Cai Yongsheng, Ke Lihui, Hao Xuefeng, Qiao Anbang, Yang Xiaoxiao, Chen Wenhui, Li Weiqing, Yang Zeng, Wei Bo
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引用次数: 0
Abstract
Background: Pneumonia is one of the most common complications after lung resection. However, there are currently no reports of postoperative pneumonia in patients with bronchiectasis.
Objectives: Our study aims to construct a new nomogram to predict the risk of postoperative pneumonia in patients with localized bronchiectasis.
Design: The clinical data of patients with localized bronchiectasis from April 2012 to August 2022 were retrospectively analyzed.
Methods: Independent risk factors were identified through simple linear regression and multiple linear regression analysis, and a new nomogram was constructed based on independent risk factors. The validity of the nomogram was evaluated using the consistency index (C-index), receiver operating characteristic curve, calibration chart, and decision curve analysis chart.
Results: The new nomogram prediction model included five independent risk factors: tuberculosis history, smoking history, platelet-lymphocyte ratio (PLR), diffusing capacity of the lung for carbon monoxide, and controlled nutritional status score. The area under the curve of the prediction model is 0.870 (95% CI: 0.750-0.892), showing good discrimination ability, and the probability threshold was set at 0.2013. In addition, the calibration curve shows that the nomogram has good calibration. In the decision curve, the nomogram model showed good clinical net benefit.
Conclusion: This study is the first to construct a nomogram prediction model for postoperative pneumonia of localized bronchiectasis, which can more accurately and directly assess the risk probability of postoperative pneumonia, and provide certain help for clinicians in prevention and treatment decisions.
期刊介绍:
Therapeutic Advances in Respiratory Disease delivers the highest quality peer-reviewed articles, reviews, and scholarly comment on pioneering efforts and innovative studies across all areas of respiratory disease.