get There? The HCUP recommendations for research design [2] are intended to ensure that the data are used in such a way that sound conclusions can be drawn. For example, it should be made clear that a hospitalization record does not necessarily describe a unique patient; if the same patient were admitted twice during a year, that patient would be counted twice, affecting the results in obvious ways. The data are not suitable for performing an analysis at the level of the state, hospital, or physician; attempts to do so will result in erroneous results. Several studies reviewed by Teng et al. [4] used the presence of secondary diagnosis codes to infer in-hospital events in order to comment on the likelihood of complications associated with surgical procedures. Unfortunately, these codes may signify preexisting comorbidities and may have little or nothing to do with the surgical procedure or hospital episode. There is a recommended method for evaluating comorbidities, but it was not always used. Teng et al. [4] found that the studies in their review did not account for the complex survey design of the NIS. An astute reader of a clinical research study will always ask, “is this finding true?” and “how generalizable is this study? Is there something about the study population that may be different from the patients I see?” A study with a large, diverse group of patients is probably going to be more generalizable than a study using a small group, and the NIS contains a large and diverse group of patients. It is not, however, a simple random sample of hospital admissions or patients. In a complex survey design, there may be deliberate oversampling of some groups relative to the general population in order to provide a sufficient number of observations for a sound statistical analysis. There may be oversampling of minority patients, children, or rural hospitals to provide enough observations. This can be addressed in an analysis by weighting observations; if I count twice as many admissions from rural hospitals as from other hospitals, I weigh each rural hospital observation half as much. Eighty percent of the orthopaedic studies using the NIS failed to account for weighting in their analysis [4]. One of the main goals of the HCUP is to provide national estimates; if weighting is not used in a research study, the results will not reflect the truth at a national level. These estimates may still be valuable, however; a study that includes a very large number of patients may provide accurate information that may be generalizable. But authors should either incorporate the weights into their analysis, which can be done using standard statistical programs such as SAS or Stata, or acknowledge that they are using a non-random sample that may not reflect the national reality, despite the large numbers. Using large datasets is the most valuable when the research question is broad and simple. The analysis is seldom simple, however. It requires an understanding of survey design and the meaning of each variable. Large numbers lead to small p values but not necessarily to a better understanding of the truth. Good research practices produce better research." @default.
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• W3041173225 date "2020-07-03" @default.
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• W3041173225 title "CORR Insights®: Most Orthopaedic Studies Using the National Inpatient Sample Fail to Adhere to Recommended Research Practices: A Systematic Review" @default.
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