Perioperative Risk Adjustment for Total Shoulder Arthroplasty: Are Simple Clinically Driven Models Sufficient?
30th November 2016, Symposium: Learning From Large-Scale Orthopaedic Databases
There is growing interest in value-based health care in the United States. Statistical analysis of large databases can inform us of the factors associated with and the probability of adverse events and unplanned readmissions that diminish quality and add expense. For example, increased operating time and high blood urea nitrogen (BUN) are associated with adverse events, whereas patients on antihypertensive medications were more likely to have an unplanned readmission. Many surgeons rely on their knowledge and intuition when assessing the risk of a procedure. Comparing clinically driven with statistically derived risk models of total shoulder arthroplasty (TSA) offers insight into potential gaps between common practice and evidence-based medicine.
28th November 2016, Symposium: Learning From Large-Scale Orthopaedic Databases
National databases are increasingly being used for research in spine surgery; however, one limitation of such databases that has received sparse mention is the frequency of missing data. Studies using these databases often do not emphasize the percentage of missing data for each variable used and do not specify how patients with missing data are incorporated into analyses. This study uses the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database to examine whether different treatments of missing data can influence the results of spine studies.