Additional value of chest CT AI-based quantification of lung involvement in predicting death and ICU admission for COVID-19 patients.
Res Diagn Interv Imaging
; 4: 100018, 2022 Dec.
Article
in English
| MEDLINE | ID: covidwho-2132214
ABSTRACT
Objectives:
We evaluated the contribution of lung lesion quantification on chest CT using a clinical Artificial Intelligence (AI) software in predicting death and intensive care units (ICU) admission for COVID-19 patients.Methods:
For 349 patients with positive COVID-19-PCR test that underwent a chest CT scan at admittance or during hospitalization, we applied the AI for lung and lung lesion segmentation to obtain lesion volume (LV), and LV/Total Lung Volume (TLV) ratio. ROC analysis was used to extract the best CT criterion in predicting death and ICU admission. Two prognostic models using multivariate logistic regressions were constructed to predict each outcome and were compared using AUC values. The first model ("Clinical") was based on patients' characteristics and clinical symptoms only. The second model ("Clinical+LV/TLV") included also the best CT criterion.Results:
LV/TLV ratio demonstrated best performance for both outcomes; AUC of 67.8% (95% CI 59.5 - 76.1) and 81.1% (95% CI 75.7 - 86.5) respectively. Regarding death prediction, AUC values were 76.2% (95% CI 69.9 - 82.6) and 79.9% (95%IC 74.4 - 85.5) for the "Clinical" and the "Clinical+LV/TLV" models respectively, showing significant performance increase (+ 3.7%; p-value<0.001) when adding LV/TLV ratio. Similarly, for ICU admission prediction, AUC values were 74.9% (IC 95% 69.2 - 80.6) and 84.8% (IC 95% 80.4 - 89.2) respectively corresponding to significant performance increase (+ 10% p-value<0.001).Conclusions:
Using a clinical AI software to quantify the COVID-19 lung involvement on chest CT, combined with clinical variables, allows better prediction of death and ICU admission.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Experimental Studies
/
Prognostic study
Language:
English
Journal:
Res Diagn Interv Imaging
Year:
2022
Document Type:
Article
Affiliation country:
J.redii.2022.100018
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