Added value of chest CT images to a personalized prognostic model in acute respiratory distress syndrome: a retrospective study.

Yuan-Cheng Wang, Shu-Hang Zhang, Wen-Hui Lv, Wei-Lang Wang, Shan Huang, Yue Qiu, Jian-Feng Xie, Yi Yang, Shenghong Ju
{"title":"Added value of chest CT images to a personalized prognostic model in acute respiratory distress syndrome: a retrospective study.","authors":"Yuan-Cheng Wang,&nbsp;Shu-Hang Zhang,&nbsp;Wen-Hui Lv,&nbsp;Wei-Lang Wang,&nbsp;Shan Huang,&nbsp;Yue Qiu,&nbsp;Jian-Feng Xie,&nbsp;Yi Yang,&nbsp;Shenghong Ju","doi":"10.1007/s42058-023-00116-x","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Acute respiratory distress syndrome (ARDS) is a critical disease in the intensive care unit (ICU) with high morbidity and mortality. The accuracy for predicting ARDS patients' outcome with mechanical ventilation is limited, and most based on clinical information.</p><p><strong>Methods: </strong>The patients diagnosed with ARDS between January 2014 and June 2019 were retrospectively recruited. Radiomics features were extracted from the upper, middle, and lower levels of the lung, and were further analyzed with the primary outcome (28-day mortality after ARDS onset). The univariate and multivariate logistic regression analyses were applied to figure out risk factors. Various predictive models were constructed and compared.</p><p><strong>Results: </strong>Of 366 ARDS patients recruited in this study, 276 (median age, 64 years [interquartile range, 54-75 years]; 208 male) survive on the Day 28. Among all factors, the APACHE II Score (OR 2.607, 95% CI 1.896-3.584, <i>P</i> < 0.001), the Radiomics_Score of the middle lung (OR 2.230, 95% CI 1.387-3.583, <i>P</i> = 0.01), the Radiomics_Score of the lower lung (OR 1.633, 95% CI 1.143-2.333, <i>P</i> = 0.01) were associated with the 28-day mortality. The clinical_radiomics predictive model (AUC 0.813, 95% CI 0.767-0.850) show the best performance compared with the clinical model (AUC 0.758, 95% CI 0.710-0.802), the radiomics model (AUC 0.692, 95% CI 0.641-0.739) and the various ventilator parameter-based models (highest AUC 0.773, 95% CI 0.726-0.815).</p><p><strong>Conclusions: </strong>The radiomics features of chest CT images have incremental values in predicting the 28-day mortality in ARDS patients with mechanical ventilation.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s42058-023-00116-x.</p>","PeriodicalId":10059,"journal":{"name":"Chinese Journal of Academic Radiology","volume":"6 1","pages":"47-56"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9884509/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chinese Journal of Academic Radiology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s42058-023-00116-x","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Background: Acute respiratory distress syndrome (ARDS) is a critical disease in the intensive care unit (ICU) with high morbidity and mortality. The accuracy for predicting ARDS patients' outcome with mechanical ventilation is limited, and most based on clinical information.

Methods: The patients diagnosed with ARDS between January 2014 and June 2019 were retrospectively recruited. Radiomics features were extracted from the upper, middle, and lower levels of the lung, and were further analyzed with the primary outcome (28-day mortality after ARDS onset). The univariate and multivariate logistic regression analyses were applied to figure out risk factors. Various predictive models were constructed and compared.

Results: Of 366 ARDS patients recruited in this study, 276 (median age, 64 years [interquartile range, 54-75 years]; 208 male) survive on the Day 28. Among all factors, the APACHE II Score (OR 2.607, 95% CI 1.896-3.584, P < 0.001), the Radiomics_Score of the middle lung (OR 2.230, 95% CI 1.387-3.583, P = 0.01), the Radiomics_Score of the lower lung (OR 1.633, 95% CI 1.143-2.333, P = 0.01) were associated with the 28-day mortality. The clinical_radiomics predictive model (AUC 0.813, 95% CI 0.767-0.850) show the best performance compared with the clinical model (AUC 0.758, 95% CI 0.710-0.802), the radiomics model (AUC 0.692, 95% CI 0.641-0.739) and the various ventilator parameter-based models (highest AUC 0.773, 95% CI 0.726-0.815).

Conclusions: The radiomics features of chest CT images have incremental values in predicting the 28-day mortality in ARDS patients with mechanical ventilation.

Supplementary information: The online version contains supplementary material available at 10.1007/s42058-023-00116-x.

Abstract Image

Abstract Image

Abstract Image

胸部CT图像对急性呼吸窘迫综合征个性化预后模型的附加价值:一项回顾性研究。
背景:急性呼吸窘迫综合征(Acute respiratory distress syndrome, ARDS)是重症监护病房(ICU)中一种发病率和死亡率高的危重疾病。预测ARDS患者机械通气预后的准确性是有限的,而且大多基于临床信息。方法:回顾性招募2014年1月至2019年6月诊断为ARDS的患者。从肺的上、中、下水平提取放射组学特征,并进一步分析主要结局(ARDS发病后28天死亡率)。采用单因素和多因素logistic回归分析找出危险因素。建立了各种预测模型并进行了比较。结果:在本研究招募的366例ARDS患者中,276例(中位年龄64岁[四分位数间距54-75岁];208只(雄性)存活到第28天。在所有因素中,APACHE II评分(OR 2.607, 95% CI 1.896 ~ 3.584, P P = 0.01)、下肺Radiomics_Score (OR 1.633, 95% CI 1.143 ~ 2.333, P = 0.01)与28天死亡率相关。与临床模型(AUC 0.758, 95% CI 0.710-0.802)、放射组学模型(AUC 0.692, 95% CI 0.641-0.739)和各种基于呼吸机参数的模型(最高AUC 0.773, 95% CI 0.726-0.815)相比,clinical_radiomics预测模型(AUC 0.813, 95% CI 0.767-0.850)表现最佳。结论:胸部CT影像放射组学特征在预测机械通气ARDS患者28天死亡率方面具有递增价值。补充信息:在线版本包含补充资料,下载地址为10.1007/s42058-023-00116-x。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
1.60
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信