Skip to main content

Advertisement

Log in

Parameters for Predicting Surgical Outcomes for Gastric Cancer Patients: Simple Is Better Than Complex

  • Gastrointestinal Oncology
  • Published:
Annals of Surgical Oncology Aims and scope Submit manuscript

Abstract

Background

Various parameters are used to predict perioperative surgical outcomes. However, no comprehensive studies in gastrectomy have been conducted. This study aimed to compare the performance of each parameter in patients with gastric cancer.

Methods

The medical records of 1032 gastric cancer patients who underwent curative gastrectomy between 2009 and 2015 were reviewed. Laboratory values and associated parameters (neutrophil count, lymphocyte count, platelet count, albumin level, Prognostic Nutritional Index, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and Systemic Immune-Inflammation Index) as well as body weight-related data and associated parameters [body mass index (BMI), percentage of weight loss, Nutritional Risk Screening 2002 assessment, the Malnutrition Universal Screening Tool, and the Nutritional Risk Index] were measured and calculated. The study end points were major complications, operative mortality, prolonged hospital stay, overall survival (OS), and recurrence-free survival (RFS).

Results

Multivariable logistic regression analysis showed that male gender, total gastrectomy, advanced-stage gastric cancer, and low albumin level were risk factors for major complications. Old age, total gastrectomy, advanced-stage cancer, and high BMI were risk factors for operative mortality. Old age, open approach, and total gastrectomy were risk factors for prolonged hospital stay. Multivariable Cox proportional hazards models showed that old age, total gastrectomy, advanced-stage cancer, and high neutrophil count were unfavorable risk factors for OS. Old age, advanced-stage cancer, high neutrophil count, and high BMI were unfavorable risk factors for RFS.

Conclusions

Albumin level, BMI, and neutrophil count are the most useful parameters for predicting short- and long-term surgical outcomes. Compared with complex parameters, simple-to-measure parameters are better for predicting surgical outcomes for gastric cancer patients.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Yang K, Choi YY, Zhang WH, et al. Strategies to improve treatment outcome in gastric cancer: a retrospective analysis of patients from two high-volume hospitals in Korea and China. Oncotarget. 2016;7:44660–75.

    PubMed  PubMed Central  Google Scholar 

  2. Berretta S, Berretta M, Fiorica F, et al. Multimodal approach of advanced gastric cancer: based therapeutic algorithm. Eur Rev Med Pharmaco Sci. 2016;20:4018–31.

    CAS  Google Scholar 

  3. Rosania R, Chiapponi C, Malfertheiner P, Venerito M. Nutrition in patients with gastric cancer: an update. Gastrointest Tumors. 2016;2:178–87.

    Article  Google Scholar 

  4. 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 

  5. Rocken C, Behrens HM. Validating the prognostic and discriminating value of the TNM-classification for gastric cancer: a critical appraisal. Eur J Cancer. 2015;51:577–86.

    Article  CAS  Google Scholar 

  6. Guo W, Ou G, Li X, Huang J, Liu J, Wei H. Screening of the nutritional risk of patients with gastric carcinoma before operation by NRS 2002 and its relationship with postoperative results. J Gastroenterol Hepatol. 2010;25:800–3.

    Article  Google Scholar 

  7. Agolli L, Maurizi Enrici R, Osti MF. Adjuvant radiochemotherapy for gastric cancer: Should we use prognostic factors to select patients? World J Gastroenterol. 2016;22:1131–8.

    Article  CAS  Google Scholar 

  8. Yamada S, Fujii T, Yabusaki N, et al. Clinical implication of inflammation-based prognostic score in pancreatic cancer: Glasgow Prognostic Score is the most reliable parameter. Med Baltimore. 2016;95:e3582.

    Article  CAS  Google Scholar 

  9. Pan QX, Su ZJ, Zhang JH, Wang CR, Ke SY. A comparison of the prognostic value of preoperative inflammation-based scores and TNM stage in patients with gastric cancer. Onco Targets Ther. 2015;8:1375–85.

    Article  CAS  Google Scholar 

  10. Sachlova M, Majek O, Tucek S. Prognostic value of scores based on malnutrition or systemic inflammatory response in patients with metastatic or recurrent gastric cancer. Nutr Cancer. 2014;66:1362–70.

    Article  CAS  Google Scholar 

  11. Ryu SW, Kim IH. Comparison of different nutritional assessments in detecting malnutrition among gastric cancer patients. World J Gastroenterol. 2010;16:3310–17.

    Article  Google Scholar 

  12. Hong X, Cui B, Wang M, Yang Z, Wang L, Xu Q. Systemic immune-inflammation index, based on platelet counts and neutrophil-lymphocyte ratio, is useful for predicting prognosis in small cell lung cancer. Tohoku J Exp Med. 2015;236:297–304.

    Article  CAS  Google Scholar 

  13. Sakurai K, Ohira M, Tamura T, et al. Predictive potential of preoperative nutritional status in long-term outcome projections for patients with gastric cancer. Ann Surg Oncol. 2016;23:525–33.

    Article  Google Scholar 

  14. Nozoe T, Ninomiya M, Maeda T, Matsukuma A, Nakashima H, Ezaki T. Prognostic nutritional index: a tool to predict the biological aggressiveness of gastric carcinoma. Surg Today. 2010;40:440–3.

    Article  Google Scholar 

  15. Sun X, Liu X, Liu J, et al. Preoperative neutrophil-to-lymphocyte ratio plus platelet-to-lymphocyte ratio in predicting survival for patients with stage I–II gastric cancer. Chin J Cancer. 2016;35:57.

    Article  Google Scholar 

  16. Feng JF, Chen S, Yang X. Systemic immune-inflammation index (SII) is a useful prognostic indicator for patients with squamous cell carcinoma of the esophagus. Med Baltimore. 2017;96:e5886.

    Article  Google Scholar 

  17. Liu BZ, Tao L, Chen YZ, et al. Preoperative body mass index, blood albumin and triglycerides predict survival for patients with gastric cancer. PLoS ONE. 2016;11:e0157401.

    Article  Google Scholar 

  18. Jun DH, Kim BJ, Park JH, et al. Preoperative body mass index may determine the prognosis of advanced gastric cancer. Nutr Cancer. 2016;68:1295–300.

    Article  CAS  Google Scholar 

  19. Lin YS, Huang KH, Lan YT, et al. Impact of body mass index on postoperative outcome of advanced gastric cancer after curative surgery. J Gastrointest Surg. 2013;17:1382–91.

    Article  Google Scholar 

  20. Rabito EI, Marcadenti A, da Silva Fink J, Figueira L, Silva FM. Nutritional Risk Screening 2002, Short Nutritional Assessment Questionnaire, Malnutrition Screening Tool, and Malnutrition Universal Screening Tool are good predictors of nutrition risk in an emergency service. Nutr Clin Pract. 2017;32:526–32.

    Article  Google Scholar 

  21. Japanese Gastric Cancer Association. Japanese gastric cancer treatment guidelines 2010 (ver. 3). Gastric Cancer. 2011;14:113–23.

    Article  Google Scholar 

  22. Lee JH, Kim JG, Jung HK, et al. Clinical practice guidelines for gastric cancer in Korea: an evidence-based approach. J Gastric Cancer 2014;14:87–104.

    Article  Google Scholar 

  23. Amin MB, Edge S, Greene F, et al. AJCC cancer staging manual. 8th ed. Springer, New York, 2017.

    Book  Google Scholar 

  24. 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 

  25. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44:837–45.

    Article  CAS  Google Scholar 

  26. Harrell FEJ. Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis. 1st ed. Springer, New York, 2001.

    Book  Google Scholar 

  27. Field AP. Discovering statistics using SPSS: (and sex and drugs and Rock ‘n’ Roll), 3rd ed. SAGE Publications, Los Angeles, 2009.

    Google Scholar 

  28. Park S, Hendry DJ. Reassessing schoenfeld residual tests of proportional hazards in political science event history analyses. Am J Political Sci. 2015;59:1072–87.

    Article  Google Scholar 

  29. Chan BA, Jang RW, Wong RK, Swallow CJ, Darling GE, Elimova E. Improving outcomes in resectable gastric cancer: a review of current and future strategies. Oncology Williston Park. 2016;30:635–45.

    PubMed  Google Scholar 

  30. Chon HJ, Hyung WJ, Kim C, et al. Differential prognostic implications of gastric signet ring cell carcinoma: stage-adjusted analysis from a single high-volume center in Asia. Ann Surg. 2017;265:946–53.

    Article  Google Scholar 

  31. Oh CA, Kim DH, Oh SJ, et al. Nutritional risk index as a predictor of postoperative wound complications after gastrectomy. World J Gastroenterol. 2012;18:673–8.

    Article  CAS  Google Scholar 

  32. Watanabe M, Iwatsuki M, Iwagami S, Ishimoto T, Baba Y, Baba H. Prognostic nutritional index predicts outcomes of gastrectomy in the elderly. World J Surg. 2012;36:1632–9.

    Article  Google Scholar 

  33. Sakurai K, Tamura T, Toyokawa T, et al. Low preoperative prognostic nutritional index predicts poor survival post-gastrectomy in elderly patients with gastric cancer. Ann Surg Oncol. 2016;23:3669–76.

    Article  Google Scholar 

  34. Liu X, Sun X, Liu J, Kong P, Chen S, Zhan Y, Xu D. Preoperative C-reactive protein/albumin ratio predicts prognosis of patients after curative resection for gastric cancer. Transl Oncol. 2015;8:339–45.

    Article  Google Scholar 

  35. Sun KY, Xu JB, Chen SL, et al. Novel immunological and nutritional-based prognostic index for gastric cancer. World J Gastroenterol. 2015;21:5961–71.

    Article  Google Scholar 

  36. Lee JY, Kim HI, Kim YN, et al. Clinical significance of the prognostic nutritional index for predicting short- and long-term surgical outcomes after gastrectomy: a retrospective analysis of 7781 gastric cancer patients. Med. Baltimore. 2016;95:e3539.

    Article  Google Scholar 

Download references

Acknowledgment

This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korea Government (MSIP) (No. 2016R1A2B4014984). We acknowledge the assistance of BioScience Writers, LLC (Houston, TX, USA) in copyediting of the manuscript and corrections of English language usage.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hyoung-Il Kim MD.

Ethics declarations

Disclosure

There are no conflicts of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guner, A., Kim, S.Y., Yu, J.E. et al. Parameters for Predicting Surgical Outcomes for Gastric Cancer Patients: Simple Is Better Than Complex. Ann Surg Oncol 25, 3239–3247 (2018). https://doi.org/10.1245/s10434-018-6684-2

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1245/s10434-018-6684-2

Keywords

Navigation