Chest computed tomography-based artificial intelligence-aided latent class analysis for diagnosis of severe pneumonia.
10.1097/CM9.0000000000003226
- Author:
Caiting CHU
1
;
Yiran GUO
2
;
Zhenghai LU
1
;
Ting GUI
1
;
Shuhui ZHAO
1
;
Xuee CUI
1
;
Siwei LU
1
;
Meijiao JIANG
1
;
Wenhua LI
1
;
Chengjin GAO
2
Author Information
1. Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China.
2. Department of Emergency, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China.
- Publication Type:Journal Article
- Keywords:
Artificial intelligence;
Clinical heterogeneity;
Computed tomography;
Latent class analysis;
Severe pneumonia;
Subphenotypes;
Ventilation
- MeSH:
Humans;
Tomography, X-Ray Computed/methods*;
Male;
Female;
Retrospective Studies;
Middle Aged;
Artificial Intelligence;
Aged;
Pneumonia/diagnosis*;
Latent Class Analysis;
Adult
- From:
Chinese Medical Journal
2025;138(18):2316-2323
- CountryChina
- Language:English
-
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.
