Discussion
inflammation, death, SARS-CoV-2 infection
SARS-COV-2 INFECTION, INFLAMMATION, DISEASE, INFECTIONS, CORONAVIRUS
In this study, the authors used a detailed dataset of patients admitted to a large academic COVID-19 treatment center in Brazil to identify factors associated with ICU admission and death. The authors built predictive scores that can be used in hospitals and emergency healthcare units to support clinical decisions and resource allocation. The final scores included age, comorbidities, and baseline laboratory data and were more accurate than the previously published NEWS-2 and 4C Mortality Score. Furthermore, the authors found that including baseline flu-like symptoms in the scores did not add substantial value to their accuracy.Several studies including both outpatient and hospitalized participants have explored prognostic scores in COVID-19. A recently published systematic reviewChen et al. developed the OURMAPCN-score using data from more than 6,000 patients admitted to seven hospitals in Wuhan as the derivation sample, with an external validation sample including more than 9,000 patients from China and Italy. The score included admission before the national maximum number of daily new cases was reached, age, oxygen saturation, blood urea nitrogen, respiratory rate, procalcitonin, C-reactive protein, and absolute neutrophil counts. Of note, this score included procalcitonin, a marker of systemic inflammation that is not readily available in most hospitals. Moreover, it included a calendar reference that is unlikely to apply to other settings. More recently, the same research group developed the PAWNN score, which used only age and complete blood count information (platelet counts; white blood cell counts; neutrophil counts; and neutrophil-to-lymphocyte ratio) as variables for a prediction tool built using a derivation sample of more than 9,000 patients and a validation sample of almost 3,000 patients in China; in this analysis, the model accuracy was 80% in an external validation sample including 227 patients from Italy.Knight et al. used data from the International Severe Acute Respiratory and Emerging Infections Coronavirus Clinical Characterisation Consortium (ISARIC-4C) to build the 4C Mortality Score. The study included more than 35,000 patients in the derivation sample and more than 22,000 patients in the validation sample. The final score included age, sex, number of comorbidities, respiratory rate, peripheral oxygen saturation, level of consciousness, urea, and C-reactive protein levels. In addition to the high discrimination for mortality, the 4C Mortality Score had the advantage of including variables usually available at the initial hospital assessment.In our prognostic score, we used variables readily available in most hospital settings that could be applied in different scenarios. We compared our scores with the 4C Mortality Score20] and the widely validated NEWS-2Despite the initial optimism brought on by the development of several effective vaccines for COVID-19, their generally slow rollout and the emergence of new SARS-CoV-2 variants have contributed to recurring waves of infected patients in several countries.This study had limitations. The authors used data from a relatively small sample (3,022 individuals) admitted to a single tertiary university hospital in a resourceful city in Brazil. However, our hospital was the primary referral center for severe COVID-19 in Sao Paulo, receiving patients from all regions of the metropolitan area, which is the most populated in Brazil with 23.5 million inhabitants. All participants were enrolled in 2020, preceding any exposure to previous SARS-CoV-2 infection or vaccination and prior to the emergence of viral genetic variants, which will likely modify the disease prognosis. As such, it is unlikely that our scores could be directly applied to contemporary cohorts of hospitalized COVID-19 patients. The authors also limited our analyses to patients aged ≥14 years and cannot extrapolate our results to pediatric populations. Nevertheless, our results are valuable in showing that prognostic scores created using readily accessible, locally sourced data and easily managed through electronic health records can be more effective in predicting clinical outcomes and improving resource allocation compared to scores developed externally.