BMC geriatrics · 2026
A short-term postoperative prediction model for colorectal cancer using comprehensive geriatric assessment.
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Yang Di, Cao Mengyu, Yang Shaokang, Chen Yujia, Jin Qinglong, Leng Jiyan
Аннотация
BACKGROUND: Older colorectal cancer (CRC) patients face significant postoperative risks. This study aimed to develop a prediction model to quantify the short-term prognosis of these patients following laparoscopic surgery.
METHODS: This prospective study enrolled patients aged ≥ 60 years undergoing elective laparoscopic radical resection (May 2021-April 2024). The primary outcome was 90-day Major Adverse Postoperative Events (MAPE). A comprehensive geriatric assessment (CGA) was performed to identify independent risk factors.
RESULTS: MAPE occurred in 23.7% of the derivation cohort and 22.5% of the validation cohort. Multivariable logistic regression identified six independent preoperative predictors: weight loss (OR = 2.20, 95% CI 1.20-4.22), ADL scores (OR = 1.11, 95% CI 1.03-1.20), preoperative BUN (OR = 1.14, 95% CI 1.01-1.29), ASMI (OR = 0.68, 95% CI 0.47-0.98), hypoalbuminemia (OR = 1.33, 95% CI 1.02-1.89), and TNM stage (OR = 1.75, 95% CI 1.18-2.60). The prediction model demonstrated robust discrimination (AUC: derivation 0.802; validation 0.779) and excellent calibration. Decision curve analysis confirmed its clinical utility across a wide range of threshold probabilities.
CONCLUSIONS: This CGA-based tool effectively predicts short-term prognosis, enabling personalized risk stratification and identifying high-risk candidates to guide future perioperative management strategies.
METHODS: This prospective study enrolled patients aged ≥ 60 years undergoing elective laparoscopic radical resection (May 2021-April 2024). The primary outcome was 90-day Major Adverse Postoperative Events (MAPE). A comprehensive geriatric assessment (CGA) was performed to identify independent risk factors.
RESULTS: MAPE occurred in 23.7% of the derivation cohort and 22.5% of the validation cohort. Multivariable logistic regression identified six independent preoperative predictors: weight loss (OR = 2.20, 95% CI 1.20-4.22), ADL scores (OR = 1.11, 95% CI 1.03-1.20), preoperative BUN (OR = 1.14, 95% CI 1.01-1.29), ASMI (OR = 0.68, 95% CI 0.47-0.98), hypoalbuminemia (OR = 1.33, 95% CI 1.02-1.89), and TNM stage (OR = 1.75, 95% CI 1.18-2.60). The prediction model demonstrated robust discrimination (AUC: derivation 0.802; validation 0.779) and excellent calibration. Decision curve analysis confirmed its clinical utility across a wide range of threshold probabilities.
CONCLUSIONS: This CGA-based tool effectively predicts short-term prognosis, enabling personalized risk stratification and identifying high-risk candidates to guide future perioperative management strategies.