Robust prediction of mortality of COVID-19 patients based on quantitative, operator-independent, lung CT densitometry. (May 2021)

• Record Type: Journal Article
• Authors: Mori, Martina; Palumbo, Diego; De Lorenzo, Rebecca; Broggi, Sara; Compagnone, Nicola; Guazzarotti, Giorgia; Giorgio Esposito, Pier; Mazzilli, Aldo; Steidler, Stephanie; Pietro Vitali, Giordano; Del Vecchio, Antonella; Rovere Querini, Patrizia; De Cobelli, Francesco; Fiorino, Claudio
• Is Part Of: Physica medica. Volume 85(2021)
• Page Start: 63
• Page End: 71
• Publication Date: 2021-05
• Subjects: COVID-19 -- CT -- Lung densitometry -- Respiratory distress syndrome; Medical physics -- Periodicals; Biophysics -- Periodicals; Biophysics -- Periodicals; Imagerie médicale -- Périodiques; Radiothérapie -- Périodiques; Rayons X -- Sécurité -- Mesures -- Périodiques; Physique -- Périodiques; Médecine -- Périodiques; 610.153
• DOI: 10.1016/j.ejmp.2021.04.022
• Languages: English
• ISSNs: 1120-1797

Abstract

Highlights: Lung densitometry of 251 COVID-19 patients was analyzed with an original method. Few-CT-features models (with/wout clinical data) predict mortality (AUC = 0.80–0.89). Training-model (n = 166) performances were confirmed in a validation group (n = 85). 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 (R 2 :0.77–0.97). Despite several patient's characteristics were different between training and validation cohorts, performances in the validation cohort confirmed good calibration (R 2 :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.