ArticleEmergency radiology2025
Automated quantitative chest CT for mortality prediction in COVID-19 patients in a resource-limited emergency setting in Gaza: a retrospective study using LungCTAnalyzer.
Article in Emergency radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeTo assess the prognostic value of automated quantitative chest CT metrics in predicting in-hospital mortality among patients with COVID-19 pneumonia admitted through the emergency department in a resource-limited setting in Gaza.
methodsThis retrospective study included 300 adult patients with RT-PCR-confirmed COVID-19 pneumonia who underwent non-contrast chest CT upon hospital admission. Automated quantitative lung metrics were derived using LungCTAnalyzer, an open-source 3D Slicer extension. Metrics included functional lung volume, affected lung volume, and the COVID-Q index (affected-to-functional lung ratio). Patients were stratified by survival status, and outcomes were analyzed using ROC curves, Kaplan-Meier survival analysis, and log-rank testing.
resultsAmong the cohort, 112 patients (37.3%) died during hospitalization. Non-survivors were older and more likely to require advanced respiratory support (p < 0.001). Quantitative CT analysis revealed significantly reduced functional lung volume (47.2% vs. 73.9%) and increased affected lung volume (52.8% vs. 26.1%) in non-survivors (p < 0.001). The COVID-Q index was markedly higher in the deceased group. ROC analysis showed good predictive performance for total affected lung volume (AUC = 0.756; 95% CI: 0.696-0.815), with an optimal threshold of approximately 42%. Right lung involvement was associated with the poorest survival outcomes (log-rank = 67.6, p < 0.001).
conclusionAutomated quantitative chest CT provides objective, reproducible metrics for early mortality risk stratification in COVID-19 pneumonia. The use of open-source tools like LungCTAnalyzer may assist emergency radiologists in prioritizing care in resource-constrained and conflict-affected healthcare systems.
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