{"title":"Chest computed tomography-based artificial intelligence-aided latent class analysis for diagnosis of severe pneumonia.","authors":"Caiting Chu, Yiran Guo, Zhenghai Lu, Ting Gui, Shuhui Zhao, Xuee Cui, Siwei Lu, Meijiao Jiang, Wenhua Li, Chengjin Gao","doi":"10.1097/CM9.0000000000003226","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>There is little literature describing the artificial intelligence (AI)-aided diagnosis of severe pneumonia (SP) subphenotypes and the association of the subphenotypes with the ventilatory treatment efficacy. The aim of our study is to illustrate whether clinical and biological heterogeneity, such as ventilation and gas-exchange, exists among patients with SP using chest computed tomography (CT)-based AI-aided latent class analysis (LCA).</p><p><strong>Methods: </strong>This retrospective study included 413 patients hospitalized at Xinhua hospital diagnosed with SP from June 1, 2015 to May 30, 2020. AI quantification results of chest CT and their combination with additional clinical variables were used to develop LCA models in an SP population. The optimal subphenotypes were determined though evaluating statistical indicators of all the LCA models, and clinical implications of them such as guiding ventilation strategies were further explored by statistical methods.</p><p><strong>Results: </strong>The two-class LCA model based on AI quantification results of chest CT can describe the biological characteristics of the SP population well and hence yielded the two clinical subphenotypes. Patients with subphenotype-1 had milder infections (P <0.001) than patients with subphenotype-2 and had lower 30-day (P <0.001) and 90-day (P <0.001) mortality, and lower in-hospital (P = 0.001) and 2-year (P <0.001) mortality. Patients with subphenotype-1 showed a better match between the percentage of non-infected lung volume (used to quantify ventilation) and oxygen saturation (used to reflect gas exchange), compared with patients with subphenotype-2. There were significant differences in the matching degree of lung ventilation and gas exchange between the two subphenotypes (P <0.001). Compared with patients with subphenotype-2, those with subphenotype-1 showed a relatively better match between CT-based AI metrics of the non-infected region and oxygenation, and their clinical outcomes were effectively improved after receiving invasive ventilation treatment.</p><p><strong>Conclusions: </strong>A two-class LCA model based on AI quantification results of chest CT in the SP population particularly revealed clinical heterogeneity of lung function. Identifying the degree of match between ventilation and gas-exchange may help guide decisions about assisted ventilation.</p>","PeriodicalId":10183,"journal":{"name":"Chinese Medical Journal","volume":null,"pages":null},"PeriodicalIF":7.5000,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chinese Medical Journal","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/CM9.0000000000003226","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
引用次数: 0
Abstract
Background: There is little literature describing the artificial intelligence (AI)-aided diagnosis of severe pneumonia (SP) subphenotypes and the association of the subphenotypes with the ventilatory treatment efficacy. The aim of our study is to illustrate whether clinical and biological heterogeneity, such as ventilation and gas-exchange, exists among patients with SP using chest computed tomography (CT)-based AI-aided latent class analysis (LCA).
Methods: This retrospective study included 413 patients hospitalized at Xinhua hospital diagnosed with SP from June 1, 2015 to May 30, 2020. AI quantification results of chest CT and their combination with additional clinical variables were used to develop LCA models in an SP population. The optimal subphenotypes were determined though evaluating statistical indicators of all the LCA models, and clinical implications of them such as guiding ventilation strategies were further explored by statistical methods.
Results: The two-class LCA model based on AI quantification results of chest CT can describe the biological characteristics of the SP population well and hence yielded the two clinical subphenotypes. Patients with subphenotype-1 had milder infections (P <0.001) than patients with subphenotype-2 and had lower 30-day (P <0.001) and 90-day (P <0.001) mortality, and lower in-hospital (P = 0.001) and 2-year (P <0.001) mortality. Patients with subphenotype-1 showed a better match between the percentage of non-infected lung volume (used to quantify ventilation) and oxygen saturation (used to reflect gas exchange), compared with patients with subphenotype-2. There were significant differences in the matching degree of lung ventilation and gas exchange between the two subphenotypes (P <0.001). Compared with patients with subphenotype-2, those with subphenotype-1 showed a relatively better match between CT-based AI metrics of the non-infected region and oxygenation, and their clinical outcomes were effectively improved after receiving invasive ventilation treatment.
Conclusions: A two-class LCA model based on AI quantification results of chest CT in the SP population particularly revealed clinical heterogeneity of lung function. Identifying the degree of match between ventilation and gas-exchange may help guide decisions about assisted ventilation.
期刊介绍:
The Chinese Medical Journal (CMJ) is published semimonthly in English by the Chinese Medical Association, and is a peer reviewed general medical journal for all doctors, researchers, and health workers regardless of their medical specialty or type of employment. Established in 1887, it is the oldest medical periodical in China and is distributed worldwide. The journal functions as a window into China’s medical sciences and reflects the advances and progress in China’s medical sciences and technology. It serves the objective of international academic exchange. The journal includes Original Articles, Editorial, Review Articles, Medical Progress, Brief Reports, Case Reports, Viewpoint, Clinical Exchange, Letter,and News,etc. CMJ is abstracted or indexed in many databases including Biological Abstracts, Chemical Abstracts, Index Medicus/Medline, Science Citation Index (SCI), Current Contents, Cancerlit, Health Plan & Administration, Embase, Social Scisearch, Aidsline, Toxline, Biocommercial Abstracts, Arts and Humanities Search, Nuclear Science Abstracts, Water Resources Abstracts, Cab Abstracts, Occupation Safety & Health, etc. In 2007, the impact factor of the journal by SCI is 0.636, and the total citation is 2315.