Opportunities and risks of ChatGPT in medicine, science, and academic publishing: a modern Promethean dilemma

Abstract

Aim To evaluate the predictive properties of several com - mon prognostic scores regarding survival outcomes in hospitalized COVID-19 patients. Methods We retrospectively reviewed the medical records of 4014 consecutive COVID-19 patients hospitalized in our tertiary level institution from March 2020 to March 2021. Prognostic properties of the WHO COVID-19 severity clas - sification, COVID-GRAM, Veterans Health Administration COVID-19 (VACO) Index, 4C Mortality Score, and CURB65 score regarding 30-day mortality, in-hospital mortality, presence of severe or critical disease on admission, need for an intensive care unit treatment, and mechanical venti - lation during hospitalization were evaluated. Results All of the investigated prognostic scores signifi - cantly distinguished between groups of patients with differ - ent 30-day mortality. The CURB-65 and 4C Mortality Score had the best prognostic properties for prediction of 30-day mortality (area under the curve [AUC] 0.761 for both) and in-hospital mortality (AUC 0.757 and 0.762, respectively). The 4C Mortality Score and COVID-GRAM best predicted the presence of severe or critical disease (AUC 0.785 and 0.717, respectively). In the multivariate analysis evaluating 30-day mortality, all scores mutually independently provid - ed additional prognostic information, except the VACO In - dex, whose prognostic properties were redundant. Conclusion Complex prognostic scores based on many parameters and comorbid conditions did not have better prognostic properties regarding survival outcomes than a simple CURB-65 prognostic score. CURB-65 also provides the largest number of prognostic categories (five), allowing more precise risk stratification than other prognostic scores.

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