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<p>Prior MAUDE studies of robotic surgery have largely characterized platform-level or procedure-specific adverse-event profiles, whereas subsystem-level surveillance of robotic stapling devices remains limited. Robotic stapling systems are multi-component devices, and post-market adverse-event reports may be distributed across separate regulatory product codes for stapler instruments and reloads. We conducted a retrospective, product-code-aware database study of Intuitive Surgical SureForm-related reports in the U.S. Food and Drug Administration Manufacturer and User Facility Device Experience database received from October 11, 2018, through March 31, 2026. Deduplicated reports were grouped by primary product code and reviewed using prespecified narrative labels for clinical consequences and failure modes. The cohort included 2,802 reports, with annual volume increasing from 7 reports in 2018 to 654 in 2025. Product-code profiles differed markedly: GDW-coded reports were predominantly Injury reports (1,336/1,912; 69.9%), whereas NAY-coded reports were predominantly Malfunction reports (718/883; 81.3%). Narrative labels also differed: bleeding, leak, tissue-pushout, misfire, and conversion terms were more frequent in GDW-coded reports, whereas stuck, jam, or freeze terminology was more frequent in NAY-coded reports. Staple-line issue was identified more frequently in GDW-coded reports but showed lower reproducibility in validation analyses and was therefore interpreted cautiously. Death-term screening identified device-specific false positives arising from technical use of “force expired.” These findings show that product-code assignment can shape the observable safety profile of a robotic stapling subsystem and suggest hypothesis-generating surveillance priorities, including product-code-integrated queries, narrative screening, and device-specific review of recurrent report-text patterns.</p>

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Adverse-event reporting profiles of the Intuitive SureForm robotic stapling system in the FDA MAUDE database differ by product code: a comparison of GDW- and NAY-coded reports

  • 期刊论文

  • 发表日期：

    2026年7月20日

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  • Qi Xu,
  • Xiaohui Chen,
  • Yuwan Gao,
  • Ou Zhang

摘要

Prior MAUDE studies of robotic surgery have largely characterized platform-level or procedure-specific adverse-event profiles, whereas subsystem-level surveillance of robotic stapling devices remains limited. Robotic stapling systems are multi-component devices, and post-market adverse-event reports may be distributed across separate regulatory product codes for stapler instruments and reloads. We conducted a retrospective, product-code-aware database study of Intuitive Surgical SureForm-related reports in the U.S. Food and Drug Administration Manufacturer and User Facility Device Experience database received from October 11, 2018, through March 31, 2026. Deduplicated reports were grouped by primary product code and reviewed using prespecified narrative labels for clinical consequences and failure modes. The cohort included 2,802 reports, with annual volume increasing from 7 reports in 2018 to 654 in 2025. Product-code profiles differed markedly: GDW-coded reports were predominantly Injury reports (1,336/1,912; 69.9%), whereas NAY-coded reports were predominantly Malfunction reports (718/883; 81.3%). Narrative labels also differed: bleeding, leak, tissue-pushout, misfire, and conversion terms were more frequent in GDW-coded reports, whereas stuck, jam, or freeze terminology was more frequent in NAY-coded reports. Staple-line issue was identified more frequently in GDW-coded reports but showed lower reproducibility in validation analyses and was therefore interpreted cautiously. Death-term screening identified device-specific false positives arising from technical use of “force expired.” These findings show that product-code assignment can shape the observable safety profile of a robotic stapling subsystem and suggest hypothesis-generating surveillance priorities, including product-code-integrated queries, narrative screening, and device-specific review of recurrent report-text patterns.