Journal of thoracic disease · 2025
The association of the advanced lung cancer inflammation index with postoperative complications in patients undergoing lung resection for bronchiectasis.
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Gu Yang, Zhang Chen, Miao Jin-Bai, Wang Hui, Hu Bin, Li Xin
Аннотация
BACKGROUND: Patients with bronchiectasis often experience systemic inflammation and malnutrition, which adversely affect their prognosis. The advanced lung cancer inflammation index (ALI) has recently emerged as a novel biomarker reflecting systemic inflammation and nutritional status. This study aims to evaluate the predictive value of ALI for postoperative complications in patients with bronchiectasis undergoing localized surgical resection.
METHODS: This retrospective study included 191 patients with localized bronchiectasis treated at our center between January 2013 and November 2023. The optimal cutoff value for ALI was determined using a receiver operating characteristic (ROC) curve. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for postoperative complications. A nomogram was constructed based on the independent risk factors. The predictive performance of the nomogram model was evaluated using calibration curve, and its accuracy was assessed via the ROC curve.
RESULTS: The optimal ALI cutoff value was 43.1. Multivariate logistic regression revealed that ALI [odds ratio (OR): 3.006; 95% confidence interval (CI): 1.351-6.686; P=0.007], low body mass index (BMI) (OR: 0.868; 95% CI: 0.760-0.992; P=0.04), and operation time (OR: 1.010; 95% CI: 1.002-1.018; P=0.02) were independent risk factors for postoperative complications. A nomogram incorporating these factors was developed, showing good predictive performance with an area under the curve (AUC) of 0.776.
CONCLUSIONS: Preoperative ALI is an independent predictor of postoperative complications in patients with localized bronchiectasis. The predictive model based on ALI provides clinicians with a valuable tool for assessing the risk of postoperative complications in bronchiectasis patients undergoing surgical treatment.
METHODS: This retrospective study included 191 patients with localized bronchiectasis treated at our center between January 2013 and November 2023. The optimal cutoff value for ALI was determined using a receiver operating characteristic (ROC) curve. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for postoperative complications. A nomogram was constructed based on the independent risk factors. The predictive performance of the nomogram model was evaluated using calibration curve, and its accuracy was assessed via the ROC curve.
RESULTS: The optimal ALI cutoff value was 43.1. Multivariate logistic regression revealed that ALI [odds ratio (OR): 3.006; 95% confidence interval (CI): 1.351-6.686; P=0.007], low body mass index (BMI) (OR: 0.868; 95% CI: 0.760-0.992; P=0.04), and operation time (OR: 1.010; 95% CI: 1.002-1.018; P=0.02) were independent risk factors for postoperative complications. A nomogram incorporating these factors was developed, showing good predictive performance with an area under the curve (AUC) of 0.776.
CONCLUSIONS: Preoperative ALI is an independent predictor of postoperative complications in patients with localized bronchiectasis. The predictive model based on ALI provides clinicians with a valuable tool for assessing the risk of postoperative complications in bronchiectasis patients undergoing surgical treatment.