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Development and validation of a noninvasive prediction model for identifying eosinophilic asthma

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机构： [1]Department of Respiratory and Critical Care Medicine, Clinical Research Center for Respiratory Disease, West China Hospital, Sichuan University, Chengdu, PR China [2]Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Kunming Medical University, Kunming, PR China [3]Laboratory of Pulmonary Immunology and Inflammation, Frontiers Science Center for Disease-related Molecular Network, Sichuan University, Chengdu, PR China [4]Pneumology Group, Department of Integrated Traditional Chinese and Western Medicine, West China Hospital, Sichuan University, Chengdu, PR China [5]School of Life Sciences, University of Technology Sydney, Ultimo, NSW, Australia [6]Respiratory Cellular and Molecule Biology, Woolcock Institute of Medical Research, The University of Sydney, Sydney, NSW, Australia [7]Priority Research Centre for Healthy Lungs, Hunter Medical Research Institute, The University of Newcastle, Newcastle, NSW, Australia [8]Department of Respiratory and Sleep Medicine, John Hunter Hospital, Newcastle, NSW, Australia, And Priority Research Centre for Healthy Lungs, Hunter Medical Research Institute, University of Newcastle, NSW, Australia [9]Respiratory Microbiome Laboratory, Frontiers Science Center for Disease-related, Molecular Network, Sichuan University, Chengdu, PR China

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Identification of eosinophilic asthma (EA) using sputum analysis is important for disease monitoring and individualized treatment. But it is laborious and technically demanding. We aimed to develop and validate an effective model to predict EA with multidimensional assessment (MDA).The asthma patients who underwent a successful sputum induction cytological analysis were consecutively recruited from March 2014 to January 2021. The variables assessed by MDA were screened by least absolute shrinkage and selection operator (LASSO) and logistic regression to develop a nomogram and an online web calculator. Validation was performed internally by a bootstrap sampling method and externally in the validation cohort. Diagnostic accuracy of the model in different asthma subgroups were also investigated.In total of 304 patients in the training cohort and 95 patients in the validation cohort were enrolled. Five variables were identified in the EA prediction model: gender, nasal polyp, blood eosinophils, blood basophils and FeNO. The C-index of the model was 0.86 (95% CI: 0.81-0.90) in the training cohort and 0.84 (95% CI: 0.72-0.89) in the validation cohort. The calibration curve showed good agreement between the prediction and actual observation. The decision curve analysis (DCA) also demonstrated that the EA prediction model was clinically beneficial. An online publicly available web calculator was constructed (https://asthmaresearcherlimin.shinyapps.io/DynNomapp/).We developed and validated a multivariable model based on MDA to help the diagnosis of EA, which has good diagnostic performance and clinical practicability. This practical tool may be a useful alternative for predicting EA in the clinic.Copyright © 2022. Published by Elsevier Ltd.

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出版当年[2023]版：

大类 | 3 区医学

小类 | 3 区心脏和心血管系统 3 区呼吸系统

最新[2023]版：

大类 | 3 区医学

小类 | 3 区心脏和心血管系统 3 区呼吸系统

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出版当年[2022]版：

Q2 CARDIAC & CARDIOVASCULAR SYSTEMS Q2 RESPIRATORY SYSTEM

最新[2023]版：

Q2 CARDIAC & CARDIOVASCULAR SYSTEMS Q2 RESPIRATORY SYSTEM

影响因子： 3.5 最新[2023版] 3.7 最新五年平均 4.3 出版当年[2022版] 4.1 出版当年五年平均 4.582 出版前一年[2021版] 3.5 出版后一年[2023版]

第一作者：

第一作者机构： [1]Department of Respiratory and Critical Care Medicine, Clinical Research Center for Respiratory Disease, West China Hospital, Sichuan University, Chengdu, PR China [2]Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Kunming Medical University, Kunming, PR China [3]Laboratory of Pulmonary Immunology and Inflammation, Frontiers Science Center for Disease-related Molecular Network, Sichuan University, Chengdu, PR China

通讯作者：

通讯机构： [1]Department of Respiratory and Critical Care Medicine, Clinical Research Center for Respiratory Disease, West China Hospital, Sichuan University, Chengdu, PR China [3]Laboratory of Pulmonary Immunology and Inflammation, Frontiers Science Center for Disease-related Molecular Network, Sichuan University, Chengdu, PR China [*1]Department of Respiratory and Critical Care Medicine, Clinical Research Center for Respiratory Disease, West China Hospital, Sichuan University, Chengdu, 610041, PR China. [*2]Laboratory of Pulmonary Immunology and Inflammation, Frontiers Science Center for Disease-related Molecular Network, Sichuan University, Chengdu, 610041, PR China.

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