Nephrocheck®: Checkmate or reality check?

Biomarkers vs Machines: The Race to Predict Acute Kidney Injury

BACKGROUND

Acute kidney injury (AKI) is a serious complication affecting up to 15% of hospitalized patients. Early diagnosis is critical to prevent irreversible kidney damage that could otherwise lead to significant morbidity and mortality. However, AKI is a clinically silent syndrome, and current detection primarily relies on measuring a rise in serum creatinine, an imperfect marker that can be slow to react to developing AKI. Over the past decade, new innovations have emerged in the form of biomarkers and artificial intelligence tools to aid in the early diagnosis and prediction of imminent AKI.

CONTENT

This review summarizes and critically evaluates the latest developments in AKI detection and prediction by emerging biomarkers and artificial intelligence. Main guidelines and studies discussed herein include those evaluating clinical utilitiy of alternate filtration markers such as cystatin C and structural injury markers such as neutrophil gelatinase-associated lipocalin and tissue inhibitor of metalloprotease 2 with insulin-like growth factor binding protein 7 and machine learning algorithms for the detection and prediction of AKI in adult and pediatric populations. Recommendations for clinical practices considering the adoption of these new tools are also provided.

SUMMARY

The race to detect AKI is heating up. Regulatory approval of select biomarkers for clinical use and the emergence of machine learning algorithms that can predict imminent AKI with high accuracy are all promising developments. But the race is far from being won. Future research focusing on clinical outcome studies that demonstrate the utility and validity of implementing these new tools into clinical practice is needed.

Case presentations of medication management for patients at risk for drug-associated acute kidney injury identified with a CDS system and a novel biomarker

PURPOSE

Traditional methods used to evaluate changes in kidney function to identify acute kidney injury (AKI) have significant limitations. Damage biomarkers can identify patients at risk for AKI prior to changes in kidney function. While clinical trials have shown that biomarker-guided treatment can improve outcomes, whether these biomarkers can influence providers' choice of treatment strategy for risk prediction, surveillance, or diagnostic evaluation in clinical practice is uncertain.

SUMMARY

This case series describes 4 patients at an academic medical center whose care was informed by kidney biomarker utilization in conjunction with a clinical decision support system (CDSS). Though each patient's clinical presentation was unique, kidney biomarkers were successfully employed as clinical tools in evaluating the risks and benefits of nephrotoxic medications.

CONCLUSION

This case series demonstrates 4 scenarios in which a kidney injury biomarker used in conjunction with CDSS and consideration of the patients' clinical presentation informed treatment strategies with the intent to prevent AKI.

Urinary TIMP-2 and IGFBP-7 protein levels as early predictors of acute kidney injury after cardiac surgery

BACKGROUND

Acute kidney injury (AKI) is a frequent complication associated with on-pump cardiac surgery. Early recognition may alter their prognosis. Therefore, the urinary concentrations of TIMP-2 (tissue inhibitor of metalloproteinases-2) and IGFBP7 (insulin-like growth factor-binding protein) as predictors for AKI were studied.

METHODS

Repetitive blood and urine samples were collected consecutively from 50 patients. Demographic, intra-, and postoperative data were recorded prospectively. To calculate the production of the TIMP-2 and IGFBP-7 protein concentrations, urinary samples were taken preoperatively, intraoperatively at 30 and 60 min after aortic clamping and at 0, 6, 12, and 24 h after admission to the intensive care unit (ICU).

RESULTS

AKI occurred in 14 patients (28%), all of them at Kidney Disease: Improving Global Outcomes stage 1. Predictive value for [TIMP-2] × [IGFBP7] was shown at 0 and 24 h after admission to ICU. At 0 h, the sensitivity was 84.6% and the specificity 55.6% for an ideal calculated cutoff at 0.07. After 24 h, the ideal cutoff amounted to 0.35 with a sensitivity of 53.8% and a specificity of 88.2%. The receiver operating characteristic curves demonstrated areas under the curve of 0.725 and 0.718. The suggested cutoffs of 0.3 and 2.0 could not be confirmed. The serum creatinine was reached to the peak median within 48 h after admission to ICU.

CONCLUSION

Postoperative risk assessment for the development of AKI can be established by [ TIMP - 2 ] × [ IGFBP 7 ] . Previously suggested cutoff values could not be confirmed. A correlation with urinary dilution parameters may enable the identification of more universal cutoffs.