Prognostic assessment of sepsis-induced acute respiratory distress syndrome in older patients using clinical and CT-based radiomic features

The nomogram developed from the fusion model demonstrated superior predictive performance for 28-day mortality in older patients with SI-ARDS compared to conventional scoring systems, though multicenter validation is required to confirm clinical utility.

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Publication Details

Journal
Experimental Gerontology
Published
2025-12-05
DOI
https://doi.org/10.1016/j.exger.2025.112987
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00
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article

Prognostic assessment of sepsis-induced acute respiratory distress syndrome in older patients using clinical and CT-based radiomic features

Xiya Wang, Yongshen Bai, Ying Chen, Bowen Zhang et al.
Experimental Gerontology
Radiomics and Machine Learning in Medical Imaging
article

Prognostic assessment of sepsis-induced acute respiratory distress syndrome in older patients using clinical and CT-based radiomic features

Xiya Wang, Yongshen Bai, Ying Chen, Bowen Zhang, Mei Xue, Shubin Guo, Shuxing Wei, Xinzhen Gao
article en

Abstract

The nomogram developed from the fusion model demonstrated superior predictive performance for 28-day mortality in older patients with SI-ARDS compared to conventional scoring systems, though multicenter validation is required to confirm clinical utility.

Experimental GerontologyVol. 213
Capital Medical University (CN), Beijing Chao-Yang Hospital, Capital Medical University (CN), Digital China Health (China) (CN)
Openalex Percentile: Top 46%
Radiomics and Machine Learning in Medical Imaging
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