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Predictors of postoperative complications in elderly patients undergoing pulmonary lobectomy: a retrospective cohort
Ke Yu1, Lijun Hua2, Xiaohao Yan3
1Health Management Center, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
Background:
Elderly patients undergoing pulmonary lobectomy for lung cancer are at increased risk of postoperative morbidity. Identifying modifiable risk factors and establishing a practical risk stratification method are essential for improving perioperative management.
Methods:
This retrospective cohort study included 212 consecutive patients aged ≥65 years who underwent pulmonary lobectomy for non-small cell lung cancer from 2020 to 2024. Demographic, clinical, and surgical data were collected. The primary outcome was major postoperative complications (Clavien-Dindo grade ≥III). Independent predictors were identified using multivariable logistic regression, and a simplified risk score was constructed. Model performance was assessed using the area under the receiver operating characteristic curve (AUC) and the Hosmer-Lemeshow goodness-of-fit test.
Results:
Major complications occurred in 73 patients (34.4%). Multivariable analysis identified three independent predictors: age ≥70 years (OR = 2.26, 95% CI: 1.18-4.42), Age-Adjusted Charlson Comorbidity Index (ACCI) ≥ 6 (OR = 3.70, 95% CI: 1.95-7.25), and preoperative hemoglobin <12 g/dL (OR = 3.04, 95% CI: 1.32-7.19). The multivariable model showed good discrimination (AUC = 0.712, 95% CI: 0.645-0.789) and calibration (Hosmer-Lemeshow p = 0.562). A simple risk score assigning one point per predictor demonstrated comparable discrimination (AUC = 0.699, 95% CI: 0.634-0.738) and a stepwise increase in complication rates: 26.0% (score 1), 54.7% (score 2), and 63.6% (score 3).
Conclusion:
A risk score based on age ≥70 years, ACCI ≥6, and preoperative anemia effectively stratifies elderly patients by their risk of major complications after pulmonary lobectomy. This simple tool may facilitate preoperative counseling, guide targeted optimization, and inform perioperative resource allocation, though external validation is warranted.
