All participants or their legal guardians provided written informed consent, and the Ethics Committee of the Changshu Hospital Affiliated to Soochow University approved the present study (Approval number: X202250).

This study included 136 SAKI patients, who were assessed between January 2021 and December 2023. All patients were obtained from Changshu Hospital Affiliated to Soochow University (Suzhou, China). According to the guidance from the KDIGO Clinical Practice Guidelines for Acute Kidney Injury and Acute Kidney Injury Network (AKIN) classification (24, 25), all patients were diagnosed and graded. Patients with an increase in serum creatinine (sCr) ≥ 26.5 μmol/L within 48 h or 1.5 times the baseline sCr value within 7 days were defined as AKI. In the present study, all patients were classified as stage 2 or stage 3. Meanwhile, the recognition of sepsis was performed according to the last revised definition of sepsis (26).

The exclusion criteria were as follows: (1) age less than 18 years; (2) receipt of nephrotoxin within 4 weeks prior to the study; (3) death within 72 h of hospital admission; (4) severe cardiovascular and cerebrovascular diseases, malnutrition, trauma, immunodeficiency, coagulation disorders, severe hepatitis, or tumors; (5) psychiatric disorders or drug addiction; (6) pregnancy; (7) poor adherence; and (8) non-infectious AKI without meeting the criteria for sepsis.

A follow-up was performed for each patient after diagnosis for 28 days or until death. Furthermore, according to the survival outcome of follow-up, the patients were further divided into two groups: the survivor group and the non-survivor group.

The clinical data including demographic characteristics, infection sites, and combined diseases, were collected from patients’ electronic medical records. Meanwhile, laboratory examinations, including sCr, blood urea nitrogen (BUN), and estimated glomerular filtration rate (eGFR), of each patient were also collected from their first electronic medical records. Furthermore, the Acute Physiology and Chronic Health Evaluation II (APACHE II) score and the Sequential Organ Failure Assessment (SOFA) score were assessed after the diagnosis of AKI.

After the diagnosis of SAKI, peripheral venous blood was collected from each individual using a coagulation tube and an EDTA-coated tube between 6:00 am and 7:00 a.m. Within 30 min of collection, the samples of the coagulation tube were centrifuged at 3,500 rpm at 4°C for 10 min, and the serum was obtained and stored at −80°C until further use. Meanwhile, the plasma samples were obtained by centrifugation at 2,000 g at 4°C for 10 min and were stored at −80°C until further use.

The plasma levels of three circulating NET markers, citrullination of citH3, NE-DNA, and MPO-DNA, were detected after the diagnosis of AKI, using commercial enzyme-linked immunosorbent assays kit (citH3: HB1518-Hu; NE-DNA: HB3168-Hu; MPO-DNA: HB2223-Hu; Hengyuan Biotech Co., Ltd., Shanghai, China). The levels of these indicators were measured in triplicate, and the inter- and intra-assay coefficients of variation were < 5%.

The Luminex xMAP array procedure was performed according to the manufacturer’s instructions to measure the serum levels of inflammation indicators. The human 12-plex panel (Product ID: FCSTM09-12) was used based on the Luminex 200 multiplexing platform (Luminex Corp., Texas, United States) (27–29) after the diagnosis of AKI. For all the biological specimens, each sample was tested in triplicate, and their average concentrations were calculated for further statistical analysis.

The sample size calculation was conducted using an online sample size calculator.¹ The sample size used in the present study was acceptable based on the results of sample size calculation (α = 0.05, β = 0.2).

Statistical analyses were performed using SPSS version 22.0 (SPSS Inc. Chicago, IL, United States) and R software (version 4.2.1). Continuous variables were presented as the mean ± standard deviation. The Kolmogorov–Smirnov test was utilized to identify the normal distribution of the data. An independent-sample t-test was used if variables were normally distributed for continuous variables. Categorical variables were analyzed using the chi-square test. The correlation analysis was performed using Pearson’s correlation coefficients, and logistic regression was used to evaluate the interaction between NETs and inflammation indices on the severity of SAKI. Receiver operating characteristic (ROC) curves were used to assess the accuracy of the markers in identifying non-survivors. Binary logistic regression was performed to compute a predicted value of each patient for obtaining a combined indicator based on NET markers (30). The Youden index (31) was utilized to determine the optimal value of sensitivity and specificity. Furthermore, to evaluate the prognostic significance of the NET markers for the 28-day survival, the Cox regression analysis was performed and the Kaplan–Meier curves were plotted. p < 0.05 were considered to indicate statistically significant differences.

According to the records of follow-up, 76 SAKI patients were included in the survivor group, and the other 60 patients were included in the non-survivor group (Table 1).

As shown in Table 1, there were no significant differences in baseline clinical features, including age, gender, BMI, infection sites, combined diseases, smoking, and drinking habits, between the survivor and non-survivor groups (all p > 0.05). Furthermore, no significant differences were observed between the survivors and non-survivors in terms of the serum levels of sCr, BUN, and eGFR (Table 1, all p > 0.05). Meanwhile, the scores of APACHE II and SOFA assessments showed no significant difference between the two groups (Table 1, all p > 0.05).

Patients in the non-survivor group have significantly higher plasma levels of NETs (citH3, NE-DNA, and MPO-DNA) than those in the survivor group (Table 2, all p < 0.05). Furthermore, a subgroup analysis further indicated that there was no significant difference in the plasma NET levels between patients with CKD and patients without CKD in both the survivor and non-survivor groups; however, survivors with or without CKD showed significantly increased NET levels as compared to non-survivors with or without CKD (Supplementary Figure S1).

In addition, 13 inflammatory indices were measured in the serum of all participants. As shown in Table 2, the non-survivor group showed significantly higher serum levels of granulocyte–macrophage colony-stimulating factor (GM-CSF), interleukin (IL)-1β, IL-2, IL-4, IL-6, IL-8, and IL-10, and tumor necrosis factor-alpha (TNF-α) than the survivor group (Table 2, all p < 0.05). However, other inflammatory indices showed no significant difference between the two groups.

As shown in Figure 1A, there were some significant correlations between plasma NET markers and serum inflammatory indices in SAKI patients. Specifically, the serum IL-6 levels showed a significantly positive correlation with plasma citH3, NE-DNA, and MPO-DNA levels. Meanwhile, the plasma citH3 levels were positively correlated with the serum GM-CSF levels, and the plasma NE-DNA levels showed a positive correlation with the serum IL-10 levels. Furthermore, positive correlations were also found between the plasma MPO-DNA levels and the serum IL-4 and TNF-α levels. Additionally, there was a significant correlation of the plasma citH3 levels with the sCr levels (r = 0.171, p = 0.047); however, the sCr levels were not correlated with the plasma levels of NE-DNA (r = 0.166, p = 0.053) and MPO-DNA (r = 0.089, p = 0.302).

Eight differential inflammatory indices and three NET markers were further included in the logistic regression analysis. Multivariate analysis indicated that IL-2, IL-8, IL-10, TNF-α, citH3, NE-DNA, and MPO-DNA displayed significant interactive effects on the survival of SAKI patients (Figure 1B).

Figure 2 displays the area under the curve (AUC) values of eight differential inflammatory indices and three NET markers for distinguishing SAKI patients in the non-survivor group from individuals in the survivor group. Five indicators (i.e., IL-10, TNF-α, citH3, NE-DNA, and MPO-DNA) provided greater diagnostic power, with AUC values >0.70 (Figure 2A; Supplementary Table S1).

To assess the clinical value of plasma NETs for the survival of SAKI, the composite marker of plasma citH3, NE-DNA, and MPO-DNA (citH3 coefficient: 0.035; NE-DNA coefficient: 0.032; MPO-DNA coefficient: 0.017) showed higher AUC values for identifying non-survivors from survivors in SAKI patients (AUC = 0.857) than any single indicators, corresponding to a specificity of 0.776 and a sensitivity of 0.767 (Figure 2B).

According to the follow-up record, all SAKI patients were evaluated in the present prognostic analysis. Based on potential risk indicators in Figure 2B and basic clinical characteristics and laboratory examinations, the Cox regression model was used to assess baseline factors to predict the 28-day survival outcomes of patients (Table 3). The results indicated that NET markers (citH3, NE-DNA, and MPO-DNA) in plasma may independently predict the survival of SAKI patients (all p < 0.001).

To facilitate the survival analysis, plasma citH3, NE-DNA, and MPO-DNA levels were divided into low expression and high expression based on the median values. The Kaplan–Meier survival curves further revealed that patients with higher plasma citH3, NE-DNA, and MPO-DNA levels had significantly poorer 28-week survival than those with lower plasma NET levels (citH3: log-rank p = 0.001; NE-DNA: log-rank p < 0.001; MPO-DNA: log-rank p < 0.001; Figure 3).