Kidney research and clinical practice · 2024
Artificial intelligence-powered chest computed tomography analysis unveils prognostic insights for COVID-19 mortality among prevalent hemodialysis patients.
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Kim Eunji, Yoon Soo-Jin, Yu Sungbong, Ko Eunsil, Shon Kyungjun, Yoon Jooyeon, Kee Youn Kyung, Kim Do Hyoung, Cho AJin, Park Hayne Cho, Lee Young-Ki
Аннотация
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 groundglass 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.
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 groundglass 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.