Nationwide Comparison of Robotic and Navigation-Assisted Total Knee Arthroplasty: Trends, Perioperative Complications, and 90-Day Readmissions (2020–2022 NRD)

Nationwide Comparison of Robotic and Navigation-Assisted Total Knee Arthroplasty: Trends, Perioperative Complications, and 90-Day Readmissions (2020–2022 NRD) | 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 Nationwide Comparison of Robotic and Navigation-Assisted Total Knee Arthroplasty: Trends, Perioperative Complications, and 90-Day Readmissions (2020–2022 NRD) David Maman, Yaniv Steinfeld, Yaron Berkovich This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8149205/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background: Both robotic-assisted (RA-TKA) and navigation-guided (NG-TKA) total knee arthroplasty were developed to enhance component accuracy and alignment. While several studies have examined intraoperative precision and early outcomes, nationwide comparisons of postoperative complications and 90-day readmissions remain limited. Methods: retrospective cohort analysis was performed using the Nationwide Readmissions Database (NRD) 2020-2022, identifying elective primary TKAs performed with computer assistance. Robotic procedures were identified using ICD-10-PCS codes 8E0Y0CZ and 8E0YXCZ, and navigation procedures using 8E0YXBZ, 8E0YXBG, and 8E0YXBF. After exclusions, 1:1 propensity-score matching (caliper 0.01) was applied across demographics, comorbidities, hospital factors, and surgical year. Primary outcome was 90-day all-cause readmission; secondary outcomes included cause-specific readmissions, in-hospital complications, and healthcare resource utilization. Results: Among 72,827 elective computer-assisted TKAs, 48,491 (66.6%) were robotic and 24,336 (33.4%) navigation. Robotic utilization rose from 61.5% in 2020 to 71.0% in 2022 (p<0.001). After matching, 49,219 cases remained. In-hospital complications were generally low, but navigation-assisted TKA showed higher rates of sepsis (0.3% vs 0.1%), venous thromboembolism (0.3% vs 0.2%), pulmonary embolism (0.2% vs 0.1%), and blood transfusion (1.5% vs 0.5%) (all p<0.05). The 90-day readmission rate was similar (4.7% vs 4.6%, p=0.56), though readmission causes differed slightly (p<0.001), with navigation cases more often readmitted for infection, sepsis, or respiratory complications. Index charges were lower in robotic cases (USD 78,123 vs 88,344, p<0.001). Conclusions: Robotic-assisted TKA demonstrated lower perioperative complication rates and comparable short-term readmission risk compared with navigation-guided TKA, with modestly reduced hospital costs. These findings from the most contemporary national cohort support the expanding adoption of robotic systems in knee arthroplasty. Levels of Evidence: Level III Total knee arthroplasty robotic navigation readmission complications NRD Introduction The demand for total knee arthroplasty (TKA) continues to grow globally, driven by aging populations and the increasing prevalence of osteoarthritis. In the United States alone, over 235 primary TKAs per 100,000 individuals are performed annually, and this figure is projected to double by 2030 and triple by 2040. As surgical volume increases, precision technologies such as computer navigation and robotic assistance have been adopted to optimize implant alignment, improve kinematics, and enhance functional recovery [ 1 – 5 ]. Navigation-guided TKA (NG-TKA) provides intraoperative real-time feedback on limb alignment and component orientation, while robotic-assisted TKA (RA-TKA) integrates preoperative planning and semi-active guidance to improve reproducibility and soft-tissue balance [ 6 – 9 ]. Both systems aim to reduce human variability, but they differ in precision, learning curve, and intraoperative workflow [ 10 – 12 ]. Previous studies have shown improved accuracy with both technologies compared with conventional TKA, but the comparative effectiveness of RA-TKA and NG-TKA remains debated. Earlier national analyses using pre-2020 datasets demonstrated lower in-hospital complication rates and shorter length of stay for robotic procedures but did not evaluate short-term readmissions, an increasingly important quality metric reflecting postoperative safety, patient optimization, and hospital resource utilization [ 13 – 16 ]. The introduction of newer-generation robotic systems and broader adoption in recent years warranted a contemporary evaluation. The present study therefore leveraged the 2020–2022 Nationwide Readmissions Database (NRD) the first U.S. dataset incorporating complete post-COVID surgical recovery years to compare 90-day readmissions, perioperative complications, and hospital resource utilization between RA-TKA and NG-TKA after rigorous propensity-score matching. We hypothesized that robotic assistance would be associated with fewer perioperative complications and similar or lower 90-day readmission rates compared with navigation guidance. Materials and Methods Data Source and Study Design We conducted a retrospective cohort study using the Nationwide Readmissions Database (NRD), 2020–2022, developed by HCUP. The NRD is a nationally representative, all-payer database specifically designed to evaluate readmissions. Each year is released as a separate dataset; therefore, only procedures performed from January through September were included to allow for complete 90-day follow-up. The 2022 dataset, the most recent available release, was incorporated, making this the most contemporary nationwide analysis to date. Cohort Identification Primary total knee arthroplasty (TKA) procedures were identified from the primary procedure field (PR1) using ICD-10-PCS codes for knee joint replacement. Computer-assisted technology use was defined by the following procedural codes: Robotic assistance: 8E0Y0CZ, 8E0YXCZ Navigation assistance: 8E0YXBZ, 8E0YXBG, 8E0YXBF When both code types were present, the case was classified as robotic-assisted. Exclusion Criteria The following were excluded: Non-elective or emergency admissions. Revision or bilateral TKAs. Patients < 18 years old. Admissions for fracture, malignancy, or reoperation. Any diagnosis of COVID-19 (U07.1). Index discharges after September of each year. Readmissions representing elective contralateral TKAs within 90 days. Outcomes The primary outcome was all-cause 90-day readmission following the index TKA. Secondary outcomes included: Cause-specific 90-day readmission, categorized by principal readmission diagnosis (prosthetic or surgical-site infection, wound dehiscence, mechanical/implant problem, cellulitis, sepsis/bacteremia, respiratory failure, venous thromboembolism, cardiovascular or cerebrovascular events, gastrointestinal complications, urinary tract infection, and other causes). In-hospital postoperative complications during the index admission (DVT, PE, sepsis, AKI, pneumonia, UTI, blood transfusion, postoperative pain, and in-hospital mortality). Resource utilization: length of stay (LOS) and total hospital charges for both the index admission and any 90-day readmission. Covariates Demographic variables included age and sex. Comorbidities were identified using ICD10 Codes and included hypertension, dyslipidemia, obesity, diabetes mellitus, chronic kidney disease, chronic lung disease, osteoporosis, liver disease, Parkinson disease, and Alzheimer disease. Hospital-level variables included bed size, teaching status, and urban or rural location. Calendar year (2020–2022) was also incorporated. Propensity-Score Matching To mitigate confounding, 1:1 propensity-score matching (PSM) was performed between robotic and navigation groups using logistic regression with all covariates listed above. Matching used a nearest-neighbor algorithm with a 0.01 caliper and no replacement. Balance was verified using standardized mean differences < 0.10. Statistical Analysis Continuous variables were summarized as mean ± standard deviation and compared with independent-sample t tests. Categorical variables were reported as counts (%) and compared with χ² tests. Relative risks (RR) with 95% confidence intervals were calculated for postoperative complications and readmission causes. Two-sided p < 0.05 was considered significant. Analyses were conducted in SPSS (IBM Corp., Armonk, NY) and MATLAB (MathWorks, Natick, MA). The NRD contains only de-identified data and is therefore exempt from institutional-review-board oversight. Results Cohort and Temporal Trends A total of 72,827 elective computer-assisted TKAs met inclusion criteria. Robotic assistance comprised 66.6% (n = 48,491) and navigation assistance 33.4% (n = 24,336) as shown in Table 1 . Robotic utilization increased from 61.5% in 2020 to 71.0% in 2022 (χ² = 907.4, p < 0.001). After PSM, 24,883 robotic and 24,336 navigation cases were retained (49,219 total). Table 1 Utilization of Robotic vs Navigation-Assisted TKA, NRD 2020–2022 Year Robotic n (%) Navigation n (%) 2020 16,842 (61.5) 10,560 (38.5) 2021 16,753 (68.6) 7,681 (31.4) 2022 14,896 (71.0) 6,095 (29.0) Total 48,491 (66.6) 24,336 (33.4) Baseline Characteristics After matching, baseline demographics and comorbidities were highly comparable between groups (Table 2 ).The mean age was 68.2 ± 9.17 years in the robotic cohort and 68.1 ± 9.42 years in the navigation cohort (p = 0.43). Females represented 63.4% vs 62.6% (p = 0.09). There were no significant differences in hypertension (57.6% vs 57.4%), dyslipidemia (54.0% vs 54.2%), obesity (43.8% vs 43.0%), diabetes (20.2% vs 20.7%), or other major comorbidities (all p > 0.05). The groups were thus well balanced before outcome comparison. Table 2 Baseline Characteristics After Propensity-Score Matching Variable Robotic % / Mean ± SD Navigation % / Mean ± SD p Age (years) 68.2 ± 9.17 68.1 ± 9.42 0.43 Female sex 63.4 62.6 0.09 Hypertension 57.6 57.4 0.69 Dyslipidemia 54 54.2 0.64 Obesity 43.8 43 0.08 Diabetes mellitus 20.2 20.7 0.13 Chronic kidney disease 9 9.1 0.8 Chronic lung disease 5.2 5.1 0.78 Osteoporosis 6 6.3 0.18 Liver disease 1.6 1.6 1 In-Hospital Complications In-hospital complication rates were uniformly low across both cohorts, generally below 2% for all individual events. Navigation-assisted TKA demonstrated higher rates of thromboembolic and infection-related complications compared with robotic-assisted procedures (Table 3 ). The relative risk for venous thromboembolism was 1.5-fold higher, and for pulmonary embolism 2-fold higher in the navigation group. Rates of sepsis and transfusion were approximately three times greater, while postoperative pain was 1.6-fold higher following navigation-assisted TKA. Acute kidney injury, pneumonia, urinary tract infection, intra-operative fracture, and in-hospital mortality did not differ significantly between groups. Table 3 In-Hospital Complications (Navigation vs Robotic, Weighted NRD 2020–2022) After Propensity-Score Matching Outcome Robotic % Navigation % Risk Ratio (NAV / ROB) [95% CI approx] p In-hospital mortality 0.1 0 Not estimable 0.242 Blood transfusion 0.5 1.5 3.0 [2.70–3.40] < 0.001 Sepsis 0.1 0.3 3.0 [1.20–7.10] 0.028 Deep-vein thrombosis 0.2 0.3 1.5 [1.14–1.95] 0.003 Pulmonary embolism 0.1 0.2 2.0 [1.25–3.21] 0.008 Acute kidney injury 2 1.8 0.9 [0.82–1.00] 0.068 Pneumonia 0.2 0.1 0.5 [0.23–1.05] 0.066 Urinary tract infection 0.8 0.6 0.8 [0.62–0.91] 0.018 Intra-operative fracture 0.2 0.2 1.0 [0.72–1.38] 0.981 Post-operative pain (G89.18) 2.5 4.1 1.6 [1.55–1.73] < 0.001 Ninety-Day Readmissions The all-cause 90-day readmission rate was 4.6% for robotic and 4.7% for navigation (χ² = 0.35, p = 0.56) as shown in Table 4 . Cause distribution differed modestly (χ² = 57.173, df = 19, p < 0.001; Cramer’s V = 0.034). Navigation-assisted cases had slightly higher readmissions for sepsis/bacteremia (0.3% vs 0.2%), pulmonary embolism (0.1% vs 0.0%), and respiratory infection/failure (0.2% vs 0.1%), while wound-related readmissions were marginally higher in robotic cases. Table 4 Ninety-Day Readmission Diagnoses (Percent Within Surgery Type) After Propensity-Score Matching Diagnosis Robotic % Navigation % PJI / post-operative infection 0.6 0.5 Wound dehiscence 0.3 0.2 Mechanical prosthesis issue 0.1 0.1 Cellulitis / SSTI 0.2 0.1 Sepsis / bacteremia 0.2 0.3 Respiratory infection / failure 0.1 0.2 Pulmonary embolism 0 0.1 Heart failure / arrhythmia 0.3 0.2 Acute kidney injury 0 0.1 GI complication 0.5 0.6 UTI / pyelonephritis 0.1 0.1 Post-operative pain / hematoma 0.1 0.1 Other / unspecified 97.3 96.9 Any readmission 4.6 4.7 Resource Utilization Robotic procedures were associated with a marginally longer index stay but lower costs (Table 5 ). Mean index LOS was 2.07 ± 2.74 days for robotic and 1.99 ± 2.13 days for navigation (p < 0.001). Mean total index charges were USD 78,123 ± 51,966 vs USD 88,344 ± 54,569 (p < 0.001). Readmission LOS was similar (4.81 ± 4.41 vs 4.74 ± 4.92 days, p = 0.719), while readmission charges were higher in navigation cases (USD 75,889 ± 102,753 vs USD 67,123 ± 68,525, p = 0.016). Table 5 Length of Stay and Hospital Charges After Propensity-Score Matching Variable Robotic Mean ± SD Navigation Mean ± SD p Index LOS (days) 2.07 ± 2.74 1.99 ± 2.13 < 0.001 Index total charges (USD) 78,123 ± 51,966 88,344 ± 54,569 < 0.001 Readmission LOS (days) 4.81 ± 4.41 4.74 ± 4.92 0.719 Readmission total charges (USD) 67,123 ± 68,525 75,889 ± 102,753 0.016 Discussion Principal findings h This large, contemporary nationwide analysis demonstrated that robotic-assisted TKA was associated with significantly lower in-hospital complication rates compared with navigation-guided TKA, including reduced rates of blood transfusion, sepsis, DVT, and pulmonary embolism, while 90-day readmission rates were equivalent between groups. These results confirm and extend earlier work by showing that the short-term safety profile of RA-TKA remains favorable at a national level, even as utilization increased to over two-thirds of all computer-assisted TKAs by 2022. Interpretation in context Our findings align with multiple institutional series and registry analyses reporting decreased intraoperative blood loss and reduced inflammatory response with robotic-assisted systems [ 17 – 21 ]. The precise bone preparation and constrained soft-tissue envelope likely contribute to lower transfusion and infection rates. The small but consistent reduction in thromboembolic complications may reflect faster postoperative mobilization and lower systemic stress responses, both facilitated by improved surgical reproducibility. Despite these clinical advantages, the overall 90-day readmission rate (~ 4.6–4.7%) was similar between groups, indicating that most early readmissions after TKA stem from medical comorbidities rather than surgical technique alone. However, cause-specific readmissions differed: navigation-assisted TKA had marginally higher returns for sepsis, pulmonary embolism, and respiratory complications, whereas robotic cases showed slightly higher wound-related readmissions differences that, although statistically significant, remain small in absolute terms. Economic and temporal trends From 2020 to 2022, robotic adoption increased from 61.5% to 71.0%, highlighting rapid national dissemination. Importantly, robotic procedures were associated with lower index hospital charges (− USD 10,000) despite slightly longer length of stay, suggesting improved cost efficiency likely related to reduced transfusion and complication rates. This contradicts earlier assumptions that robotic technology universally increases perioperative costs and supports the growing evidence that cost parity has been achieved as system efficiency and surgical throughput improved [ 22 – 25 ]. Comparison with previous data In contrast to our earlier analysis (2010–2019), which focused solely on in-hospital outcomes, the current dataset captures both immediate complications and 90-day post-discharge readmissions[ 13 , 17 ]. The present findings strengthen prior conclusions by showing that the safety advantages of RA-TKA persist after hospital discharge, without increased readmission burden or cost escalation. Moreover, by incorporating 2022 NRD data, this study reflects the transition period following the COVID-19 pandemic, when both patient optimization and perioperative pathways had stabilized -lending additional relevance to contemporary practice. Clinical implications The results reinforce that robotic assistance provides incremental safety benefits over navigation while maintaining economic sustainability. For high-volume centers, adoption may yield measurable reductions in perioperative morbidity and transfusion-related complications.[ 3 , 7 , 11 ] As reimbursement models shift toward bundled payments and readmission penalties, the ability of RA-TKA to maintain equivalent readmission rates with fewer complications could enhance institutional quality metrics and value-based performance. Limitations Several limitations merit consideration. The NRD lacks implant-level detail and does not differentiate between active versus semi-active robotic systems or image-based versus imageless navigation. Coding inaccuracies and unmeasured confounders (e.g., surgeon experience, intraoperative efficiency) may exist, although large sample size and propensity matching mitigate these effects. Readmissions to out-of-state hospitals may be missing, and functional outcomes or patient-reported measures were unavailable. Lastly, while the study establishes short-term safety equivalence, long-term survivorship and functional outcomes remain unknown. [ 26 , 27 ] Future directions Future research should incorporate device-specific identifiers and longitudinal datasets linking registry and claims data to assess long-term implant survival, functional recovery, and cumulative cost trajectories. Comparative cost-effectiveness analyses will also clarify whether initial capital investment in robotics is offset by downstream savings from reduced complications and revisions. Conclusion In this nationwide matched analysis of more than 49,000 computer-assisted TKAs, robotic-assisted procedures demonstrated lower perioperative complication rates and similar 90-day readmission rates compared with navigation-guided TKA, while also incurring lower hospital charges. These findings, derived from the most recent national cohort, support the continued integration of robotic systems into modern knee arthroplasty practice as a safe and efficient advancement in surgical precision. Funding Declaration No Funding Consent to Publish declaration: not applicable Ethics and Consent to Participate declarations: not applicable Funding Declaration: No funding Clinical trial number: not applicable. Declarations Funding Declaration: No Funding Consent to Publish declaration: not applicable Ethics and Consent to Participate declarations: not applicable Funding Declaration: No funding Author Contribution D.M. performed the data analysis and wrote the majority of the manuscript. Y.B. and Y.S. contributed to the study conception, interpretation of results, and critical revision of the manuscript. All authors reviewed and approved the final version of the manuscript. Data Availability Data Availability StatementThis study utilized data from the Healthcare Cost and Utilization Project (HCUP) Nationwide Readmissions Database (NRD) 2020–2022, which is a publicly available, de-identified dataset. Access to the NRD is available through the HCUP Central Distributor (https://www.hcup-us.ahrq.gov/tech_assist/centdist.jsp) upon completion of a data use agreement and payment of the associated fee. The authors do not have special access privileges, and all researchers can obtain the data in the same manner. References Singh JA, Yu S, Chen L, Cleveland JD. Rates of total joint replacement in the United States: future projections to 2020–2040 using the National Inpatient Sample. J Rheumatol. 2019;46(9):1134–40. https://doi.org/10.3899/jrheum.170990 . Sloan M, Premkumar A, Sheth NP. Projected volume of primary total joint arthroplasty in the United States, 2014 to 2030. J Bone Joint Surg Am. 2018;100(17):1455–60. https://doi.org/10.2106/JBJS.17.01617 . 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10:21:50","extension":"html","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":98816,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8149205/v1/c0f574b7e52f43b7ac615a8c.html"},{"id":98783238,"identity":"78c2f70a-58e2-4732-b385-b6e7172a1f67","added_by":"auto","created_at":"2025-12-22 12:41:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":837551,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8149205/v1/4e8a875c-b1c8-4970-8329-18dac5575b29.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Nationwide Comparison of Robotic and Navigation-Assisted Total Knee Arthroplasty: Trends, Perioperative Complications, and 90-Day Readmissions (2020–2022 NRD)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe demand for total knee arthroplasty (TKA) continues to grow globally, driven by aging populations and the increasing prevalence of osteoarthritis. In the United States alone, over 235 primary TKAs per 100,000 individuals are performed annually, and this figure is projected to double by 2030 and triple by 2040. As surgical volume increases, precision technologies such as computer navigation and robotic assistance have been adopted to optimize implant alignment, improve kinematics, and enhance functional recovery [\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNavigation-guided TKA (NG-TKA) provides intraoperative real-time feedback on limb alignment and component orientation, while robotic-assisted TKA (RA-TKA) integrates preoperative planning and semi-active guidance to improve reproducibility and soft-tissue balance [\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Both systems aim to reduce human variability, but they differ in precision, learning curve, and intraoperative workflow [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious studies have shown improved accuracy with both technologies compared with conventional TKA, but the comparative effectiveness of RA-TKA and NG-TKA remains debated. Earlier national analyses using pre-2020 datasets demonstrated lower in-hospital complication rates and shorter length of stay for robotic procedures but did not evaluate short-term readmissions, an increasingly important quality metric reflecting postoperative safety, patient optimization, and hospital resource utilization [\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe introduction of newer-generation robotic systems and broader adoption in recent years warranted a contemporary evaluation. The present study therefore leveraged the 2020\u0026ndash;2022 Nationwide Readmissions Database (NRD) the first U.S. dataset incorporating complete post-COVID surgical recovery years to compare 90-day readmissions, perioperative complications, and hospital resource utilization between RA-TKA and NG-TKA after rigorous propensity-score matching.\u003c/p\u003e \u003cp\u003eWe hypothesized that robotic assistance would be associated with fewer perioperative complications and similar or lower 90-day readmission rates compared with navigation guidance.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Source and Study Design\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective cohort study using the Nationwide Readmissions Database (NRD), 2020\u0026ndash;2022, developed by HCUP. The NRD is a nationally representative, all-payer database specifically designed to evaluate readmissions. Each year is released as a separate dataset; therefore, only procedures performed from January through September were included to allow for complete 90-day follow-up. The 2022 dataset, the most recent available release, was incorporated, making this the most contemporary nationwide analysis to date.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCohort Identification\u003c/h3\u003e\n\u003cp\u003ePrimary total knee arthroplasty (TKA) procedures were identified from the primary procedure field (PR1) using ICD-10-PCS codes for knee joint replacement. Computer-assisted technology use was defined by the following procedural codes:\u003c/p\u003e \u003cp\u003eRobotic assistance: 8E0Y0CZ, 8E0YXCZ\u003c/p\u003e \u003cp\u003eNavigation assistance: 8E0YXBZ, 8E0YXBG, 8E0YXBF\u003c/p\u003e \u003cp\u003eWhen both code types were present, the case was classified as robotic-assisted.\u003c/p\u003e\n\u003ch3\u003eExclusion Criteria\u003c/h3\u003e\n\u003cp\u003eThe following were excluded:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eNon-elective or emergency admissions.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRevision or bilateral TKAs.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePatients\u0026thinsp;\u0026lt;\u0026thinsp;18 years old.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAdmissions for fracture, malignancy, or reoperation.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAny diagnosis of COVID-19 (U07.1).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIndex discharges after September of each year.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eReadmissions representing elective contralateral TKAs within 90 days.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e\n\u003ch3\u003eOutcomes\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was all-cause 90-day readmission following the index TKA.\u003c/p\u003e \u003cp\u003eSecondary outcomes included:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eCause-specific 90-day readmission, categorized by principal readmission diagnosis (prosthetic or surgical-site infection, wound dehiscence, mechanical/implant problem, cellulitis, sepsis/bacteremia, respiratory failure, venous thromboembolism, cardiovascular or cerebrovascular events, gastrointestinal complications, urinary tract infection, and other causes).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIn-hospital postoperative complications during the index admission (DVT, PE, sepsis, AKI, pneumonia, UTI, blood transfusion, postoperative pain, and in-hospital mortality).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eResource utilization: length of stay (LOS) and total hospital charges for both the index admission and any 90-day readmission.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eDemographic variables included age and sex.\u003c/p\u003e \u003cp\u003eComorbidities were identified using ICD10 Codes and included hypertension, dyslipidemia, obesity, diabetes mellitus, chronic kidney disease, chronic lung disease, osteoporosis, liver disease, Parkinson disease, and Alzheimer disease.\u003c/p\u003e \u003cp\u003eHospital-level variables included bed size, teaching status, and urban or rural location. Calendar year (2020\u0026ndash;2022) was also incorporated.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePropensity-Score Matching\u003c/h2\u003e \u003cp\u003eTo mitigate confounding, 1:1 propensity-score matching (PSM) was performed between robotic and navigation groups using logistic regression with all covariates listed above. Matching used a nearest-neighbor algorithm with a 0.01 caliper and no replacement. Balance was verified using standardized mean differences\u0026thinsp;\u0026lt;\u0026thinsp;0.10.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eContinuous variables were summarized as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation and compared with independent-sample t tests. Categorical variables were reported as counts (%) and compared with χ\u0026sup2; tests. Relative risks (RR) with 95% confidence intervals were calculated for postoperative complications and readmission causes. Two-sided p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant. Analyses were conducted in SPSS (IBM Corp., Armonk, NY) and MATLAB (MathWorks, Natick, MA). The NRD contains only de-identified data and is therefore exempt from institutional-review-board oversight.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCohort and Temporal Trends\u003c/h2\u003e \u003cp\u003eA total of 72,827 elective computer-assisted TKAs met inclusion criteria. Robotic assistance comprised 66.6% (n\u0026thinsp;=\u0026thinsp;48,491) and navigation assistance 33.4% (n\u0026thinsp;=\u0026thinsp;24,336) as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Robotic utilization increased from 61.5% in 2020 to 71.0% in 2022 (χ\u0026sup2; = 907.4, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After PSM, 24,883 robotic and 24,336 navigation cases were retained (49,219 total).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUtilization of Robotic vs Navigation-Assisted TKA, NRD 2020\u0026ndash;2022\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRobotic n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNavigation n (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16,842 (61.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10,560 (38.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16,753 (68.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,681 (31.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14,896 (71.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6,095 (29.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48,491 (66.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24,336 (33.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eBaseline Characteristics\u003c/h2\u003e \u003cp\u003eAfter matching, baseline demographics and comorbidities were highly comparable between groups (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).The mean age was 68.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.17 years in the robotic cohort and 68.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.42 years in the navigation cohort (p\u0026thinsp;=\u0026thinsp;0.43).\u003c/p\u003e \u003cp\u003eFemales represented 63.4% vs 62.6% (p\u0026thinsp;=\u0026thinsp;0.09). There were no significant differences in hypertension (57.6% vs 57.4%), dyslipidemia (54.0% vs 54.2%), obesity (43.8% vs 43.0%), diabetes (20.2% vs 20.7%), or other major comorbidities (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eThe groups were thus well balanced before outcome comparison.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristics After Propensity-Score Matching\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRobotic % / Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNavigation % / Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic kidney disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic lung disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsteoporosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eIn-Hospital Complications\u003c/h2\u003e \u003cp\u003eIn-hospital complication rates were uniformly low across both cohorts, generally below 2% for all individual events.\u003c/p\u003e \u003cp\u003eNavigation-assisted TKA demonstrated higher rates of thromboembolic and infection-related complications compared with robotic-assisted procedures (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe relative risk for venous thromboembolism was 1.5-fold higher, and for pulmonary embolism 2-fold higher in the navigation group.\u003c/p\u003e \u003cp\u003eRates of sepsis and transfusion were approximately three times greater, while postoperative pain was 1.6-fold higher following navigation-assisted TKA.\u003c/p\u003e \u003cp\u003eAcute kidney injury, pneumonia, urinary tract infection, intra-operative fracture, and in-hospital mortality did not differ significantly between groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIn-Hospital Complications (Navigation vs Robotic, Weighted NRD 2020\u0026ndash;2022) After Propensity-Score Matching\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRobotic %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNavigation %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRisk Ratio (NAV / ROB) [95% CI approx]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn-hospital mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot estimable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood transfusion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3.0 [2.70\u0026ndash;3.40]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSepsis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3.0 [1.20\u0026ndash;7.10]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeep-vein thrombosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.5 [1.14\u0026ndash;1.95]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulmonary embolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.0 [1.25\u0026ndash;3.21]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute kidney injury\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9 [0.82\u0026ndash;1.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePneumonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5 [0.23\u0026ndash;1.05]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrinary tract infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8 [0.62\u0026ndash;0.91]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntra-operative fracture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0 [0.72\u0026ndash;1.38]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.981\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-operative pain (G89.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.6 [1.55\u0026ndash;1.73]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eNinety-Day Readmissions\u003c/h2\u003e \u003cp\u003eThe all-cause 90-day readmission rate was 4.6% for robotic and 4.7% for navigation (χ\u0026sup2; = 0.35, p\u0026thinsp;=\u0026thinsp;0.56) as shown in Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Cause distribution differed modestly (χ\u0026sup2; = 57.173, df\u0026thinsp;=\u0026thinsp;19, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Cramer\u0026rsquo;s V\u0026thinsp;=\u0026thinsp;0.034). Navigation-assisted cases had slightly higher readmissions for sepsis/bacteremia (0.3% vs 0.2%), pulmonary embolism (0.1% vs 0.0%), and respiratory infection/failure (0.2% vs 0.1%), while wound-related readmissions were marginally higher in robotic cases.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNinety-Day Readmission Diagnoses (Percent Within Surgery Type) After Propensity-Score Matching\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiagnosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRobotic %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNavigation %\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePJI / post-operative infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWound dehiscence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanical prosthesis issue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCellulitis / SSTI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSepsis / bacteremia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory infection / failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulmonary embolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart failure / arrhythmia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute kidney injury\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGI complication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUTI / pyelonephritis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-operative pain / hematoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther / unspecified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAny readmission\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e4.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e4.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eResource Utilization\u003c/h2\u003e \u003cp\u003eRobotic procedures were associated with a marginally longer index stay but lower costs (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Mean index LOS was 2.07\u0026thinsp;\u0026plusmn;\u0026thinsp;2.74 days for robotic and 1.99\u0026thinsp;\u0026plusmn;\u0026thinsp;2.13 days for navigation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Mean total index charges were USD 78,123\u0026thinsp;\u0026plusmn;\u0026thinsp;51,966 vs USD 88,344\u0026thinsp;\u0026plusmn;\u0026thinsp;54,569 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Readmission LOS was similar (4.81\u0026thinsp;\u0026plusmn;\u0026thinsp;4.41 vs 4.74\u0026thinsp;\u0026plusmn;\u0026thinsp;4.92 days, p\u0026thinsp;=\u0026thinsp;0.719), while readmission charges were higher in navigation cases (USD 75,889\u0026thinsp;\u0026plusmn;\u0026thinsp;102,753 vs USD 67,123\u0026thinsp;\u0026plusmn;\u0026thinsp;68,525, p\u0026thinsp;=\u0026thinsp;0.016).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLength of Stay and Hospital Charges After Propensity-Score Matching\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRobotic Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNavigation Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndex LOS (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.07\u0026thinsp;\u0026plusmn;\u0026thinsp;2.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.99\u0026thinsp;\u0026plusmn;\u0026thinsp;2.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndex total charges (USD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e78,123\u0026thinsp;\u0026plusmn;\u0026thinsp;51,966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e88,344\u0026thinsp;\u0026plusmn;\u0026thinsp;54,569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReadmission LOS (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.81\u0026thinsp;\u0026plusmn;\u0026thinsp;4.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.74\u0026thinsp;\u0026plusmn;\u0026thinsp;4.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.719\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReadmission total charges (USD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e67,123\u0026thinsp;\u0026plusmn;\u0026thinsp;68,525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e75,889\u0026thinsp;\u0026plusmn;\u0026thinsp;102,753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003ePrincipal findings\u003c/h2\u003e \u003cp\u003eh This large, contemporary nationwide analysis demonstrated that robotic-assisted TKA was associated with significantly lower in-hospital complication rates compared with navigation-guided TKA, including reduced rates of blood transfusion, sepsis, DVT, and pulmonary embolism, while 90-day readmission rates were equivalent between groups. These results confirm and extend earlier work by showing that the short-term safety profile of RA-TKA remains favorable at a national level, even as utilization increased to over two-thirds of all computer-assisted TKAs by 2022.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eInterpretation in context\u003c/h2\u003e \u003cp\u003eOur findings align with multiple institutional series and registry analyses reporting decreased intraoperative blood loss and reduced inflammatory response with robotic-assisted systems [\u003cspan additionalcitationids=\"CR18 CR19 CR20\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The precise bone preparation and constrained soft-tissue envelope likely contribute to lower transfusion and infection rates. The small but consistent reduction in thromboembolic complications may reflect faster postoperative mobilization and lower systemic stress responses, both facilitated by improved surgical reproducibility.\u003c/p\u003e \u003cp\u003eDespite these clinical advantages, the overall 90-day readmission rate (~\u0026thinsp;4.6\u0026ndash;4.7%) was similar between groups, indicating that most early readmissions after TKA stem from medical comorbidities rather than surgical technique alone. However, cause-specific readmissions differed: navigation-assisted TKA had marginally higher returns for sepsis, pulmonary embolism, and respiratory complications, whereas robotic cases showed slightly higher wound-related readmissions differences that, although statistically significant, remain small in absolute terms.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eEconomic and temporal trends\u003c/h2\u003e \u003cp\u003eFrom 2020 to 2022, robotic adoption increased from 61.5% to 71.0%, highlighting rapid national dissemination. Importantly, robotic procedures were associated with lower index hospital charges (\u0026minus;\u0026thinsp;USD 10,000) despite slightly longer length of stay, suggesting improved cost efficiency likely related to reduced transfusion and complication rates. This contradicts earlier assumptions that robotic technology universally increases perioperative costs and supports the growing evidence that cost parity has been achieved as system efficiency and surgical throughput improved [\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eComparison with previous data\u003c/h2\u003e \u003cp\u003eIn contrast to our earlier analysis (2010\u0026ndash;2019), which focused solely on in-hospital outcomes, the current dataset captures both immediate complications and 90-day post-discharge readmissions[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The present findings strengthen prior conclusions by showing that the safety advantages of RA-TKA persist after hospital discharge, without increased readmission burden or cost escalation. Moreover, by incorporating 2022 NRD data, this study reflects the transition period following the COVID-19 pandemic, when both patient optimization and perioperative pathways had stabilized -lending additional relevance to contemporary practice.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eClinical implications\u003c/h2\u003e \u003cp\u003eThe results reinforce that robotic assistance provides incremental safety benefits over navigation while maintaining economic sustainability. For high-volume centers, adoption may yield measurable reductions in perioperative morbidity and transfusion-related complications.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] As reimbursement models shift toward bundled payments and readmission penalties, the ability of RA-TKA to maintain equivalent readmission rates with fewer complications could enhance institutional quality metrics and value-based performance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eSeveral limitations merit consideration. The NRD lacks implant-level detail and does not differentiate between active versus semi-active robotic systems or image-based versus imageless navigation. Coding inaccuracies and unmeasured confounders (e.g., surgeon experience, intraoperative efficiency) may exist, although large sample size and propensity matching mitigate these effects. Readmissions to out-of-state hospitals may be missing, and functional outcomes or patient-reported measures were unavailable. Lastly, while the study establishes short-term safety equivalence, long-term survivorship and functional outcomes remain unknown. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eFuture directions\u003c/h2\u003e \u003cp\u003eFuture research should incorporate device-specific identifiers and longitudinal datasets linking registry and claims data to assess long-term implant survival, functional recovery, and cumulative cost trajectories. Comparative cost-effectiveness analyses will also clarify whether initial capital investment in robotics is offset by downstream savings from reduced complications and revisions.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this nationwide matched analysis of more than 49,000 computer-assisted TKAs, robotic-assisted procedures demonstrated lower perioperative complication rates and similar 90-day readmission rates compared with navigation-guided TKA, while also incurring lower hospital charges. These findings, derived from the most recent national cohort, support the continued integration of robotic systems into modern knee arthroplasty practice as a safe and efficient advancement in surgical precision.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFunding Declaration\u003c/strong\u003e \u003cp\u003eNo Funding\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to Publish\u003c/strong\u003e \u003cp\u003edeclaration: not applicable\u003c/p\u003e \u003c/p\u003e \u003cp\u003eEthics and Consent to Participate declarations: not applicable\u003c/p\u003e \u003cp\u003eFunding Declaration: No funding\u003c/p\u003e \u003cp\u003eClinical trial number: not applicable.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Declaration:\u003c/strong\u003e\u0026nbsp;No Funding\u003c/p\u003e\n\u003cp\u003eConsent to Publish declaration: not applicable\u003c/p\u003e\n\u003cp\u003eEthics and Consent to Participate declarations: not applicable\u003c/p\u003e\n\u003cp\u003eFunding Declaration: No funding\u003c/p\u003e\u003cp\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eD.M. performed the data analysis and wrote the majority of the manuscript. Y.B. and Y.S. contributed to the study conception, interpretation of results, and critical revision of the manuscript. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eData Availability StatementThis study utilized data from the Healthcare Cost and Utilization Project (HCUP) Nationwide Readmissions Database (NRD) 2020\u0026ndash;2022, which is a publicly available, de-identified dataset. Access to the NRD is available through the HCUP Central Distributor (https://www.hcup-us.ahrq.gov/tech_assist/centdist.jsp) upon completion of a data use agreement and payment of the associated fee. The authors do not have special access privileges, and all researchers can obtain the data in the same manner.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSingh JA, Yu S, Chen L, Cleveland JD. 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Disagreement in readmission rates after total hip and knee arthroplasty across data sets. Arthroplast Today. 2021;9:73\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.artd.2021.04.002\u003c/span\u003e\u003cspan address=\"10.1016/j.artd.2021.04.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHow to Create an Orthopaedic Arthroplasty Administrative Database Project. A Step-by-Step Guide Part I: Study Design Ng, Mitchell. K. J Arthroplast, 38, Issue 3, 407\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"bmc-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bsur","sideBox":"Learn more about [BMC Surgery](http://bmcsurg.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bsur/default.aspx","title":"BMC Surgery","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Total knee arthroplasty, robotic, navigation, readmission, complications, NRD","lastPublishedDoi":"10.21203/rs.3.rs-8149205/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8149205/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eBoth robotic-assisted (RA-TKA) and navigation-guided (NG-TKA) total knee arthroplasty were developed to enhance component accuracy and alignment. While several studies have examined intraoperative precision and early outcomes, nationwide comparisons of postoperative complications and 90-day readmissions remain limited.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eretrospective cohort analysis was performed using the Nationwide Readmissions Database (NRD) 2020-2022, identifying elective primary TKAs performed with computer assistance. Robotic procedures were identified using ICD-10-PCS codes 8E0Y0CZ and 8E0YXCZ, and navigation procedures using 8E0YXBZ, 8E0YXBG, and 8E0YXBF. After exclusions, 1:1 propensity-score matching (caliper 0.01) was applied across demographics, comorbidities, hospital factors, and surgical year. Primary outcome was 90-day all-cause readmission; secondary outcomes included cause-specific readmissions, in-hospital complications, and healthcare resource utilization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Among 72,827 elective computer-assisted TKAs, 48,491 (66.6%) were robotic and 24,336 (33.4%) navigation. Robotic utilization rose from 61.5% in 2020 to 71.0% in 2022 (p\u0026lt;0.001). After matching, 49,219 cases remained. In-hospital complications were generally low, but navigation-assisted TKA showed higher rates of sepsis (0.3% vs 0.1%), venous thromboembolism (0.3% vs 0.2%), pulmonary embolism (0.2% vs 0.1%), and blood transfusion (1.5% vs 0.5%) (all p\u0026lt;0.05). The 90-day readmission rate was similar (4.7% vs 4.6%, p=0.56), though readmission causes differed slightly (p\u0026lt;0.001), with navigation cases more often readmitted for infection, sepsis, or respiratory complications. Index charges were lower in robotic cases (USD 78,123 vs 88,344, p\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eRobotic-assisted TKA demonstrated lower perioperative complication rates and comparable short-term readmission risk compared with navigation-guided TKA, with modestly reduced hospital costs. These findings from the most contemporary national cohort support the expanding adoption of robotic systems in knee arthroplasty.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLevels of Evidence:\u003c/strong\u003e Level III\u003c/p\u003e","manuscriptTitle":"Nationwide Comparison of Robotic and Navigation-Assisted Total Knee Arthroplasty: Trends, Perioperative Complications, and 90-Day Readmissions (2020–2022 NRD)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 10:21:40","doi":"10.21203/rs.3.rs-8149205/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-12-19T10:49:54+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-26T07:35:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-22T05:45:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-22T05:44:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Surgery","date":"2025-11-18T22:05:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bsur","sideBox":"Learn more about [BMC Surgery](http://bmcsurg.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bsur/default.aspx","title":"BMC Surgery","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"99a92ae5-fc7c-40b4-95a4-67c4c429b118","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-22T10:21:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-22 10:21:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8149205","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8149205","identity":"rs-8149205","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}