A total of 893 patients were admitted to hospital due to COVID-19. Baseline characteristics are described in Table 1. Median age was 68.2±15.2 years, and 453 were female (50.7%). 87 (9.7%) patients were admitted to Intensive Care Unit (ICU) and 72 (8.1%) also needed mechanical ventilation support and 171 (19.1%) died.
In relation with cardiovascular outcomes, heart failure was the most common complication (61cases, 6.8%). Acute myocardial infarction, myocarditis and pericarditis were also described (3 (0.3%); 2 (0.2%); 3 (0.3%), respectively). Pulmonary embolism was diagnosed in 8 patients (0.9%).
Univariate analysis with clinical characteristics and treatment to predict mortality was made (Supplementary table 1). Those that were found to have significant difference (p<0.05) (age, comorbidities (hypertension, dyslipemia, diabetes mellitus, peripheral artery disease, heart disease, COPD/asthma), days of symptoms, respiratory insufficiency, in-hospital drugs (antiviral, chloroquine, ceftriaxone, corticosteroids, anticoagulation, antiplatelet)) were included in the multivariate analysis.
After multivariate adjustment, most laboratory parameters were significantly associated with mortality (haemoglobin, leukocytes, neutrophils, neutrophils/lymphocytes ratio, lymphocytesx100leucocyte, GOT, GGT, creatinine, CRP, IL-6, procalcitonin, LDH and D-dimer), only lymphocytes, platelets and ferritin values were not associated with high mortality (Table 2). The incremental value of each of these significant biomarkers, when it was added to a clinical base model, was assessed with the change in the c-index, achieving all of them significant difference regarding the reference (Table 3).
In order to convert these variables to categorical ones, the optimal cut-off point definition for each different biomarker was established based on ROC curves, as is represented in Figure 1.
After categorizing the biomarkers according to their cut-off points, those resulting as independent predictors of mortality by the multivariate logistic regression analysis were incorporated into a risk score (Table 4). The scores assigned to each biomarker were determined according to the value of the odds ratio (OR). The one with an individual higher score was procalcitonin < 0.2 ng/mL with 5 points (OR 5.72, CI 95% 3.35-9.76, p<0.001), in relation with the severity of bacterial coinfection. Hemoglogin <12 g/dL (OR 1.07, CI 95% 1.05-1.09, p<0.001; 1 point), erythrocytes < 4.1 per 106/mm3 (OR 2.14, CI 95% 1.19-3.84, p 0.011;2 points), leukocytes > 8.3 per 103/mm3 (OR 2.51, CI 95% 1.56-4.03, p<0.001; 3 points), neutrophils > 8.1 per103/mm3 (OR 2.13, CI 95% 1.14-3.95, p 0.017; 2 points), lymphocytes < 6.5 per 100 leukocytes (OR 2.85, CI 95% 1.82-4.46, p<0.001; 3 points), creatinine (OR 4.10, CI 95% 2.56-6.55, p<0.001; 4 points), C-reactive protein > 4.5 mg/L (OR 3.05, CI 95% 1.08-8.58, p 0.035; 4 points), interleukin-6 > 24 pg/mL (OR 1.83, CI 95% 1.17-2.88, p 0.009; 2 points), lactate dehydrogenase (LDH) ≥ 393 UI/L (OR 4.29, CI 95% 2.49-7.39, p<0.001; 4 points), and D-dimer > 1116 ng/mL (OR 1.92, CI 95% 1.22-3.02, 2 points).
With these individual scores, the COVID-19 Lab score is performed, ranging from 0 to 30 points. The predicted probability of death based on the risk score was graphically represented after modelling by fractional polynomials (Figure 2).
Depending on the COVID-19 Lab score, mortality varies as shown in Figures 2 and 3, the higher is the score, the poorer outcome. Three groups were set, dividing patients in low (<12 points), moderate (12-18 points) and high (19 or higher points) risk of death, with significant mortality differences between them (3.9%, 16.1%, 49.1% respectively, p>0.001).
The performance of this risk score was tested by assessing its discrimination and calibration capacity for all-cause death. Discrimination was evaluated by calculating the C statistic, 0.85 (0.82-0.88), and calibration was assessed by the Hosmer-Lemeshow test, p-value 0.63 (table 5)