Development of a Scoring Method Based on a Chest CT Scan to Determine the Outcomes of COVID-19 Patients
Introduction As COVID-19 shifts from pandemic urgency to endemic management, healthcare systems are faced with the evolving challenge of providing optimized care and adept resource allocation in this evolving landscape of the disease. However, the timely management and accurate assessment of disease severity remains a cornerstone of effective treatment. This study presents a pioneering scoring system, based on the primary chest CT scan findings, to predict patient outcomes and to equip clinicians with a tool that can expedite decision-making.
Method A retrospective cohort study was conducted involving 406 confirmed COVID-19 cases referred to two of our hospitals in Tehran, between February and April 2020. Radiographic and CT scan data were sourced from the imaging archive system and evaluated by a certified radiologist. We devised distinct severity scores for CT findings, demographic factors, and clinical indicators. These were synthesized into a comprehensive severity score to forecast critical patient outcomes, such as mortality, ICU admission, intubation, or extended hospitalization. Of the total cases, 161 (39.7%) were classified as severe, while 245 (60%) fell into the low or moderate severity category.
Results The mean score of demographic, CT scan, and clinical characteristics was significantly higher for those in the severe COVID-19 than the non-severe group. The cutoff score for predicting the outcomes in COVID-19 patients for demographic, clinical, and chest CT scan factors was 2.5, 9.5, and 8.5, respectively. Multivariate analysis indicated that each unit increase in these scores elevated the odds of fatal outcomes by 24%, 2.8%, and 12%, respectively. Then, using the comprehensive severity score, which is the sum of the above scores, we further predicted the disease severity.
Conclusion The findings suggest that our innovative scoring system, based on initial chest CT scan findings, serves as a robust predictor of COVID-19 outcomes.
Additional Information Author Contributions All authors have reviewed the final version to be published and agreed to be accountable for all aspects of the work.
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