Predicting Severe COVID-19 Infection on Initial Diagnosis: Comparison and Validation of CT Imaging Triage Tools.

BACKGROUND: Developing a reliable predictive tool of disease severity in COVID-19 infection is important to help triage patients and ensure the appropriate utilization of health-care resources. OBJECTIVE: To develop, validate, and compare three CT scoring systems (CTSS) to predict severe disease on initial diagnosis of COVID-19 infection. METHODS: One hundred and twenty and 80 symptomatic adults with confirmed COVID-19 infection who presented to emergency department were evaluated retrospectively in the primary and validation groups, respectively. All patients had non-contrast CT chest within 48 hours of admission. Three lobarbased CTSS were assessed and compared. The simple lobar system was based on the extent of pulmonary infiltration. Attenuation corrected lobar system (ACL) assigned further weighting factor based on attenuation of pulmonary infiltrates. Attenuation and volume-corrected lobar system incorporated further weighting factor based on proportional lobar volume. The total CT severity score (TSS) was calculated by adding individual lobar scores. The disease severity assessment was based on Chinese National Health Commission guidelines. Disease severity discrimination was assessed by the area under the receiver operating characteristic curve (AUC). RESULTS: The ACL CTSS demonstrated the best predictive and consistent accuracy of disease severity with an AUC of 0.93(95%CI:0.88-0.97) in the primary cohort and 0.97 (95%CI:0.91.5-1) in the validation group. Applying a TSS cut-off value of 9.25, the sensitivities were 96.4% and 100% and the specificities were 75% and 91% in the primary and validation groups, respectively. CONCLUSION: The ACL CTSS showed the highest accuracy and consistency in predicting severe disease on initial diagnosis of COVID-19. This scoring system may provide frontline physicians with a triage tool to guide admission, discharge, and early detection of severe illness.

Predicting Severe Disease and Critical Illness on Initial Diagnosis of COVID-19: Simple Triage Tools.

RATIONALE: This study was conducted to develop, validate, and compare prediction models for severe disease and critical illness among symptomatic patients with confirmed COVID-19. METHODS: For development cohort, 433 symptomatic patients diagnosed with COVID-19 between April 15th 2020 and June 30th, 2020 presented to Tawam Public Hospital, Abu Dhabi, United Arab Emirates were included in this study. Our cohort included both severe and non-severe patients as all cases were admitted for purpose of isolation as per hospital policy. We examined 19 potential predictors of severe disease and critical illness that were recorded at the time of initial assessment. Univariate and multivariate logistic regression analyses were used to construct predictive models. Discrimination was assessed by the area under the receiver operating characteristic curve (AUC). Calibration and goodness of fit of the models were assessed. A cohort of 213 patients assessed at another public hospital in the country during the same period was used to validate the models. RESULTS: One hundred and eighty-six patients were classified as severe while the remaining 247 were categorized as non-severe. For prediction of progression to severe disease, the three independent predictive factors were age, serum lactate dehydrogenase (LDH) and serum albumin (ALA model). For progression to critical illness, the four independent predictive factors were age, serum LDH, kidney function (eGFR), and serum albumin (ALKA model). The AUC for the ALA and ALKA models were 0.88 (95% CI, 0.86-0.89) and 0.85 (95% CI, 0.83-0.86), respectively. Calibration of the two models showed good fit and the validation cohort showed excellent discrimination, with an AUC of 0.91 (95% CI, 0.83-0.99) for the ALA model and 0.89 (95% CI, 0.80-0.99) for the ALKA model. A free web-based risk calculator was developed. CONCLUSIONS: The ALA and ALKA predictive models were developed and validated based on simple, readily available clinical and laboratory tests assessed at presentation. These models may help frontline clinicians to triage patients for admission or discharge, as well as for early identification of patients at risk of developing critical illness.