Background

Coronavirus disease (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has affected almost all countries and regions, posing a great threat to human health. SARS-CoV-2 has evolved into various variants with different virulence and transmission, including Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Delta (B.1.617.2) and Omicron (B.1.1.529) [1]. B.1.1.529 was first discovered in South Africa in November 2021, and was listed as a Variants of Concern by the World Health Organization and named Omicron [2]. Increased transmissibility and reduced protection from neutralising antibodies have led to the rapid spread of this variant, which rapidly became a major variant in many countries [3]. Since the cancellation of the zero-COVID policy in China in December 2022, many cases of SARS-CoV-2 Omicron infection have been reported across the country [4]. The clinical manifestations of Omicron infection vary widely, ranging from asymptomatic illness to pneumonia and life-threatening complications, including acute respiratory distress syndrome and multiple organ failure, and death [12]. The independent validation data were collected from seven hospitals in multiple provinces and cities across the country. Despite differences from the training set, the scoring systems also had high accuracy in the validation set, indicating that the scoring system is generalisable. In addition, some laboratory assays differed by hospital. For example, IL-6 measurement is not as standardised as other inflammatory markers, which also suggests that different laboratory techniques can be used without affecting the model performance.

In our study, the risk factors for death in patients with COVID-19 were similar to those identified in previous studies [13, 14]. Older patients have more underlying diseases, are more likely to have secondary infections, and develop critical illness and have a higher case fatality rate [15]. IL-6 is a multifunctional cytokine that regulates humoral and cellular responses, and has been identified in many previous studies as an important biomarker associated with adverse clinical outcomes of COVID-19 [16, 17]. It is released by immune cells, including macrophages and T cells, and elevated levels of IL-6 reflect viral load and lung damage. Overexpression of proinflammatory cytokines and chemokines is involved in the occurrence of severe pneumonia, acute respiratory distress syndrome, and multiple organ failure in patients with COVID-19 [18]. Our study suggests that monitoring IL-6 levels can also help identify high-risk patients. In our study, elevated D-dimer levels were associated with mortality. D-dimer is a fibrin degradation product. An increase in D-dimer level indicates activation of coagulation, which may be related to thrombosis and inflammation [19]. D-dimer levels are elevated in 3.75–74.6% of patients with COVID-19 [20, 21]. A multicentre retrospective study conducted in Wuhan found that D-dimer greater than 1 µg/mL on admission was associated with an increased risk of in-hospital death [22]. Consistent with previous studies, we found that elevated BUN levels were associated with an increased risk of death from COVID-19 [23]. BUN, a nitrogenous end-product of protein metabolism, can be used to assess renal function and Hypovolemia. One study reported that after correcting for renal function, a high BUN concentration on admission was still closely related to the adverse outcomes of critically ill patients in the ICU [24]. In addition, the BUN-to-serum albumin ratio is an important prognostic factor for mortality and severity in patients with aspiration pneumonia, hospital-acquired pneumonia, and community-acquired pneumonia [25, 26]. LDH is an enzyme present in the cytoplasm that is involved in lactate metabolism. An elevated LDH level is an indicator of cell damage or necrosis [27]. Several studies have shown that the LDH levels reflects disease severity and is significantly higher in patients in ICUs than in other patients [28, 29]. A meta-analysis of 18 studies (total sample size: 5394 patients) showed that an elevated LDH level was associated with a 5-fold increase in the risk of adverse outcomes in patients with COVID-19 [30]. The discovery of these biomarkers also has implications for the treatment of COVID-19, such as timely anti-inflammation, blocking cytokine storm, appropriate anticoagulation, and prevention of gastrointestinal bleeding may help to improve prognosis.

It is worth noting that underlying disease (especially cardiac disease) have been associated with poor prognosis [31], the prediction of COVID-19 death in our study was mainly captured by age and biological examination at adimission. Machine learning variable selection techniques essentially retain only those variables that have the greatest impact on prognosis, and the extent of individual systemic inflammatory response syndrome appears to drive patient outcomes to a greater extent than underlying conditions. Therefore, underlying disease was not included in the final risk score because its effect was offset by other factors.

Our scoring system has several advantages. First, it is based on the data of patients with SARS-CoV-2 Omicron infection, and so adds to the prognostic indicators available for different variants of SARS-CoV-2. Second, it is based on readily available objective indicators, and is easy to calculate and use in clinical practice. Third, it has good prediction performance and was verified using data from different hospitals in China, so has generalisability. However, our research has some limitations. First, this study was retrospective, not all patients underwent all laboratory tests, and all patients in this study were hospitalized and none were outpatients, resulting in incomplete data and selection bias. Second, it is not a large-sample study. All the data come from China and may not fully represent the world ‘s population. Third, our risk score was calculated from baseline variables at admission, regardless of the effect of various treatments during hospitalization on prognosis such as antiviral therapy, which was an important independent predictor of COVID-19-related mortality [32]. In addition, most of the patients in our study received COVID-19 vaccines on national appeal, but we did not collect specific information on the number, type, and timing of vaccinations, which have been reported to reduce the risk of COVID-19-related death in a dose-response manner [33]. These limitations may limit its implementation. Large-scale prospective data are needed to optimize the model in the future.

In summary, the scoring system based on age and four laboratory indicators on admission can timely and effectively assess the risk of patients with SARS-CoV-2 Omicron infection, and help clinicians identify high-risk patients for monitoring and immediate intervention.