Development of a machine learning-based risk model for postoperative complications of lung cancer surgery.
Complications
Lung cancer
Machine learning
Risk scoring
Thoracic surgery
Journal
Surgery today
ISSN: 1436-2813
Titre abrégé: Surg Today
Pays: Japan
ID NLM: 9204360
Informations de publication
Date de publication:
19 Jun 2024
19 Jun 2024
Historique:
received:
18
03
2024
accepted:
30
04
2024
medline:
19
6
2024
pubmed:
19
6
2024
entrez:
19
6
2024
Statut:
aheadofprint
Résumé
To develop a comorbidity risk score specifically for lung resection surgeries. We reviewed the medical records of patients who underwent lung resections for lung cancer, and developed a risk model using data from 2014 to 2017 (training dataset), validated using data from 2018 to 2019 (validation dataset). Forty variables were analyzed, including 35 factors related to the patient's overall condition and five factors related to surgical techniques and tumor-related factors. The risk model for postoperative complications was developed using an elastic net regularized generalized linear model. The performance of the risk model was evaluated using receiver operating characteristic curves and compared with the Charlson Comorbidity Index (CCI). The rate of postoperative complications was 34.7% in the training dataset and 21.9% in the validation dataset. The final model consisted of 20 variables, including age, surgical-related factors, respiratory function tests, and comorbidities, such as chronic obstructive pulmonary disease, a history of ischemic heart disease, and 12 blood test results. The area under the curve (AUC) for the developed risk model was 0.734, whereas the AUC for the CCI was 0.521 in the validation dataset. The new machine learning model could predict postoperative complications with acceptable accuracy. 2020-0375.
Identifiants
pubmed: 38896280
doi: 10.1007/s00595-024-02878-y
pii: 10.1007/s00595-024-02878-y
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024. The Author(s) under exclusive licence to Springer Nature Singapore Pte Ltd.
Références
Chen-Yoshikawa TF, Fukui T, Nakamura S, Ito T, Kadomatsu Y, Tsubouchi H, et al. Current trends in thoracic surgery. Nagoya J Med Sci. 2020;82:161–74.
Medbery RL, Fernandez FG, Kosinski AS, Tong BC, Furnary AP, Feng L, et al. Costs associated with lobectomy for lung cancer: an analysis merging STS and medicare data. Ann Thorac Surg. 2021;111:1781–90.
doi: 10.1016/j.athoracsur.2020.08.073
Asban A, Chung SK, Tresler MA, Huilgol P, Xie R, Kirklin JK, et al. Hyperthyroidism is underdiagnosed and undertreated in 3336 patients: an opportunity for improvement and intervention. Ann Surg. 2018;268:506–12.
doi: 10.1097/SLA.0000000000002922
Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40:373–83.
doi: 10.1016/0021-9681(87)90171-8
Colinet B, Jacot W, Bertrand D, Lacombe S, Bozonnat MC, Daurès JP, et al. A new simplified comorbidity score as a prognostic factor in non-small-cell lung cancer patients: description and comparison with the Charlson’s index. Br J Cancer. 2005;93:1098–105.
doi: 10.1038/sj.bjc.6602836
Gajra A. Assessment of comorbidity in lung cancer: how, why, and in whom? J Geriatr Oncol. 2016;7:64–7.
doi: 10.1016/j.jgo.2016.02.003
Sandfeld-Paulsen BP, Meldgaard P, Aggerholm-Pedersen N. Comorbidity in lung cancer: a prospective cohort study of self-reported versus register-based comorbidity. J Thorac Oncol. 2018;13:54–62.
doi: 10.1016/j.jtho.2017.10.002
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.
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.
doi: 10.1093/ejcts/ezx190
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.
doi: 10.1016/j.athoracsur.2016.02.098
Meyer A, Zverinski D, Pfahringer B, Kempfert J, Kuehne T, Sündermann SH, et al. Machine learning for real-time prediction of complications in critical care: a retrospective study. Lancet Respir Med. 2018;6:905–14.
doi: 10.1016/S2213-2600(18)30300-X
Viswanathan VS, Toro P, Corredor G, Mukhopadhyay S, Madabhushi A. The state of the art for artificial intelligence in lung digital pathology. J Pathol. 2022;257:413–29.
doi: 10.1002/path.5966
Zhong Y, She Y, Deng J, Chen S, Wang T, Yang M, et al. Deep learning for prediction of N2 metastasis and survival for clinical stage I non-small cell lung cancer. Radiology. 2022;302:200–11.
doi: 10.1148/radiol.2021210902
Endo S, Ikeda N, Kondo T, Nakajima J, Kondo H, Yokoi K, et al. Development of an annually updated Japanese national clinical database for chest surgery in 2014. Gen Thorac Cardiovasc Surg. 2016;64:569–76.
doi: 10.1007/s11748-016-0697-1
Kozower BD, Sheng S, O’Brien SM, Liptay MJ, Lau CL, Jones DR, et al. STS database risk models: predictors of mortality and major morbidity for lung cancer resection. Ann Thorac Surg. 2010;90:875–81.
doi: 10.1016/j.athoracsur.2010.03.115
Kadomatsu Y, Nakamura S, Ueno H, Goto M, Ozeki N, Fukumoto K, et al. Prognostic value of uncertain resection for overall survival in non-small cell lung cancer. Ann Thorac Surg. 2022;114:1262–8.
doi: 10.1016/j.athoracsur.2021.07.087
Varma S, Simon R. Bias in error estimation when using cross-validation for model selection. BMC Bioinformatics. 2006;7:91.
doi: 10.1186/1471-2105-7-91
Rogers MP, Janjua H, Fishberger G, Harish A, Sujka J, Toloza EM, et al. A machine learning approach to high-risk cardiac surgery risk scoring. J Card Surg. 2022;37:4612–20.
doi: 10.1111/jocs.17110
Zhang K, Ye B, Wu L, Ni S, Li Y, Wang Q, et al. Machine learning-based prediction of survival prognosis in esophageal squamous cell carcinoma. Sci Rep. 2023;13:13532.
doi: 10.1038/s41598-023-40780-8
Seastedt KP, Moukheiber D, Mahindre SA, Thammineni C, Rosen DT, Watkins AA, et al. A scoping review of artificial intelligence applications in thoracic surgery. Eur J Cardiothorac Surg. 2022;61:239–48.
doi: 10.1093/ejcts/ezab422
Saji H, Okada M, Tsuboi M, Nakajima R, Suzuki K, Aokage K, et al. Segmentectomy versus lobectomy in small-sized peripheral non-small-cell lung cancer (JCOG0802/WJOG4607L): a multicentre, open-label, phase 3, randomised, controlled, non-inferiority trial. Lancet. 2022;399:1607–17.
doi: 10.1016/S0140-6736(21)02333-3
Divisi D, Pipitone M, Perkmann R, Bertolaccini L, Curcio C, Baldinelli F, et al. Prolonged air leak after video-assisted thoracic anatomical pulmonary resections: a clinical predicting model based on data from the Italian VATS group registry, a machine learning approach. J Thorac Dis. 2023;15:849–57.
doi: 10.21037/jtd-21-1484