Research progress on risk assessment and predictive early warning of severe perioperative complications in elderly lung cancer patients_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

HE Zixiao ¹ , LUO Ziyue ¹ ,  ZHOU Ruihao ¹ , WANG Yanbingshi ² , SHEN Le ² , CHEN Guo ¹ , ZHU Tao ¹ ,  LIU Lunxu ³

1. Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, 610041, P. R. China;
2. Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, P. R. China;
3. Department of Thoracic Surgery, Institute of Thoracic Oncology, West China Hospital, Sichuan University, Chengdu, 610041, P. R. China;

Corresponding?author：

ZHOU Ruihao, Email: riozhou@163.com; LIU Lunxu, Email: lunxu_liu@aliyun.com

Keywords：

Elderly lung cancer; perioperative complications; risk factor; predictive models; machine learning; frailty; enhanced recovery after surgery

DOI：

10.7507/1007-4848.202604040

Video：

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Abstract Full text Figures/Tables Video References Cited by

With the aging population and the widespread implementation of lung cancer screening programs, an increasing number of elderly patients are undergoing curative lung resection. Due to diminished physiological reserve and complex comorbidities, this demographic exhibits a significantly higher incidence of severe perioperative complications (defined as Clavien-Dindo grade≥Ⅲ), which adversely affects both perioperative safety and long-term prognosis. In recent years, the research paradigm has shifted from univariate analysis toward multidimensional risk integration and the development of predictive models. This article systematically reviews the key risk factors for severe perioperative complications in elderly lung cancer patients, encompassing biological aging processes, frailty and sarcopenia, cardiopulmonary and renal functional reserves, inflammatory-immune and coagulation status, and perioperative interventions. Furthermore, it traces the evolution of risk assessment tools from traditional regression models to machine learning models that integrate multimodal data. The review also discusses common challenges in this field, including the standardization of outcome definitions, external validation, calibration assessment, and clinical translation. Future efforts should prioritize the deep integration of predictive tools with clinical decision support systems to establish a closed-loop care pathway from risk identification to stratified intervention, thereby effectively reducing complication rates and enhancing surgical outcomes for elderly lung cancer patients.

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2. de Koning HJ, van der Aalst CM, de Jong PA, et al. Reduced lung-cancer mortality with volume CT screening in a randomized trial. N Engl J Med, 2020, 382(6): 503-513.
3. Fernandez-Bustamante A, Parker RA, Frendl G, et al. Perioperative lung expansion and pulmonary outcomes after open abdominal surgery versus usual care in the USA (PRIME-AIR): a multicentre, randomised, controlled, phase 3 trial. Lancet Respir Med, 2025, 13(5): 447-459.
4. Volkert D, Delzenne N, Demirkan K, et al. Nutrition for the older adult-current concepts. Report from an ESPEN symposium. Clin Nutr, 2024, 43(8): 1815-1824.
5. Engel JS, Tran J, Khalil N, et al. A systematic review of perioperative clinical practice guidelines for care of older adults living with frailty. Br J Anaesth, 2023, 130(3): 262-271.
6. Tong BC, Bonnell LN, Habib RH, et al. The Society of Thoracic Surgeons 2024 risk models for lung cancer resection: continued refinement and improved outcomes. Ann Thorac Surg, 2025, 119(4): 777-785.
7. Brunelli A, Salati M, Rocco G, 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(3): 490-497.
8. Garabinovic Z, Savic M, Colic N, et al. Artificial intelligence as a diagnostic tool in preoperative surgical planning for early non-small cell lung cancer: a single-center experience. J Clin Med, 2025, 14(21): 7609.
9. Chen Y, Chen D, Liu X, et al. Deep learning-driven multimodal integration of miRNA and radiomic for lung cancer diagnosis. Biosensors, 2025, 15(9): 610.
10. Kuang Q, Feng B, Xu K, et al. Multimodal deep learning radiomics model for predicting postoperative progression in solid stageⅠnon-small cell lung cancer. Cancer Imaging, 2024, 24(1): 140.
11. 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(2): 205-213.
12. Trotti A, Colevas AD, Setser A, et al. CTCAE v3. 0: development of a comprehensive grading system for the adverse effects of cancer treatment. Semin Radiat Oncol, 2003, 13(3): 176-181.
13. Sato Y, Mizusawa J, Katayama H, et al. Validation of the Japan Clinical Oncology Group postoperative complications criteria against CTCAE for evaluation of postoperative complications (JCOG1903A). Eur J Surg Oncol, 2025, 51(6): 109669.
14. Katayama H, Kurokawa Y, Nakamura K, et al. Extended Clavien-Dindo classification of surgical complications: Japan Clinical Oncology Group postoperative complications criteria. Surg Today, 2016, 46(6): 668-685.
15. Liu Z, Kuo PL, Horvath S, et al. A new aging measure captures morbidity and mortality risk across diverse subpopulations from NHANES Ⅳ: a cohort study. PLoS Med, 2018, 15(12): e1002718.
16. Jiang J, Yin X, Kong S, et al. Associations of accelerated biological aging with intraoperative hypotension in major surgery: a multicenter cohort study of 116, 996 patients. Ann Med, 2025, 57(1): 2603045.
17. Soysal P, Stubbs B, Lucato P, et al. Inflammation and frailty in the elderly: a systematic review and meta-analysis. Ageing Res Rev, 2016, 31: 1-8.
18. Pan Y, Ma L. Inflammatory markers and physical frailty: towards clinical application. Immun Ageing, 2024, 21(1): 4.
19. Church S, Rogers E, Rockwood K, et al. A scoping review of the Clinical Frailty Scale. BMC Geriatr, 2020, 20(1): 393.
20. Rockwood K, Song X, MacKnight C, et al. A global clinical measure of fitness and frailty in elderly people. CMAJ, 2005, 173(5): 489-495.
21. McIsaac DI, Lee S, Fergusson D, et al. Home-based prehabilitation for older surgical patients with frailty: a randomized clinical trial. JAMA Surg, 2026, 161(2): 113-123.
22. Cruz-Jentoft AJ, Bahat G, Bauer J, et al. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing, 2019, 48(4): 601.
23. Han DJ, Na KJ, Yun T, et al. Effects of respiratory sarcopenia on the postoperative course in elderly lung cancer patient: a retrospective study. J Cardiothorac Surg, 2025, 20(1): 71.
24. Brunelli A, Charloux A, Bolliger CT, et al. ERS/ESTS clinical guidelines on fitness for radical therapy in lung cancer patients (surgery and chemo-radiotherapy). Eur Respir J, 2009, 34(1): 17-41.
25. Clark JM, Marrufo AS, Kozower BD, et al. Cardiopulmonary testing prior to lung resection: what are thoracic surgeons doing? Ann Thorac Surg, 2019, 108(4): 1006-1012.
26. Lee AH, Seyednejad N, Yang Y, et al. Analysis of pulmonary complications and predicted postoperative pulmonary function in oncologic lung resections. J Thorac Dis, 2024, 16(11): 7574-7581.
27. Axtell AL, David EA, Block MI, et al. Association between interstitial lung disease and outcomes after lung cancer resection. Ann Thorac Surg, 2023, 116(3): 533-541.
28. Writing Committee Members, Thompson A, Fleischmann KE, et al. 2024 AHA/ACC/ACS/ASNC/HRS/SCA/SCCT/SCMR/SVM guideline for perioperative cardiovascular management for noncardiac surgery: a report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. J Am Coll Cardiol, 2024, 84(19): 1869-1969.
29. Brunelli A, Kim AW, Berger KI, et al. 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(5 Suppl): e166S-e190S.
30. Chaudhry RA, Zarmer L, West K, et al. Obstructive sleep apnea and risk of postoperative complications after non-cardiac surgery. J Clin Med, 2024, 13(9): 2538.
31. Zhang X, Li X, Li Y, et al. Comparison of high-flow nasal cannula with conventional oxygen therapy for preventing postoperative hypoxemia in patients with lung resection surgery: a systematic review and meta-analysis. J Thorac Dis, 2024, 16(5): 2906-2917.
32. Yu Y, Xu S, Yan B, et al. Incidence and associations of acute kidney injury after general thoracic surgery: a system review and meta-analysis. J Clin Med, 2022, 12(1): 37.
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Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Latest ArticlesResearch progress on risk assessment and predictive early warning of severe perioperative complications in elderly lung cancer patients

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