Research progress on risk assessment and predictive early warning of severe perioperative complications in elderly lung cancer patients
- VernacularTitle:老年肺癌患者围术期严重并发症风险评估与预测预警研究进展
- Author:
Zixiao HE
1
;
Ziyue LUO
1
;
Ruihao ZHOU
1
;
Yanbingshi WANG
2
;
Le SHEN
2
;
Guo CHEN
1
;
Tao ZHU
1
;
Lunxu LIU
3
Author Information
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
- Publication Type:Journal Article
- Keywords:
Elderly lung cancer;
perioperative complications;
risk factor;
predictive models;
machine learning;
frailty;
enhanced recovery after surgery
- From:
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery
2026;33(07):1129-1141
- CountryChina
- Language:Chinese
-
Abstract:
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.
