Use of [TIMP-2] • [IGFBP7] to identify sepsis-related acute kidney injury in the emergency department

Abstract

Acute kidney injury (AKI) is a common and harmful syndrome associated with sepsis. It is difficult to predict and has an adverse impact on both short- and long-term prognosis. Two urinary biomarkers, tissue inhibitor of metalloproteinases-2 (TIMP-2), and insulin-like growth factor-binding protein 7 (IGFBP7) have been validated for predicting moderate and severe AKI and improving the recognition of organ failure in critically ill patients with sepsis. We aimed to evaluate the performance of these markers for predicting sepsis-related AKI and its evolution in a cohort of patients admitted to the Emergency Department with infectious disease and high risk of sepsis.

This single-centre, prospective, observational study included adult patients presenting to the Emergency Department (ED) with suspected infection. Patients were included in the study if they had a National Early Warning Score 2 (NEWS 2) of 3 or higher. Three blood samples were drawn during the first 24 h, and we defined AKI according to the Kidney Disease: Improving Global Outcomes (KDIGO) definition. The first urine sample during ED stay was collected for urinary [TIMP-2]•[ IGFBP7] determination.

Higher admission levels of urinary [TIMP-2]•[IGFBP7] were associated with the development of AKI moderate-severe within 24 h (adjusted Odds Ratio 1.24; 95% Confidence Interval 1.07–1.43; p < 0.01). The biomarker showed only modest overall discrimination (AUC 0.67). Still, predefined cut-offs revealed complementary diagnostic roles: low values (<0.3) may help exclude AKI with a negative predictive value of 0.86, whereas the highest values (>2.0) increase its likelihood. Creatinine trajectories and inflammatory profiles were largely similar across groups, except for elevations in IL-10 and IL-1RN in high-risk patients.

In conclusion, urinary [TIMP-2]•[IGFBP7] measured on presentation to the ED had only moderate discrimination for sepsis-associated AKI. However, values (<0.3 units) could help to rule out the risk of developing AKI, while values >2.0 indicated high risk.

According to the latest Global Burden of Disease 2021 analysis, there were an estimated 166 million cases of sepsis (95% CI 135–201 million) and 21.4 million sepsis-related deaths (20.3–22.5 million) worldwide in 2021. This represents about 31.5% of all global deaths [1]. Current efforts to reduce the global burden of sepsis are focused on improving biomarker awareness among the public and healthcare professionals to avoid delays in diagnosis and treatment, and rapid treatment using appropriate antibiotics and bundles of supportive care focused on the common bacterial causes of sepsis [2]. Acute kidney injury (AKI) is a common and harmful syndrome associated with sepsis. It is difficult to predict and adversely impacts short-and long-term prognosis [3,4]. Its incidence in critically-ill patients is variable and may exceed 60% [[5], [6], [7], [8]]. Early and precise diagnosis of AKI is of great significance for the prognosis of critically ill patients [9]. Since serum creatinine and urine output are late and functional markers, novel AKI biomarkers have been identified [[10], [11], [12], [13], [14]]. Two urinary biomarkers, tissue inhibitor of metalloproteinases-2 (TIMP-2), and insulin-like growth factor-binding protein 7 (IGFBP7) have been validated for predicting moderate and severe AKI in critically ill patients with cardiovascular or respiratory failure [10,11] and perform well in patients with established sepsis [13]. Both are markers of cellular stress in the early phase of tubular cell injury caused by a wide variety of insults. The product of these two biomarkers outperformed all other known biomarkers for predicting AKI and has been proven to be a highly sensitive predictor of AKI in cardiac surgery and septic patients [[12], [13], [14]]. Still, all these studies were performed in Intensive Care Units (ICU) [15,16]. The test may extend the therapeutic window for renal protection [14] and potentially enhance specific therapeutic interventions to prevent or attenuate AKI in a more homogeneous at-risk population [17].

Data on urinary [TIMP-2]·[IGFBP7] in emergency department (ED) populations are scarce. A prospective ED cohort reported that [TIMP-2]·[IGFBP7] added to clinical variables improved discrimination for imminent moderate–severe AKI [18,19], whereas a subsequent biomarker-guided randomised ED trial did not demonstrate a reduction in AKI with an early Kidney Disease: Improving Global Outcomes (KDIGO) bundle [20].

In this study, we aimed to evaluate the early performance of [TIMP-2]•[ IGFBP7] for risk stratification of stage 2–3 AKI in patients on presentation to the ED with a clinical diagnosis of infection.

Methods

Study design

This was a single-centre, prospective, observational study conducted at Hospital Universitari Parc Taulí de Sabadell, in collaboration with the Institut d’Investigació i Innovació Parc Taulí (I3PT). The study was purely observational and did not involve any interventions or modifications to standard clinical care.

Study population

We included adult patients with suspected infection who presented to the ED of Hospital Universitari Parc Taulí de Sabadell between September 2022 and June 2024.

Patients were included if they fulfilled the following criteria: age ≥18 years, suspected infection with a National Early Warning Score 2 (NEWS) score ≥3 [21], and willingness to provide written informed consent. NEWS score sums weighted scores from routinely recorded vital signs, including: respiratory rate, oxygen saturation, systolic blood pressure, pulse rate, temperature, and level of consciousness/new confusion—and adds 2 points for supplemental oxygen use, with higher totals indicating greater acuity and need for clinical response [21].

Patients were excluded if they presented with surgical abdominal sepsis that required surgery; were pregnant or breastfeeding; receiving immunosuppressive therapy including, antineoplastic chemotherapy, chronic corticosteroids (prednisone ≥10 mg/day for ≥3 weeks), or had received an organ transplant; had previously been included in another study within the previous 60 days; had been hospitalized for >48 h; had a life expectancy of less than 6 months, or had any limitation of therapeutic effort. These exclusion criteria were chosen to avoid conditions that may alter the host response significantly.

Study procedures

Eligible patients were assessed by ED physicians. Upon obtaining informed consent, a 4 mL blood sample was collected at baseline (0 h), 4 h, and 24 h. Variables collected were: Demographics (age, sex), comorbidity burden (Charlson Comorbidity Index and individual comorbidities), vital signs (heart rate, respiratory rate, blood pressure, oxygen saturation, temperature, mental status), respiratory support, and severity scores (NEWS2, SOFA, APACHE, qSOFA). Routine laboratory markers of inflammation and organ dysfunction were measured at the same time points (e.g., white blood cell count with differential, lactate, bilirubin, CRP, procalcitonin, creatinine and other standard biochemistry), and arterial blood gas variables were recorded when available. Data was collected from electronic records. Patients were treated following local guidelines and the survival sepsis campaign recommendations [2,22,23].

Patients were followed up until hospital discharge or death.

TIMP-2 and IGFBP7 measurement

Urine specimens were collected at the first available sampling after patient arrival to the ED for the determination of TIMP-2 and IGFBP7. Midstream or indwelling-catheter urine was obtained in sterile containers, aliquoted, and immediately frozen at −80 °C until analysis. Analytical determinations were performed in urine samples using a validated immunoassay for TIMP-2 and IGFBP7 on the VIDAS 3 platform (bioMérieux, Marcy-l’Étoile, France), following the manufacturer’s instructions. The biomarker product was calculated as (TIMP-2 [ng/mL] × IGFBP7 [ng/mL]) / 1000 and expressed as (ng/mL)²/1000. According to previously validated thresholds, values <0.3 were considered low risk, values between 0.3 and 2.0 as high risk, and values ≥2.0 as highest risk for AKI.

Cytokines measures

To characterise the host inflammatory response at ED presentation, we performed exploratory measurements of inflammatory biomarkers.

Blood samples were centrifuged, and plasma was separated and stored at −80 °C. At the end of the study, samples were thawed and analysed for inflammatory mediators. A pre-specified inflammatory panel was selected to cover key pathways in sepsis and organ dysfunction: IL-1 axis [interleukin-1β (IL1B) and interleukin-1 receptor antagonist (IL1RN)], acute-phase/immune regulation [interleukin-6 (IL6), interleukin-10 (IL10), interleukin-7 (IL7)], neutrophil recruitment [CXCL8/IL-8], Th1/Th17 responses [interferon-γ (IFNG), interleukin-17A (IL17A)], TNF signaling [TNF-α (TNF) and soluble TNFRSF1A (sTNFR1)], and monocyte chemotaxis [CCL2/MCP-1 and CCL7/MCP-3]. Cytokines/chemokines were measured by multiplex bead-based immunoassay, and procalcitonin (PCT) and soluble TREM-1 (STREM-1) by ELISA.

Outcomes

The primary outcome was the diagnosis of moderate-to-severe acute kidney injury (AKI), defined as modified KDIGO stage 2–3 [24].

AKI was diagnosed using data from the first 24 h after presentation to the Emergency Department, rather than the 7-day assessment window proposed in the original KDIGO criteria (24). Baseline creatinine was defined as the most recent outpatient value available within the preceding three months. When no recent reference value was available, baseline creatinine was estimated by back-calculation using the modification of diet in renal disease (MDRD) equation, assuming an estimated glomerular filtration rate (eGFR) of 75 mL/min/1.73 m².

AKI severity was classified according to KDIGO as follows:

Stage 0 (no AKI): No change in serum creatinine or urine output above diagnostic thresholds. Stage 1: Increase in serum creatinine to 1.5–1.9 times baseline or by ≥0.3 mg/dL (≥26.5 µmol/L); or urine output <0.5 mL/kg/h for 6–12 h. Stage 2: Increase in serum creatinine to 2.0–2.9 times baseline; or urine output <0.5 mL/kg/h for ≥12 h. Stage 3: Increase in serum creatinine to ≥3.0 times baseline, or serum creatinine ≥4.0 mg/dL (≥353.6 µmol/L), or initiation of renal replacement therapy; or urine output <0.3 mL/kg/h for ≥24 h, or anuria for ≥12 h.

Sepsis was diagnosed according to Sepsis-3 criteria [25] and confirmed independently by two blinded investigators. In-hospital mortality was defined as death during hospitalisation.

Ethic statement

This study is reported following STROBE guidelines [26]. The institution’s Ethical Review Board approved the study (Comité de Ética de Investigación con medicamentos del Parc Taulí 2022/3004), which was performed following the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. Written informed consent was obtained from all patients or their legally authorised surrogates.

Statistical analysis

Quantitative variables were summarized using means, medians, standard deviations, and interquartile ranges. Categorical variables were described as frequencies and percentages. Comparisons between groups were performed using the chi-square or Fisher’s exact test for categorical variables, and the Student’s t-test or Mann-Whitney U test for continuous variables, as appropriate.

The diagnostic performance of [TIMP-2]•[IGFBP7] for identifying AKI within the first 24 h was assessed by receiver operating characteristic (ROC) curve analysis, calculating area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and likelihood ratios (LHR) for the predefined cut-off values of 0.3 and 2.0 (ng/mL)²/1000. A post hoc power analysis was performed to evaluate the biomarker's discriminative performance using the Hanley and McNeil method for ROC curves [27], estimating the statistical power associated with both the observed AUC and a predefined clinically relevant AUC.

Logistic regression analyses were performed to evaluate the independent association between [TIMP-2]•[IGFBP7] or categories (low, high, and highest risk) and the occurrence of AKI, adjusting for relevant clinical covariates.

Time-to-event data were analysed using Kaplan–Meier curves and compared using the log-rank or Gehan–Breslow–Wilcoxon tests.

Missing data was considered as missing not at random, and was not imputed. For those measures below or above the limit of quantification, we imputed the minimum or maximum value. Due to the non-normal distribution of cytokine data, the log10 + 1 transformation was applied.

Creatinine trajectories over time were analysed using general mixed-effects models. A mixed model was fitted on creatinine value, with group (AKI risk according to [TIMP-2]•[IGFBP7] values), time (numeric), and their interaction included as fixed effects, and patient-specific intercept and time slope included as random effects.

All analyses were performed using R 4.4 version [28], using rstatix package to perform pipe-friendly basic statistical tests (e.g., t-test/Wilcoxon, ANOVA/Kruskal–Wallis) [29], ggplot2 for data visualization [30], pROC for ROC curve/AUC analyses [31], nlme for linear mixed-effects models [32], and the survival and survminer packages for survival analyses and Kaplan–Meier/Cox visualizations [33,34].

Results

Between September 2022 and June 2024, 214 patients were screened and accepted as participants. Of these eligible patients, 196 met the inclusion/exclusion criteria, and 156 had available data for analysis. At 24 h, 33 patients (21%) had developed moderate or severe AKI (KDIGO stage 2–3) (Fig. 1). Of them, 14 (9%) had already presented AKI at the ED presentation. Creatinine trajectories are shown in eFig. S1.

Fig. 1.

Flowchart.

Flow chart of patient selection. AKI, acute kidney injury; KDIGO, Kidney Disease: Improving Global Outcomes.

Baseline characteristics and comorbidities are summarised in Table 1 and eTable S1. The outcomes for patients with AKI were worse, requiring more ICU admissions and significantly lower survival rates, with an in-hospital mortality of 13 (39%) in the group of moderate or severe AKI compared to 16 (13%) in patients with mild or non-AKI. Survival curves according to KDIGO stages are shown in eFig. S2.

Table 1.

Baseline characteristics according to Acute Kidney Injury diagnosis.

Variable	Total n = 156	No AKI or mild AKI n = 123	Moderate-Severe AKI n = 33	p value
Age (years), Median [Q1, Q3]	77 [65−85]	77 [65−85]	78 [69−85]	0.7995
Sex, male, n(%)	106 (67.9%)	82 (66.7%)	24 (72.7%)	0.6510
BMI (kg/m2), Median [Q1, Q3]	26.4 [23−29.9]	26.7 [22.9−30.3]	25.2 [23−29.4]	0.5914
CCI (points), Median [Q1, Q3]	5 [4–7]	5 [4–7]	5 [3–7]	0.7381
Coronary disease, n (%)	18 (11.5%)	12 (9.8%)	6 (18.2%)	0.2182
Chronic Heart Failure, n (%)	31 (19.9%)	24 (19.5%)	7 (21.2%)	1.0000
Cerebrovascular Disease, n (%)	32 (20.5%)	26 (21.1%)	6 (18.2%)	0.8960
Chronic Pulmonary Disease, n (%)	47 (30.1%)	40 (32.5%)	7 (21.2%)	0.2967
Dementia, n (%)	37 (23.7%)	29 (23.6%)	8 (24.2%)	1.0000
Liver Disease, n (%)	10 (6.4%)	9 (7.3%)	1 (3%)	0.6897
Diabetes, n (%)	39 (25%)	33 (26.8%)	6 (18.2%)	0.4282
Chronic Kidney Disease, n (%)	36 (23.1%)	29 (23.6%)	7 (21.2%)	0.9572
Prior neoplasia, n (%)	25 (16%)	19 (15.4%)	6 (18.2%)	0.9100
Previous Antibiotic Use, n (%)	10 (6.4%)	9 (7.3%)	1 (3%)	0.4974
Leukocytes (×109/L), Median [Q1, Q3]	12 [7.8−17.2]	12 [7.7−16.8]	13.2 [10−18.9]	0.4923
CRP (mg/dL), Median [Q1, Q3]	9.4 [3.7−24]	6.7 [2.6−16.9]	25.8 [11.9−30.2]	<0.0001
PCT (ng/mL), Median [Q1, Q3]	3 [0.7−8.2]	1.8 [0.6−8]	5.5 [2−8.7]	0.0036
pH, Median [Q1, Q3]	7.4 [7.3−7.4]	7.4 [7.3−7.4]	7.4 [7.3−7.4]	0.5787
PaO2 (mmHg), Median [Q1, Q3]	80 [77−92.5]	80 [76.5−91]	87.3 [81.2−92.4]	0.6009
PaCO₂ (mmHg), Median [Q1, Q3]	38.2 [33−45.3]	39.2 [34−46]	32 [29.2−33.8]	0.0033
Glasgow Coma Scale (points), Median [Q1, Q3]	15 [15–15]	15 [15–15]	15 [15–15]	0.3571
SpO2 (%), Median [Q1, Q3]	95 [94−97]	95 [93.4−97]	96 [95−97]	0.7126
FiO₂ (%), Median [Q1, Q3]	21 [21−27.5]	21 [21–28]	21 [21–26]	0.6732
Temperature (°C), Median [Q1, Q3]	37 [36–38]	37 [36–38]	36.9 [35.9−38]	0.5093
Systolic Blood Pressure (mmHg), Median [Q1, Q3]	99 [86−120]	100 [88.2−126]	91 [80−110]	0.0078
Diastolic Blood Pressure (mmHg), Median [Q1, Q3]	60 [50−69.5]	61 [50−70]	55 [49−66]	0.1895
Respiratory Rate (breaths/min), Mean ± SD	27.5 ± 8.1	29.3 ± 7.6	20 ± 5.8	0.0175
Heart Rate (beats/min), Mean ± SD	97.7 ± 24.3	98.2 ± 24.1	95.7 ± 25.5	0.6113
SOFA Score (points), Median [Q1, Q3]	3 [2–4]	3 [1–4]	5 [4–7]	<0.0001
APACHE Score (points), Median [Q1, Q3]	11 [9–15]	11 [8–14]	15 [13–18]	0.0117
NEWS2 Score (points), Median [Q1, Q3]	5 [4−7.2]	5 [3–7]	5 [4–8]	0.8227
qSOFA Score (points), Median [Q1, Q3]	1 [0−1]	1 [0−1]	1 [0.8−1.2]	0.2269
Creatinine (mg/dL), Median [Q1, Q3]	1.4 [1−2.1]	1.2 [0.9−1.6]	2.6 [2.1−4.4]	<0.0001
[TIMP-2]•[IGFBP7] ((ng/mL)2/1000), Median [Q1, Q3]	0.7 [0.2−2.4]	0.6 [0.2−1.5]	1.6 [0.5−6.3]	0.0030
Days since symptom onset (days), Median [Q1, Q3]	2 [0−4]	1 [0−3]	2 [1.5−4.5]	0.0224
Shock, n(%)	17 (10.9%)	7 (5.7%)	10 (30.3%)	0.0003
Nephrotoxic antibiotic use, n(%)	21 (13.5%)	20 (16.3%)	1 (3%)	0.0486
ICU admission, n(%)	24 (15.4%)	14 (11.4%)	10 (30.3%)	0.0162
Sepsis diagnosis, n(%)	123 (78.8%)	90 (73.2%)	33 (100%)	0.0019
In-hospital mortality, n(%)	27 (17.3%)	16 (13%)	11 (33.3%)	0.0131

Descriptive statistics by Acute Kidney Injury (AKI) diagnosis. The results shown in this table represent the results of the first vital sign- blood sample collected. Continuous variables are presented as Median [Q1, Q3] when non-normally distributed and as Mean ± SD when normally distributed. P-values for continuous variables were computed using the Wilcoxon rank-sum test for non-normal distributions, and Student’s t-test for normal distributions. Categorical variables are presented as n (%) and compared using the chi-square test or Fisher’s exact test when appropriate. Nephrotoxic antibiotics were considered: vancomycin, aminoglycosides, piperacillin–tazobactam, and colistin. BMI body mass index; CCI Charlson Comorbidity Index; CRP C-reactive protein; FiO₂ fraction of inspired oxygen; MV mechanical ventilation; PaCO₂/PCO₂ arterial partial pressure of carbon dioxide; PaO₂ partial pressure of arterial oxygen; PCT procalcitonin; qSOFA Quick Sequential Organ Failure Assessment; SOFA Sequential Organ Failure Assessment; NEWS National Early Warning Score; ICU Intensive Care Unit.

Urinary [TIMP-2]•[IGFBP7] levels measured on presentation to ED showed clear differences between patients who developed AKI and those who did not. Individuals with AKI moderate-severe (KDIGO stage 2–3) within 24 h exhibited substantially higher concentrations of the biomarker (Table 1 and eTable S1), a finding that was consistent with associations across both univariate and multivariate analyses. In univariate analysis, [TIMP-2]•[IGFBP7] was associated with AKI (OR 1.22; 95%CI 1.08–1.39; p = 0.01). This association remained significant after adjustment for age, NEWS2 score, and septic shock (adjusted OR 1.24; 95%CI 1.07–1.43; p < 0.01). When applying predefined cut-offs, only high-risk levels (>2.0 (ng/mL)²/1000) showed a significant association with AKI (adjusted OR 3.41; 95%CI 1.10–10.57; p = 0.03), whereas intermediate-risk values did not (adjusted OR 1.27; 95%CI 0.43–3.74; p = 0.65). [TIMP-2]•[IGFBP7] levels were higher in those patients who developed sepsis (non-sepsis median values 0.5 (ng/mL)²/1000 [1st-3rd quartile range 0.1–1.1] vs. septic median values 0.8 (ng/mL)²/1000 [1st-3rd quartile range 0.2–2.9]).

The evaluation of diagnostic performance using the biomarker as a continuous variable revealed only modest accuracy to predict moderate-severe AKI (KDIGO stage 2–3) at 24 h, with an AUC of 0.67 (95%CI: 0.56−0.77) (Fig. 2). For prediction of mild AKI (KDIGO stage 1), the test obtained a low AUC 0.55 (95%CI: 0.44−0.67). When using established cut-offs, distinct performance profiles emerged: low-risk values (<0.3) offered strong sensitivity and negative predictive power, may rule out early AKI, whereas highest-risk values (>2.0) provided good specificity and a higher negative predictive value as well (Table 2). Pre-test and post-test probability estimates based on likelihood ratios are displayed in eFig. S3. Sensitivity analysis excluding patients with AKI diagnosis at baseline is shown in eFig. S4. Also, an analysis of discriminatory ability, only including septic patients, is shown in eFigure S5. Using the Hanley and McNeil approach, the observed AUC of 0.67 yielded an estimated statistical power of 85.7%, while a hypothetical AUC of 0.75 would correspond to a power of 99.7%, indicating that the sample size was adequate to detect clinically meaningful discriminatory performance.

Fig. 2.

Received Operative Characteristic curve evaluating predictive performance of [TIMP-2]•[ IGFBP7] for prediction of AKI moderate or severe (KDIGO 2 or 3).

Receiver operating characteristic (ROC) curve, plotting sensitivity against specificity; area under the curve (AUC) = 0.67 (95%CI: 0.5633−0.7743).

Table 2.

Predictive accuracy of [TIMP-2]•[ IGFBP7] for Acute Kidney Injury diagnosis.

Cut-off	Sensitivity	Specificity	Positive Predictive Value	Negative Predictive Value	Likelihood Ratio Positive	Likelihood Ratio Negative
0.3 (ng/mL)2/1000	0.81 [0.65, 0.91]	0.31 [0.24, 0.40]	0.24 [0.17, 0.33]	0.86 [0.73, 0.93]	1.19 [0.98, 1.46]	0.57 [0.26, 1.23]
2 (ng/mL)2/1000	0.48 [0.32, 0.64]	0.76 [0.68, 0.83]	0.35 [0.232, 0.50]	0.84 [0.76, 0.90]	2.05 [1.28, 3.30]	0.674 [0.47, 0.95]

Sensitivity, specificity, positive predictive value, negative predictive value and likelihood ratios were calculated for the diagnosis of AKI defined according to KDIGO stage 2 or 3, using the urinary values of [TIMP-2]•[ IGFBP7] expressed as (ng/mL)2/1000.

Mixed effect models showed a non-significant association among time and [TIMP-2]•[IGFBP7] categories with creatinine (Time β estimate – 0.08 CI95% -0.02 to 0.004; high risk β estimate 0.14 CI95% -0.55 to 0.84; highest risk β estimate 0.39 CI95% -0.37 to 1.15)(eTable S2). The model-based trajectories showed that creatinine evolution over the first 24 h followed similar trends, although patients in the high-risk group tended to present with slightly higher baseline creatinine and a somewhat faster early decline. Beyond these initial differences, the biomarker categories did not predict distinct creatinine trajectories (Fig. 3).

Fig. 3.

Trajectories of creatinine concentration during the first 24 h by groups assigned by AKI risk measured by categorised results of [TIMP-2]•[ IGFBP7].

Lines represent the fitted linear mixed model. Each point represents the concentration of creatinine for each subject at the referenced time point.

Finally, analysis of inflammatory markers revealed no major differences between risk groups, with the exception of IL-10 and IL-1RN, which were higher in patients classified as highest risk according to [TIMP-2]•[IGFBP7] (Fig. 4, eTable S3).

Fig. 4.

Biomarker concentrations by acute kidney injury (AKI) risk as measured by categorised results of [TIMP-2]•[IGFBP7].

Boxplots and scatter plots display log-transformed concentrations of biomarkers in patient groups classified by AKI risk: low (red), high (green), and highest (blue). Concentrations are plotted on a logarithmic scale. Abbreviations: IFNG, Interferon gamma; IL10, Interleukin 10; IL17A, Interleukin 17A; IL1B, Interleukin 1 beta; IL1RN, Interleukin 1 receptor antagonist; IL6, Interleukin 6; IL7, Interleukin 7; IL8, Interleukin 8; CCL2, Chemokine (C-C motif) ligand 2 (MCP-1); CCL7, Chemokine (C-C motif) ligand 7 (MCP-3); TNFA, Tumor necrosis factor alpha; TNFRSF1A, Tumor necrosis factor receptor superfamily member 1A.

Discussion

In this study, we examined the performance of urinary [TIMP-2]•[IGFBP7] on urine samples obtained from patients presenting to the ED at risk of developing sepsis. We found that the biomarker results were correlated with the development of AKI, even after adjusting for confounding variables. However, the AUC was only moderate for predicting stage 2–3 AKI at 24 h. Predefined cut-offs showed good sensitivity at low risk and good specificity at high risk, but intermediate results were less useful. Regarding predictive capacity, a linear mixed model was constructed to predict creatinine trajectories. Predefined cut-offs allowed detection of higher values of creatinine at baseline, and a rapid decline during the first 24 h for those with the highest risk, but these predictions were probably not clinically useful. Inflammatory profile showed higher values of IL-10 and IL-1RN in those with the highest risk.

The sepsis-associated AKI is multifactorial; beyond underlying conditions that are non-modifiable, several factors contribute to its development, including macro- and microcirculatory dysfunction, inflammatory insults, and metabolic reprogramming [15,35,36]. Cell cycle arrest is a process that may provide protection during cellular stress, whereby tubular cells decrease their energy consumption to protect themselves. This phenomenon may result in the activation of the tubulo-glomerular feedback mechanism, which contributes to a decrease in GFR and attenuates ultrafiltration [10,15].

TIMP-2 and IGFBP7 are urinary cell-cycle arrest markers that have been validated for early detection of risk for development of moderate–severe AKI. Studies have evaluated their performance in different clinical contexts, including sepsis, mixed medical-surgical ICU cohorts, ED presentations at risk for AKI, and perioperative populations. A meta-analysis reported an AUROC of 0.81 (95% CI 0.78–0.84) for predicting AKI [37]. This analysis included a wide range of heterogeneous studies. The predictive accuracy may depend on the setting, the timing of the measurement and the clinical endpoint used. Studies of septic patients and mixed ICU cohorts generally show that the marker predicts AKI with an AUROC of 0.80 (10). Furthermore, Kellum et al. [13] found that the use of urinary TIMP-2 and IGFBP7 levels could identify AKI in ICU patients with infection nearly a day before clinical diagnosis. So, the use of TIMP-2 and IGFBP7 may predict the development of sepsis in patients with infection by identifying a key organ injury earlier. However, patients in this study were already in the ICU. Our study attempted to test the performance of the markers on the first available urine sample in the ED. This time point may have been too early, however. Fiorentino et al. [38] showed that risk stratification was significantly better 6 h after initial resuscitation in patients with septic shock [39]. Serial measures may also provide more information, and this allows identification of risk after exposure to renal insults [40].

Our findings align with prior ED evidence showing only modest discrimination of [TIMP-2]·[IGFBP7] for subsequent AKI, even when the biomarker adds information beyond routine variables [18,19]. These findings may suggest that, in the ED, a single early (often pre-resuscitation) urine sample may capture heterogeneous and rapidly evolving physiology (e.g., hemodynamic instability, volume status, and acute illness trajectories), limiting overall predictive performance. Nevertheless, extreme values may still support clinical triage by helping to rule out or rule in imminent AKI (19). In this context, it is also noteworthy that a biomarker-guided ED randomized trial (enrolling patients with [TIMP-2]·[IGFBP7] >0.3) did not reduce moderate–severe AKI with an early nephrologist-led KDIGO bundle(20). In our study, [TIMP-2]·[IGFBP7] did not provide additional predictive value for trajectories and/or their relationship with inflammatory status, further supporting the concept that a single early ED measurement may be insufficient for nuanced risk stratification in this setting.

Our study has limitations and strengths that should be acknowledged. The main strength is the homogeneous population of patients at risk of sepsis and AKI in a real-world clinical setting. The limitations are, firstly, that it is a single-centre study, and the results could be influenced by the population studied; the results should be validated before they can be generalised. Secondly, the follow-up period for AKI development was short. We determined the presence of AKI within 24 h from presentation to the ED, whereas the urinary biomarker test was developed for a 12 -h window. Thirdly, the patients included in this study may have had less severe disease or rehydration status than those in other studies, such as those in the ICU setting or those who received better fluid resuscitation. Also, we have not collected of which specimens were collected by indwelling catheter or midstream spontaneous collection, so some patients may have experienced transient oliguria. Therefore, despite representing a real-life scenario in the ED, differences could be explained by these factors. Fourthly, no a priori sample size or power calculation was performed before study initiation. As a result, the sample size was not determined on the basis of pre-specified statistical assumptions, which should be considered when interpreting the study findings. Lastly, we only performed one determination of TIMP-2 and IGFBP7. Since the time zero in sepsis is difficult to determine, we may lose the peak value. Serial measurements may help to determine the response to treatment and the trend values.

In conclusion, in a real-world ED cohort where baseline AKI status may be uncertain, [TIMP-2]·[IGFBP7] at first assessment provided modest discrimination for moderate-to-severe AKI. The biomarker alone is unlikely to be sufficient for definitive risk prediction, but may add incremental information to clinical evaluation and support early management decisions in selected patients.

Authors' contribution

AC & AA Study supervision. All authors reviewed and approved the manuscript for submission. BC, VM, AN, TT, MCR, ED, LLB, EG, MO, JK, PH, AA and AC participated in protocol development, study design, study management, statistical analysis, and data interpretation. BC and AC participated in statistical analysis. BC, AT, IM, MV, MC, GGe, MSO, XB, ML, TT, MS CS PS, CG, JT and GG participated in data collection. VM, MCR, AAB, and ECD participated in sample analyses. All authors read and approved the final manuscript.

Consent for publication

NA Availability of supporting data: The datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request.

Ethical approval and consent to participate

The institution’s Ethical Review Board approved the study (Comité de Ética de Investigación con medicamentos del Parc Taulí 2022/3004), which was performed following the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. Written informed consent was obtained from all patients or their legally authorised surrogates.

Funding

This study was supported by the Ministry of Science: CPP2021-008394. CIBERES, and Research and Innovation Institute Parc Tauli (I3PT-CERCA). AC acknowledges receiving financial support from Instituto de Salud Carlos III CD21/00087 and PI23/00106.

Declaration of competing interest

The authors have no conflict of interest.

Appendix A. Supplementary data

The following are Supplementary data to this article: