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Perioperative Outcomes and Learning Curve of Robot-Assisted McKeown Esophagectomy

  • Original Article
  • Published:
Journal of Gastrointestinal Surgery

Abstract

Background

This study aimed to evaluate the perioperative outcomes of patients undergoing robot-assisted McKeown esophagectomy (RAME) and the learning curves of surgeons performing RAME at a single center.

Methods

Perioperative outcomes of RAME and video-assisted McKeown esophagectomy (VAME) were compared after eliminating confounding factors by propensity score matching (PSM). The cumulative sum (CUSUM) method was used to evaluate the learning curves of RAME for a single surgical team.

Results

In general, a total of 198 patients with esophageal cancer (RAME: 45 patients, VAME: 153 patients) were included in this study, and 43 pairs of patients receiving RAME or VAME were matched using 1:1 PSM analysis. Those in the RAME group had more lymph nodes dissected in the total lymph nodes (median 29.0 vs. 26.0, P = 0.011) and the upper mediastinum (median 8.0 vs. 6.0, P < 0.001), especially the left recurrent laryngeal nerve (RLN) lymph node (median 4.0 vs. 2.0, P = 0.001). According to the trend of the CUSUM plot, the learning curve was divided into two stages at the 20th RAME procedure. After mastering the learning curve, RAME harvested a significantly higher number of upper mediastinal lymph nodes (median 9.0 vs. 6.0, P = 0.001), left RLN lymph nodes (median 5.0 vs. 3.5, P = 0.003), and right RLN lymph nodes (median 4.0 vs. 2.0, P = 0.002). Meanwhile, the incidence of postoperative pneumonia in the proficiency phase was significantly lower than that in the learning phase (4.0% vs. 25.0%, P = 0.04).

Conclusions

RAME improved left RLN lymph node dissection. Surgeons with extensive VAME experience need at least 20 cases to achieve early proficiency in RAME.

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References

  1. Galldiks N, Niyazi M, Grosu AL, et al. Contribution of PET imaging to radiotherapy planning and monitoring in glioma patients - a report of the PET/RANO group. Neuro Oncol 2021;23:881-893.

    Article  CAS  Google Scholar 

  2. Domper Arnal MJ, Ferrandez Arenas A, Lanas Arbeloa A. Esophageal cancer: Risk factors, screening and endoscopic treatment in Western and Eastern countries. World J Gastroenterol 2015;21:7933-43.

    Article  Google Scholar 

  3. Grille VJ, Campbell S, Gibbs JF, et al. Esophageal cancer: the rise of adenocarcinoma over squamous cell carcinoma in the Asian belt. J Gastrointest Oncol 2021;12:S339-S349.

    Article  Google Scholar 

  4. Pennathur A, Gibson MK, Jobe BA, et al. Oesophageal carcinoma. Lancet 2013;381:400-12.

    Article  Google Scholar 

  5. Biere SS, van Berge Henegouwen MI, Maas KW, et al. Minimally invasive versus open oesophagectomy for patients with oesophageal cancer: a multicentre, open-label, randomised controlled trial. Lancet 2012;379:1887-92.

    Article  Google Scholar 

  6. Haverkamp L, Seesing MF, Ruurda JP, et al. Worldwide trends in surgical techniques in the treatment of esophageal and gastroesophageal junction cancer. Dis Esophagus 2017;30:1-7.

    CAS  Google Scholar 

  7. Chen J, Liu Q, Zhang X, et al. Comparisons of short-term outcomes between robot-assisted and thoraco-laparoscopic esophagectomy with extended two-field lymph node dissection for resectable thoracic esophageal squamous cell carcinoma. J Thorac Dis 2019;11:3874-3880.

    Article  Google Scholar 

  8. van der Sluis PC, van der Horst S, May AM, et al. Robot-assisted minimally invasive thoracolaparoscopic esophagectomy versus open transthoracic esophagectomy for resectable esophageal cancer: a randomized controlled trial. Ann Surg 2019;269:621-630.

    Article  Google Scholar 

  9. Deng HY, Luo J, Li SX, et al. Does robot-assisted minimally invasive esophagectomy really have the advantage of lymphadenectomy over video-assisted minimally invasive esophagectomy in treating esophageal squamous cell carcinoma? A propensity score-matched analysis based on short-term outcomes. Dis Esophagus 2019;32.

  10. Hosoda K, Niihara M, Ushiku H, et al. Prevention of intra-thoracic recurrent laryngeal nerve injury with robot-assisted esophagectomy. Langenbecks Arch Surg 2020;405:533-540.

    Article  Google Scholar 

  11. de Groot EM, van der Horst S, Kingma BF, et al. Robot-assisted minimally invasive thoracolaparoscopic esophagectomy versus open esophagectomy: long-term follow-up of a randomized clinical trial. Dis Esophagus 2020;33.

  12. van der Sluis PC, Ruurda JP, Verhage RJ, et al. Oncologic long-term results of robot-assisted minimally invasive thoraco-laparoscopic esophagectomy with two-field lymphadenectomy for esophageal cancer. Ann Surg Oncol 2015;22 Suppl 3:S1350-6.

    Article  Google Scholar 

  13. D'Journo XB. Clinical implication of the innovations of the 8(th) edition of the TNM classification for esophageal and esophago-gastric cancer. J Thorac Dis 2018;10:S2671-S2681.

    Article  Google Scholar 

  14. Sun HB, Li Y, Liu XB, et al. Early Oral Feeding Following McKeown Minimally Invasive Esophagectomy: An Open-label, Randomized, Controlled, Noninferiority Trial. Ann Surg 2018;267:435-442.

    Article  Google Scholar 

  15. Sun HB, Li Y, Liu XB, et al. Embedded three-layer esophagogastric anastomosis reduces morbidity and improves short-term outcomes after esophagectomy for cancer. Ann Thorac Surg 2016;101:1131-8.

    Article  Google Scholar 

  16. Low DE, Alderson D, Cecconello I, et al. International consensus on standardization of data collection for complications associated with esophagectomy: esophagectomy complications consensus group (ECCG). Ann Surg 2015;262:286-94.

    Article  Google Scholar 

  17. Kubo Y, Tanaka K, Yamasaki M, et al. Influences of the Charlson Comorbidity Index and nutrition status on prognosis after esophageal cancer surgery. Ann Surg Oncol 2021;28:7173-7182.

    Article  Google Scholar 

  18. He H, Wu Q, Wang Z, et al. Short-term outcomes of robot-assisted minimally invasive esophagectomy for esophageal cancer: a propensity score matched analysis. J Cardiothorac Surg 2018;13:52.

    Article  Google Scholar 

  19. Chao YK, Hsieh MJ, Liu YH, et al. Lymph node evaluation in robot-assisted versus video-assisted thoracoscopic esophagectomy for esophageal squamous cell carcinoma: a propensity-matched analysis. World J Surg 2018;42:590-598.

    Article  Google Scholar 

  20. Motoyama S, Sato Y, Wakita A, et al. Extensive lymph node dissection around the left laryngeal nerve achieved with robot-assisted thoracoscopic esophagectomy. Anticancer Res 2019;39:1337-1342.

    Article  Google Scholar 

  21. Yang Y, Li B, Yi J, et al. Robot-assisted versus conventional minimally invasive esophagectomy for resectable esophageal squamous cell carcinoma: early results of a multicenter randomized controlled trial: the RAMIE Trial. Ann Surg 2022;275:646-653.

    Article  Google Scholar 

  22. Xu ZJ, Zhuo ZG, Song TN, et al. Pretreatment-assisted robot intrathoracic layered anastomosis: our exploration in Ivor-Lewis esophagectomy. J Thorac Dis 2021;13:4349-4359.

    Article  Google Scholar 

  23. van der Sluis PC, Tagkalos E, Hadzijusufovic E, et al. Robot-assisted minimally invasive esophagectomy with intrathoracic anastomosis (Ivor Lewis): promising results in 100 consecutive patients (the European Experience). J Gastrointest Surg 2021;25:1-8.

    Article  Google Scholar 

  24. Thammineedi SR, Patnaik SC, Reddy P, et al. The emerging role of ICG fluorescence during minimally invasive esophagectomy. Indian J Surg Oncol 2021;12:635-636.

    Article  Google Scholar 

  25. Zhang H, Chen L, Wang Z, et al. The learning curve for robotic McKeown Esophagectomy in Patients With Esophageal Cancer. Ann Thorac Surg 2018;105:1024-1030.

    Article  Google Scholar 

  26. Han Y, Zhang Y, Zhang W, et al. Learning curve for robot-assisted Ivor Lewis esophagectomy. Dis Esophagus 2022;35.

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Funding

This study was funded by the Province-Ministry Co-construction Project of Health Committee of Henan Province (SB201901108), Talent Youth Project of Henan Youth Health Science and Technology Innovation Foundation (YXKC2020022), and Henan Province Key Project of Science and Technology (222102310170).

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Correspondence to Hai-Bo Sun.

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Sun, HB., Jiang, D., Liu, XB. et al. Perioperative Outcomes and Learning Curve of Robot-Assisted McKeown Esophagectomy. J Gastrointest Surg 27, 17–26 (2023). https://doi.org/10.1007/s11605-022-05484-w

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  • DOI: https://doi.org/10.1007/s11605-022-05484-w

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