{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T05:43:41Z","timestamp":1782798221152,"version":"3.54.5"},"reference-count":36,"publisher":"The Korean Society of Nephrology","issue":"4","license":[{"start":{"date-parts":[[2024,9,26]],"date-time":"2024-09-26T00:00:00Z","timestamp":1727308800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["krcp-ksn.org"],"crossmark-restriction":true},"short-container-title":["Kidney Res Clin Pract"],"published-print":{"date-parts":[[2026,7,1]]},"abstract":"<jats:p>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 \u00b1 12.6 years, 61.0% were male, and 58.6% were diabetic. Chest CT showed that 18.1% had lung involvement &gt;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 &gt;2.7% (odds ratio [OR], 16.70; 95% confidence interval [CI], 4.35\u201365.63), pleural effusion (OR, 3.28; 95% CI, 1.15\u20139.35), nodular consolidation (OR, 4.08; 95% CI, 1.12\u201314.82), and patchy infiltration (OR, 3.75; 95% CI, 1.17\u201312.03) as significant mortality risk factors.Conclusion: Chest CT findings, including lung involvement &gt;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.<\/jats:p>","DOI":"10.23876\/j.krcp.24.079","type":"journal-article","created":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T02:56:38Z","timestamp":1728528998000},"page":"587-597","update-policy":"https:\/\/doi.org\/10.23876\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence-powered chest computed tomography analysis unveils prognostic insights for COVID-19 mortality among prevalent hemodialysis 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