Development and Validation of a Prediction Model for Positive Findings of Preoperative Flexible Bronchoscopy in Patients with Peripheral Lung Cancer

Author:

Li DongyuORCID,Li Zaishan,Li Shaolei,Zhang Hongbing,Yao Siqing,Li Yi,Chen Jun

Abstract

(1) Background: It has yet to be determined whether preoperative flexible bronchoscopy (FB) should be routinely performed in patients with peripheral lung cancer. The aim of this study was to construct a model to predict the probability of positive FB findings, which would help assess the necessity of preoperative FB. (2) Methods: A total of 380 consecutive patients with peripheral lung cancer who underwent preoperative FB were recruited for this study. A prediction model was developed through univariate and multivariate logistic regression, with predictors including gender, age, body mass index (BMI), smoking, history of chronic lung diseases, respiratory symptoms, lesion size, lesion type, lesion location in the bronchi, and lesion location in the lobe. The predictive performance of the model was evaluated by validation using 1000 iterations of bootstrap resampling. Model discrimination was assessed using the area under the receiver operating characteristics curve (AUC), and calibration was assessed using the Brier score and calibration plots. (3) Results: The model suggested that male patients with respiratory symptoms, decreased BMI, solid lesions, and lesions located in lower-order bronchi were more likely to have positive FB findings. The AUC and Brier score of the model for internal validation were 0.784 and 0.162, respectively. The calibration curve for the probability of positive FB findings showed convincing concordance between the predicted and actual results. (4) Conclusions: Our prediction model estimated the pretest probability of positive FB findings in patients with peripheral lung cancers. Males and patients with lower BMI, the presence of respiratory symptoms, larger lesions, solid lesions, and lesions located in lower-order bronchi were associated with increased positive FB findings. The use of our model can be of assistance when making clinical decisions about preoperative FB.

Funder

Beijing Municipal Science & Technology Commission

Publisher

MDPI AG

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3