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<p>Robotic-assisted Total Knee Arthroplasty (RA-TKA) enhances precision but is historically associated with increased operative times and workflow disruption. This study evaluates whether integrating a novel open-platform robotic effector into an established, familiar navigation workflow mitigates the learning curve and maintains total operative time compared to standard navigation. A retrospective comparative analysis was performed on 236 primary TKAs (142 NAV-TKA vs. 94 RA-TKA) performed by a single high-volume surgical team. Operative times were segmented into five phases using synchronized system logs. Educational cases were excluded. The learning curve was analyzed using Cumulative Sum (CUSUM) control charts. Multi-way analysis across independent cohorts showed a significant global variation in total skin-to-skin time (<i>p</i> &lt; 0.001). However, post-hoc pairwise testing demonstrated that while the initial learning phase was significantly longer (Median: 84.00&#xa0;min), the steady-state proficiency phase (Median: 77.00&#xa0;min) achieved a comparable time profile to the legacy navigation workflow (Median: 81.00&#xa0;min; <i>p</i> &gt; 0.017), avoiding an overall sustained time penalty. CUSUM analysis identified a learning curve of 58 cases. In the proficiency phase, the active robotic resection time was significantly faster than manual navigated resection (17.95 vs. 19.08&#xa0;min; <i>p</i> = 0.009). The open-platform robotic system successfully integrated into the clinical workflow without introducing an overall sustained time penalty. Retaining a familiar interface effectively cushions the initial efficiency loss typical of closed platforms. Once proficiency is attained, active robotic assistance significantly enhances mechanical resection speed, offsetting the mandatory intra-operative planning time investment.</p>

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Introduction of robotics into a well-established navigation OR team for TKA does not increase surgical time. A one center evaluation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月19日

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  • Lorenzo Maggi,
  • Yves Vanderschelden,
  • David Burlot,
  • Nicola Secciani,
  • Benedetto Allotta

摘要

Robotic-assisted Total Knee Arthroplasty (RA-TKA) enhances precision but is historically associated with increased operative times and workflow disruption. This study evaluates whether integrating a novel open-platform robotic effector into an established, familiar navigation workflow mitigates the learning curve and maintains total operative time compared to standard navigation. A retrospective comparative analysis was performed on 236 primary TKAs (142 NAV-TKA vs. 94 RA-TKA) performed by a single high-volume surgical team. Operative times were segmented into five phases using synchronized system logs. Educational cases were excluded. The learning curve was analyzed using Cumulative Sum (CUSUM) control charts. Multi-way analysis across independent cohorts showed a significant global variation in total skin-to-skin time (p < 0.001). However, post-hoc pairwise testing demonstrated that while the initial learning phase was significantly longer (Median: 84.00 min), the steady-state proficiency phase (Median: 77.00 min) achieved a comparable time profile to the legacy navigation workflow (Median: 81.00 min; p > 0.017), avoiding an overall sustained time penalty. CUSUM analysis identified a learning curve of 58 cases. In the proficiency phase, the active robotic resection time was significantly faster than manual navigated resection (17.95 vs. 19.08 min; p = 0.009). The open-platform robotic system successfully integrated into the clinical workflow without introducing an overall sustained time penalty. Retaining a familiar interface effectively cushions the initial efficiency loss typical of closed platforms. Once proficiency is attained, active robotic assistance significantly enhances mechanical resection speed, offsetting the mandatory intra-operative planning time investment.