Clinical characteristics and laboratory findings of non-survivors and survivors with COVID-19
281 (28.30%) non-survivors and 712 (71.70%) survivors with COVID-19 were enrolled in this study. Considering that the age, sex and comorbidities had been reported as the common death risk factors of COVID-19, enrolled survivors and non-survivors were statistically matched based on these risk factors. As summarized in Table 1 and supplementary table 1, the most common symptoms of all patients were fever and cough, non-survivors were more likely to have dyspnea (50.39% vs 42.23%, P=0.026) than survivors. Besides, non-survivors tended to have more abnormal vital signs on admission, such as higher temperature, heart rate and respiratory rate.
The abnormalities in chest CT images among these patients were also observed (Table 1 and Figure 1). The patchy shadows presence was the most typical manifestation of both groups, while pleural thickening (41.38% vs 26.90%; P=0.018) was more frequently observed in non-survivors, compared with survivors. Consistently, blood gas analysis revealed the degree of hypoxia was more evident in non-survivors, which presented lower concentration of PaO2, SaO2, PaCO2, CtCO2 and bicarbonate than survivors. These findings suggest more serious impairment of lung function in non-survivors.
Previous studies reported that cytokine storm and lymphopenia were common features in severe COVID-19 patients [2, 15]. We observed that the serum levels of inflammatory cytokines including IL6 (61.35 vs 6.85 pg/mL; P<0.0001), IL10 (10.30 vs 5.00 pg/mL; P<0.0001), IL8 (28.40 vs 11.60 pg/mL; P<0.0001), TNF-α (11.45 vs 8.10 pg/mL; P<0.0001), IL-1β (8.15 vs 6.49 pg/mL; P<0.0001) and IL2R (1148.00 vs 599.50 U/mL; P<0.0001) were significantly elevated in non-survivors compared with survivors. Moreover, the infection-related biomarkers including ferritin, CRP and procalcitonin also exhibited higher levels in non-survivors. Conversely, the baseline counts of lymphocytes (0.63 vs 1.09×109/L; P<0.0001), CD3+CD19- T cells (276.50 vs 905.00/μL, P<0.0001), CD3+CD8+ T cells (62.00 vs 280.00/μL; P<0.0001), B cells (73.50 vs 156.00/μL, P<0.0001), NK cells (36.50 vs 209.00/μL, P<0.0001) as well as the total number of T cells, B cells and NK cells (406.00 vs 1337.00/μL; P<0.0001) were drastically decreased in non-survivors, compared with survivors. Collectively, these findings demonstrate that aggravated inflammatory responses and severe lymphopenia might be correlated with the poor clinical outcome of COVID-19 patients.
Multiple-organ damage was more pronounced in non-survivors. We observed higher levels of ALT, TBIL, LDH, homocysteine, NT-proBNP, hs-cTnI, CK-MB and lower level of ALB/GLO in non-survivors. Besides, non-survivors had a falling count of eosinophils while elevated leukocytes and neutrophils, compared with survivors. Of note, in non-survivors, coagulation-related biomarkers of platelets counts were also substantially decreased, followed by the increased D-dimer and prolonged PT and APTT.
Additionally, in term of several scores evaluating disease severity, the fatal cases had more serious SOFA (4.00 vs 2.00, P<0.0001), qSOFA (1.00 vs 0.00, P<0.0001), APACHE II (17.00 vs 10.00, P<0.0001) and SIRS scores (2.00 vs 1.00, P<0.0001), compared to recovered patients (Table 1).
Complications and clinical treatments of non-survivors and survivors with COVID-19
SARS-COV-2 infection can cause both pulmonary and multi-system inflammation, leading critical complications (Table 2). The frequency of acute cardiac injury (79.72% vs 11.80%, P<0.0001), heart failure (71.53% vs 6.32%, P<0.0001), acute kidney injury (48.40% vs 4.35%, P<0.0001), acute liver injury (24.20% vs 2.39%, P<0.0001), acute pancreatic injury (4.27% vs 1.54%, P=0.010) especially acute respiratory distress syndrome (ARDS, 96.80% vs 53.79%, P<0.0001) and disseminated intravascular coagulation (DIC, 19.22% vs 0.56%, P<0.0001) were more higher in non-survivors than survivors. These critical complications could be the main cause of death, and the underlying mechanisms warrant further investigations.
Almost all deceased patients received antibiotic treatment (93.95% vs 88.48%, P=0.0094), more than the number of recovered patients (Table 2). Fatal patients received more glucocorticoid therapy, intravenous immunoglobulin therapy or transfusion than recovered cases, since the cytokine storm and DIC were more often observed in non-survivors. Similarly, due to the higher proportions of patients developed acute kidney injury or ARDS in non-survivors, decreased patients received more mechanical ventilation, continuous renal replacement therapy, high flow nasal cannula and extracorporeal membrane oxygenation than recovered patients. These factors might result in more frequent ICU admission (52.69% vs 0.56%, P<0.0001) and shorter time of hospital stay (22[IQR 15-28] vs 8 [IQR 4-14], P<0.0001) in non-survivors than survivors. The clinical interventions were more intensive in non-survivors due to the more severe illness in fatal cases. Of note, recovered patients were undergoing more, antiviral therapy (91.15% vs 80.07%, P<0.0001) as compared to decreased cases.
Risk factors associated with the death of COVID-19 patients
Furthermore, we performed Cox analysis to identify the potential death risk factors of COVID-19 with adjustment of age, sex, comorbidities. As shown in Table 3 and supplementary table 2, the higher temperature, faster heart rate and respiratory rate and lower mean arterial pressure were associated with increased risk of death. In terms of laboratory parameters, elevated inflammatory cytokines and infection-related factors, such as TNF-α, IL-6, IL-10, IL-8, IL-1β, IL-2R, ferritin, hs-CRP and procalcitonin were significantly associated with higher death risk of COVID-19. Conversely, the immune cells subsets, such as lymphocytes (HR=0.220, 95%CI=0.161-0.302, P<0.0001), B cells (HR=0.994, 95%CI=0.990-0.998, P=0.002), NK cells (HR=0.985, 95%CI=0.981-0.990, P<0.0001), CD3+CD19- T cells (HR=0.996, 95%CI=0.995-0.997, P<0.0001) and CD3+CD8+ T cells (HR=0.989, 95%CI=0.985-0.992, P<0.0001), were significantly associated with the lower death risk of COVID-19.
Indicators that represented organ damages, such as elevated ALT, AST, LDH, creatinine, amylase, hs-cTnI, NT-proBNP, CK-MB, and decreased ALB/GLO aggrandized the risk for COVID-19 death. Furthermore, coagulation-related biomarkers, including declined platelet counts (HR=0.994, 95%CI=0.992-0.995, P<0.0001), and increased PT, APTT, D-Dimer (HR=1.025~1.359, P<0.0001), might be strong indicators for death risk of COVID-19. Moreover, apart from abnormal biochemical dynamics, metabolism indices and blood gas analysis such as decreased serum level of PaO2, SaO2, PaCO2, and CtCO2 that could result in the degree of electrolyte disturbance or hypoxia, increasing the risk death of COVID-19.
We also analyzed the risk of death for patients with complications, patients with ARDS (HR=16.216, 95%CI=8.341-31.527, P<0.0001) had highest risk of death, followed by acute cardiac injury (HR=13.023, 95%CI=9.678-17.524, P<0.0001), heart failure (HR=10.722, 95%CI=8.227-13.974, P<0.0001), acute kidney injury (HR= 6.063, 95%CI=4.759-7.724, P<0.0001) and DIC (HR=5.819, 95%CI=4.281-7.910, P<0.0001). Besides, the higher levels of SOFA, qSOFA, APACHE II and SIRS scores were significantly associated with increased death risk (HR=1.195~3.471, P<0.0001). These findings provided evidence supporting that dynamic of inflammatory cytokines, immune cells subsets, blood gas, organ damage biomarkers, and especially complications should be closely monitored, in case of poor outcomes.
Comprehensive prediction models for death risk of COVID-19 patients
In light of the SOFA, qSOFA, APACHE II and SIRS scores have been reported as good diagnostic indicators for sepsis, septic shock and multi-organ failure [3, 4, 6-8, 16], we then calculated the prediction accuracy of these four scores in assessing death risk of COVID-19. As presented in Figure 2, these four scores had prominent prediction capacities evaluating COVID-19 death risk. The AUCs of SOFA, qSOFA, APACHE II and SIRS scores attained 0.697(95%CI=0.546-0.849), 0.610(95%CI=0.474-0.747), 0.826(95%CI=0.671-0.981) and 0.749(95%CI=0.629-0.869). Of note, discrimination of death risk models were better using APACHE II (AUC=0.826, P=0.022) and SIRS scores (AUC=0.749, P=0.013) than SOFA or qSOFA scores, which might partly be attributed to aggravated pro-inflammatory responses in non-survivors.
Besides, we also established another four prediction models based on inflammatory-related indices, immune cells subsets, organ damage biomarkers and complications, all of which were significantly associated with the COVID-19 death risk. Among death risk prediction models of each group alone, the predictive accuracy of the immune cells subsets group was the highest (AUC=0.901, 95%CI=0.801-1.000). Similarly, multiple-organ damage biomarkers (AUC=0.894, 95%CI=0.829-0.959), inflammatory-related indices (AUC=0.757, 95%CI=0.665-0.850) and complications (AUC=0.878, 95%CI=0.817-0.938) had better predictive effects in the discrimination of mortality, outperforming abovementioned SOFA, qSOFA scores (P<0.05) (Figure 2).
Finally, we integrated four score predictive systems, inflammatory-related indices, immune cells subsets, organ damage biomarkers and complications to construct a combine group. The combine score (AUC=0.950, 95%CI=0.853-1.000) was significantly higher than that of each risk group alone (Figure 2), suggesting the combined score system can comprehensively reflect the death risk of COVID-19.