The novel coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has become a major health concern worldwide at the time of this study with more than 1 million direct deaths according to the World Health Organization (1). Respiratory failure is the leading cause of mortality in patients with COVID-19 (2). Myocardial injury, kidney or liver injury, and multi-organ dysfunction are among the other complications leading to death (3). Several prognostic factors, such as older age, male gender, presence of comorbidities, and smoking, have been found to be associated with severe disease or death (4–7).
The National Institute for Health and Care Excellence (NICE) in its guidelines for the management of COVID-19 recommended the use of NEWS2 in critical care (8, 9). National Early Warning Score (NEWS) is a standardized clinical scoring system developed to improve detection of deterioration in acutely ill patients (Fig. 1) and it’s based on a logistic regression model designed to predict in-hospital patient mortality within 24 hours of a set of vital signs observation (10). Originally it consisted in evaluation of pulse rate, respiratory rate, blood pressure, temperature and oxygen saturation. NEWS-2 is the latest version of the NEWS score, which adds new onset of confusion to the parameters and then 2 points are added for people requiring supplemental oxygen to maintain their recommended oxygen saturation. A recent in-hospital study showed that NEWS2 did not appear to add predictive value over NEWS, even in patients with type 2 respiratory failure (11–14), for the purposes of our model we opted to integrate sensitivities and specificities of the original NEWS score.
Other scoring systems, like the CURB-65 (Fig-2) are widely used in predicting 30-day mortality in community-acquired pneumonia (15). CURB-65 has also been found to be useful in predicting 14-day mortality in hospital-acquired pneumonia (16). Recently a simple predictive tool for estimating the risk of 30-day mortality, and to stratify patients with COVID-19 was developed integrating CURB-65 (17).
The Complex Vulnerability Index (IVC-COV2 for its abbreviation in Spanish) (18) is a population-based index designed by Dominican health authorities which takes into account sex, age, and comorbidities in order to assess how much risk a specific patient has to suffer a critical outcome if infected with COVID-19, for the purposes of this study patients were stratified as low, intermediate, and high risk based on the DR-IVC-COV2 score. The use of clinical scoring systems to predict severe disease and mortality in patients with COVID-19 should be investigated further in larger prospective studies.
Bayesian statistics have been utilized to evaluate uncertainty using mathematical probability instruments. Our group has been studying Bayesian statistics in clinical/ medical decision making and as a “data recycling” tool and can be used to compare the diagnostic quality of different serum biomarkers, with a methodology that outputs the probability of an event based on criteria related to the specific event (19–26). Our group has developed a simple mathematical method for interpreting diagnostic impact called “Bayesian Diagnostic Gains (BDG)”, where relative diagnostic gain (RDG) and absolute diagnostic gain (ADG) were calculated based on the differences deducted from pre and posttest probabilities (ADG = post-test – pre-test) and (RDG = 100 × post-test – pre-test/Pre-test). This particular study is our first attempt at integrating BDGs in a COVID-19 Critical Care prediction multi-item model.
To develop a hybrid mathematical model that assists in predicting critical care disposition in patients with COVID-19 by means of Bayesian statistics assessing comparatively the IVC-COV2 score integrated with both NEWS and the CURB-65 score.