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 clinical parameters (CLIN_model) and both CT/clinical parameters (COMB_model) were developed. Model's performances were assessed by goodness-of-fit (H&L-test), calibration and discrimination. Model's performances were tested in the validation group.

Results: Forty-three patients died (25/18 in training/validation). CT_model included AVmax (i.e. maximum AV between lungs), CV and CV/AE, while CLIN_model included random glycemia, C-reactive protein and biological drugs (protective). Goodness-of-fit and discrimination were similar (H&L:0.70 vs 0.80; AUC:0.80 vs 0.80). COMB_model including AVmax, CV, CV/AE, random glycemia, biological drugs and active cancer, outperformed both models (H&L:0.91; AUC:0.89, 95%CI:0.82-0.93). All models showed good calibration (R2:0.77-0.97). Despite several patient's characteristics were different between training and validation cohorts, performances in the validation cohort confirmed good calibration (R2:0-70-0.81) and discrimination for CT_model/COMB_model (AUC:0.72/0.76), while CLIN_model performed worse (AUC:0.64).

Conclusions: Few automatically extracted densitometry parameters with clear functional meaning predicted mortality of COVID-19 patients. Combined with clinical features, the resulting predictive model showed higher discrimination/calibration.

Keywords: COVID-19; CT; Lung densitometry; Respiratory distress syndrome.

Copyright © 2021 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Figures

Fig. 1

Graphical representation of the threshold values found by searching inflexion points of the HU-density histograms. The Aerated Volume (AV) in white ranges between −1000 HU and HU Threshold 1; the Intermediate Volume (IV) in light grey ranges between HU Threshold 1 and HU Threshold 2; the Consolidated Volumes (CV) in dark grey ranges from HU Threshold 2 until higher HU values.

Fig. 2

ROC curves of the predictive indexes of the three models and their comparison in the training and validation group.

Fig. 2

ROC curves of the predictive indexes of the three models and their comparison in the training and validation group.

Fig. 3

Calibration plots of the predictive indexes in the training and validation group.