ArticleScientific reports2022
Predefined and data driven CT densitometric features predict critical illness and hospital length of stay in COVID-19 patients.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prognostic models in COVID-19 infection that predict severity: a systematic review.European journal of epidemiology · 2023Pooled it
- Imaging biomarkers of post-COVID dyspnea: insights from machine learning CT patterns and parametric response mapping.Respiratory research · 2026Article
- Quantile-Adaptive Sufficient Variable Screening by Controlling False Discovery.Entropy (Basel, Switzerland) · 2023Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of this study was to compare whole lung CT density histograms to predict critical illness outcome and hospital length of stay in a cohort of 80 COVID-19 patients. CT chest images on segmented lungs were retrospectively analyzed. Functional Principal Component Analysis (FPCA) was used to find the main modes of variations on CT density histograms. CT density features, the CT severity score, the COVID-GRAM score and the patient clinical data were assessed for predicting the patient outcome using logistic regression models and survival analysis. ROC analysis predictors of critically ill status: 87.5th percentile CT density (Q875)-AUC 0.88 95% CI (0.79 0.94), F1-CT-AUC 0.87 (0.77 0.93) Standard Deviation (SD-CT)-AUC 0.86 (0.73, 0.93). Multivariate models combining CT-density predictors and Neutrophil-Lymphocyte Ratio showed the highest accuracy. SD-CT, Q875 and F1 score were significant predictors of hospital length of stay (LOS) while controlling for hospital death using competing risks models. Moreover, two multivariate Fine-Gray regression models combining the clinical variables: age, NLR, Contrast CT factor with either Q875 or F1 CT-density predictors revealed significant effects for the prediction of LOS incidence in presence of a competing risk (death) and acceptable predictive performances (Bootstrapped C-index 0.74 [0.70 0.78]).
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