Elicit: Real-Time Kidney Damage Markers (public)

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

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

Screening

We screened in sources based on their abstracts that met these criteria:

Real-time Biomarker Detection: Does this study evaluate biomarkers that can detect kidney cell damage in real-time or near real-time (within 24 hours maximum)?
Human Participants: Does this study involve human participants with actual or potential kidney cell damage?
Diagnostic Performance Assessment: Does this study assess diagnostic performance metrics (such as sensitivity, specificity, positive/negative predictive values, AUC, or diagnostic accuracy)?
Appropriate Study Design: Is this study a prospective cohort study, cross-sectional diagnostic accuracy study, randomized controlled trial, systematic review, or meta-analysis?
Comparative Assessment: Does this study compare novel biomarkers to established reference standards or other biomarkers?
Clinical Study Setting: Is this study conducted in a clinical setting (not exclusively animal studies, in vitro studies, or cell culture studies)?
Primary Research Quality: Is this study a primary research study or high-quality secondary research (not a case report, case series, editorial, or opinion piece)?
Clinical Utility Assessment: Does this study assess the diagnostic performance or clinical utility of biomarkers (not just measure biomarker levels without performance evaluation)?

We considered all screening questions together and made a holistic judgement about whether to screen in each paper.

Data extraction

Summary of Study Characteristics:

Most commonly evaluated biomarkers:
- Neutrophil gelatinase-associated lipocalin (NGAL):29 studies
- Kidney injury molecule-1 (KIM-1):21 studies
- [TIMP-2]·[IGFBP7]:12 studies
- Interleukin-18 (IL-18):11 studies
- Liver-type fatty acid-binding protein (L-FABP):8 studies
- Cystatin C (CysC):14 studies
AKI definitions used:
- KDIGO:15 studies
- AKIN:8 studies
Primary outcomes:
- Early AKI detection/diagnosis:19 studies
- Prediction (risk stratification):13 studies

Results

Individual Biomarker 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%
IL-18 (urine)	0.72–0.94	75–85%	63–73%
Urine microscopy/sediment	0.79–0.865	6–30%	91–98.6%

Combined Biomarker Performance

Several studies reported that combinations of biomarkers outperformed individual markers. Key findings from these studies include:

Combinations typically included [TIMP-2]·[IGFBP7] and NGAL, sometimes with cystatin C or clinical variables.

Timing of Detection and Clinical Context

Biomarker/Combination	Optimal Detection Window	Clinical Context	Performance Metrics
NGAL (urine)	2–6 hours post-injury	Pediatric cardiopulmonary bypass	AUC >0.9 (early), Sensitivity 84–100%
[TIMP-2]·[IGFBP7]	6–12 hours post-injury	ICU, cardiac surgery, ED	AUC 0.70–0.94, Sensitivity 89–96%
KIM-1 (urine)	12–24 hours post-injury	Drug-induced, contrast nephropathy	AUC 0.66–0.84, Sensitivity 75.9–91.6%

Factors Affecting Biomarker Performance

Patient population: Performance varied by clinical context, including ICU, surgery, sepsis, drug-induced injury.

References

Ravi J. Desai, et al. (2022). Kidney Damage and Stress Biomarkers for Early Identification of Drug-Induced Kidney Injury: A Systematic Review.
W. Han, et al. (2008). Urinary biomarkers in the early diagnosis of acute kidney injury. Kidney International.
Sarah Jarvis (2011). The ROMA (Risk of Ovarian Malignancy Algorithm).
J. Koyner, et al. (2010). Urinary biomarkers in the clinical prognosis and early detection of acute kidney injury. Clinical Journal.
Heng-Chih Pan, et al. (2022). Comparative accuracy of biomarkers for the prediction of hospital-acquired acute kidney injury.
T. Nickolas, et al. (2012). Diagnostic and prognostic stratification in the emergency department using urinary biomarkers of nephron damage.
Liqun Dong, et al. (2017). Urinary biomarkers of cell cycle arrest are delayed predictors of acute kidney injury after pediatric cardiopulmonary bypass.