Robust prediction of mortality of COVID-19 patients based on quantitative, operator-independent, lung CT densitometry.

ABSTRACT:

Purpose

To train and validate a predictive model of mortality for hospitalized COVID-19 patients based on lung densitometry.

Methods

Two-hundred-fifty-one patients with respiratory symptoms underwent CT few days after hospitalization. "Aerated" (AV), "consolidated" (CV) and "intermediate" (IV) lung sub-volumes were quantified by an operator-independent method based on individual HU maximum gradient recognition. AV, CV, IV, CV/AV, IV/AV, and HU of the first peak position were extracted. Relevant clinical parameters were prospectively collected. The population was composed by training (n = 166) and validation (n = 85) consecutive cohorts, and backward multi-variate logistic regression was applied on the training group to build a CT_model. Similarly, models including only clini

SUBMITTER: Mori M 

PROVIDER: S-EPMC8084622 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature