Progress in predictive models for the risk of postoperative pulmonary complications after lung cancer surgery_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

DENG Ting ^1,2 , SONG Jiamei ^1,2 , LI Jin ³ , WU Xiaoyan ³ , WU Lishan ³ ,  CHEN Shaolin ^1,2

1. Nursing Department, Affiliated Hospital of Zunyi Medical University, Zunyi, 563000, Guizhou, P. R. China;
2. School of Nursing, Zunyi Medical University, Zunyi, 563000, Guizhou, P. R. China;
3. Department of Thoracic Surgery, Affiliated Hospital of Zunyi Medical University, Zunyi, 563000, Guizhou, P. R. China;

Corresponding author：

CHEN Shaolin, Email: 30363284@qq.com

Keywords：

Lung cancer; postoperative pulmonary complications; risk prediction models; review

DOI：

10.7507/1007-4848.202405056

Video：

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Postoperative pulmonary complications (PPCs) risk prediction models can help healthcare professionals identify the probability of PPCs occurring in patients after surgery and provide a foundation for rapid decision-making by clinical healthcare professionals. This study evaluated PPCs of lung cancer models' merits, limitations, and challenges, covering construction methods, model performance, and clinical applications. The current risk prediction models for PPCs after lung cancer surgery have a certain predictive effect on the occurrence of PPCs. However, deficiencies persist in study design, clinical implementation, and reporting transparency. Future research should prioritize large-sample, prospective, multi-center studies for multiomics models, ensuring robust data for precise predictions, thereby facilitating clinical translation, adoption, and promotion.

1.	Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2024, 74(3): 229-263.
2.	中华医学会肿瘤学分会, 中华医学会杂志社. 中华医学会肺癌临床诊疗指南(2023版). 中华医学杂志, 2023, 103(27): 2037-2074.Oncology Society of Chinese Medical Association. Chinese Medical Association guideline for clinical diagnosis and treatment of lung cancer (2023 edition). Natl Med J China, 2023, 103(27): 2037-2074.
3.	王天佑, 李单青, 崔永, 等. 胸外科围手术期肺保护中国专家共识(2019版). 中国胸心血管外科临床杂志, 2019, 26(9): 835-842.Wang TY, Li DQ, Cui Y, et al. Chinese expert consensus on perioperative lung protection in thoracic surgery (2019 edition). Chin J Clin Thorac Cardiovasc Surg, 2019, 26(9): 835-842.
4.	Zheng Y, Mao M, Li F, et al. Effects of enhanced recovery after surgery plus pulmonary rehabilitation on complications after video-assisted lung cancer surgery: A multicentre randomised controlled trial. Thorax, 2023, 78(6): 574-586.
5.	Liu W, Jin F, Wang HM, et al. The association between double-lumen tube versus bronchial blocker and postoperative pulmonary complications in patients after lung cancer surgery. Front Oncol, 2022, 12: 1011849.
6.	Moons KGM, Wolff RF, Riley RD, et al. PROBAST: A tool to assess risk of bias and applicability of prediction model studies: Explanation and elaboration. Ann Intern Med, 2019, 170(1): W1-W33.
7.	Collins GS, Moons KGM, Dhiman P, et al. TRIPOD+AI statement: Updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ, 2024, 385: e078378.
8.	Miskovic A, Lumb AB. Postoperative pulmonary complications. Br J Anaesth, 2017, 118(3): 317-334.
9.	张雨桐, 金亮, 玉红, 等. 三种术后肺部并发症评估标准的临床效果比较. 中华麻醉学杂志, 2022, 42(9): 1054-1058.Zhang YT, Jin L, Yu H, et al. Comparison of clinical effects of three criteria for postoperative pulmonary complications. Chin J Anesthesiol, 2022, 42(9): 1054-1058.
10.	Seder CW, Salati M, Kozower BD, et al. Variation in pulmonary resection practices between the Society of Thoracic Surgeons and the European Society of Thoracic Surgeons General Thoracic Surgery Databases. Ann Thorac Surg, 2016, 101(6): 2077-2084.
11.	Jammer I, Wickboldt N, Sander M, et al. Standards for definitions and use of outcome measures for clinical effectiveness research in perioperative medicine: European Perioperative Clinical Outcome (EPCO) definitions: A statement from the ESA-ESICM joint taskforce on perioperative outcome measures. Eur J Anaesthesiol, 2015, 32(2): 88-105.
12.	Abbott TEF, Fowler AJ, Pelosi P, et al. A systematic review and consensus definitions for standardised end-points in perioperative medicine: Pulmonary complications. Br J Anaesth, 2018, 120(5): 1066-1079.
13.	Takamori S, Oizumi H, Suzuki J, et al. Thoracoscopic anatomical individual basilar segmentectomy. Eur J Cardiothorac Surg, 2022, 62(1): ezab509.
14.	Fraser S, Baranowski R, Patrini D, et al. Maintaining safe lung cancer surgery during the COVID-19 pandemic in a global city. EClinicalMedicine, 2021, 39: 101085.
15.	E H, Yang C, Wu J, et al. Hybrid uniportal robotic-assisted thoracoscopic surgery using video-assisted thoracoscopic surgery staplers: Technical aspects and results. Ann Cardiothorac Surg, 2023, 12(1): 34-40.
16.	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.
17.	García-Hidalgo MC, Benítez ID, Perez-Pons M, et al. MicroRNA-guided drug discovery for mitigating persistent pulmonary complications in critical COVID-19 survivors: A longitudinal pilot study. Br J Pharmacol, 2024 Feb 15.
18.	Zhou CM, Xue Q, Li H, et al. A predictive model for post-thoracoscopic surgery pulmonary complications based on the PBNN algorithm. Sci Rep, 2024, 14(1): 7035.
19.	Song Y, Liu J, Lei M, et al. An external-validated algorithm to predict postoperative pneumonia among elderly patients with lung cancer after video-assisted thoracoscopic surgery. Front Oncol, 2021, 11: 777564.
20.	Zhao D, Ma A, Li S, et al. Development and validation of a nomogram for predicting pulmonary complications after video-assisted thoracoscopic surgery in elderly patients with lung cancer. Front Oncol, 2023, 13: 1265204.
21.	Choi JW, Jeong H, Ahn HJ, et al. The impact of pulmonary function tests on early postoperative complications in open lung resection surgery: An observational cohort study. Sci Rep, 2022, 12(1): 1277.
22.	Jin F, Liu W, Qiao X, et al. Nomogram prediction model of postoperative pneumonia in patients with lung cancer: A retrospective cohort study. Front Oncol, 2023, 13: 1114302.
23.	Tibshirani R. The LASSO method for variable selection in the Cox model. Stat Med, 1997, 16(4): 385-395.
24.	Copeland GP, Jones D, Walters M. POSSUM: A scoring system for surgical audit. Br J Surg, 1991, 78(3): 355-360.
25.	Whiteley MS, Prytherch DR, Higgins B, et al. An evaluation of the POSSUM surgical scoring system. Br J Surg, 1996, 83(6): 812-815.
26.	Vashistha N, Singhal S, Budhiraja S, et al. Evaluation of ACS-NSQIP and CR-POSSUM risk calculators for the prediction of mortality after colorectal surgery: A retrospective cohort study. J Minim Access Surg, 2024, 20(2): 142-147.
27.	Fugazzola P, Cobianchi L, Di Martino M, et al. Prediction of morbidity and mortality after early cholecystectomy for acute calculous cholecystitis: Results of the S. P. Ri. M. A. C. C. study. World J Emerg Surg, 2023, 18(1): 20.
28.	许川, 刘迪, 金星, 等. 胸腔镜下肺部术后并发症发生率的预测模型构建. 肿瘤学杂志, 2020, 26(7): 596-599.Xu C, Liu D, Jin X, et al. Construction of predictive model for postoperative complications in thoracoscopic lung surgery. J Chin Oncol, 2020, 26(7): 596-599.
29.	杨福耀. 改良POSSUM评分联合Clavien-Dindo分级预测高龄患者胸腔镜肺手术术后严重并发症发生率的研究. 河北医科大学, 2022.Yang FY. Modified POSSUM score combined with Clavien-Dindo grading to predict the incidence of serious complications after thoracoscopic lung surgery in elderly patients. Hebei Medical University, 2022.
30.	Haug CJ, Drazen JM. Artificial intelligence and machine learning in clinical medicine, 2023. N Engl J Med, 2023, 388(13): 1201-1208.
31.	Cierco Jimenez R, Lee T, Rosillo N, et al. Machine learning computational tools to assist the performance of systematic reviews: A mapping review. BMC Med Res Methodol, 2022, 22(1): 322.
32.	王新, 王炯杰, 王雷, 等. 基于CHAID决策树和Logistic回归的肺癌患者术后肺部并发症预测效果的研究. 临床肿瘤学杂志, 2021, 26(10): 898-902.Wang X, Wang JJ, Wang L, et al. Prediction of postoperative pulmonary complications in patients with lung cancer based on CHAID decision tree and logistic regression. Chin Clin Oncol, 2021, 26(10): 898-902.
33.	Jandy K, Weichbroth P. A machine learning approach to classifying New York Heart Association (NYHA) heart failure. Sci Rep, 2024, 14(1): 11496.
34.	Battista K, Diao L, Patte KA, et al. Examining the use of decision trees in population health surveillance research: An application to youth mental health survey data in the COMPASS study. Health Promot Chronic Dis Prev Can, 2023, 43(2): 73-86.
35.	Jin X, Pan Y, Zhai C, et al. Exploration and machine learning model development for T2 NSCLC with bronchus infiltration and obstructive pneumonia/atelectasis. Sci Rep, 2024, 14(1): 4793.
36.	Ju H, Kim K, Kim BI, et al. Graph neural network model for prediction of non-small cell lung cancer lymph node metastasis using protein-protein interaction network and 18F-FDG PET/CT radiomics. Int J Mol Sci, 2024, 25(2): 698.
37.	Tane S, Nishikubo M, Kitazume M, et al. Cluster analysis of emphysema for predicting pulmonary complications after thoracoscopic lobectomy. Eur J Cardiothorac Surg, 2021, 60(3): 607-613.
38.	Li T, Li W, Chen F, et al. The chest X-ray score baseline in predicting continuous oxygen therapy failure in low-risk aged patients after thoracic surgery. J Thorac Dis, 2024, 16(3): 1885-1899.
39.	Chen Y, Yao L, Chen Q, et al. A retrospective study on the impact of radiotherapy on the survival outcomes of small cell lung cancer patients based on the SEER database. Sci Rep, 2024, 14(1): 15552.
40.	Collins GS, Reitsma JB, Altman DG, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement. BMJ, 2015, 350: g7594.
41.	Wynants L, Van Calster B, Collins GS, et al. Prediction models for diagnosis and prognosis of covid-19: Systematic review and critical appraisal. BMJ, 2020, 369: m1328.
42.	Chen T, Chan EG, Huang B, et al. Outcomes following minimally invasive approaches vs. open extended lobectomy for non-small cell lung cancer: A propensity-matched analysis of the National Cancer Database. Transl Lung Cancer Res, 2024, 13(2): 334-344.
43.	Salazar de Pablo G, Iniesta R, Bellato A, et al. Individualized prediction models in ADHD: A systematic review and meta-regression. Mol Psychiatry, 2024 May 23.
44.	Tabja Bortesi JP, Ranisau J, Di S, et al. Machine learning approaches for the image-based identification of surgical wound infections: Scoping review. J Med Internet Res, 2024, 26: e52880.
45.	Snell KIE, Levis B, Damen JAA, et al. Transparent reporting of multivariable prediction models for individual prognosis or diagnosis: Checklist for systematic reviews and meta-analyses (TRIPOD-SRMA). BMJ, 2023, 381: e073538.
46.	Riley RD, Ensor J, Snell KIE, et al. Calculating the sample size required for developing a clinical prediction model. BMJ, 2020, 368: m441.
47.	Riley RD, Snell KIE, Archer L, et al. Evaluation of clinical prediction models (part 3): Calculating the sample size required for an external validation study. BMJ, 2024, 384: e074821.

1. Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2024, 74(3): 229-263.
2. 中华医学会肿瘤学分会, 中华医学会杂志社. 中华医学会肺癌临床诊疗指南(2023版). 中华医学杂志, 2023, 103(27): 2037-2074.Oncology Society of Chinese Medical Association. Chinese Medical Association guideline for clinical diagnosis and treatment of lung cancer (2023 edition). Natl Med J China, 2023, 103(27): 2037-2074.
3. 王天佑, 李单青, 崔永, 等. 胸外科围手术期肺保护中国专家共识(2019版). 中国胸心血管外科临床杂志, 2019, 26(9): 835-842.Wang TY, Li DQ, Cui Y, et al. Chinese expert consensus on perioperative lung protection in thoracic surgery (2019 edition). Chin J Clin Thorac Cardiovasc Surg, 2019, 26(9): 835-842.
4. Zheng Y, Mao M, Li F, et al. Effects of enhanced recovery after surgery plus pulmonary rehabilitation on complications after video-assisted lung cancer surgery: A multicentre randomised controlled trial. Thorax, 2023, 78(6): 574-586.
5. Liu W, Jin F, Wang HM, et al. The association between double-lumen tube versus bronchial blocker and postoperative pulmonary complications in patients after lung cancer surgery. Front Oncol, 2022, 12: 1011849.
6. Moons KGM, Wolff RF, Riley RD, et al. PROBAST: A tool to assess risk of bias and applicability of prediction model studies: Explanation and elaboration. Ann Intern Med, 2019, 170(1): W1-W33.
7. Collins GS, Moons KGM, Dhiman P, et al. TRIPOD+AI statement: Updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ, 2024, 385: e078378.
8. Miskovic A, Lumb AB. Postoperative pulmonary complications. Br J Anaesth, 2017, 118(3): 317-334.
9. 张雨桐, 金亮, 玉红, 等. 三种术后肺部并发症评估标准的临床效果比较. 中华麻醉学杂志, 2022, 42(9): 1054-1058.Zhang YT, Jin L, Yu H, et al. Comparison of clinical effects of three criteria for postoperative pulmonary complications. Chin J Anesthesiol, 2022, 42(9): 1054-1058.
10. Seder CW, Salati M, Kozower BD, et al. Variation in pulmonary resection practices between the Society of Thoracic Surgeons and the European Society of Thoracic Surgeons General Thoracic Surgery Databases. Ann Thorac Surg, 2016, 101(6): 2077-2084.
11. Jammer I, Wickboldt N, Sander M, et al. Standards for definitions and use of outcome measures for clinical effectiveness research in perioperative medicine: European Perioperative Clinical Outcome (EPCO) definitions: A statement from the ESA-ESICM joint taskforce on perioperative outcome measures. Eur J Anaesthesiol, 2015, 32(2): 88-105.
12. Abbott TEF, Fowler AJ, Pelosi P, et al. A systematic review and consensus definitions for standardised end-points in perioperative medicine: Pulmonary complications. Br J Anaesth, 2018, 120(5): 1066-1079.
13. Takamori S, Oizumi H, Suzuki J, et al. Thoracoscopic anatomical individual basilar segmentectomy. Eur J Cardiothorac Surg, 2022, 62(1): ezab509.
14. Fraser S, Baranowski R, Patrini D, et al. Maintaining safe lung cancer surgery during the COVID-19 pandemic in a global city. EClinicalMedicine, 2021, 39: 101085.
15. E H, Yang C, Wu J, et al. Hybrid uniportal robotic-assisted thoracoscopic surgery using video-assisted thoracoscopic surgery staplers: Technical aspects and results. Ann Cardiothorac Surg, 2023, 12(1): 34-40.
16. 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.
17. García-Hidalgo MC, Benítez ID, Perez-Pons M, et al. MicroRNA-guided drug discovery for mitigating persistent pulmonary complications in critical COVID-19 survivors: A longitudinal pilot study. Br J Pharmacol, 2024 Feb 15.
18. Zhou CM, Xue Q, Li H, et al. A predictive model for post-thoracoscopic surgery pulmonary complications based on the PBNN algorithm. Sci Rep, 2024, 14(1): 7035.
19. Song Y, Liu J, Lei M, et al. An external-validated algorithm to predict postoperative pneumonia among elderly patients with lung cancer after video-assisted thoracoscopic surgery. Front Oncol, 2021, 11: 777564.
20. Zhao D, Ma A, Li S, et al. Development and validation of a nomogram for predicting pulmonary complications after video-assisted thoracoscopic surgery in elderly patients with lung cancer. Front Oncol, 2023, 13: 1265204.
21. Choi JW, Jeong H, Ahn HJ, et al. The impact of pulmonary function tests on early postoperative complications in open lung resection surgery: An observational cohort study. Sci Rep, 2022, 12(1): 1277.
22. Jin F, Liu W, Qiao X, et al. Nomogram prediction model of postoperative pneumonia in patients with lung cancer: A retrospective cohort study. Front Oncol, 2023, 13: 1114302.
23. Tibshirani R. The LASSO method for variable selection in the Cox model. Stat Med, 1997, 16(4): 385-395.
24. Copeland GP, Jones D, Walters M. POSSUM: A scoring system for surgical audit. Br J Surg, 1991, 78(3): 355-360.
25. Whiteley MS, Prytherch DR, Higgins B, et al. An evaluation of the POSSUM surgical scoring system. Br J Surg, 1996, 83(6): 812-815.
26. Vashistha N, Singhal S, Budhiraja S, et al. Evaluation of ACS-NSQIP and CR-POSSUM risk calculators for the prediction of mortality after colorectal surgery: A retrospective cohort study. J Minim Access Surg, 2024, 20(2): 142-147.
27. Fugazzola P, Cobianchi L, Di Martino M, et al. Prediction of morbidity and mortality after early cholecystectomy for acute calculous cholecystitis: Results of the S. P. Ri. M. A. C. C. study. World J Emerg Surg, 2023, 18(1): 20.
28. 许川, 刘迪, 金星, 等. 胸腔镜下肺部术后并发症发生率的预测模型构建. 肿瘤学杂志, 2020, 26(7): 596-599.Xu C, Liu D, Jin X, et al. Construction of predictive model for postoperative complications in thoracoscopic lung surgery. J Chin Oncol, 2020, 26(7): 596-599.
29. 杨福耀. 改良POSSUM评分联合Clavien-Dindo分级预测高龄患者胸腔镜肺手术术后严重并发症发生率的研究. 河北医科大学, 2022.Yang FY. Modified POSSUM score combined with Clavien-Dindo grading to predict the incidence of serious complications after thoracoscopic lung surgery in elderly patients. Hebei Medical University, 2022.
30. Haug CJ, Drazen JM. Artificial intelligence and machine learning in clinical medicine, 2023. N Engl J Med, 2023, 388(13): 1201-1208.
31. Cierco Jimenez R, Lee T, Rosillo N, et al. Machine learning computational tools to assist the performance of systematic reviews: A mapping review. BMC Med Res Methodol, 2022, 22(1): 322.
32. 王新, 王炯杰, 王雷, 等. 基于CHAID决策树和Logistic回归的肺癌患者术后肺部并发症预测效果的研究. 临床肿瘤学杂志, 2021, 26(10): 898-902.Wang X, Wang JJ, Wang L, et al. Prediction of postoperative pulmonary complications in patients with lung cancer based on CHAID decision tree and logistic regression. Chin Clin Oncol, 2021, 26(10): 898-902.
33. Jandy K, Weichbroth P. A machine learning approach to classifying New York Heart Association (NYHA) heart failure. Sci Rep, 2024, 14(1): 11496.
34. Battista K, Diao L, Patte KA, et al. Examining the use of decision trees in population health surveillance research: An application to youth mental health survey data in the COMPASS study. Health Promot Chronic Dis Prev Can, 2023, 43(2): 73-86.
35. Jin X, Pan Y, Zhai C, et al. Exploration and machine learning model development for T2 NSCLC with bronchus infiltration and obstructive pneumonia/atelectasis. Sci Rep, 2024, 14(1): 4793.
36. Ju H, Kim K, Kim BI, et al. Graph neural network model for prediction of non-small cell lung cancer lymph node metastasis using protein-protein interaction network and 18F-FDG PET/CT radiomics. Int J Mol Sci, 2024, 25(2): 698.
37. Tane S, Nishikubo M, Kitazume M, et al. Cluster analysis of emphysema for predicting pulmonary complications after thoracoscopic lobectomy. Eur J Cardiothorac Surg, 2021, 60(3): 607-613.
38. Li T, Li W, Chen F, et al. The chest X-ray score baseline in predicting continuous oxygen therapy failure in low-risk aged patients after thoracic surgery. J Thorac Dis, 2024, 16(3): 1885-1899.
39. Chen Y, Yao L, Chen Q, et al. A retrospective study on the impact of radiotherapy on the survival outcomes of small cell lung cancer patients based on the SEER database. Sci Rep, 2024, 14(1): 15552.
40. Collins GS, Reitsma JB, Altman DG, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement. BMJ, 2015, 350: g7594.
41. Wynants L, Van Calster B, Collins GS, et al. Prediction models for diagnosis and prognosis of covid-19: Systematic review and critical appraisal. BMJ, 2020, 369: m1328.
42. Chen T, Chan EG, Huang B, et al. Outcomes following minimally invasive approaches vs. open extended lobectomy for non-small cell lung cancer: A propensity-matched analysis of the National Cancer Database. Transl Lung Cancer Res, 2024, 13(2): 334-344.
43. Salazar de Pablo G, Iniesta R, Bellato A, et al. Individualized prediction models in ADHD: A systematic review and meta-regression. Mol Psychiatry, 2024 May 23.
44. Tabja Bortesi JP, Ranisau J, Di S, et al. Machine learning approaches for the image-based identification of surgical wound infections: Scoping review. J Med Internet Res, 2024, 26: e52880.
45. Snell KIE, Levis B, Damen JAA, et al. Transparent reporting of multivariable prediction models for individual prognosis or diagnosis: Checklist for systematic reviews and meta-analyses (TRIPOD-SRMA). BMJ, 2023, 381: e073538.
46. Riley RD, Ensor J, Snell KIE, et al. Calculating the sample size required for developing a clinical prediction model. BMJ, 2020, 368: m441.
47. Riley RD, Snell KIE, Archer L, et al. Evaluation of clinical prediction models (part 3): Calculating the sample size required for an external validation study. BMJ, 2024, 384: e074821.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Latest ArticlesProgress in predictive models for the risk of postoperative pulmonary complications after lung cancer surgery

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