Trends in Practice Patterns of Conventional and Computer-Assisted Knee Arthroplasty

An Analysis of 570,671 Knee Arthroplasties Between 2010 and 2017

Abdalrahman G. Ahmed; Raymond Kang, MA; Mohamed Hasan, MD, MPH; Yao Tian, PhD, MS, MPH; Hassan M. Ghomrawi, PhD, MPH

Disclosures

J Am Acad Orthop Surg. 2021;29(22):e1117-e1125. 

In This Article

Discussion

The purpose of this article was to examine the trends in utilization of CAKA from 2010 to 2017 in both New York and Florida as a surrogate for national trends and to identify independent predictors of adoption of computer-assisted navigation technology. We found an overall increasing trend in the proportion of KAs performed as CAKAs over the study period. However, the transition from ICD-9-CM to ICD-10-CM codes did not result in an increase the use of modality-specific codes. We also found that disadvantaged populations were not receiving CAKAs at the same rate with an increased disparity on the basis of race and insurance type found in New York compared with Florida.

To our knowledge, this is the first study that examined CAKA trends beyond the ICD-9-CM coding era (Q3 of 2015). We found that 7.52% of all the KAs in New York were CAKAs and 5.39% of all the KAs in Florida were CAKAs during the study period from 2010Q1–2017Q3 (Table 2). These results show a higher proportion of CAKAs out of KAs than reported in previous studies.[1,19–21] Similar to all of these studies, except that by Gholson et al,[20] our study showed an increase in the adoption of computer-assisted navigation technology over time. Our results are likely showing conservative estimates of this increase because it is likely that the code is underreported with no financial incentives associated with reporting computer-assisted codes. This increase while modest may suggest that in more recent years, more surgeons and hospitals have embraced the many advantages of computer-assisted navigation technology over conventional KA to overcome the high initial costs associated with CAKA.[1,4,20,33,34] Cost-effectiveness analysis studies comparing KA and CAKA have shown that costs can be saved from the societal and hospital perspective over the long run.[35–37] Despite these findings, not all hospitals may realize the cost savings. Studies show that CAKA may be cost-effective in high-volume centers but not in low-volume centers, which may restrict the availability of this technology to high-volume centers.[35,36] Further cost-effectiveness studies of CAKAs versus KA are needed as this technology evolves and more data are available on patient outcomes.

Although we had hoped to shed some light on the different modalities used in CAKA using our data, especially with the more detailed ICD-10-CM codes, the general ICD code for computer-assisted surgery was used to code most CAKAs. In fact, the general code was used more frequently during the ICD-10-CM era than during the ICD-9-CM era, and this may indicate a lack of knowledge among institutions of the best code to use shortly after the shift to ICD-10-CM occurred. Based on this finding, it is unclear how this technology is evolving and whether this overall trend is a mix of certain modalities emerging while others are dying off. This issue is expected to continue, especially with no financial incentives to code using the specific modality.[1,19]

Our study also highlights that not all patients are equally likely to receive CAKA. In both New York and Florida, Black patients and Hispanic patients were less likely to receive CAKAs compared with non-Hispanic White patients. Mounting evidence exists on the persistence of racial disparities in receiving KA and revision KA, with minorities having the lowest rates of total KA and the highest revision incidence.[38–40] Other studies have shown that patients who are Black are more likely to receive this surgery in low-volume hospitals, which are typically located in lower-resourced areas.[41–43] In addition, hospitals in these areas are less likely to provide computer-assisted technology.[22] With a recent study showing that low-volume surgeons have high rates of malalignment similar to that of trainees,[14] providing incentives to low-volume hospitals to start using CAKA could significantly help address the inferior implant positioning outcomes associated with a low-volume hospital, and this may reflect positively on the outcomes of minority and disadvantaged populations undergoing KA. Our study also found that having Medicaid coverage was associated with less likelihood of receiving CAKAs in New York but not in Florida, highlighting the potential importance of coverage provisions on access. Medicaid patients having lower rates of technology-assisted surgery in our study is consistent with the prior literature on technology utilization in KAs, and with broader surgical literature.[22,44] Medicaid programs differ significantly across states, and a broader comparison across multiple states is needed to understand the association of different provisions with access to CAKA.

Despite being the first study to examine CAKA trends using ICD-10-CM codes, our study has a number of limitations. The study relies on administrative databases, which are susceptible to coding accuracy issues and may likely result in underreporting of the number of CAKAs performed in these two states. Also, this study was conducted during a major change from ICD-9 to ICD-10 codes. Separate models were run for the ICD-9 and ICD-10 eras, and increasing trends in the utilization of CAKA were still found. Variables found in the combined models were found in the separate models, and results were robust to sensitivity analysis. We did not include in our models information on hospital characteristics such as geographical location, teaching status, and size, which may affect the utilization rates of CAKA. Nonteaching status, urban location, and large size were all found to be hospital characteristics associated with higher volume outcomes of CAKAs and technology-assisted KAs.[19,21] The data were retrieved only from two states, New York and Florida. Although both states are large and diverse and could provide a reasonable perspective on the utilization of CAKA, the findings are not representative of all utilization trends across the United States. Despite these limitations, we believe that the large sample size of the findings gives insight on the increased adoption of computer-assisted navigation technology and the presence of disparities therein.

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