The study population consisted of 115 patients with COVID-19 diagnosed between January 2022 and May 2022. Diagnosis was based on reverse transcriptase-polymerase chain reaction detection of viral RNA from nasopharyngeal swabs from patients with clinical symptoms. Patients aged <18 years of age were excluded. Blood and urine samples were collected within 48 h of admission using EDTA tubes (367835, BD, Franklin Lakes, NJ, USA) for plasma and Serum Separator Clot Activator tubes (456073, BD) for serum. Plasma was obtained by centrifugation of blood samples at 1500 g for 10 minutes at 4°C, and serum was obtained by incubation at room temperature for 20-30 minutes followed by centrifugation as described for plasma. Both plasma and serum were aliquoted and stored at -70°C until analysis. This study was approved by our Institutional Ethics Committee (IRB No 2022-02-014). Informed consent was obtained from all study participants or, in the case of incapacity, from their next of kin.

Demographic information, data on comorbidities, and the history of vaccination against COVID-19 at the time of admission were extracted from the patients’ medical records. Initial vital signs, information on ICU admission, and the need for mechanical ventilation (MV) were also determined. The cycle threshold value was used to measure viral load. Biochemical parameters, white blood cell (WBC) count, neutrophil count, lymphocyte count, platelet count, albumin levels, the neutrophil/lymphocyte ratio (NLR), and platelet/WBC ratio (PWR), and serum procalcitonin, lactic acid, and brain natriuretic peptide (BNP) levels, were obtained from the patients’ medical records.

Inflammatory cytokines were further assessed by measuring serum levels of interleukin (IL)-6, high-sensitivity C-reactive protein (hs-CRP), and monocyte chemotactic protein-1 (MCP-1) levels, using the appropriate kits according to the manufacturer’s instructions. The levels of these cytokines were determined using ELISA kits (R&D Systems, Minneapolis, MN, USA).

AKI at the time of admission was diagnosed according to the Kidney Disease: Improving Global Outcomes (KDIGO) consensus definition for AKI, which includes the serum creatinine (SCr) level and urine output. The use of Scr has been reported to be very accurate in predicting COVID-19 AKI (28). The presence of underlying chronic kidney disease (CKD) was determined based on laboratory data obtained in our hospital (n=81) within the previous 2 years. For the 34 patients without previous laboratory data in our hospital, the presence of CKD was determined from their medical history. In addition, SCr level was measured serially during hospitalization, and AKI was diagnosed retrospectively if the SCr level improved and the urine volume increased after intravenous hydration.

Urinary specimens at the time of admission were stored at -80’C until thawed for measurement of biomarkers of renal tubular cell damage. We used two markers, β2-microglobulin (β2M) and kidney injury molecule 1 (KIM-1), which are known to be associated with AKI.

In vivo NET levels were quantified by measuring plasma levels of circulating nucleosome (histone-DNA) and MPO-DNA (Cell Death Detection ELISA Plus Kit; Roche Diagnostics, Basel, Switzerland) levels, as described in our previous paper (27). The degree of endothelial injury or damage was assessed by plasma von Willebrand factor (vWF) levels determined using a commercially available ELISA kit (Ray Biotech, Peachtree Corners, GA, USA).

The primary outcome was in-hospital mortality according to baseline NET levels. The duration of hospital stays, ICU admission rates, and COVID-19 AKI occurrence were also compared between higher and lower NET groups.

Variables with normal distributions, confirmed in Kolmogorov-Smirnov tests, were expressed as the mean ± standard deviation (SD). Categorical variables were expressed as percentages and were compared in chi-squared tests. An independent sample t-test was used to identify differences among groups based on continuous values. Pearson’s correlation coefficient was calculated for circulating nucleosomes, MPO-DNA, IL-6, BNP, various biochemical factors, and comorbidities. Multiple logistic regression analyses were performed to evaluate the role of increased NET levels as a determinant of AKI. Cumulative survival curves were derived using the Kaplan-Meier method; differences between survival curves were compared using a log-rank test. A Cox proportional hazards model was used to identify independent factors in the development of the study’s endpoints. The predictive value was expressed as a hazard ratio (HR) with the corresponding 95% confidence intervals (CIs). A p-value <0.05 was defined as indicating significance. All statistical analyses were performed using SPSS version 24.0 (IBM Corp., Armonk, NY, USA).

The 115 patients included in the analysis had a mean age of 67.6 ± 17.1 years, with ~50% of the patients being older than 70 years. Of the COVID-19 patients, 28 (24.3%) died. Differences in baseline demographic and clinical characteristics according to mortality are described in Table 1 . Patients who died during hospitalization were significantly older than survivors and had unstable vital signs on admission (low oxygen saturation, high heart rate, and high respiratory rate). As expected, the rate of ICU admission rate and the need for MV were much higher in these patients. Of all patients, 75 (65.2%) had received a COVID-19 vaccination prior to admission. However, COVID-19 vaccination was not associated with mortality, and the viral load measured at the time of admission did not differ between patients who died and those who survived. However, the time from the last vaccination to infection was significantly longer in the former (152 vs. 111 days, p=0.022). Hypertension (56.5%) and diabetes (40.9%) were the most common comorbidities, but neither was associated with mortality. Pre-existing CKD was present in 35 (30.4%) patients, with a significantly higher prevalence of CKD and pre-existing heart failure (HF) in patients who died than in those who survived (53.6% vs. 23.0% for CKD, and 57.1% vs. 14.9% for HF). Other comorbidities were similar between the two groups.

Baseline biochemical parameters according to mortality are compared in Table 2 . In patients who died during hospitalization, the WBC count, neutrophil count, and N/L ratio were significantly higher but the PWR and albumin levels were lower than in surviving patients. In addition, the levels of lactic acid, inflammatory cytokines, IL-6, hs-CRP, MCP-1, and procalcitonin were significantly higher in the patients who died than in those who survived.

On admission, 43 patients (37.4%) had AKI, and the prevalence of AKI was significantly higher in patients who died than in those who survived (57.1% vs. 31.0%, p=0.009) ( Table 1 ). Approximately one third of deceased patients required renal replacement therapy. AKI was also common in patients with pre-existing CKD or HF (60.0% in CKD vs. 26.3% in non-CKD, p=0.001, 40.5% with HF vs. 16.4% without HF, p=0.005).

COVID-19 AKI was very strongly associated with poor outcome ( Figure 1 ). Patients with AKI had significantly higher rates of in-hospital mortality (38.1% vs. 16.4%, p=0.009), initial ICU admission (64.3% vs. 37.0%, p=0.004) and MV (33.3% vs. 11.0%, p=0.004) and a significantly longer hospital stay (22.4 ± 19.5 vs. 12.4 ± 10.0 days, p<0.001) than those without AKI ( Figures 1A, B ). A comparison of COVID-19 AKI and non-AKI patients at baseline showed that WBC (11,000 ± 5,760 vs. 7,900 ± 4,120) and neutrophil (9,280 ± 5,777 vs. 5,980 ± 3,940) counts were significantly higher (p=0.001 and p<0.001) and PWR was significantly lower (20.7 ± 9.0 vs. 31.2 ± 16.2, p<0.001) in the former. In addition, the levels of all inflammatory markers, including serum ln_IL-6 (4.2 ± 1.4 vs. 3.2 ± 1.4, p=0.001), ln_hs-CRP (4.0 ± 1.4 vs. 3.1 ± 1.5, p=0.004), blood lactate (2.6 ± 1.6 vs. 1. 8 ± 1.2, p=0.040), procalcitonin (6.4 ± 11.4 vs. 1.1 ± 2.4, p=0.038) and BNP (792.5 ± 601.4 vs. 207.5 ± 311.4, p=0.001) were significantly higher in patients with AKI than in those without AKI ( Figures 1C, D ). For urinary biomarkers, urinary KIM-1/creatine (p=0.022) and β2M (p=0.006) levels were significantly higher in patients with AKI compared to non-AKI patients.

In the COVID-19 patients, we measured two representative markers of NETs, circulating nucleosomes and MPO-DNA, and both were closely associated ( Figure 2A ). Parameters associated with circulating nucleosome levels were previous coronary artery disease (r=0.185, p=0.048), HF (r=0.241, p=0.010), WBC count (r=0.258, p<0.001), neutrophil count (r=0.348, p<0.001), PWR (r=−0.347, p<0.001), and eGFR (r=-0.272, p=0.003), ( Figures 2B–D ). In contrast, the levels of inflammatory markers, including serum IL-6, hs-CRP, and MCP-1, did not correlate with circulating nucleosome levels ( Figure 2E ). Only procalcitonin levels showed a marginally significant correlation (r=0.173, p=0.072). However, in contrast to inflammatory cytokine levels, higher nucleosome levels had a strong positive correlation with vWF levels (r=0.356, p<0.001), a marker of endothelial damage ( Figure 2F ). This finding suggests an association between NETs and endothelial damage. There were no significant correlations between vWF and either the IL-6 or MCP-1 levels.

In addition, higher levels of NETs were significantly associated with AKI. As shown in Figure 3A , patients with COVID-19 AKI had significantly higher circulating nucleosomes (p=0.008) and peripheral neutrophil counts (p<0.001) but lower PWR (p<0.001) compared to patients without AKI ( Figure 3B ). The prevalence of AKI was 50.8% in the high NET group but 21.4% in the low NET group (p=0.001). Supporting these findings, urinary markers of AKI, urinary KIM-1/creatine (r=0.368, p=0.001) and β2M (r=0.218, p=0.049) levels were well-correlated with circulating nucleosome levels ( Figure 3C ).

We also found that the NET markers, nucleosomes and MPO-DNA, were significantly higher in patients who died than in those who survived ( Figure 4A ). Similarly, there were significant differences in peripheral neutrophil counts and PWR between the two groups ( Figure 4B ). When the nucleosome levels were divided into two groups based on the median levels, patients in the higher NET group had significantly worse clinical outcomes than those in the lower NET group ( Figures 4C, D ). In a Cox regression analysis, higher NET level was independently associated with the risk of in-hospital mortality (unadjusted HR 3.81; 95% CI 1.43–10.16, p=0.007). Even after adjustment for risk factors, higher NET level remained a significant predictor of death (HR 3.21, 95% CI 1.08–9.19, p=0.035) ( Table 3 ).

Given the strong association between NET and AKI, the risk of COVID-19 AKI associated with higher levels of NET was investigated. In a logistic regression model, significant predictors of AKI in unadjusted analysis, were age >70 years (OR 2.58, 95% CI 1.17–5.65, p=0.018), pre-existing CKD (OR 4.21, 95% CI 1.82–9.76, p=0.001), higher IL-6 (OR 1.65, 95% CI 1.19–2.30, p=0.002), higher BNP levels (OR 2.31, 95% CI 1.05–5.04, p=0.036) and higher NET (OR 3.79, 95% CI 1.87–8.58, p=0.001). After model adjustment for age >70 years, sex, diabetes, pre-existing comorbidities, and IL-6 and BNP levels, a higher NET (OR 3.67, 95% CI 1.30–10.41, p=0.014), pre-existing CKD (OR 8.55, 95% CI 2.07–35.38, p=0.010) and increased IL-6 (OR 1.59, 95% CI 1.09-2.32, p=0.016) were significant determinants of COVID-19 AKI (model 3) ( Table 4 ).

As the neutrophil count or the PWR at the time of admission was found to be as good as the nucleosome level in predicting clinical outcome, the sensitivity and specificity of these variables in predicting prognosis were compared using ROC analysis. We found that the AUCs of circulating nucleosome, peripheral neutrophil count, and P/W ratio were very similar; 0.678, 0.640, and 0.761 for mortality prediction and 0.681, 0.666, and 0.717 for AKI prediction, respectively. Pairwise comparisons showed that the difference between AUCs was not statistically significant, suggesting the usefulness of peripheral neutrophil count and PWR in predicting outcomes ( Supplementary Figure 1 ).