Prognostic utility of the chest computed tomography severity score for the requirement of mechanical ventilation and mortality in hospitalized patients with COVID-19.

Purpose: To correlate the chest computed tomography severity score (CT-SS) with the need for mechanical ventilation and mortality in hospitalized patients with COVID-19. Materials and methods: The chest CT images of 224 inpatients with COVID-19, confirmed by reverse transcriptase-polymerase chain reaction (RT-PCR), were retrospectively reviewed from April 1 to 25, 2020, in a tertiary health care center. We calculated the CT-SS (dividing each lung into 20 segments and assigning scores of 0, 1, and 2 due to opacification involving 0%, <50%, and ≥50% of each region for a global range of 0-40 points, including both lungs), and collected clinical data. The receiver operating characteristic curve and Youden Index analysis was performed to calculate the CT-SS threshold and accuracy for classification for risk of mortality or MV requirement. Results: 136 men and 88 women were recruited, with an age range of 23-91 years and a mean of 50.17 years; 79 met the MV criteria, and 53 were nonsurvivors. The optimal threshold was >27.5 points for mortality (area under ROC curve >0.96), with a sensitivity of 93% and specificity of 87%, and >25.5 points for the need for MV (area under ROC curve >0.94), with a sensitivity of 90% and specificity of 89%. The Kaplan-Meier curves show a significant difference in mortality by the CT-SS threshold (Log Rank p < 0.001). Conclusions: In our cohort of hospitalized patients with COVID-19, the CT-SS accurately discriminates the need for MV and mortality risk. In conjunction with clinical status and laboratory data, the CT-SS may be a useful imaging tool that could be included in establishing a prognosis for this population.

Validation of Chest Computed Tomography Artificial Intelligence to Determine the Requirement for Mechanical Ventilation and Risk of Mortality in Hospitalized Coronavirus Disease-19 Patients in a Tertiary Care Center In Mexico City.

BACKGROUND: Artificial intelligence (AI) in radiology has improved diagnostic performance and shortened reading times of coronavirus disease 2019 (COVID-19) patients' studies. OBJECTIVES: The objectives pf the study were to analyze the performance of a chest computed tomography (CT) AI quantitative algorithm for determining the risk of mortality/mechanical ventilation (MV) in hospitalized COVID-19 patients and explore a prognostic multivariate model in a tertiary-care center in Mexico City. METHODS: Chest CT images of 166 COVID-19 patients hospitalized from April 1 to 20, 2020, were retrospectively analyzed using AI algorithm software. Data were collected from their medical records. We analyzed the diagnostic yield of the relevant CT variables using the area under the ROC curve (area under the curve [AUC]). Optimal thresholds were obtained using the Youden index. We proposed a predictive logistic model for each outcome based on CT AI measures and predetermined laboratory and clinical characteristics. RESULTS: The highest diagnostic yield of the assessed CT variables for mortality was the percentage of total opacity (threshold >51%; AUC = 0.88, sensitivity = 74%, and specificity = 91%). The AUC of the CT severity score (threshold > 12.5) was 0.88 for MV (sensitivity = 65% and specificity = 92%). The proposed prognostic models include the percentage of opacity and lactate dehydrogenase level for mortality and troponin I and CT severity score for MV requirement. CONCLUSION: The AI-calculated CT severity score and total opacity percentage showed good diagnostic accuracy for mortality and met MV criteria. The proposed prognostic models using biochemical variables and imaging data measured by AI on chest CT showed good risk classification in our population of hospitalized COVID-19 patients.