基于卷积神经网络胸片评分与临床数据相结合的COVID-19住院患者插管和死亡率预测

BJR open Pub Date : 2022-01-01 DOI:10.1259/bjro.20210062
Aileen O'Shea, Matthew D Li, Nathaniel D Mercaldo, Patricia Balthazar, Avik Som, Tristan Yeung, Marc D Succi, Brent P Little, Jayashree Kalpathy-Cramer, Susanna I Lee
{"title":"基于卷积神经网络胸片评分与临床数据相结合的COVID-19住院患者插管和死亡率预测","authors":"Aileen O'Shea,&nbsp;Matthew D Li,&nbsp;Nathaniel D Mercaldo,&nbsp;Patricia Balthazar,&nbsp;Avik Som,&nbsp;Tristan Yeung,&nbsp;Marc D Succi,&nbsp;Brent P Little,&nbsp;Jayashree Kalpathy-Cramer,&nbsp;Susanna I Lee","doi":"10.1259/bjro.20210062","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>To predict short-term outcomes in hospitalized COVID-19 patients using a model incorporating clinical variables with automated convolutional neural network (CNN) chest radiograph analysis.</p><p><strong>Methods: </strong>A retrospective single center study was performed on patients consecutively admitted with COVID-19 between March 14 and April 21 2020. Demographic, clinical and laboratory data were collected, and automated CNN scoring of the admission chest radiograph was performed. The two outcomes of disease progression were intubation or death within 7 days and death within 14 days following admission. Multiple imputation was performed for missing predictor variables and, for each imputed data set, a penalized logistic regression model was constructed to identify predictors and their functional relationship to each outcome. Cross-validated area under the characteristic (AUC) curves were estimated to quantify the discriminative ability of each model.</p><p><strong>Results: </strong>801 patients (median age 59; interquartile range 46-73 years, 469 men) were evaluated. 36 patients were deceased and 207 were intubated at 7 days and 65 were deceased at 14 days. Cross-validated AUC values for predictive models were 0.82 (95% CI, 0.79-0.86) for death or intubation within 7 days and 0.82 (0.78-0.87) for death within 14 days. Automated CNN chest radiograph score was an important variable in predicting both outcomes.</p><p><strong>Conclusion: </strong>Automated CNN chest radiograph analysis, in combination with clinical variables, predicts short-term intubation and death in patients hospitalized for COVID-19 infection. Chest radiograph scoring of more severe disease was associated with a greater probability of adverse short-term outcome.</p><p><strong>Advances in knowledge: </strong>Model-based predictions of intubation and death in COVID-19 can be performed with high discriminative performance using admission clinical data and convolutional neural network-based scoring of chest radiograph severity.</p>","PeriodicalId":72419,"journal":{"name":"BJR open","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9459864/pdf/","citationCount":"4","resultStr":"{\"title\":\"Intubation and mortality prediction in hospitalized COVID-19 patients using a combination of convolutional neural network-based scoring of chest radiographs and clinical data.\",\"authors\":\"Aileen O'Shea,&nbsp;Matthew D Li,&nbsp;Nathaniel D Mercaldo,&nbsp;Patricia Balthazar,&nbsp;Avik Som,&nbsp;Tristan Yeung,&nbsp;Marc D Succi,&nbsp;Brent P Little,&nbsp;Jayashree Kalpathy-Cramer,&nbsp;Susanna I Lee\",\"doi\":\"10.1259/bjro.20210062\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>To predict short-term outcomes in hospitalized COVID-19 patients using a model incorporating clinical variables with automated convolutional neural network (CNN) chest radiograph analysis.</p><p><strong>Methods: </strong>A retrospective single center study was performed on patients consecutively admitted with COVID-19 between March 14 and April 21 2020. Demographic, clinical and laboratory data were collected, and automated CNN scoring of the admission chest radiograph was performed. The two outcomes of disease progression were intubation or death within 7 days and death within 14 days following admission. Multiple imputation was performed for missing predictor variables and, for each imputed data set, a penalized logistic regression model was constructed to identify predictors and their functional relationship to each outcome. Cross-validated area under the characteristic (AUC) curves were estimated to quantify the discriminative ability of each model.</p><p><strong>Results: </strong>801 patients (median age 59; interquartile range 46-73 years, 469 men) were evaluated. 36 patients were deceased and 207 were intubated at 7 days and 65 were deceased at 14 days. Cross-validated AUC values for predictive models were 0.82 (95% CI, 0.79-0.86) for death or intubation within 7 days and 0.82 (0.78-0.87) for death within 14 days. Automated CNN chest radiograph score was an important variable in predicting both outcomes.</p><p><strong>Conclusion: </strong>Automated CNN chest radiograph analysis, in combination with clinical variables, predicts short-term intubation and death in patients hospitalized for COVID-19 infection. Chest radiograph scoring of more severe disease was associated with a greater probability of adverse short-term outcome.</p><p><strong>Advances in knowledge: </strong>Model-based predictions of intubation and death in COVID-19 can be performed with high discriminative performance using admission clinical data and convolutional neural network-based scoring of chest radiograph severity.</p>\",\"PeriodicalId\":72419,\"journal\":{\"name\":\"BJR open\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9459864/pdf/\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BJR open\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1259/bjro.20210062\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BJR open","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1259/bjro.20210062","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

摘要

目的:利用结合临床变量和自动卷积神经网络(CNN)胸片分析的模型预测住院COVID-19患者的短期预后。方法:对2020年3月14日至4月21日连续住院的COVID-19患者进行回顾性单中心研究。收集人口统计学、临床和实验室数据,并对入院胸片进行自动CNN评分。疾病进展的两个结局是入院后7天内插管或死亡和14天内死亡。对缺失的预测变量进行了多次输入,并对每个输入的数据集构建了一个惩罚逻辑回归模型,以确定预测变量及其与每个结果的函数关系。估计特征曲线下的交叉验证面积(AUC),量化每个模型的判别能力。结果:801例患者(中位年龄59岁;四分位数范围为46-73岁,469名男性)。7 d死亡36例,插管207例,14 d死亡65例。7天内死亡或插管预测模型的交叉验证AUC值为0.82 (95% CI, 0.79-0.86), 14天内死亡预测模型的AUC值为0.82(0.78-0.87)。自动CNN胸片评分是预测两种结果的重要变量。结论:自动CNN胸片分析结合临床变量可预测COVID-19感染住院患者的短期插管和死亡。胸片评分越严重的疾病与短期不良预后的可能性越大相关。知识进步:利用入院临床数据和基于卷积神经网络的胸片严重程度评分,可以对COVID-19患者的插管和死亡进行基于模型的预测,具有很高的判别性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Intubation and mortality prediction in hospitalized COVID-19 patients using a combination of convolutional neural network-based scoring of chest radiographs and clinical data.

Intubation and mortality prediction in hospitalized COVID-19 patients using a combination of convolutional neural network-based scoring of chest radiographs and clinical data.

Intubation and mortality prediction in hospitalized COVID-19 patients using a combination of convolutional neural network-based scoring of chest radiographs and clinical data.

Intubation and mortality prediction in hospitalized COVID-19 patients using a combination of convolutional neural network-based scoring of chest radiographs and clinical data.

Objective: To predict short-term outcomes in hospitalized COVID-19 patients using a model incorporating clinical variables with automated convolutional neural network (CNN) chest radiograph analysis.

Methods: A retrospective single center study was performed on patients consecutively admitted with COVID-19 between March 14 and April 21 2020. Demographic, clinical and laboratory data were collected, and automated CNN scoring of the admission chest radiograph was performed. The two outcomes of disease progression were intubation or death within 7 days and death within 14 days following admission. Multiple imputation was performed for missing predictor variables and, for each imputed data set, a penalized logistic regression model was constructed to identify predictors and their functional relationship to each outcome. Cross-validated area under the characteristic (AUC) curves were estimated to quantify the discriminative ability of each model.

Results: 801 patients (median age 59; interquartile range 46-73 years, 469 men) were evaluated. 36 patients were deceased and 207 were intubated at 7 days and 65 were deceased at 14 days. Cross-validated AUC values for predictive models were 0.82 (95% CI, 0.79-0.86) for death or intubation within 7 days and 0.82 (0.78-0.87) for death within 14 days. Automated CNN chest radiograph score was an important variable in predicting both outcomes.

Conclusion: Automated CNN chest radiograph analysis, in combination with clinical variables, predicts short-term intubation and death in patients hospitalized for COVID-19 infection. Chest radiograph scoring of more severe disease was associated with a greater probability of adverse short-term outcome.

Advances in knowledge: Model-based predictions of intubation and death in COVID-19 can be performed with high discriminative performance using admission clinical data and convolutional neural network-based scoring of chest radiograph severity.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
审稿时长
18 weeks
×
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