Elicit: Real-Time Kidney Damage Markers (public)

What are the most effective real-time markers of kidney cell damage?

NGAL, [TIMP‑2]·[IGFBP7], and KIM-1 are the most effective real-time markers of kidney cell damage, showing high diagnostic accuracy when measured at appropriate time windows and achieving superior performance when combined in multi-marker panels.

Abstract

Real‐time markers for kidney cell damage most consistently report high diagnostic accuracy when measured in the appropriate clinical context and time window. Urinary neutrophil gelatinase‐associated lipocalin (NGAL) rises within 2–6 hours after injury, with studies reporting area under the curve (AUC) values up to 0.91, sensitivities of 84–100%, and specificities as high as 99.5%. Urinary [TIMP‑2]·[IGFBP7] is typically measured at 6–12 hours post‐injury and shows AUC values ranging from 0.70 to 0.94, with reported sensitivities of 42–96% and specificities up to 95%. Urinary kidney injury molecule‑1 (KIM‑1) measured at 12–24 hours post‐insult yields moderate performance (AUC 0.66–0.84) with sensitivities between 75.9% and 91.6% and specificities up to 95.2%.

When combined in multi‐marker panels, these biomarkers often achieve superior performance. For example, panels including NGAL, [TIMP‑2]·[IGFBP7], and cystatin C have produced AUC values as high as 0.98, while combinations with additional markers have reached similar levels of discrimination. These findings, drawn from a diverse set of populations and clinical settings—ranging from cardiac surgery and intensive care to emergency presentations—support the use of NGAL, [TIMP‑2]·[IGFBP7], and KIM‑1 as effective real‐time indicators of kidney cell damage.

Methods

We analyzed 40 sources from an initial pool of 999, using 8 screening criteria. Each paper was reviewed for 5 key aspects that mattered most to the research question.

Papers identified with Elicit search

n = 999

Papers screened using: Real-time Biomarker Detection, Human Participants, Diagnostic Performance Assessment, Appropriate Study Design, Comparative Assessment, Clinical Study Setting, Primary Research Quality, Clinical Utility Assessment

n = 999

Papers screened out

n = 959

Papers included for extraction

n = 40

Data extraction

We asked a large language model to extract each data column below from each paper.

Study design:

Specify the exact type of study design used. Look in the methods section for precise description. Possible types include:

If multiple design elements are present, list all. If design is not clearly stated, write “design not clearly specified”.

Participant characteristics:

Extract the following details:

Biomarkers investigated:

List ALL biomarkers examined in the study for detecting kidney cell damage. Include:

Biomarker performance metrics:

Extract quantitative performance data for biomarkers, specifically:

Primary outcome definition:

Describe how acute kidney injury (AKI) was defined and diagnosed in the study. Look for:

Results

Characteristics of Included Studies

Study Study Population Biomarkers Evaluated AKI Definition Primary Outcome
Bihorac et al., 2014 Critically ill patients (n=420) Urinary [TIMP-2]·[IGFBP7] No mention found Prediction of moderate to severe acute kidney injury (AKI) within 12h
Piedrafita et al., 2022 Cardiac surgery (n=1170), ICU (n=1569) Urinary peptide signature, NGAL, calprotectin, [TIMP-2]/[IGFBP7] Kidney Disease: Improving Global Outcomes (KDIGO) 2012 Early AKI prediction (7-day KDIGO)
Sun et al., 2024 Critically ill patients (n=417+164) 12 urinary biomarkers, U-AKIpredTM (alpha-1-microglobulin, L-FABP, IGFBP7) KDIGO AKI within 12h of panel measurement
Desai et al., 2022 Hospitalized patients (systematic review) B2M, IL-18, KIM-1, L-FABP, NGAL, [TIMP-2]*[IGFBP7] No mention found Early detection of drug-induced AKI
Han et al., 2008 Acute/chronic kidney disease, pediatric CPB MMP-9, NAG, KIM-1 >50% serum creatinine (SCr) increase in 48h Early AKI diagnosis
Jarvis, 2011 ICU patients (n=529) GGT, AP, NGAL, CysC, KIM-1, IL-18 No mention found Diagnosis/prediction of AKI, dialysis, death
Koyner et al., 2010 Cardiac surgery (n=123) NGAL, CysC, KIM-1, HGF, π-GST, α-GST No mention found Early detection and prognosis of AKI
Pan et al., 2022 Adults >18, mixed settings (n=38,725) NGAL (urine/serum), KIM-1, L-FABP, IL-18, [TIMP-2]·[IGFBP7] KDIGO, AKIN, RIFLE Predictive performance of biomarkers for AKI
Nickolas et al., 2012 Emergency department (ED) patients (n=1,635) NGAL, KIM-1, L-FABP, IL-18, CysC No mention found Diagnostic/prognostic value of biomarkers
Dong et al., 2017 Pediatric cardiac surgery (n=150) NGAL, IL-18, L-FABP, KIM-1, [TIMP-2], IGFBP7 KDIGO (≥50% SCr increase) Sequential biomarker elevation for AKI

Summary of Study Characteristics:

Summary of Diagnostic Performance:

Biomarker Area Under the Curve (AUC) Range Sensitivity Range Specificity Range
[TIMP-2]·[IGFBP7] 0.70–0.94 42–96% 50–95%
NGAL (urine) 0.62–0.91 55–100% 64–99.5%
KIM-1 (urine) 0.66–0.84 75.9–91.6% 61.8–95.2%
Cystatin C (urine/plasma) 0.68–0.88 71–79.3% 74.2–92%
Albumin (urine) 0.44–0.94 No mention found No mention found
IL-18 (urine) 0.72–0.94 75–85% 63–73%
Midkine (urine) 0.88–0.96 87–97% 85–90%
Urine microscopy/sediment 0.79–0.865 6–30% 91–98.6%

Factors Affecting Biomarker Performance

References

This report contains the insightful analysis of real-time kidney damage biomarkers and their clinical significance over time.