Eunji Kim, Soo-Jin Yoon, Sungbong Yu, Eunsil Ko, Kyungjun Shon, Jooyeon Yoon, Youn Kyung Kee, Do Hyoung Kim, AJin Cho, Hayne Cho Park, Young-Ki Lee
{"title":"人工智能驱动的胸部计算机断层扫描分析揭示了流行性血液透析患者 COVID-19 死亡率的预后见解。","authors":"Eunji Kim, Soo-Jin Yoon, Sungbong Yu, Eunsil Ko, Kyungjun Shon, Jooyeon Yoon, Youn Kyung Kee, Do Hyoung Kim, AJin Cho, Hayne Cho Park, Young-Ki Lee","doi":"10.23876/j.krcp.24.079","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Coronavirus disease 2019 (COVID-19) has led to severe pneumonia and mortality worldwide, however, clinical outcomes in end-stage renal disease patients remain unclear. This study evaluates the prognostic value of chest computed tomography (CT) findings in predicting COVID-19-related outcomes in prevalent hemodialysis patients.</p><p><strong>Methods: </strong>We retrospectively analyzed 326 prevalent hemodialysis patients diagnosed with COVID-19 who underwent chest CT scans. Characteristics assessed included pleural effusion, lung involvement volume, nodular consolidation, patchy infiltration, and ground-glass opacity. Artificial intelligence (AI)-assisted CT analysis quantified lung involvement. The primary endpoint was in-hospital mortality. Clinical data were collected, and logistic regression analysis assessed the association between CT findings and mortality.</p><p><strong>Results: </strong>The mean age of the patients was 66.7 ± 12.6 years, 61.0% were male, and 58.6% were diabetic. Chest CT showed that 18.1% had lung involvement >10%, 32.5% had pleural effusion, 68.7% had nodular consolidation, 57.1% had patchy infiltration, and 58.0% had ground-glass opacity. Seventy patients (21.5%) died. Multivariate logistic regression analysis identified lung involvement >2.7% (odds ratio [OR], 16.70; 95% confidence interval [CI], 4.35-65.63), pleural effusion (OR, 3.28; 95% CI, 1.15-9.35), nodular consolidation (OR, 4.08; 95% CI, 1.12-14.82), and patchy infiltration (OR, 3.75; 95% CI, 1.17-12.03) as significant mortality risk factors.</p><p><strong>Conclusion: </strong>Chest CT findings, including lung involvement >2.7% and the presence of pleural effusion, nodular consolidation, and patchy infiltrates, significantly indicated mortality in COVID-19 pneumonia among prevalent hemodialysis patients. AI-assisted CT analysis proved useful in assessing lung involvement extent, showing that even minimal lung involvement can be associated with increased mortality.</p>","PeriodicalId":17716,"journal":{"name":"Kidney Research and Clinical Practice","volume":null,"pages":null},"PeriodicalIF":2.9000,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial intelligence-powered chest computed tomography analysis unveils prognostic insights for COVID-19 mortality among prevalent hemodialysis patients.\",\"authors\":\"Eunji Kim, Soo-Jin Yoon, Sungbong Yu, Eunsil Ko, Kyungjun Shon, Jooyeon Yoon, Youn Kyung Kee, Do Hyoung Kim, AJin Cho, Hayne Cho Park, Young-Ki Lee\",\"doi\":\"10.23876/j.krcp.24.079\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Coronavirus disease 2019 (COVID-19) has led to severe pneumonia and mortality worldwide, however, clinical outcomes in end-stage renal disease patients remain unclear. This study evaluates the prognostic value of chest computed tomography (CT) findings in predicting COVID-19-related outcomes in prevalent hemodialysis patients.</p><p><strong>Methods: </strong>We retrospectively analyzed 326 prevalent hemodialysis patients diagnosed with COVID-19 who underwent chest CT scans. Characteristics assessed included pleural effusion, lung involvement volume, nodular consolidation, patchy infiltration, and ground-glass opacity. Artificial intelligence (AI)-assisted CT analysis quantified lung involvement. The primary endpoint was in-hospital mortality. Clinical data were collected, and logistic regression analysis assessed the association between CT findings and mortality.</p><p><strong>Results: </strong>The mean age of the patients was 66.7 ± 12.6 years, 61.0% were male, and 58.6% were diabetic. Chest CT showed that 18.1% had lung involvement >10%, 32.5% had pleural effusion, 68.7% had nodular consolidation, 57.1% had patchy infiltration, and 58.0% had ground-glass opacity. Seventy patients (21.5%) died. Multivariate logistic regression analysis identified lung involvement >2.7% (odds ratio [OR], 16.70; 95% confidence interval [CI], 4.35-65.63), pleural effusion (OR, 3.28; 95% CI, 1.15-9.35), nodular consolidation (OR, 4.08; 95% CI, 1.12-14.82), and patchy infiltration (OR, 3.75; 95% CI, 1.17-12.03) as significant mortality risk factors.</p><p><strong>Conclusion: </strong>Chest CT findings, including lung involvement >2.7% and the presence of pleural effusion, nodular consolidation, and patchy infiltrates, significantly indicated mortality in COVID-19 pneumonia among prevalent hemodialysis patients. AI-assisted CT analysis proved useful in assessing lung involvement extent, showing that even minimal lung involvement can be associated with increased mortality.</p>\",\"PeriodicalId\":17716,\"journal\":{\"name\":\"Kidney Research and Clinical Practice\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2024-09-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Kidney Research and Clinical Practice\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.23876/j.krcp.24.079\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"UROLOGY & NEPHROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Kidney Research and Clinical Practice","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.23876/j.krcp.24.079","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"UROLOGY & NEPHROLOGY","Score":null,"Total":0}
Artificial intelligence-powered chest computed tomography analysis unveils prognostic insights for COVID-19 mortality among prevalent hemodialysis patients.
Background: Coronavirus disease 2019 (COVID-19) has led to severe pneumonia and mortality worldwide, however, clinical outcomes in end-stage renal disease patients remain unclear. This study evaluates the prognostic value of chest computed tomography (CT) findings in predicting COVID-19-related outcomes in prevalent hemodialysis patients.
Methods: We retrospectively analyzed 326 prevalent hemodialysis patients diagnosed with COVID-19 who underwent chest CT scans. Characteristics assessed included pleural effusion, lung involvement volume, nodular consolidation, patchy infiltration, and ground-glass opacity. Artificial intelligence (AI)-assisted CT analysis quantified lung involvement. The primary endpoint was in-hospital mortality. Clinical data were collected, and logistic regression analysis assessed the association between CT findings and mortality.
Results: The mean age of the patients was 66.7 ± 12.6 years, 61.0% were male, and 58.6% were diabetic. Chest CT showed that 18.1% had lung involvement >10%, 32.5% had pleural effusion, 68.7% had nodular consolidation, 57.1% had patchy infiltration, and 58.0% had ground-glass opacity. Seventy patients (21.5%) died. Multivariate logistic regression analysis identified lung involvement >2.7% (odds ratio [OR], 16.70; 95% confidence interval [CI], 4.35-65.63), pleural effusion (OR, 3.28; 95% CI, 1.15-9.35), nodular consolidation (OR, 4.08; 95% CI, 1.12-14.82), and patchy infiltration (OR, 3.75; 95% CI, 1.17-12.03) as significant mortality risk factors.
Conclusion: Chest CT findings, including lung involvement >2.7% and the presence of pleural effusion, nodular consolidation, and patchy infiltrates, significantly indicated mortality in COVID-19 pneumonia among prevalent hemodialysis patients. AI-assisted CT analysis proved useful in assessing lung involvement extent, showing that even minimal lung involvement can be associated with increased mortality.
期刊介绍：
Kidney Research and Clinical Practice (formerly The Korean Journal of Nephrology; ISSN 1975-9460, launched in 1982), the official journal of the Korean Society of Nephrology, is an international, peer-reviewed journal published in English. Its ISO abbreviation is Kidney Res Clin Pract. To provide an efficient venue for dissemination of knowledge and discussion of topics related to basic renal science and clinical practice, the journal offers open access (free submission and free access) and considers articles on all aspects of clinical nephrology and hypertension as well as related molecular genetics, anatomy, pathology, physiology, pharmacology, and immunology. In particular, the journal focuses on translational renal research that helps bridging laboratory discovery with the diagnosis and treatment of human kidney disease. Topics covered include basic science with possible clinical applicability and papers on the pathophysiological basis of disease processes of the kidney. Original researches from areas of intervention nephrology or dialysis access are also welcomed. Major article types considered for publication include original research and reviews on current topics of interest. Accepted manuscripts are granted free online open-access immediately after publication, which permits its users to read, download, copy, distribute, print, search, or link to the full texts of its articles to facilitate access to a broad readership. Circulation number of print copies is 1,600.
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