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E-PASS scoring system serves as a predictor of short- and long-term outcomes in gastric cancer surgery

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This study aimed to evaluate the estimation of the physiological ability and surgical stress (E-PASS) scoring system for predicting the short- and long-term outcomes in gastric cancer (GC) surgery.


We analyzed a multi-institutional dataset to study patients who underwent gastrectomy with a curative intent between 2010 and 2014. This study evaluated the associations between the optimal E-PASS score cutoff value and the following outcomes: (1) the incidence of postoperative complications in stage I–III GC patients and (2) the prognosis in stage II–III GC patients.


A total of 2495 GC patients were included. A cutoff value of 0.419 was determined using the ROC curve analysis. Postoperative complications were observed more frequently in the E-PASS-high group than that in the E-PASS-low group (30% vs. 17%, p < 0.0001). Among pStage II–III GC patients (n = 1009), the overall survival time of the E-PASS-high group was significantly shorter than that of the E-PASS-low group (hazard ratio 2.08; 95% confidence interval 1.64–2.65; p < 0.0001). A forest plot revealed that E-PASS-high was associated with a greater prognostic factor for overall survival in most subgroups.


The E-PASS scoring system may therefore be a useful predictor of the short- and long-term outcomes in patients with GC who have undergone radical gastrectomy.

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  1. Persiani R, Antonacci V, Biondi A, Rausei S, La Greca A, Zoccali M, et al. Determinants of surgical morbidity in gastric cancer treatment. J Am Coll Surg. 2008;207:13–9.

    Article  Google Scholar 

  2. Park DJ, Lee HJ, Kim HH, Yang HK, Lee KU, Choe KJ. Predictors of operative morbidity and mortality in gastric cancer surgery. Br J Surg. 2005;92:1099–102.

    Article  CAS  Google Scholar 

  3. Kanda M, Ito S, Mochizuki Y, Teramoto H, Ishigure K, Murai T, et al. Multi-institutional analysis of the prognostic significance of postoperative complications after curative resection for gastric cancer. Cancer Med. 2019;8:5194–201.

    Article  CAS  Google Scholar 

  4. Kurita N, Miyata H, Gotoh M, Shimada M, Imura S, Kimura W, et al. Risk model for distal gastrectomy when treating gastric cancer on the basis of data from 33,917 Japanese patients collected using a nationwide web-based data entry system. Ann Surg. 2015;262:295–303.

    Article  Google Scholar 

  5. Nakanishi K, Kanda M, Kodera Y. Long-lasting discussion: adverse effects of intraoperative blood loss and allogeneic transfusion on prognosis of patients with gastric cancer. World J Gastroenterol. 2019;25:2743–51.

    Article  CAS  Google Scholar 

  6. Wang X, Yao Y, Qian H, Li H, Zhu X. Longer operating time during gastrectomy has adverse effects on short-term surgical outcomes. J Surg Res. 2019;243:151–9.

    Article  CAS  Google Scholar 

  7. Nakanishi K, Kanda M, Ito S, Mochizuki Y, Teramoto H, Ishigure K, et al. Propensity-score-matched analysis of a multi-institutional dataset to compare postoperative complications between Billroth I and Roux-en-Y reconstructions after distal gastrectomy. Gastric Cancer. 2020;23:734–45.

    Article  Google Scholar 

  8. Kanda M. Preoperative predictors of postoperative complications after gastric cancer resection. Surg Today. 2020;50:3–11.

    Article  Google Scholar 

  9. Nakanishi K, Kanda M, Ito S, Mochizuki Y, Teramoto H, Ishigure K, et al. Delay in initiation of postoperative adjuvant chemotherapy with S-1 monotherapy and prognosis for gastric cancer patients: analysis of a multi-institutional dataset. Gastric Cancer. 2019;22:1215–25.

    Article  CAS  Google Scholar 

  10. Kanda M, Suh YS, Park DJ, Tanaka C, Ahn SH, Kong SH, et al. Serum levels of ANOS1 serve as a diagnostic biomarker of gastric cancer: a prospective multicenter observational study. Gastric Cancer. 2020;23:203–11.

    Article  Google Scholar 

  11. Nakanishi K, Kanda M, Umeda S, Tanaka C, Kobayashi D, Hayashi M, et al. The levels of SYT13 and CEA mRNAs in peritoneal lavages predict the peritoneal recurrence of gastric cancer. Gastric Cancer. 2019;22:1143–52.

    Article  Google Scholar 

  12. Kitano Y, Iwatsuki M, Kurashige J, Kuroda D, Kosumi K, Baba Y, et al. Estimation of Physiologic Ability and Surgical Stress (E-PASS) versus modified E-PASS for prediction of postoperative complications in elderly patients who undergo gastrectomy for gastric cancer. Int J Clin Oncol. 2017;22:80–7.

    Article  Google Scholar 

  13. Haga Y, Wada Y, Takeuchi H, Ikejiri K, Ikenaga M, Kimura O. Evaluation of modified estimation of physiologic ability and surgical stress in gastric carcinoma surgery. Gastric Cancer. 2012;15:7–14.

    Article  Google Scholar 

  14. Ariake K, Ueno T, Takahashi M, Goto S, Sato S, Akada M, et al. E-PASS comprehensive risk score is a good predictor of postsurgical mortality from comorbid disease in elderly gastric cancer patients. J Surg Oncol. 2014;109:586–92.

    Article  Google Scholar 

  15. Murakami Y, Saito H, Shimizu S, Kono Y, Shishido Y, Miyatani K, et al. Evaluation of the estimation of physiologic ability and surgical stress score as a prognostic indicator for older patients with gastric cancer. Dig Surg. 2020;37:171–8.

    Article  Google Scholar 

  16. Takahashi R, Nunobe S, Makuuchi R, Ida S, Kumagai K, Ohashi M, et al. Survival outcomes of elderly patients with pathological stages II and III gastric cancer following curative gastrectomy. Ann Gastroenterol Surg. 2020;4:433–40.

    Article  Google Scholar 

  17. Haga Y, Ikei S, Ogawa M. Estimation of Physiologic Ability and Surgical Stress (E-PASS) as a new prediction scoring system for postoperative morbidity and mortality following elective gastrointestinal surgery. Surg Today. 1999;29:219–25.

    Article  CAS  Google Scholar 

  18. Haga Y, Wada Y, Takeuchi H, Ikejiri K, Ikenaga M. Prediction of anastomotic leak and its prognosis in digestive surgery. World J Surg. 2011;35:716–22.

    Article  Google Scholar 

  19. Asociation JGC. Japanese classification of gastric carcinoma. 15th ed. Tokyo: Kanehara Publisher; 2017.

    Google Scholar 

  20. Japanese Gastric Cancer Association. Japanese gastric cancer treatment guidelines 2014 (ver. 4). Gastric Cancer. 2017;20:1–19.

    Article  Google Scholar 

  21. Japanese Gastric Cancer Association. Japanese gastric cancer treatment guidelines 2018 (5th edition). Gastric Cancer. 2021;24:1–21.

    Article  Google Scholar 

  22. Dindo D, Demartines N, Clavien PA. Classification of surgical complications: a new proposal with evaluation in a cohort of 6336 patients and results of a survey. Ann Surg. 2004;240:205–13.

    Article  Google Scholar 

  23. Clavien PA, Barkun J, de Oliveira ML, Vauthey JN, Dindo D, Schulick RD, et al. The Clavien-Dindo classification of surgical complications: five-year experience. Ann Surg. 2009;250:187–96.

    Article  Google Scholar 

  24. Yamamoto M, Saito H, Uejima C, Tanio A, Tada Y, Matsunaga T, et al. Estimation of physiological ability and surgical stress score is a useful prognostic indicator for elderly patients with colorectal cancer. Dig Surg. 2020;37:145–53.

    Article  Google Scholar 

  25. Nakanishi K, Kanda M, Sakamoto J, Kodera Y. Is the measurement of drain amylase content useful for predicting pancreas-related complications after gastrectomy with systematic lymphadenectomy? World J Gastroenterol. 2020;26:1594–600.

    Article  Google Scholar 

  26. Miki Y, Makuuchi R, Tokunaga M, Tanizawa Y, Bando E, Kawamura T, et al. Risk factors for postoperative pneumonia after gastrectomy for gastric cancer. Surg Today. 2016;46:552–6.

    Article  Google Scholar 

  27. Shoka M, Kanda M, Ito S, Mochizuki Y, Teramoto H, Ishigure K, et al. Systemic inflammation score as a predictor of pneumonia after radical resection of gastric cancer: analysis of a multi-institutional dataset. Dig Surg. 2020;37:401–10.

    Article  Google Scholar 

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Correspondence to Mitsuro Kanda.

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All procedures performed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1964 and later versions.

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Supplementary file1 (DOCX 23 kb)


Supplementary file2. Supplemental Figure 1. Overall survival curves according to the pathological disease stage. A pStage I, B pStage II, and C pStage III. (TIF 927 kb)


Supplementary file3. Supplemental Figure 2. Disease-free survival curves according to pathological disease stage. A pStage I, B pStage II, and C pStage III. (TIF 916 kb)

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Nakanishi, K., Kanda, M., Ito, S. et al. E-PASS scoring system serves as a predictor of short- and long-term outcomes in gastric cancer surgery. Surg Today 52, 914–922 (2022).

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