Details

Autor(en) / Beteiligte

• 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

Titel

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

Ist Teil von

• Physica medica, 2021-05, Vol.85, p.63-71

Ort / Verlag

Italy: Elsevier Ltd

Erscheinungsjahr

2021

Quelle

Elsevier ScienceDirect Journals

Beschreibungen/Notizen

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