The Who, What, and Where of Primary TKAs: An Analysis of HCUP Data from 2009 to 2015.
Chukwuweike U GwamSamuel RosasRashad SullivanT David LuoCynthia L EmoryJohannes F PlatePublished in: The journal of knee surgery (2019)
The aim of this study was to assess (1) temporal trends, (2) primary indication, (3) patient-level demographics (age, race, gender, health status, and median income quartile), and (4) region and hospital type for all patients receiving primary total knee arthroplasty (TKA) between 2009 and the third quarter of 2015. The National Inpatient Sample Database (NIS) was used to identify all patients who underwent a TKA between 2009 and the third quarter of 2015. Regression analysis was utilized to assess trends. Chi-square analysis was used to explore categorical variables whereas Kruskal-Wallis test was used to explore nonparametric continuous variables. TKA utilization increased between 2009 and 2015 with the highest volume occurring during the fall. Primary osteoarthritis was the primary indication in 98% of cases. There was an increase in minority representation among recipients. Black TKA recipients were younger and had lower median age-adjusted Charlson's comorbidity index (CCI). Black recipients were most likely to be of the lowest 25% of median income than any other races. The Midwest demonstrated the greatest increase in TKAs performed per 100,000 between 2009 and 2014. Case volumes shifted to urban teaching hospitals between 2009 and 2014. There were differences in age of presentation, preoperative morbidity, and income status among races. Furthermore, our findings revealed a more rapid growth in TKA procedures per 100,000 in the Midwest, in addition to volume shifts toward urban teaching hospitals. Future studies are needed to update our findings as well as explore trends in racial disparities for primary TKA recipients.
Keyphrases
- total knee arthroplasty
- total hip
- mental health
- healthcare
- physical activity
- case report
- kidney transplantation
- patients undergoing
- end stage renal disease
- ejection fraction
- chronic kidney disease
- palliative care
- medical students
- neuropathic pain
- single cell
- spinal cord
- deep learning
- acute care
- health insurance
- patient reported
- neural network
- sensitive detection