Chest computed tomography-based artificial intelligence-aided latent class analysis for diagnosis of severe pneumonia.

IF 7.5 3区 医学 Q1 MEDICINE, GENERAL & INTERNAL Chinese Medical Journal Pub Date : 2024-11-06 DOI:10.1097/CM9.0000000000003226
Caiting Chu, Yiran Guo, Zhenghai Lu, Ting Gui, Shuhui Zhao, Xuee Cui, Siwei Lu, Meijiao Jiang, Wenhua Li, Chengjin Gao
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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.

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基于胸部计算机断层扫描的人工智能辅助潜类分析用于重症肺炎诊断。
背景:关于人工智能(AI)辅助诊断重症肺炎(SP)亚型以及亚型与通气治疗效果的关联的文献很少。我们的研究旨在利用基于胸部计算机断层扫描(CT)的人工智能辅助潜类分析(LCA),说明重症肺炎患者的临床和生物学异质性(如通气和气体交换)是否存在:这项回顾性研究纳入了2015年6月1日至2020年5月30日期间在新华医院住院的413名确诊为SP的患者。胸部 CT 的 AI 量化结果及其与其他临床变量的结合被用于建立 SP 患者的 LCA 模型。通过评估所有LCA模型的统计指标,确定最佳亚型,并通过统计方法进一步探讨其临床意义,如指导通气策略等:结果:基于胸部 CT AI 定量结果的两类 LCA 模型能很好地描述 SP 群体的生物学特征,因此得出了两种临床亚型。亚型-1 患者的感染程度较轻(P基于胸部 CT AI 定量结果的两类 LCA 模型特别揭示了 SP 患者肺功能的临床异质性。确定通气和气体交换之间的匹配程度有助于指导辅助通气的决策。
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来源期刊
Chinese Medical Journal
Chinese Medical Journal 医学-医学:内科
CiteScore
9.80
自引率
4.90%
发文量
19245
审稿时长
6 months
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.
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