How Large a Study Is Needed to Detect TKA Revision Rate Reductions Attributable to Robotic or Navigated Technologies? A Simulation-based Power Analysis

Abstract

Background Robotic and navigated TKA procedures have been introduced to improve component placement precision in the hope of improving implant survivorship and other clinical outcomes. Although numerous comparative studies have shown enhanced precision and accuracy in placing components, most comparative studies have not shown that such interventions result in improved implant survival. Given what we know about effect sizes from large arthroplasty registries, large cohort studies, and large randomized controlled trials (RCTs), we wondered how large randomized trials would need to be to detect such small differences, and if the number is very high, what that would tell us about the value of these treatments for preventing revision surgery. Questions/purposes In this simulation study, we asked: Given that survivorship differences between technology-assisted TKA (TA-TKA, which we defined as either navigated or robot-assisted TKA) and conventional TKA are either small or absent based on large arthroplasty registries, large cohort studies, and large RCTs, how large would randomized trials need to be to detect small differences between TA-TKA and conventional TKA if they exist, and how long would the follow-up period need to be to have a reasonable chance to detect those differences? Methods We used estimated effect sizes drawn from previous clinical and registry studies, combined with estimates of the accuracy and precision of various navigation and robotic systems, to model and simulate the likely outcomes of potential comparative clinical study designs. To characterize the ranges of patients enrolled and general follow-up times associated with traditional RCT studies, we conducted a structured search of previously published studies evaluating the effect of robotics and navigation on revision rates compared with that of conventional TKA. The structured search of the University of British Columbia’s library database (which automatically searches medical publication databases such as PubMed, Embase, Medline, and Web of Science) and subsequent searching through included studies' reference lists yielded 103 search results. Only clinical studies assessing implant survival differences between patient cohorts of TA-TKA and conventional TKA were included. Studies analyzing registry data, using cadaver specimens, assessing revision TKA, conference proceedings, and preprint services were excluded. Twenty studies met all our inclusion criteria, but only one study reported a statistically significant difference between the conventional and robotic or navigated groups. Next, we generated a large set of patients with simulated TKA (1.5 million), randomly assigning each simulated patient a set of patient-specific factors (age at the index surgery, gender, and BMI) drawn from data from registries and published information. We divided this set of simulated procedures into four groups, each associated with a coronal alignment precision reported for different types of surgical procedures, and randomly assigned each patient an overall coronal alignment consistent with their group’s precision. TA procedures were modeled based on the alignment precision that an intervention could deliver, regardless of whether the technology used was navigation- or robot-assisted. To evaluate the power associated with using different cohort sizes, we ran a Monte Carlo simulation generating 3000 simulated populations that were drawn (with replacement) from the large set of simulated patients with TKA. We simulated the time to revision for aseptic loosening for each patient, computed the corresponding Kaplan-Meier survival curves, and applied a log-rank test to each study for statistical differences in revision rates at concurrent follow-up timepoints (1-25 years). From each simulation associated with a given cohort size, we determined the percentage of simulated studies that found a statistically significant difference at each follow-up interval. For each alternative precision, we then also calculated the expected reduction in revision rates (effect size) attributable to TA-TKA intervention and the number needed to treat (NNT) using TA-TKA to prevent one revision at 2, 5, 10, and 15 years after index surgery for the entire set of Kaplan-Meier survival analyses. Results The results from our simulation found survivorship differences favoring TA-TKA ranging from 1.4% to 2.0% at 15 years of follow-up. Comparative studies would need to enroll between 2500 and 4000 patients in each arm of the study, depending on the precision of the navigated or robotic procedure, to have an 80% chance of showing this reduction in revision rates at 15 years of follow-up. For the highest precision simulated intervention, the NNT using TA-TKA to prevent one revision was 1000 at 2 years, 334 at 5 years, 100 at 10 years, and 50 at 15 years post–index surgery. Conclusion Based on these simulations, it appears that TA-TKA interventions could potentially result in a relative reduction in revision rates as large as 27% (from 7.5% down to about 5.5% at 15 years for the intervention with the most precise coronal alignment); however, since this 2% absolute reduction in revision rates is relatively small in comparison with the baseline success rate of TKA and would not be realized until 15 years after the index surgery, traditional RCT studies would require excessively large numbers of patients to be enrolled and excessively long follow-up times to demonstrate whether such a reduction actually exists. Clinical Relevance Given that the NNTs to avoid revisions at various time points are predicted to be high, it would require correspondingly low system costs to justify broad adoption of TA-TKA based on avoided revision costs alone, though we speculate that technology assistance could perhaps prove to be cost effective in the care of patients who are at an elevated risk of revision.

Robotics versus personalized 3D preoperative planning in total knee arthroplasty: a propensity score-matched analysis

“…On the other hand, various factors could affect postoperative outcomes, including the target coronal alignment, soft tissue balance, rehabilitation and mental expectation. The decrease in malalignment may be more intuitive in reducing revision procedure, just as Hickey et al projected [ 31 ], which requires a much longer follow-up.…”

Can Technology Assistance be Cost Effective in TKA? A Simulation-Based Analysis of a Risk-prioritized, Practice-specific Framework

“…For each simulated clinical scenario, we generated 10,000 simulated TKA patient populations by randomly assigning demographic parameters (age at index surgery, BMI, gender) to each patient drawn from the distributions characterizing each population, using the method reported in a previous study [25]. We then calculated patient-specific reductions in revision risk and the change in QALYs (∆QALY) attributable to each form of TA-TKA that might be used in each of the two clinical scenarios by simulating the outcomes of 5000 operations per patient using the precisions associated with the conventional TKA technique and the three TA-TKA techniques studied (along with one hypothetical ideal-TKA technology that could deliver perfect overall coronal alignment); we repeated this three times to evaluate sensitivity to the estimated effect of surgical precision.…”

“…This metric estimates the expected lifetime reduction in a specific patient’s revision risk (based on their set of demographic factors) because of the improved precision offered by a particular form of TA. To calculate R i , we created a cohort of simulated patients with TKA with the desired demographics (age at index surgery, gender, and BMI) using the procedure described by Hickey et al [25]. For each patient ( i ) in our simulation, we simulated 3 x 5 x 5000 TKA procedures, with each set of 5000 procedures characterized by a variation in the effect size of deviations from neutral overall coronal alignment on revision risk (-25%, +0%, and +25% from the baseline rate) and different TKA techniques (conventional, three TA-TKA systems, and a hypothetical TA intervention that could consistently deliver neutral overall coronal alignment) ( a ) and corresponding coronal alignment precision: ± 3° for a conventional approach ( a = conventional TKA), ± 2.4° for a PSI approach ( a = PSI-TKA [9]), ± 1.9° for a NAV-TA approach ( a = NAV-TKA [9]), and ± 1.0° for an RA-TA approach ( a = RA-TKA [44]), where these estimated precisions were either sourced directly from the cited literature or calculated using outlier percentages (Supplementary Digital Content 1; http://links.lww.com/CORR/A924).…”

“…In a previous study [25], we presented a method for creating large sets of simulated TKA patient populations with distributions of patient-specific factors including age at index surgery, gender, and BMI. In that study, we estimated the relative risks associated with age at index surgery and gender using data acquired from a large registry study on the risk of revision from the Clinical Practice Research Datalink [6].…”

“…We used demographic information drawn from previously reported studies [45,57] to characterize two primary contrasting clinical scenarios in which the distributions of factors describing patients undergoing TKA (in particular, age, gender, and BMI) place one population at a comparatively elevated risk of revision and the second at a comparatively reduced risk of revision. For the three main technologies that could be used in technology-assisted procedures (PSI, navigated, or robotic), we used results from previously reported studies [6,10,22,25,34] to estimate the implant precision in coronal plane alignment that each technology would produce.…”