Skip to main content
Log in

The predictive value of preoperative risk assessments and frailty for surgical complications in lung cancer patients

  • Original Article
  • Published:
Surgery Today Aims and scope Submit manuscript

Abstract

Purpose

To assess the predictive value of frailty and risk assessments for postoperative complications in lung cancer patients, we reviewed various risk indicators: including FEV1, ppoFEV1, the Zubrod performance status, the American Society of Anesthesiologist score, and risk models based on the Japan National Clinical Database (NCD) and the European Society of Thoracic Surgeons (ESTS) database.

Methods

Patients who underwent elective surgery between April 2016 and May 2019 were enrolled. A statistical analysis was performed to compare any differences among the risk indicators.

Results

The total number of patients enrolled was 193. Thirteen patients (6.7%) were classified as frail and 28 (14.5%) as pre-frail. Among the various risk indicators, the risk models based on the Japan NCD and the ESTS database revealed statistically significant differences in patients with and without postoperative complications (p value < 0.0001 and 0.0049, respectively), although there were no significant differences in frailty. The area under the receiver operating characteristic curve for risk models based on the Japan NCD registry and the ESTS registry was 0.70 and 0.64, respectively.

Conclusions

Our analyses of a series of lung cancer patients showed that frailty was not a significant predictor of postoperative outcomes, while risk models based on academic society databases were found to have a significant predictive value.

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.

Similar content being viewed by others

References

  1. Brunelli A, Kim AW, Berger KI, Addrizzo-Harris DJ. Physiologic evaluation of the patient with lung cancer being considered for resectional surgery. Diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines. Chest. 2013;143:e166S-90S.

    Article  Google Scholar 

  2. Lim E, Baldwin D, Beckles M, Duffy J, Entwisle J, Faivre-Finn C, et al. Guidelines on the radical management of patients with lung cancer. Thorax. 2010;65:iii1–27.

    PubMed  Google Scholar 

  3. Etzioni DA, Liu JH, Maggard MA, Ko CY. The aging population and its impact on the surgery workforce. Ann Surg. 2003;238:170–7.

    PubMed  PubMed Central  Google Scholar 

  4. Chand M, Armstrong T, Britton G, Nash GF. How and why do we measure surgical risk? J R Soc Med. 2007;100:508–12.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Marx GF, Mateo CV, Orkin LR. Computer analysis of postanesthetic deaths. Anesthesiology. 1973;39:54–8.

    Article  CAS  PubMed  Google Scholar 

  6. Mayhew D, Mendonca V, Murthy BVS. A review of ASA physical status—historical perspectives and modern developments. Anaesthesia. 2019;74:373–9.

    Article  CAS  PubMed  Google Scholar 

  7. Knaus WA, Zimmerman JE, Wagner DP, Draper EA, Lawrence DE. APACHE-acute physiology and chronic health evaluation: a physiologically based classification system. Crit Care Med. 1981;9:591–7.

    Article  CAS  PubMed  Google Scholar 

  8. Nag DS, Dembla A, Mahanty PR, Kant S, Chatterjee A, Samaddar DP, et al. Comparative analysis of APACHE-II and P-POSSUM scoring systems in predicting postoperative mortality in patients undergoing emergency laparotomy. World J Clin Cases. 2019;7:2227–37.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Copeland GP, Jones D, Walters M. POSSUM: a scoring system for surgical audit. Br J Surg. 1991;78:355–60.

    Article  CAS  PubMed  Google Scholar 

  10. Hong S, Wang S, Xu G, Liu J. Evaluation of the POSSUM, p-POSSUM, o-POSSUM, and APACHE II scoring systems in predicting postoperative mortality and morbidity in gastric cancer patients. Asian J Surg. 2017;40:89–94.

    Article  PubMed  Google Scholar 

  11. Brunelli A, Fianchini A, Gesuita R, Carle F. POSSUM scoring system as an instrument of audit in lung resection surgery. Physiological and operative severity score for the enumeration of mortality and morbidity. Ann Thorac Surg. 1999;67:329–31.

    Article  CAS  PubMed  Google Scholar 

  12. Fernandez FG, Kosinski AS, Burfeind W, Park B, DeCamp MM, Seder C, et al. The society of thoracic surgeons lung cancer resection risk model: higher quality data and superior outcomes. Ann Thorac Surg. 2016;102:370–7.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Brunelli A, Salati M, Rocco G, Varela G, Van Raemdonck D, Decaluwe H, et al. European risk models for morbidity (EuroLung1) and mortality (EuroLung2) to predict outcome following anatomic lung resections: an analysis from the European Society of Thoracic Surgeons database. Eur J Cardiothorac Surg. 2017;51:490–7.

    Article  PubMed  Google Scholar 

  14. Endo S, Ikeda N, Kondo T, Nakajima J, Kondo H, Yokoi K, et al. Model of lung cancer surgery risk derived from a Japanese nationwide web-based database of 78 594 patients during 2014–2015. Eur J Cardiothorac Surg. 2017;52:1182–9.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56:M146–56.

    Article  CAS  Google Scholar 

  16. Lin HS, Watts JN, Peel NM, Hubbard RE. Frailty and post-operative outcomes in older surgical patients: a systematic review. BMC Geriatr. 2016;16:157.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Clegg A, Young J, Iliffe S, Rikkert MO, Rockwood K. Frailty in elderly people. Lancet. 2013;381:752–62.

    Article  PubMed  Google Scholar 

  18. Hanlon P, Nicholl BI, Jani BD, Lee D, McQueenie R, Mair FS. Frailty and pre-frailty in middle-aged and older adults and its association with multimorbidity and mortality: a prospective analysis of 493 737 UK Biobank participants. Lancet Public Health. 2018;3:e323–32.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Shinall MC Jr, Arya S, Youk A, Varley P, Shah R, Massarweh NN, et al. Association of preoperative patient frailty and operative stress with postoperative mortality. JAMA Surg. 2019;155:e194620.

    Article  PubMed Central  Google Scholar 

  20. Panayi AC, Orkaby AR, Sakthivel D, Endo Y, Varon D, Roh D, et al. Impact of frailty on outcomes in surgical patients: a systematic review and meta-analysis. Am J Surg. 2019;218:393–400.

    Article  CAS  PubMed  Google Scholar 

  21. Satake S, Shimada H, Yamada M, Kim H, Yoshida H, Gondo Y, et al. Prevalence of frailty among community-dwellers and outpatients in Japan as defined by the Japanese version of the Cardiovascular Health Study criteria. Geriatr Gerontol Int. 2017;17:2629–34.

    Article  PubMed  Google Scholar 

  22. Kaneda H, Saito Y, Okamoto M, Maniwa T, Minami K, Imamura H. Early postoperative mobilization with walking at 4 hours after lobectomy in lung cancer patients. Gen Thorac Cardiovasc Surg. 2007;55:493–8.

    Article  PubMed  Google Scholar 

  23. Bolliger CT, Jordan P, Soler M, Stulz P, Gradel E, Skarvan K, et al. Exercise capacity as a predictor of postoperative complications in lung resection candidates. Am J Respir Crit Care Med. 1995;151:1472–80.

    Article  CAS  PubMed  Google Scholar 

  24. Duque JL, Ramos G, Castrodeza J, Cerezal J, Castanedo M, Yuste MG, et al. Early complications in surgical treatment of lung cancer: a prospective, multicenter study. Ann Thorac Surg. 1997;63:944–50.

    Article  CAS  PubMed  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. Kaneda H, Saito Y. The prognostic factor tumor, node, metastasis classification: how helpful is it as a predictive factor of the success of a specific treatment? J Thorac Oncol. 2014;9:e64.

    Article  PubMed  Google Scholar 

  27. Buchner DM, Wagner EH. Preventing frail health. Clin Geriatr Med. 1992;8:1–17.

    Article  CAS  PubMed  Google Scholar 

  28. Dent E, Kowal P, Hoogendijk EO. Frailty measurement in research and clinical practice: a review. Eur J Intern Med. 2016;31:3–10.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

We thank Shie Wakita and Tomoko Hasegawa for their data management.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hiroyuki Kaneda.

Ethics declarations

Conflict of interest

Hiroyuki Kaneda and other co-authors have no conflicts of interest to declare in association with this study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kaneda, H., Nakano, T. & Murakawa, T. The predictive value of preoperative risk assessments and frailty for surgical complications in lung cancer patients. Surg Today 51, 86–93 (2021). https://doi.org/10.1007/s00595-020-02058-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00595-020-02058-8

Keywords

Navigation