Predictive modeling for eosinophilic chronic rhinosinusitis: Nomogram and four machine learning approaches

作者全名："Xiong, Panhui; Chen, Junliang; Zhang, Yue; Shu, Longlan; Shen, Yang; Gu, Yue; Liu, Yijun; Guan, Dayu; Zheng, Bowen; Yang, Yucheng"

作者地址："[Xiong, Panhui; Zhang, Yue; Shu, Longlan; Shen, Yang; Gu, Yue; Liu, Yijun; Guan, Dayu; Zheng, Bowen; Yang, Yucheng] Chongqing Med Univ, Dept Otorhinolaryngol, Affiliated Hosp 1, Chongqing 400016, Peoples R China; [Chen, Junliang] Xishui Peoples Hosp, Dept Otorhinolaryngol, Zunyi 564600, Guizhou, Peoples R China"

通信作者："Yang, YC (通讯作者)，Chongqing Med Univ, Dept Otorhinolaryngol, Affiliated Hosp 1, Chongqing 400016, Peoples R China."

来源：ISCIENCE

ESI学科分类：　

WOS号：WOS:001181328300001

JCR分区：Q1

影响因子：4.6

年份：2024

卷号：27

期号：2

开始页：　

结束页：　

文献类型：Article

关键词：　

摘要："Eosinophilic chronic rhinosinusitis (ECRS) is a distinct subset of chronic rhinosinusitis characterized by heightened eosinophilic infiltration and increased symptom severity, often resisting standard treatments. Traditional diagnosis requires invasive histological evaluation. This study aims to develop predictive models for ECRS based on patient clinical parameters, eliminating the need for invasive biopsy. Utilizing logistic regression with lasso regularization, random forest (RF), gradient -boosted decision tree (GBDT), and deep neural network (DNN), we trained models on common clinical data. The predictive performance was evaluated using metrics such as area under the curve (AUC) for receiver operator characteristics, decision curves, and feature ranking analysis. In a cohort of 437 eligible patients, the models identified peripheral blood eosinophil ratio, absolute peripheral blood eosinophil, and the ethmoidal/maxillary sinus density ratio (E/M) on computed tomography as crucial predictors for ECRS. This predictive model offers a valuable tool for identifying ECRS without resorting to histological biopsy, enhancing clinical decisionmaking."

基金机构："Chongqing Middle and Youth Medical High-end Talent Studio Project [cstc2021ycjhbgzxm0080]; Chongqing Health and Family Planning Commission Research Project [Yu Wei (2018), 2]; Chongqing medical scientific research project (Joint project of Chongqing Health Commission and Science and Technology Bureau) [2020jstg004]; China Post-doctoral Science Foundation [:2023GDRC003]; Foundation of State Key Laboratory of Ultrasound in Medicine and Engineering [2022M720608]; [2023KFKT001]"

基金资助正文："We thank Qiuling Shi for guidance in the statistical methods of our study and Xiuqin XU for participating in data collection. This study was supported by the National Natural Science Foundation of China, Grant/Award Number: 81970864; the Chongqing Talents Project, Grant/Award Number: cstc2021ycjhbgzxm0080; the Chongqing Middle and Youth Medical High-end Talent Studio Project (Yu Wei (2018) No. 2) ; the Chongqing Health and Family Planning Commission Research Project (2020jstg004) ; Chongqing medical scientific research project (Joint project of Chongqing Health Commission and Science and Technology Bureau) , Grant/Award Number:2023GDRC003; and China Post-doctoral Science Foundation (2022M720608) , Foundation of State Key Laboratory of Ultrasound in Medicine and Engineering (Grant No.2023KFKT001) .r Grant/Award Number: cstc2021ycjhbgzxm0080; the Chongqing Middle and Youth Medical High-end Talent Studio Project (Yu Wei (2018) No. 2) ; the Chongqing Health and Family Planning Commission Research Project (2020jstg004) ; Chongqing medical scientific research project (Joint project of Chongqing Health Commission and Science and Technology Bureau) , Grant/Award Number:2023GDRC003; and China Post-doctoral Science Foundation (2022M720608) , Foundation of State Key Laboratory of Ultrasound in Medicine and Engineering (Grant No.2023KFKT001) ."