Robust prediction of mortality of COVID-19 patients based on quantitative, operator-independent, lung CT densitometry.
Phys Med
; 85: 63-71, 2021 May.
Article
em En
| MEDLINE
| ID: mdl-33971530
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&L0.70 vs 0.80; AUC0.80 vs 0.80). COMB_model including AVmax, CV, CV/AE, random glycemia, biological drugs and active cancer, outperformed both models (H&L0.91; AUC0.89, 95%CI0.82-0.93). All models showed good calibration (R20.77-0.97). Despite several patient's characteristics were different between training and validation cohorts, performances in the validation cohort confirmed good calibration (R20-70-0.81) and discrimination for CT_model/COMB_model (AUC0.72/0.76), while CLIN_model performed worse (AUC0.64).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.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
COVID-19
Tipo de estudo:
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Phys Med
Assunto da revista:
BIOFISICA
/
BIOLOGIA
/
MEDICINA
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Itália