Robotic-Assisted Total Knee Arthroplasty Does Not Increase Procedure Duration or Adverse Event Incidence: A Retrospective Comparative Cohort Study in a Secondary Public Hospital

Robotic-Assisted Total Knee Arthroplasty Does Not Increase Procedure Duration or Adverse Event Incidence: A Retrospective Comparative Cohort Study in a Secondary Public Hospital | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Robotic-Assisted Total Knee Arthroplasty Does Not Increase Procedure Duration or Adverse Event Incidence: A Retrospective Comparative Cohort Study in a Secondary Public Hospital Michael Singh, Fraser Labrom, Jonathan Bigwood, Corey Scholes, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8724932/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Background Total knee arthroplasty (TKA) with various guidance methods for bone cuts and soft-tissue balancing yield excellent outcomes for end-stage knee osteoarthritis, offering durable pain relief and functional restoration. However, there is a growing interest in further enhancing intraoperative accuracy and consistency, with the aim of reducing complications and reducing recovery times through the introduction of robotic-assisted surgery (RAS). As with any new technology, concerns remain regarding the surgeon learning curve, operative efficiency, and safety profile of RAS compared to conventional techniques. This study evaluates the clinical utility of a RAS system (ROSA, Zimmer Biomet) in a public hospital setting, assessing its impact on procedure duration and adverse event incidence at 90 days postoperatively. Methods A retrospective cohort study was conducted at a mid-sized metropolitan public hospital in Australia. Data was extracted from a departmental registry, electronic medical records, and intraoperative reports from September 2017 to February 2023. The study included 568 TKA cases: 173 instrumented or navigated TKAs performed before RAS introduction (Pre-RAS), 258 robotic-assisted TKAs after RAS adoption (RAS group), and 139 TKAs performed by other department surgeons who did not use RAS (non-RAS), serving as a benchmark for department-wide outcomes. The primary outcomes were procedure duration and adverse event incidence including surgical site infection (SSI), venous thromboembolism (VTE), knee stiffness, and all-cause readmission within 90 days postoperatively. Results The pre-RAS group had significantly longer operative times than the RAS group (128 ± 21.6 min vs. 121.4 ± 19.5 min, p < 0.01), suggesting improved efficiency with RAS adoption. The non-RAS department group had shorter procedure durations than the pre-RAS group (118.3 ± 20.1 min, p < 0.01). Among 67 recorded adverse events, no significant differences in total adverse event incidence were observed between the pre-RAS and RAS groups (12.1% vs. 11.6%, p = 0.78). A non-significant increase in superficial infections in males undergoing RAS-TKA was observed ( p = 0.062). Conclusion RAS-TKA demonstrated reduced operative time and a comparative safety profile to non-robotic TKA. These findings suggest that RAS integration improves surgical efficiency without compromising safety, warranting further investigation into long-term functional and economic outcomes. Figures Figure 1 Figure 2 Figure 3 Introduction Total knee arthroplasty (TKA) remains the gold-standard treatment modality for end-stage knee osteoarthritis [ 1 ]. Despite excellent short- and long-term clinical outcomes [ 2 , 3 ], there remains great interest in pursuing optimisation of TKA delivery to maximise clinical outcomes, while minimising adverse events and resource usage. As such, robotic-assisted surgery (RAS) is rapidly developing as a variation upon the traditional instrumented TKA which has shown promise in achieving these goals [ 4 ]. RAS TKA purported benefits include improved pre-operative planning resources and contemporaneous intra-operative assessment of dynamic ligamentous and osseous balancing [ 5 , 6 ]. Recent meta-analysis has shown that RAS enables a more precise implantation of prosthesis components and reduction in overall mean blood loss [ 7 ]. However, there is a lack of data which shows this consistently translates into improved efficiency and reduction of patient adverse outcomes incidence. The hesitancy surrounding RAS TKA uptake has largely been attributed to assumed increased operative time, costs/resource usage, potential complications, in addition to the expected learning curve with adopting a new operative protocol [ 8 ]. Earlier Markov decision modelling studies suggested computer-navigated TKA as initially more costly at the time of index surgery compared to instrumented methods [ 9 , 10 ], with the later cost benefits of decreased revision rates only being realised in higher volume tertiary centres [ 11 ]. In a recent study, RAS was shown to be comparable to computer-navigated TKA in total in-hospital costs despite a reduced in-hospital rehabilitation timeframe [ 12 ]. However, this study was again performed in a high-volume tertiary centre. Although benefits have been shown in tertiary centres, there remains a clear gap in the literature for an investigation which details the implementation of these robotic-assisted arthroplasty systems in a smaller, secondary public hospital setting. This study aims to assess the use of a RAS system (ROSA, Zimmer Biomet) for TKA on procedure duration, readmission and reoperation rates (and other adverse events) at 90 days followup, in a public hospital setting. It was hypothesised that RAS for TKA provides equivalent procedure durations, readmissions and reoperation rates compared to either non robotic delivery systems within the department. Methods Study Design This case-control retrospective cohort study sourced data from a quality departmental registry, electronic medical records and case reports from the intraoperative systems of interest. The case-control retrospective analysis designed for this analysis provides the most robust comparison between the conditions of having the RAS present versus absent in the same surgeon group, as well as benchmarking against the outcomes of the hospital department. Setting A medium-sized metropolitan public hospital in Australia. The hospital is part of a local health network spanning a region of the city and performs the majority of total knee arthroplasty cases within the network. The department registry was implemented in July 2017. The registry utilises prospectively collected data to aid in the construction of observational cohorts for case-control analyses. The implementation and quality assessment of this particular department registry has been previously published [13]. The robotic assisted surgical system (RAS) was introduced in January 2021 and the first case for the department performed 19-Feb-2021. Registration and Ethics Ethical approval was obtained from an institutional human research ethics committee, with a waiver of consent approved for retrospective chart review. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. No funding sources had any role in the design, conduct, analysis, or reporting of this study Participants and Grouping This study included a total of 568 patients undergoing primary total knee arthroplasty at the study institution between September 2017 and February 2023. This included 173 computer assisted navigation or intramedullary jig based TKAs using Zimmer Persona (Zimmer Biomet) TKA implants (pre-RAS group) performed prior to the introduction of robotic-assisted knee arthroplasty systems at the study site by surgeons who now routinely employ robotic-assisted TKA (Surgeons A, B, C). The RAS Group included 258 primary robotic TKAs using Zimmer Persona (Zimmer Biomet) implants, irrespective of surgeon (contributions from Surgeons A, B, C, E, F, G). The remaining 139 instrumented or navigated TKAs performed by four other departmental surgeons (Surgeons D, E, F, G) were assigned to a non-RAS group, using a variety of implants. Patients were included if they underwent primary total knee arthroplasty between September 2017 and February 2023, with degenerative knee joint disease as the primary indication for surgery. All cases were performed under the care of a consultant orthopaedic surgeon. Records for patients receiving a total knee arthroplasty with or without the involvement of the robotic-assisted surgical (RAS) system (ROSA, Zimmer-Biomet, USA) were included for review and analysis. Exclusion criteria for the RAS system of interest included; Hip pathology with significant bone loss (e.g. avascular necrosis of the femoral head with collapse, severe dysplasia of the femoral head or the acetabulum) Hip pathology severely limiting range of motion (e.g. arthrodesis, severe contractures, chronic severe dislocation) Active infections of the knee joint area Revision TKA surgery Outcomes All demographic data and patient outcomes were retrospectively collected through chart review by three researchers. Demographic data included age, sex, BMI, comorbidities and side of surgery. The primary outcomes of this study are adverse events within 90 days post-surgery and procedure duration (time from skin incision to final skin closure). Adverse events (up to 3 months) is defined as any deviation from the normal clinical course of treatment or recovery including: in-hospital complication event, post-discharge representation, post-discharge readmission, and/or reoperation. Adverse events have been subcategorised into the following categories [14]: Bleed, Thromboembolisms, Neural injury, Vascular injury, Stiffness, Infection (Superficial, Deep, Systemic), Periprosthetic fracture, Patellofemoral pain, Delivery, Readmission, Reoperation, Revision . Data Sources and Measurement Chart Review All relevant registry encounters between July 1 2017 and March 1 2023 were reviewed. All Individual patient charts were accessed through the institutional electronic medical record system. The record of encounters was reviewed by three reviewers and descriptions of any deviation from the normal trajectory of recovery (adverse events) were documented in an electronic form for linkage to the study master database. Details regarding procedure duration, intraoperative surgical technique (including robotic/navigation/ instrumented), implant components, patient demographics and comorbidities were also recorded from the electronic medical record. Intraoperative surgical technique was also cross referenced from the departmental registry as well as case records from the ROSA system records to ensure accuracy. Any conflicting information was physically reviewed to ensure data collection was accurate to the procedure. Study Size The sample sizes are based on the available data from the departmental registry combined with all cases performed on the robotic-assisted surgical system since its introduction in February 2021. Post-hoc power analysis of the available sample demonstrates sufficient sample size to detect non-inferiority between groups (Appendix 6.8). The non-inferiority margin was set a priori at a 17% relative increase over the baseline 90-day superficial surgical site infection rate. The baseline rate was taken from the pre-RAS cohort (2.3% at 90 days). This corresponds to an absolute increase threshold of 0.39 percentage points (2.3% × 0.17), i.e., an upper limit of ~2.7% for the RAS group. This margin was chosen as a clinically small increase in minor SSI risk that would be acceptable given the anticipated workflow and alignment benefits of RAS. Arthroplasty Intervention Patient selection Patients undergoing primary TKA at the study institution between September 2017 and February 2023 were included in the study. They were excluded if there was active infection, underwent revision TKA, had hip pathology with bony loss or had hip pathology significantly reducing ROM. Complex primary (e.g. those requiring varus/valgus constraint or hinged options, post osteotomy and post traumatic arthritis) arthroplasty cases were excluded. Hospital Setting The procedures were performed at a medium size metropolitan public teaching hospital in Australia. All cases were performed either by a consultant orthopaedic surgeon (71% of all cases) or by a training surgeon under the supervision of a consultant orthopaedic surgeon (29% cases overall). General Surgical Technique Considerations General surgical technique, including approach, alignment philosophy, decision for patella resurfacing/release, use of tourniquet, and choice of implants were up to the discretion of the lead surgeon. However, on balance, the medial parapatellar approach, patella resurfacing and tourniquet use are general commonalities within the surgeon cohort (see appendix for full breakdown). Instrumented Surgical Technique Instrumented technique was performed with intramedullary femoral alignment and extramedullary tibial alignment as per the surgical technique from Zimmer-Biomet (Persona, Zimmer-Biomet, USA), Stryker (Triathlon, Stryker, Germany), Depuy (Attune, Depuy Synthes, USA) or Smith and Nephew (Genesis II, Smith and Nephew, USA). Computer Assisted Navigation Surgical Technique Computer assisted navigated technique was performed as per the surgical technique from Zimmer-Biomet (Orthosoft, Zimmer-Biomet, USA) or Stryker (Orthomap Precision, Stryker, Germany). Robot Assisted Surgical Technique RAS surgical technique was performed as per the surgical technique from Zimmer-Biomet (ROSA, Zimmer-Biomet, USA) using Zimmer Persona implants. Data and Statistical Analysis All data for the primary outcome or procedure duration (secondary outcome) was present. To assess the effect of Group on the total incidence of adverse events at up to 90 days follow up, a gamma regression (see supplementary material, table 22) was generated to assess the effect of group on proportions of events, with adjustment for covariates. A directed-acyclic-graph (DAG) method (see supplementary material, figures 9,11,13) was used to map the relationships between the estimand (adverse event incidence) and the primary exposure (group - use of RAS or otherwise) to establish a reasonable model. Odds ratios were calculated and retrieved for the model variables with standard errors and p-values. Model-predicted margins with 95% confidence intervals were used to assess the difference between the RAS group and the non-inferiority margin. Non-inferiority was declared if the upper limit of the one-sided 95% confidence interval for the marginal estimate of adverse event incidence in the RAS group did not exceed a relative difference of 17% from the baseline incidence (as per sample size justification). Procedure duration was fitted to a mixed-effects gamma regression model utilizing the minimal adjustment set identified in the DAG (see supplementary material, figure 18 and 19). Residual fits and leverage points were plotted to assess the fit of the procedure duration model. Sensitivity analyses were performed to assess the impact of early learning curve cases on operative duration and complication incidence. These analyses confirmed that inclusion of initial robotic-assisted cases did not bias the findings. Temporal analysis of adverse event development was conducted using Cox proportional hazards models to generate adjusted survival curves stratified by surgical group and adjusted for relevant covariates. Missing data patterns were assessed and visualised using naniar, and relevant demographic data were supplemented using cross-linked registry records where available. Full data preparation and modelling procedures are documented in the supplementary report. Subgroup interaction effects, including those by sex, were evaluated by incorporating interaction terms into the regression models; significance was assessed using the p-value of the interaction term, with p < 0.05 considered statistically significant. Results Patient characteristics and group comparison Registry data collated over the study course included the record of 588 total surgical encounters across the three groups. Encounters were subsequently examined for eligibility, with 568 surgical encounters ultimately included for comparison at the primary outcome timepoint of 3-months post-operatively. 20 records were excluded: four due to pre-operative cancellation, four due to unavailable medical records, one due to duplication, and eleven due to incorrectly recorded surgery type. The RAS group of surgeons recorded 258 surgical encounters, 3 of the 6 surgeons, who contributed cases to the RAS group had routinely performed non robotic assisted surgery using the same implant from 2017-2021 prior to introduction of the RAS. These ‘pre robotic’ cases numbered 173 and were assigned to pre-RAS group. All encounters in the pre-RAS and RAS groups used the same TKA implants (Zimmer Biomet Persona). The non-RAS group recorded 137 surgical encounters. The Pre-RAS surgeons contributed 59% of RAS cases with surgeons EFG contributing 0% to Pre-RAS and 42% RAS. There were no statistically significant differences between groups in respect to patient demographics, time-frame of recruitment, and surgeon (Table 1). Table 1: Surgeon breakdown, cohort demographics and alignment referencing modality as stratified by surgeon TKA delivery groups Characteristic Overall N = 568 1 RAS N = 258 1 Pre-RAS N = 173 1 Non-RAS N = 137 1 Surgeon A 55 (9.7%) 10 (3.9%) 45 (26%) 0 (0%) B 98 (17%) 44 (17%) 54 (31%) 0 (0%) C 170 (30%) 96 (37%) 74 (43%) 0 (0%) D 46 (8.1%) 0 (0%) 0 (0%) 46 (34%) E 38 (6.7%) 31 (12%) 0 (0%) 7 (5.1%) F 26 (4.6%) 25 (9.7%) 0 (0%) 1 (0.7%) G 135 (24%) 52 (20%) 0 (0%) 83 (61%) Surgery Date 2017-09-15 - 2023-02-07 2021-02-19 - 2023-01-19 2017-09-15 - 2023-02-07 2021-02-02 - 2023-01-30 Surgery Side Left 278 (49%) 131 (51%) 88 (51%) 59 (43%) Right 290 (51%) 127 (49%) 85 (49%) 78 (57%) Alignment Referencing Im Femur/Em Tibia 116 (21%) 0 (0%) 23 (14%) 93 (68%) Navigation 183 (33%) 0 (0%) 140 (86%) 43 (32%) Robotic 254 (46%) 254 (100%) 0 (0%) 0 (0%) Unknown 15 4 10 1 Age At Surgery 69 (62, 76) 69 (64, 76) 68 (61, 75) 70 (63, 77) Unknown 3 3 0 0 Sex Female 322 (57%) 141 (55%) 93 (54%) 88 (64%) Male 246 (43%) 117 (45%) 80 (46%) 49 (36%) 1 n (%); Min - Max; Median (Q1, Q3) Adverse Events From the 568 encounters, a total of 67 adverse events were recorded within the primary outcome variable timeframe of 90 days. Of these, 38 were re-admissions, 24 were re-operation procedures and two revision arthroplasty procedures. Adverse event incidence and adjusted likelihood of adverse event rate was calculated by multi-state models. Re-admission (16 (6.2%) vs 8 (4.8%) vs 14 (10%) (p=0.13)), re-operation (12 (4.7%) vs 4 (2.3%) vs 8 (5.8%) (p=0.3)) and revision rates (0 vs 0 vs 2 (1.5%) p=0.58) were comparable between groups. A complete breakdown is tabulated in Table 2. TKA delivery modality had no significant effect upon incidence of SSI, VTE, stiffness or all-cause readmission after TKA in the first 90 days after surgery (Table 3). Temporal development of these adverse events were examined utilising adjusted survival curves (Figures 1 and 2). When examined via sub-group interaction effect analysis, the cumulative incidence of superficial infection at 90-day follow up for male sex was 6% [95% CI 2% - 10%], but this did not reach significance when compared to the male population within other groups (p=0.062). Further statistical analysis data outputs are available within supplementary material. Procedure Duration The introduction of RAS resulted in a statistically significant decrease in operative procedure time for those surgeons adopting the system (RAS vs Pre-RAS) (∆6.55(2.27) mins; p < 0.01). Mean ± standard deviation (SD) procedure durations were: 128 ± 21.6 min (Pre-RAS), 121.4 ± 19.5 min (RAS), and 118.3 ± 20.1 min (non-RAS). The Pre-RAS group had a procedure duration of 128 (110-151) minutes, RAS group 123 (108-143), and non-RAS group of 120 (103-142). When comparing procedure duration, the non-RAS group was statistically significantly shorter compared to the pre-RAS group (∆-9.68(3.24) (mean(standard error) minutes (mins); p < 0.01). The procedure duration was statistically comparable between non-RAS and RAS groups (∆-3.13(3.13)mins; p = 0.32). This data is graphically represented in Figure 3. Discussion Our key findings indicate that robotic-assisted surgery can be delivered in a public hospital setting without increasing the incidence of adverse events in the 90-day post-operative period. We also observed a modest reduction in procedure duration in the RAS group when compared with the pre-RAS group. Importantly, this study design included a pragmatic comparison group of procedures performed by the same surgeons in the same setting, prior to and during adoption of RAS. This corroborates with current knowledge that RAS or non-RAS technique implementation does not affect the rates of overall adverse event occurrence [6, 15]. The strength of our study is that we were able to directly compare non-robotic arthroplasty with RAS through a pragmatic study with largely the same surgeons and implants in a teaching public hospital. We were able to track this through the temporal transition to RAS from CAS in the public hospital department. Of the 568 total cases included in this study, there were 67 overall adverse events with no significant difference observed between groups for total adverse event incidence. The rates of readmission and reoperation (non-revision) were comparable between RAS and non-RAS groups, Whilst post-operative stiffness was observed at a higher rate in the RAS group (2.7%) than the non-robotic groups (0%, 1.5%), this was not a statistically significant result when corrected for multiple testing (q=0.4). These findings are further supported by the adjusted survival curves presented in Figures 1 and, which illustrate no significant temporal differences in the incidence of superficial infection, venous thromboembolism, postoperative stiffness, or all-cause readmission across the RAS, pre-RAS, and non-RAS groups during the 90-day follow-up period. Current literature suggests that RAS TKA yields lower rates of post-operative stiffness as evidenced by lower rates of manipulation under anaesthesia in robotic cases [15, 17]. Whilst there were no statistically significant differences identified in sex-stratified subgroup analysis of adverse event occurrence, there was a trend towards males who underwent RAS TKA in our cohort experiencing more superficial infections (survival rate: 94% [95% CI 90% - 98%] p=0.062). Low numbers of event occurrence limit capacity to make sound conclusions, this difference approaches our statistical significance threshold and certainly represents an area for further investigation. Current literature reliably demonstrates that increased surgical duration is associated with higher risk of subsequent prosthetic joint infection [24]. Since introduction of RAS TKA at the study site, there was a small but statistically significant decrease in operative time. There was no significant difference in procedure duration between the RAS group and non-RAS control cohort. Current trends in literature tend to suggest that conventional TKA techniques yield shorter operative times than RAS TKA [25]. Of note, our pre-RAS cohort was composed of primarily navigated TKA cases (86%) in contrast to our non-RAS group which predominantly utilized instrumented techniques (68%). Navigated TKA has previously been demonstrated to produce longer procedure durations than instrumented TKA [26] and there is emerging evidence to suggest navigated TKA may in fact yield longer procedure durations than RAS TKA [27]. These findings are corroborated by our study. An additional point of consideration when interpreting operative times trends is surgeon experience with RAS. Chen et al. [28] have described the significant learning curve associated with RAS TKA. They have also shown that a given surgeons’ operative time tends to decrease for approximately the first twenty RAS TKA cases and approximately two-thirds of RAS-utilizing surgeons will achieve the same operative times as instrumented technique following this initial learning curve. Given the study design, our RAS Group was inclusive of the initial learning curve cases for each individual surgeon who took up the RAS system in question. This may, in fact, suggest that a post-learning curve analysis of procedure duration would further favour RAS TKA in regard to decreased operative time. There is a paucity of high-level evidence on the incidence of surgical site infections in robotic-assisted TKA in comparison to instrumented and navigated methods. Meta-analysis [15] of studies investigating surgical site infection in robotic knee arthroplasty has reported superficial surgical site infection incidence as low as 0.57% (CI = 0.209–0.927). Our findings demonstrate no change in superficial infection incidence since the introduction of RAS at the study site. Whilst higher incidence of prosthetic joint infections amongst males undergoing arthroplasty has been well-documented [18], Keemu et al. [19] attributes this to largely to the increased rates of smoking and alcohol consumption in the male population - both independent risk factors for prosthetic joint infections [20]. The interaction between gender and TKA delivery system on post-operative infection has not been previously reported to the authors’ knowledge. The clinical significance of early superficial infection post-TKA is poorly documented in current literature. Galat et al. [21] has previously described an increased occurrence of deep tissue infection (6.0% vs 0.8%) and re-operation (5.3% vs 0.6%) within two years amongst those requiring incision and drainage for superficial infections post-TKA. Previous studies have identified infection depth as a key prognostic factor in post-operative infections following TKA. Extrafascial infections treated with irrigation and debridement have been demonstrated to have higher long-term prosthesis retention rates than deeper (subfascial) infections treated the same [22, 23]. Long-term outcomes for TKAs complicated by superficial infections treated non-surgically are currently not well reported in literature. Limitations This study has several limitations. A key limitation of this study is the variability within groups, which reflects the real-world way robotic-assisted surgery was introduced at our institution. Surgeons adopted the technology at different times, and the case-mix naturally shifted as new consultants joined the department. This means the groups are not perfectly homogenous in terms of surgeon experience, technique history or patient selection. While this captures actual clinical practice, it also introduces within-group variability that may dilute subtle differences between delivery methods. The inclusion of low-volume surgeons may also influence short-term adverse event rates, given the recognised relationship between surgical volume and early complications. These factors should be considered when interpreting comparisons across groups. The retrospective cohort design introduces inherent risk of information bias, as it relies on the accuracy and completeness of routinely collected clinical and registry data. While three investigators independently reviewed charts and cross-checked registry and intraoperative system data, inter-rater reliability was not formally assessed, and misclassification of variables may have occurred. The grouping of patients based on surgeon use of robotic-assisted surgery (RAS) may introduce selection bias. Surgeons may have selected cases based on patient complexity, anatomical variation, or familiarity with technology, which were not fully captured in the dataset. Although statistical adjustment was undertaken using DAG-informed models, residual confounding from unmeasured variables remains possible. While there was no missing data for primary outcomes, demographic and procedural variables had variable completeness. These were addressed through cross-linking with the departmental registry and hospital records. Despite thorough sensitivity analyses, residual confounding from unmeasured factors (e.g. surgeon experience beyond RAS volume) remains possible. The inclusion of early robotic cases may underestimate efficiency gains post-learning curve, though sensitivity testing suggests this impact is modest. Generalisability is limited by the single-centre design and the exclusive use of one robotic platform (ROSA, Zimmer Biomet) as well as a single implant (Persona, Zimmer Biomet). These results may not apply to institutions with different patient populations, surgical workflows, or alternative robotic systems. Whilst the total sample size was sufficient for the primary outcomes, the study was underpowered to detect differences in rare events such as deep infections, periprosthetic fractures, or revisions. Findings related to these outcomes should be interpreted with caution. Our RAS group included each surgeon’s early cases during their learning curve, which may have underestimated potential efficiency gains. Operative time may further decrease with ongoing experience. Finally, the study did not assess long-term outcomes such as implant survivorship or patient-reported functional scores. These measures are critical for evaluating the broader clinical impact of robotic-assisted TKA and should be the focus of future prospective studies. Conclusion RAS-TKA demonstrated reduced operative time and a comparative safety profile to navigated TKA. These findings suggest that RAS integration improves surgical efficiency without compromising safety, warranting further investigation into long-term functional and economic outcomes. The clinical significance of these findings requires further research focused upon long-term outcomes. Declarations Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. No funding sources had any role in the design, conduct, analysis, or reporting of this study Author Contribution M.S., F.L., and J.B. contributed to data collection, chart review, and verification of adverse events. M.S. and F.L. contributed to data curation and initial data interpretation.C.S. contributed to study conception, oversight of the departmental registry, methodological guidance, and critical revision of the manuscript for important intellectual content.L.C. conceived and designed the study, supervised data collection and analysis, performed the statistical analysis, and drafted the manuscript. L.C. is the guarantor of the study.All authors contributed to interpretation of the data, critically reviewed the manuscript, approved the final version for submission, and agree to be accountable for all aspects of the work. Data Availability The data supporting the findings of this study were generated from a prospectively maintained departmental orthopaedic registry and linked hospital electronic medical records. These data contain potentially identifiable patient information and are subject to institutional and ethics approval restrictions. As such, the datasets are not publicly available. De-identified data may be made available from the corresponding author upon reasonable request and with appropriate institutional and ethics approvals. References Price AJ, Alvand A, Troelsen A, Katz JN, Hooper G, Gray A et al (2018) Knee replacement. Lancet 392(10158):1672–1682 Cacciola G, Mancino F, De Meo F, Di Matteo V, Sculco PK, Cavaliere P et al (2021) Mid-term survivorship and clinical outcomes of the medial stabilized systems in primary total knee arthroplasty: A systematic review. J Orthop 24:157–164 Rodricks DJ, Patil S, Pulido P, Colwell CW (2007) Jr. Press-fit condylar design total knee arthroplasty. 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J Bone Joint Surg Am 90(7):1492–1500 Steffens D, Karunaratne S, McBride K, Gupta S, Horsley M, Fritsch B (2022) Implementation of robotic-assisted total knee arthroplasty in the public health system: a comparative cost analysis. Int Orthop 46(3):481–488 Lee B, Ebrahimi M, Ektas N, Ting CH, Cowley M, Scholes C, Bell C (2020) Implementation and quality assessment of a clinical orthopaedic registry in a public hospital department. BMC Health Serv Res 20. 10.1186/s12913-020-05203-8 Healy WL, Della Valle CJ, Iorio R, Berend KR, Cushner FD, Dalury DF et al (2013) Complications of total knee arthroplasty: standardized list and definitions of the Knee Society. Clin Orthop Relat Res 471(1):215–220 Alrajeb R, Zarti M, Shuia Z, Alzobi O, Ahmed G, Elmhiregh A (2024) Robotic-assisted versus conventional total knee arthroplasty: a systematic review and meta-analysis of randomized controlled trials. Eur J Orthop Surg Traumatol 34(3):1333–1343 Dabuzhsky L, Neuhauser-Daley K, Plaskos C (2017) Post-operative manipulation rates in robotic-assisted TKA using a gap referencing technique. Orthopaedic Proceedings. ;99-B(SUPP_3):87 Malkani AL, Roche MW, Kolisek FR, Gustke KA, Hozack WJ, Sodhi N et al (2020) Manipulation Under Anesthesia Rates in Technology-Assisted versus Conventional-Instrumentation Total Knee Arthroplasty. Surg Technol Int 36:336–340 De Mauro D, Meschini C, Balato G, Ascione T, Festa E, Bizzoca D et al (2024) Sex-related differences in periprosthetic joint infection research. J Bone Joint Infect 9(2):137–142 Keemu H, Alakylä KJ, Klén R, Panula VJ, Venäläinen MS, Haapakoski JJ et al (2023) Risk factors for revision due to prosthetic joint infection following total knee arthroplasty based on 62,087 knees in the Finnish Arthroplasty Register from 2014 to 2020. Acta Orthop 94:215–223 Raj S, Bola H, York T (2023) Robotic-assisted knee replacement surgery & infection: A historical foundation, systematic review and meta-analysis. J Orthop 40:38–46 Galat DD, McGovern SC, Larson DR, Harrington JR, Hanssen AD, Clarke HD (2009) Surgical treatment of early wound complications following primary total knee arthroplasty. J Bone Joint Surg Am 91(1):48–54 Rasul AT, Tsukayama D, Gustilo RB (1991) Effect of Time of Onset and Depth of Infection on the Outcome of Total Knee Arthroplasty Infections. Clin Orthop Relat Research®. ;273 Van Kleunen JP, Knox D, Garino JP, Lee GC (2010) Irrigation and débridement and prosthesis retention for treating acute periprosthetic infections. Clin Orthop Relat Res 468(8):2024–2028 Ravi B, Jenkinson R, O'Heireamhoin S, Austin PC, Aktar S, Leroux TS et al (2019) Surgical duration is associated with an increased risk of periprosthetic infection following total knee arthroplasty: A population-based retrospective cohort study. EClinicalMedicine 16:74–80 Ren Y, Cao S, Wu J, Weng X, Feng B (2019) Efficacy and reliability of active robotic-assisted total knee arthroplasty compared with conventional total knee arthroplasty: a systematic review and meta-analysis. Postgrad Med J 95(1121):125–133 Meyer M, Renkawitz T, Völlner F, Benditz A, Grifka J, Weber M (2021) Pros and cons of navigated versus conventional total knee arthroplasty-a retrospective analysis of over 2400 patients. Arch Orthop Trauma Surg 141(11):1983–1991 Clark TC, Schmidt FH (2013) Robot-Assisted Navigation versus Computer-Assisted Navigation in Primary Total Knee Arthroplasty: Efficiency and Accuracy. ISRN Orthop 2013:794827 Chen Z, Bhowmik-Stoker M, Palmer M, Coppolecchia A, Harder B, Mont MA et al (2023) Time-Based Learning Curve for Robotic-Assisted Total Knee Arthroplasty: A Multicenter Study. J Knee Surg 36(8):873–877 Tables Tables 2 and 3 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table2and3.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 17 May, 2026 Reviews received at journal 26 Mar, 2026 Reviewers agreed at journal 25 Mar, 2026 Reviews received at journal 25 Mar, 2026 Reviewers agreed at journal 25 Mar, 2026 Reviewers invited by journal 11 Mar, 2026 Editor assigned by journal 06 Feb, 2026 Submission checks completed at journal 06 Feb, 2026 First submitted to journal 28 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8724932","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":604413945,"identity":"cdcba391-9d2c-4351-89a7-4608bd38dc2f","order_by":0,"name":"Michael Singh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYDACZgY2IGkBYgBRBVjMgBgtElAtZ4jRwgDTAtLO2EaEFt125mcPflRIMMi38x5+XTjvsGwDe/M2CYaawzi1mB1mMzfsOSPBYHCYL8165rbDxg08x8okGI7h08LDJsHbBtTCzGNmzLvtcGKDRI6ZBAMbfi2Sf/8BHdYM0jIHqEX+DVDLP/xapHkbgN4/zGP8mLcBZAuPmQRjG16/mEnLHJPgMTjMY8bMcyzduI0nrdgisS8dt5bzh59JvqmxkZPvP2P8mafGWraf/fDGGx++WePUAgM8QMwGih1I1CQQ1AABzB9AWhqIVD0KRsEoGAUjBwAAgJhHzIVLntYAAAAASUVORK5CYII=","orcid":"","institution":"University of Queensland","correspondingAuthor":true,"prefix":"","firstName":"Michael","middleName":"","lastName":"Singh","suffix":""},{"id":604413953,"identity":"5e56bb34-199e-4d2c-a3d2-4319a462d0a2","order_by":1,"name":"Fraser Labrom","email":"","orcid":"","institution":"Queen Elizabeth II Jubilee Hospital","correspondingAuthor":false,"prefix":"","firstName":"Fraser","middleName":"","lastName":"Labrom","suffix":""},{"id":604413956,"identity":"383ccc4a-a7f5-488c-8015-12b01c02d0fc","order_by":2,"name":"Jonathan Bigwood","email":"","orcid":"","institution":"Queen Elizabeth II Jubilee Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jonathan","middleName":"","lastName":"Bigwood","suffix":""},{"id":604413957,"identity":"a19c8b67-524b-47d0-9d13-416f0c3026da","order_by":3,"name":"Corey Scholes","email":"","orcid":"","institution":"Queen Elizabeth II Jubilee Hospital","correspondingAuthor":false,"prefix":"","firstName":"Corey","middleName":"","lastName":"Scholes","suffix":""},{"id":604413962,"identity":"f1992c8d-f69a-4460-b15c-4a10eedead11","order_by":4,"name":"Lorenzo Calabro","email":"","orcid":"","institution":"Queen Elizabeth II Jubilee Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lorenzo","middleName":"","lastName":"Calabro","suffix":""}],"badges":[],"createdAt":"2026-01-28 20:23:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8724932/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8724932/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104668285,"identity":"e12e180d-3c88-4c9b-aafd-670943c20fcc","added_by":"auto","created_at":"2026-03-15 16:53:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":9241,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival curve of primary adverse event outcome variable of superficial infection (% incidence) (top) and VTE (bottom) over the postoperative follow up period (days). Survival rates are stratified by Group (RAS, Pre-RAS, Non-RAS).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8724932/v1/b4f9868b46d27bc5263bb9f8.png"},{"id":104668284,"identity":"0e152093-60f5-4633-9779-9e6881af11d5","added_by":"auto","created_at":"2026-03-15 16:53:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":9162,"visible":true,"origin":"","legend":"\u003cp\u003eIncidence model curve of primary adverse event outcome variable of stiffness (% incidence) (top) and all-cause readmission (bottom) over the postoperative follow up period (days). Incidence rates are stratified by TKA mode of delivery surgeon groups (RAS, pre-RAS, non-RAS)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8724932/v1/2d44813f01b51ed99d16d99e.png"},{"id":104668287,"identity":"49ceb02d-535b-408c-989c-535b7fbc9d64","added_by":"auto","created_at":"2026-03-15 16:53:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":7762,"visible":true,"origin":"","legend":"\u003cp\u003eProcedure duration (minutes) for individual study groups with depicted median, inter-quartile ranges and minimum and maximum values shown. Median (IQR1-IQR3) procedure times were 120 (103-142) minutes (mins) for the non-RAS department group; 128 (110-151) mins for the pre-RAS group; and 123 (108-143) mins for the RAS group.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8724932/v1/77dc42ae6672692c40dbcd03.png"},{"id":104668288,"identity":"df0fda0a-c8ff-4837-a614-613c8619db2a","added_by":"auto","created_at":"2026-03-15 16:53:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":758323,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8724932/v1/174c5e12-de48-4de2-93a9-34534e716044.pdf"},{"id":104668286,"identity":"89a0155a-7295-4479-b25f-783d434b19ed","added_by":"auto","created_at":"2026-03-15 16:53:03","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20684,"visible":true,"origin":"","legend":"","description":"","filename":"Table2and3.docx","url":"https://assets-eu.researchsquare.com/files/rs-8724932/v1/62d30d8223b79b76f7039541.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eRobotic-Assisted Total Knee Arthroplasty Does Not Increase Procedure Duration or Adverse Event Incidence: A Retrospective Comparative Cohort Study in a Secondary Public Hospital\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTotal knee arthroplasty (TKA) remains the gold-standard treatment modality for end-stage knee osteoarthritis [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite excellent short- and long-term clinical outcomes [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], there remains great interest in pursuing optimisation of TKA delivery to maximise clinical outcomes, while minimising adverse events and resource usage. As such, robotic-assisted surgery (RAS) is rapidly developing as a variation upon the traditional instrumented TKA which has shown promise in achieving these goals [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRAS TKA purported benefits include improved pre-operative planning resources and contemporaneous intra-operative assessment of dynamic ligamentous and osseous balancing [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Recent meta-analysis has shown that RAS enables a more precise implantation of prosthesis components and reduction in overall mean blood loss [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, there is a lack of data which shows this consistently translates into improved efficiency and reduction of patient adverse outcomes incidence.\u003c/p\u003e \u003cp\u003eThe hesitancy surrounding RAS TKA uptake has largely been attributed to assumed increased operative time, costs/resource usage, potential complications, in addition to the expected learning curve with adopting a new operative protocol [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Earlier Markov decision modelling studies suggested computer-navigated TKA as initially more costly at the time of index surgery compared to instrumented methods [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], with the later cost benefits of decreased revision rates only being realised in higher volume tertiary centres [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In a recent study, RAS was shown to be comparable to computer-navigated TKA in total in-hospital costs despite a reduced in-hospital rehabilitation timeframe [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, this study was again performed in a high-volume tertiary centre.\u003c/p\u003e \u003cp\u003eAlthough benefits have been shown in tertiary centres, there remains a clear gap in the literature for an investigation which details the implementation of these robotic-assisted arthroplasty systems in a smaller, secondary public hospital setting. This study aims to assess the use of a RAS system (ROSA, Zimmer Biomet) for TKA on procedure duration, readmission and reoperation rates (and other adverse events) at 90 days followup, in a public hospital setting. It was hypothesised that RAS for TKA provides equivalent procedure durations, readmissions and reoperation rates compared to either non robotic delivery systems within the department.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch3\u003eStudy Design\u003c/h3\u003e\n\u003cp\u003eThis case-control retrospective cohort study sourced data from a quality departmental registry, electronic medical records and case reports from the intraoperative systems of interest. The case-control retrospective analysis designed for this analysis provides the most robust comparison between the conditions of having the RAS present versus absent in the same surgeon group, as well as benchmarking against the outcomes of the hospital department.\u003c/p\u003e\n\u003ch3\u003eSetting\u003c/h3\u003e\n\u003cp\u003eA medium-sized metropolitan public hospital in Australia. The hospital is part of a local health network spanning a region of the city and performs the majority of total knee arthroplasty cases within the network. The department registry was implemented in July 2017. The registry utilises prospectively collected data to aid in the construction of observational cohorts for case-control analyses.\u0026nbsp;The implementation and quality assessment of this particular department registry has been previously published [13]. The robotic assisted surgical system (RAS) was introduced in January 2021 and the first case for the department performed 19-Feb-2021.\u003c/p\u003e\n\u003ch3\u003eRegistration and Ethics\u003c/h3\u003e\n\u003cp\u003eEthical approval was obtained from an institutional human research ethics committee, with a waiver of consent approved for retrospective chart review.\u003c/p\u003e\n\u003ch3\u003eFunding\u003c/h3\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. No funding sources had any role in the design, conduct, analysis, or reporting of this study\u003c/p\u003e\n\u003ch3\u003eParticipants and Grouping\u003c/h3\u003e\n\u003cp\u003eThis study included a total of 568 patients undergoing primary total knee arthroplasty at the study institution between September 2017 and February 2023. This included 173 computer assisted navigation or intramedullary jig based TKAs using Zimmer Persona (Zimmer Biomet) TKA implants (pre-RAS group) performed prior to the introduction of robotic-assisted knee arthroplasty systems at the study site by surgeons who now routinely employ robotic-assisted TKA (Surgeons A, B, C). The RAS Group included 258 primary robotic TKAs using Zimmer Persona (Zimmer Biomet) implants, irrespective of surgeon (contributions from Surgeons A, B, C, E, F, G). The remaining 139 instrumented or navigated TKAs performed by four other departmental surgeons (Surgeons D, E, F, G) were assigned to a non-RAS group, using a variety of implants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePatients were included if they underwent primary total knee arthroplasty between September 2017 and February 2023, with degenerative knee joint disease as the primary indication for surgery. All cases were performed under the care of a consultant orthopaedic surgeon. Records for patients receiving a total knee arthroplasty with or without the involvement of the robotic-assisted surgical (RAS) system (ROSA, Zimmer-Biomet, USA) were included for review and analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eExclusion criteria for the RAS system of interest included;\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eHip pathology with significant bone loss (e.g. avascular necrosis of the femoral head with collapse, severe dysplasia of the femoral head or the acetabulum)\u003c/li\u003e\n \u003cli\u003eHip pathology severely limiting range of motion (e.g. arthrodesis, severe contractures, chronic severe dislocation)\u003c/li\u003e\n \u003cli\u003eActive infections of the knee joint area\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eRevision TKA surgery\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003eOutcomes\u003c/h3\u003e\n\u003cp\u003eAll demographic data and patient outcomes were retrospectively collected through chart review by three researchers. Demographic data included age, sex, BMI, comorbidities and side of surgery.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe primary outcomes of this study are adverse events within 90 days post-surgery and procedure duration (time from skin incision to final skin closure). Adverse events (up to 3 months) is defined as any deviation from the normal clinical course of treatment or recovery including: in-hospital complication event, post-discharge representation, post-discharge readmission, and/or reoperation. Adverse events have been subcategorised into the following categories [14]: \u003cem\u003eBleed, Thromboembolisms, Neural injury, Vascular injury, Stiffness, Infection (Superficial, Deep, Systemic), Periprosthetic fracture, Patellofemoral pain, Delivery, Readmission, Reoperation, Revision\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eData Sources and Measurement\u003c/h3\u003e\n\u003ch4\u003eChart Review\u003c/h4\u003e\n\u003cp\u003eAll relevant registry encounters between July 1 2017 and March 1 2023 were reviewed. All Individual patient charts were accessed through the institutional electronic medical record system. The record of encounters was reviewed by three reviewers and descriptions of any deviation from the normal trajectory of recovery (adverse events) were documented in an electronic form for linkage to the study master database. Details regarding procedure duration, intraoperative surgical technique (including robotic/navigation/ instrumented), implant components, patient demographics and comorbidities were also recorded from the electronic medical record. Intraoperative surgical technique was also cross referenced from the departmental registry as well as case records from the ROSA system records to ensure accuracy. Any conflicting information was physically reviewed to ensure data collection was accurate to the procedure.\u003c/p\u003e\n\u003ch3\u003eStudy Size\u003c/h3\u003e\n\u003cp\u003eThe sample sizes are based on the available data from the departmental registry combined with all cases performed on the robotic-assisted surgical system since its introduction in February 2021. Post-hoc power analysis of the available sample demonstrates sufficient sample size to detect non-inferiority between groups (Appendix 6.8). The non-inferiority margin was set a priori at a 17% relative increase over the baseline 90-day superficial surgical site infection rate. The baseline rate was taken from the pre-RAS cohort (2.3% at 90 days). This corresponds to an absolute increase threshold of 0.39 percentage points (2.3% \u0026times; 0.17), i.e., an upper limit of ~2.7% for the RAS group. This margin was chosen as a clinically small increase in minor SSI risk that would be acceptable given the anticipated workflow and alignment benefits of RAS.\u003c/p\u003e\n\u003ch3\u003eArthroplasty Intervention\u003c/h3\u003e\n\u003ch4\u003ePatient selection\u003c/h4\u003e\n\u003cp\u003ePatients undergoing primary TKA at the study institution between September 2017 and February 2023 were included in the study. They were excluded if there was active infection, underwent revision TKA, had hip pathology with bony loss or had hip pathology significantly reducing ROM. Complex primary (e.g. those requiring varus/valgus constraint or hinged options, post osteotomy and post traumatic arthritis) arthroplasty cases were excluded.\u0026nbsp;\u003c/p\u003e\n\u003ch4\u003eHospital Setting\u003c/h4\u003e\n\u003cp\u003eThe procedures were performed at a medium size metropolitan public teaching hospital in Australia. All cases were performed either by a consultant orthopaedic surgeon (71% of all cases) or by a training surgeon under the supervision of a consultant orthopaedic surgeon (29% cases overall).\u003c/p\u003e\n\u003ch4\u003eGeneral Surgical Technique Considerations\u003c/h4\u003e\n\u003cp\u003eGeneral surgical technique, including approach, alignment philosophy, decision for patella resurfacing/release, use of tourniquet, and choice of implants were up to the discretion of the lead surgeon. However, on balance, the medial parapatellar approach, patella resurfacing and tourniquet use are general commonalities within the surgeon cohort (see appendix for full breakdown).\u0026nbsp;\u003c/p\u003e\n\u003ch4\u003eInstrumented Surgical Technique\u003c/h4\u003e\n\u003cp\u003eInstrumented technique was performed with intramedullary femoral alignment and extramedullary tibial alignment as per the surgical technique from Zimmer-Biomet (Persona, Zimmer-Biomet, USA), Stryker (Triathlon, Stryker, Germany), Depuy (Attune, Depuy Synthes, USA) or Smith and Nephew (Genesis II, Smith and Nephew, USA).\u003c/p\u003e\n\u003ch4\u003eComputer Assisted Navigation Surgical Technique\u003c/h4\u003e\n\u003cp\u003eComputer assisted navigated technique was performed as per the surgical technique from Zimmer-Biomet (Orthosoft, Zimmer-Biomet, USA) or Stryker (Orthomap Precision, Stryker, Germany).\u003c/p\u003e\n\u003ch4\u003eRobot Assisted Surgical Technique\u003c/h4\u003e\n\u003cp\u003eRAS surgical technique was performed as per the surgical technique from Zimmer-Biomet (ROSA, Zimmer-Biomet, USA) using Zimmer Persona implants.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eData and Statistical Analysis\u003c/h3\u003e\n\u003cp\u003eAll data for the primary outcome or procedure duration (secondary outcome) was present.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo assess the effect of \u003cem\u003eGroup\u003c/em\u003e on the total incidence of adverse events at up to 90 days follow up, a gamma regression (see supplementary material, table 22) was generated to assess the effect of \u003cem\u003egroup\u003c/em\u003e on proportions of events, with adjustment for covariates. A directed-acyclic-graph (DAG) method (see supplementary material, figures 9,11,13) was used to map the relationships between the estimand (adverse event incidence) and the primary exposure (group - use of RAS or otherwise) to establish a reasonable model. Odds ratios were calculated and retrieved for the model variables with standard errors and p-values. Model-predicted margins with 95% confidence intervals were used to assess the difference between the RAS group and the non-inferiority margin. Non-inferiority was declared if the upper limit of the one-sided 95% confidence interval for the marginal estimate of adverse event incidence in the RAS group did not exceed a relative difference of 17% from the baseline incidence (as per sample size justification).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eProcedure duration was fitted to a mixed-effects gamma regression model utilizing the minimal adjustment set identified in the DAG (see supplementary material, figure 18 and 19). Residual fits and leverage points were plotted to assess the fit of the procedure duration model.\u003c/p\u003e\n\u003cp\u003eSensitivity analyses were performed to assess the impact of early learning curve cases on operative duration and complication incidence. These analyses confirmed that inclusion of initial robotic-assisted cases did not bias the findings. Temporal analysis of adverse event development was conducted using Cox proportional hazards models to generate adjusted survival curves stratified by surgical group and adjusted for relevant covariates. Missing data patterns were assessed and visualised using naniar, and relevant demographic data were supplemented using cross-linked registry records where available. Full data preparation and modelling procedures are documented in the supplementary report. Subgroup interaction effects, including those by sex, were evaluated by incorporating interaction terms into the regression models; significance was assessed using the p-value of the interaction term, with p \u0026lt; 0.05 considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003ch3\u003ePatient characteristics and group comparison\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eRegistry data collated over the study course included the record of 588 total surgical encounters across the three groups. Encounters were subsequently examined for eligibility, with 568 surgical encounters ultimately included for comparison at the primary outcome timepoint of 3-months post-operatively. 20 records were excluded: four due to pre-operative cancellation, four due to unavailable medical records, one due to duplication, and eleven due to incorrectly recorded surgery type. The RAS group of surgeons recorded 258 surgical encounters, 3 of the 6 surgeons, who contributed cases to the RAS group had routinely performed non robotic assisted surgery using the same implant from 2017-2021 prior to introduction of the RAS. These \u0026lsquo;pre robotic\u0026rsquo; cases numbered 173 and were assigned to pre-RAS group. All encounters in the pre-RAS and RAS groups used the same TKA implants (Zimmer Biomet Persona). The non-RAS group recorded 137 surgical encounters. The Pre-RAS surgeons contributed 59% of RAS cases with surgeons EFG contributing 0% to Pre-RAS and 42% RAS. There were no statistically significant differences between groups in respect to patient demographics, time-frame of recruitment, and surgeon (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1: Surgeon breakdown, cohort demographics and alignment referencing modality as stratified by surgeon TKA delivery groups\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"591\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e N = 568\u003cem\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRAS\u003cbr\u003e\u003c/strong\u003e N = 258\u003cem\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-RAS\u003cbr\u003e\u003c/strong\u003eN = 173\u003cem\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-RAS\u003cbr\u003e\u003c/strong\u003e N = 137\u003cem\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eSurgeon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e55 (9.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e10 (3.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e45 (26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e98 (17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e44 (17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e54 (31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e170 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e96 (37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e74 (43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e46 (8.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e46 (34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e38 (6.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e31 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e7 (5.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e26 (4.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e25 (9.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e1 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e135 (24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e52 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e83 (61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eSurgery Date\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e2017-09-15 - 2023-02-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e2021-02-19 - 2023-01-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e2017-09-15 - 2023-02-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e2021-02-02 - 2023-01-30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eSurgery Side\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Left\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e278 (49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e131 (51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e88 (51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e59 (43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Right\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e290 (51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e127 (49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e85 (49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e78 (57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eAlignment Referencing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Im Femur/Em Tibia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e116 (21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e23 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e93 (68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Navigation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e183 (33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e140 (86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e43 (32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Robotic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e254 (46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e254 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eAge At Surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e69 (62, 76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e69 (64, 76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e68 (61, 75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e70 (63, 77)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e322 (57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e141 (55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e93 (54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e88 (64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e246 (43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e117 (45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e80 (46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e49 (36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 591px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/em\u003en (%); Min - Max; Median (Q1, Q3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003eAdverse Events\u003c/h3\u003e\n\u003cp\u003eFrom the 568 encounters, a total of 67 adverse events were recorded within the primary outcome variable timeframe of 90 days. Of these, 38 were re-admissions, 24 were re-operation procedures and two revision arthroplasty procedures. Adverse event incidence and adjusted likelihood of adverse event rate was calculated by multi-state models. Re-admission (16 (6.2%) vs 8 (4.8%) vs 14 (10%) (p=0.13)), re-operation (12 (4.7%) vs 4 (2.3%) vs 8 (5.8%) (p=0.3)) and revision rates (0 vs 0 vs 2 (1.5%) p=0.58) were comparable between groups. A complete breakdown is tabulated in Table 2.\u003c/p\u003e\n\u003cp\u003eTKA delivery modality had no significant effect upon incidence of SSI, VTE, stiffness or all-cause readmission after TKA in the first 90 days after surgery (Table 3). Temporal development of these adverse events were examined utilising adjusted survival curves (Figures 1 and 2). When examined via sub-group interaction effect analysis, the cumulative incidence of superficial infection at 90-day follow up for male sex was 6% [95% CI 2% - 10%], but this did not reach significance when compared to the male population within other groups (p=0.062). Further statistical analysis data outputs are available within supplementary material.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eProcedure Duration\u003c/h3\u003e\n\u003cp\u003eThe introduction of RAS resulted in a statistically significant decrease in operative procedure time for those surgeons adopting the system (RAS vs Pre-RAS) (∆6.55(2.27) mins; p \u0026lt; 0.01). Mean \u0026plusmn; standard deviation (SD) procedure durations were: 128 \u0026plusmn; 21.6 min (Pre-RAS), 121.4 \u0026plusmn; 19.5 min (RAS), and 118.3 \u0026plusmn; 20.1 min (non-RAS). The Pre-RAS group had a procedure duration of 128 (110-151) minutes, RAS group 123 (108-143), and non-RAS group of 120 (103-142). When comparing procedure duration, the non-RAS group was statistically significantly shorter compared to the pre-RAS group (∆-9.68(3.24) (mean(standard error) minutes (mins); p \u0026lt; 0.01). The procedure duration was statistically comparable between non-RAS and RAS groups (∆-3.13(3.13)mins; p = 0.32). This data is graphically represented in Figure 3.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur key findings indicate that robotic-assisted surgery can be delivered in a public hospital setting without increasing the incidence of adverse events in the 90-day post-operative period.\u0026nbsp;We also observed a modest reduction in procedure duration in the RAS group when compared with the pre-RAS group. Importantly, this study design included a pragmatic comparison group of procedures performed by the same surgeons in the same setting, prior to and during adoption of RAS.\u003c/p\u003e\n\u003cp\u003eThis corroborates with current knowledge that RAS or non-RAS technique implementation does not affect the rates of overall adverse event occurrence [6, 15]. The strength of our study is that we were able to directly compare non-robotic arthroplasty with RAS through a pragmatic study with largely the same surgeons and implants in a teaching public hospital. We were able to track this through the temporal transition to RAS from CAS in the public hospital department. Of the 568 total cases included in this study, there were 67 overall adverse events with no significant difference observed between groups for total adverse event incidence. The rates of readmission and reoperation (non-revision) were comparable between RAS and non-RAS groups, Whilst post-operative stiffness was observed at a higher rate in the RAS group (2.7%) than the non-robotic groups (0%, 1.5%), this was not a statistically significant result when corrected for multiple testing (q=0.4). These findings are further supported by the adjusted survival curves presented in Figures 1 and, which illustrate no significant temporal differences in the incidence of superficial infection, venous thromboembolism, postoperative stiffness, or all-cause readmission across the RAS, pre-RAS, and non-RAS groups during the 90-day follow-up period. Current literature suggests that RAS TKA yields lower rates of post-operative stiffness as evidenced by lower rates of manipulation under anaesthesia in robotic cases [15, 17]. Whilst there were no statistically significant differences identified in sex-stratified subgroup analysis of adverse event occurrence, there was a trend towards males who underwent RAS TKA in our cohort experiencing more superficial infections (survival rate: 94% [95% CI 90% - 98%] p=0.062). Low numbers of event occurrence limit capacity to make sound conclusions, this difference approaches our statistical significance threshold and certainly represents an area for further investigation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCurrent literature reliably demonstrates that increased surgical duration is associated with higher risk of subsequent prosthetic joint infection [24]. Since introduction of RAS TKA at the study site, there was a small but statistically significant decrease in operative time. There was no significant difference in procedure duration between the RAS group and non-RAS control cohort. Current trends in literature tend to suggest that conventional TKA techniques yield shorter operative times than RAS TKA [25]. Of note, our pre-RAS cohort was composed of primarily navigated TKA cases (86%) in contrast to our non-RAS group which predominantly utilized instrumented techniques (68%). Navigated TKA has previously been demonstrated to produce longer procedure durations than instrumented TKA [26] and there is emerging evidence to suggest navigated TKA may in fact yield longer procedure durations than RAS TKA [27]. These findings are corroborated by our study. An additional point of consideration when interpreting operative times trends is surgeon experience with RAS. Chen et al. [28] have described the significant learning curve associated with RAS TKA. They have also shown that a given surgeons’ operative time tends to decrease for approximately the first twenty RAS TKA cases and approximately two-thirds of RAS-utilizing surgeons will achieve the same operative times as instrumented technique following this initial learning curve. Given the study design, our RAS Group was inclusive of the initial learning curve cases for each individual surgeon who took up the RAS system in question. This may, in fact, suggest that a post-learning curve analysis of procedure duration would further favour RAS TKA in regard to decreased operative time.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere is a paucity of high-level evidence on the incidence of surgical site infections in robotic-assisted TKA in comparison to instrumented and navigated methods. Meta-analysis [15] of studies investigating surgical site infection in robotic knee arthroplasty has reported superficial surgical site infection incidence as low as 0.57% (CI = 0.209–0.927). Our findings demonstrate no change in superficial infection incidence since the introduction of RAS at the study site. Whilst higher incidence of prosthetic joint infections amongst males undergoing arthroplasty has been well-documented [18], Keemu et al. [19] attributes this to largely to the increased rates of smoking and alcohol consumption in the male population - both independent risk factors for prosthetic joint infections [20]. The interaction between gender and TKA delivery system on post-operative infection has not been previously reported to the authors’ knowledge.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe clinical significance of early superficial infection post-TKA is poorly documented in current literature. Galat et al. [21] has previously described an increased occurrence of deep tissue infection (6.0% vs 0.8%) and re-operation (5.3% vs 0.6%) within two years amongst those requiring incision and drainage for superficial infections post-TKA. Previous studies have identified infection depth as a key prognostic factor in post-operative infections following TKA. Extrafascial infections treated with irrigation and debridement have been demonstrated to have higher long-term prosthesis retention rates than deeper (subfascial) infections treated the same [22, 23]. Long-term outcomes for TKAs complicated by superficial infections treated non-surgically are currently not well reported in literature. \u0026nbsp;\u003c/p\u003e\n\u003ch1\u003eLimitations\u0026nbsp;\u003c/h1\u003e\n\u003cp\u003eThis study has several limitations. A key limitation of this study is the variability within groups, which reflects the real-world way robotic-assisted surgery was introduced at our institution. Surgeons adopted the technology at different times, and the case-mix naturally shifted as new consultants joined the department. This means the groups are not perfectly homogenous in terms of surgeon experience, technique history or patient selection. While this captures actual clinical practice, it also introduces within-group variability that may dilute subtle differences between delivery methods. The inclusion of low-volume surgeons may also influence short-term adverse event rates, given the recognised relationship between surgical volume and early complications. These factors should be considered when interpreting comparisons across groups.\u003c/p\u003e\n\u003cp\u003eThe retrospective cohort design introduces inherent risk of information bias, as it relies on the accuracy and completeness of routinely collected clinical and registry data. While three investigators independently reviewed charts and cross-checked registry and intraoperative system data, inter-rater reliability was not formally assessed, and misclassification of variables may have occurred.\u003c/p\u003e\n\u003cp\u003eThe grouping of patients based on surgeon use of robotic-assisted surgery (RAS) may introduce selection bias. Surgeons may have selected cases based on patient complexity, anatomical variation, or familiarity with technology, which were not fully captured in the dataset. Although statistical adjustment was undertaken using DAG-informed models, residual confounding from unmeasured variables remains possible.\u003c/p\u003e\n\u003cp\u003eWhile there was no missing data for primary outcomes, demographic and procedural variables had variable completeness. These were addressed through cross-linking with the departmental registry and hospital records. Despite thorough sensitivity analyses, residual confounding from unmeasured factors (e.g. surgeon experience beyond RAS volume) remains possible. The inclusion of early robotic cases may underestimate efficiency gains post-learning curve, though sensitivity testing suggests this impact is modest.\u003c/p\u003e\n\u003cp\u003eGeneralisability is limited by the single-centre design and the exclusive use of one robotic platform (ROSA, Zimmer Biomet) as well as a single implant (Persona, Zimmer Biomet). These results may not apply to institutions with different patient populations, surgical workflows, or alternative robotic systems.\u003c/p\u003e\n\u003cp\u003eWhilst the total sample size was sufficient for the primary outcomes, the study was underpowered to detect differences in rare events such as deep infections, periprosthetic fractures, or revisions. Findings related to these outcomes should be interpreted with caution.\u003c/p\u003e\n\u003cp\u003eOur RAS group included each surgeon’s early cases during their learning curve, which may have underestimated potential efficiency gains. Operative time may further decrease with ongoing experience.\u003c/p\u003e\n\u003cp\u003eFinally, the study did not assess long-term outcomes such as implant survivorship or patient-reported functional scores. These measures are critical for evaluating the broader clinical impact of robotic-assisted TKA and should be the focus of future prospective studies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eRAS-TKA demonstrated reduced operative time and a comparative safety profile to navigated TKA. These findings suggest that RAS integration improves surgical efficiency without compromising safety, warranting further investigation into long-term functional and economic outcomes. The clinical significance of these findings requires further research focused upon long-term outcomes. \u0026nbsp;\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. No funding sources had any role in the design, conduct, analysis, or reporting of this study\u003c/p\u003e \u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM.S., F.L., and J.B. contributed to data collection, chart review, and verification of adverse events. M.S. and F.L. contributed to data curation and initial data interpretation.C.S. contributed to study conception, oversight of the departmental registry, methodological guidance, and critical revision of the manuscript for important intellectual content.L.C. conceived and designed the study, supervised data collection and analysis, performed the statistical analysis, and drafted the manuscript. L.C. is the guarantor of the study.All authors contributed to interpretation of the data, critically reviewed the manuscript, approved the final version for submission, and agree to be accountable for all aspects of the work.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data supporting the findings of this study were generated from a prospectively maintained departmental orthopaedic registry and linked hospital electronic medical records. These data contain potentially identifiable patient information and are subject to institutional and ethics approval restrictions. As such, the datasets are not publicly available. De-identified data may be made available from the corresponding author upon reasonable request and with appropriate institutional and ethics approvals.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePrice AJ, Alvand A, Troelsen A, Katz JN, Hooper G, Gray A et al (2018) Knee replacement. Lancet 392(10158):1672\u0026ndash;1682\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCacciola G, Mancino F, De Meo F, Di Matteo V, Sculco PK, Cavaliere P et al (2021) Mid-term survivorship and clinical outcomes of the medial stabilized systems in primary total knee arthroplasty: A systematic review. J Orthop 24:157\u0026ndash;164\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRodricks DJ, Patil S, Pulido P, Colwell CW (2007) Jr. Press-fit condylar design total knee arthroplasty. Fourteen to seventeen-year follow-up. J Bone Joint Surg Am 89(1):89\u0026ndash;95\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJacofsky DJ, Allen M (2016) Robotics in Arthroplasty: A Comprehensive Review. J Arthroplasty 31(10):2353\u0026ndash;2363\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarchand RC, Sodhi N, Bhowmik-Stoker M, Scholl LY, Condrey C, Khlopas A et al (2018) Does the Robotic Arm and Preoperative CT Planning Help with 3D Intraoperative Total Knee Arthroplasty Planning? J Knee Surg 32:742\u0026ndash;749\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHampp EL, Chughtai M, Scholl LY, Sodhi N, Bhowmik-Stoker M, Jacofsky DJ et al (2019) Robotic-Arm Assisted Total Knee Arthroplasty Demonstrated Greater Accuracy and Precision to Plan Compared with Manual Techniques. J Knee Surg 32(3):239\u0026ndash;250\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOnggo JR, Onggo JD, De Steiger R, Hau R (2020) Robotic-assisted total knee arthroplasty is comparable to conventional total knee arthroplasty: a meta-analysis and systematic review. Arch Orthop Trauma Surg 140(10):1533\u0026ndash;1549\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCacciola G, Bosco F, Giustra F, Risitano S, Capella M, Bistolfi A et al (2022) Learning Curve in Robotic-Assisted Total Knee Arthroplasty: A Systematic Review of the Literature. Appl Sci 12(21):11085\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNovak EJ, Silverstein MD, Bozic KJ (2007) The cost-effectiveness of computer-assisted navigation in total knee arthroplasty. J Bone Joint Surg Am 89(11):2389\u0026ndash;2397\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWatters TS, Mather RC 3rd, Browne JA, Berend KR, Lombardi AV Jr., Bolognesi MP (2011) Analysis of procedure-related costs and proposed benefits of using patient-specific approach in total knee arthroplasty. J Surg Orthop Adv 20(2):112\u0026ndash;116\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSlover JD, Tosteson AN, Bozic KJ, Rubash HE, Malchau H (2008) Impact of hospital volume on the economic value of computer navigation for total knee replacement. J Bone Joint Surg Am 90(7):1492\u0026ndash;1500\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteffens D, Karunaratne S, McBride K, Gupta S, Horsley M, Fritsch B (2022) Implementation of robotic-assisted total knee arthroplasty in the public health system: a comparative cost analysis. Int Orthop 46(3):481\u0026ndash;488\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee B, Ebrahimi M, Ektas N, Ting CH, Cowley M, Scholes C, Bell C (2020) Implementation and quality assessment of a clinical orthopaedic registry in a public hospital department. BMC Health Serv Res 20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12913-020-05203-8\u003c/span\u003e\u003cspan address=\"10.1186/s12913-020-05203-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHealy WL, Della Valle CJ, Iorio R, Berend KR, Cushner FD, Dalury DF et al (2013) Complications of total knee arthroplasty: standardized list and definitions of the Knee Society. Clin Orthop Relat Res 471(1):215\u0026ndash;220\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlrajeb R, Zarti M, Shuia Z, Alzobi O, Ahmed G, Elmhiregh A (2024) Robotic-assisted versus conventional total knee arthroplasty: a systematic review and meta-analysis of randomized controlled trials. Eur J Orthop Surg Traumatol 34(3):1333\u0026ndash;1343\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDabuzhsky L, Neuhauser-Daley K, Plaskos C (2017) Post-operative manipulation rates in robotic-assisted TKA using a gap referencing technique. Orthopaedic Proceedings. ;99-B(SUPP_3):87\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMalkani AL, Roche MW, Kolisek FR, Gustke KA, Hozack WJ, Sodhi N et al (2020) Manipulation Under Anesthesia Rates in Technology-Assisted versus Conventional-Instrumentation Total Knee Arthroplasty. Surg Technol Int 36:336\u0026ndash;340\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Mauro D, Meschini C, Balato G, Ascione T, Festa E, Bizzoca D et al (2024) Sex-related differences in periprosthetic joint infection research. J Bone Joint Infect 9(2):137\u0026ndash;142\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKeemu H, Alakyl\u0026auml; KJ, Kl\u0026eacute;n R, Panula VJ, Ven\u0026auml;l\u0026auml;inen MS, Haapakoski JJ et al (2023) Risk factors for revision due to prosthetic joint infection following total knee arthroplasty based on 62,087 knees in the Finnish Arthroplasty Register from 2014 to 2020. Acta Orthop 94:215\u0026ndash;223\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaj S, Bola H, York T (2023) Robotic-assisted knee replacement surgery \u0026amp; infection: A historical foundation, systematic review and meta-analysis. J Orthop 40:38\u0026ndash;46\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGalat DD, McGovern SC, Larson DR, Harrington JR, Hanssen AD, Clarke HD (2009) Surgical treatment of early wound complications following primary total knee arthroplasty. J Bone Joint Surg Am 91(1):48\u0026ndash;54\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRasul AT, Tsukayama D, Gustilo RB (1991) Effect of Time of Onset and Depth of Infection on the Outcome of Total Knee Arthroplasty Infections. Clin Orthop Relat Research\u0026reg;. ;273\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Kleunen JP, Knox D, Garino JP, Lee GC (2010) Irrigation and d\u0026eacute;bridement and prosthesis retention for treating acute periprosthetic infections. Clin Orthop Relat Res 468(8):2024\u0026ndash;2028\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRavi B, Jenkinson R, O'Heireamhoin S, Austin PC, Aktar S, Leroux TS et al (2019) Surgical duration is associated with an increased risk of periprosthetic infection following total knee arthroplasty: A population-based retrospective cohort study. EClinicalMedicine 16:74\u0026ndash;80\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRen Y, Cao S, Wu J, Weng X, Feng B (2019) Efficacy and reliability of active robotic-assisted total knee arthroplasty compared with conventional total knee arthroplasty: a systematic review and meta-analysis. Postgrad Med J 95(1121):125\u0026ndash;133\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeyer M, Renkawitz T, V\u0026ouml;llner F, Benditz A, Grifka J, Weber M (2021) Pros and cons of navigated versus conventional total knee arthroplasty-a retrospective analysis of over 2400 patients. Arch Orthop Trauma Surg 141(11):1983\u0026ndash;1991\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClark TC, Schmidt FH (2013) Robot-Assisted Navigation versus Computer-Assisted Navigation in Primary Total Knee Arthroplasty: Efficiency and Accuracy. ISRN Orthop 2013:794827\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen Z, Bhowmik-Stoker M, Palmer M, Coppolecchia A, Harder B, Mont MA et al (2023) Time-Based Learning Curve for Robotic-Assisted Total Knee Arthroplasty: A Multicenter Study. J Knee Surg 36(8):873\u0026ndash;877\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 2 and 3 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-orthopaedic-surgery-and-traumatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejos","sideBox":"Learn more about [European Journal of Orthopaedic Surgery \u0026 Traumatology](http://link.springer.com/journal/590)","snPcode":"590","submissionUrl":"https://submission.springernature.com/new-submission/590/3","title":"European Journal of Orthopaedic Surgery \u0026 Traumatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8724932/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8724932/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTotal knee arthroplasty (TKA) with various guidance methods for bone cuts and soft-tissue balancing yield excellent outcomes for end-stage knee osteoarthritis, offering durable pain relief and functional restoration. However, there is a growing interest in further enhancing intraoperative accuracy and consistency, with the aim of reducing complications and reducing recovery times through the introduction of robotic-assisted surgery (RAS). As with any new technology, concerns remain regarding the surgeon learning curve, operative efficiency, and safety profile of RAS compared to conventional techniques. This study evaluates the clinical utility of a RAS system (ROSA, Zimmer Biomet) in a public hospital setting, assessing its impact on procedure duration and adverse event incidence at 90 days postoperatively.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA retrospective cohort study was conducted at a mid-sized metropolitan public hospital in Australia. Data was extracted from a departmental registry, electronic medical records, and intraoperative reports from September 2017 to February 2023. The study included 568 TKA cases: 173 instrumented or navigated TKAs performed before RAS introduction (Pre-RAS), 258 robotic-assisted TKAs after RAS adoption (RAS group), and 139 TKAs performed by other department surgeons who did not use RAS (non-RAS), serving as a benchmark for department-wide outcomes. The primary outcomes were procedure duration and adverse event incidence including surgical site infection (SSI), venous thromboembolism (VTE), knee stiffness, and all-cause readmission within 90 days postoperatively.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe pre-RAS group had significantly longer operative times than the RAS group (128\u0026thinsp;\u0026plusmn;\u0026thinsp;21.6 min vs. 121.4\u0026thinsp;\u0026plusmn;\u0026thinsp;19.5 min, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), suggesting improved efficiency with RAS adoption. The non-RAS department group had shorter procedure durations than the pre-RAS group (118.3\u0026thinsp;\u0026plusmn;\u0026thinsp;20.1 min, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Among 67 recorded adverse events, no significant differences in total adverse event incidence were observed between the pre-RAS and RAS groups (12.1% vs. 11.6%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.78). A non-significant increase in superficial infections in males undergoing RAS-TKA was observed (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.062).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eRAS-TKA demonstrated reduced operative time and a comparative safety profile to non-robotic TKA. These findings suggest that RAS integration improves surgical efficiency without compromising safety, warranting further investigation into long-term functional and economic outcomes.\u003c/p\u003e","manuscriptTitle":"Robotic-Assisted Total Knee Arthroplasty Does Not Increase Procedure Duration or Adverse Event Incidence: A Retrospective Comparative Cohort Study in a Secondary Public Hospital","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-15 16:52:58","doi":"10.21203/rs.3.rs-8724932/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-17T19:49:45+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-26T07:52:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"59005276593111447595312714677088776671","date":"2026-03-25T14:28:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-25T14:27:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"180943625270159217522583193232617008192","date":"2026-03-25T12:35:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-11T05:02:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-07T04:37:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-07T04:35:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Orthopaedic Surgery \u0026 Traumatology","date":"2026-01-28T20:10:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-orthopaedic-surgery-and-traumatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejos","sideBox":"Learn more about [European Journal of Orthopaedic Surgery \u0026 Traumatology](http://link.springer.com/journal/590)","snPcode":"590","submissionUrl":"https://submission.springernature.com/new-submission/590/3","title":"European Journal of Orthopaedic Surgery \u0026 Traumatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b10e8b4b-a8ee-43cb-8467-579752529021","owner":[],"postedDate":"March 15th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-17T19:49:45+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-17T19:53:47+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-15 16:52:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8724932","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8724932","identity":"rs-8724932","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}