Predictive value of urinary cell cycle arrest biomarkers for all cause-acute kidney injury: a meta-analysis

The efficacy of novel biomarkers for the early detection and management of acute kidney injury: A systematic review

Acute kidney injury (AKI) is a frequent clinical complication lacking early diagnostic tests and effective treatments. Novel biomarkers have shown promise for enabling earlier detection, risk stratification, and guiding management of AKI. We conducted a systematic review to synthesize evidence on the efficacy of novel biomarkers for AKI detection and management. Database searches yielded 17 relevant studies which were critically appraised. Key themes were biomarker efficacy in predicting AKI risk and severity before functional changes; potential to improve clinical management through earlier diagnosis, prognostic enrichment, and guiding interventions; emerging roles as therapeutic targets and prognostic tools; and ongoing challenges requiring further validation. Overall, novel biomarkers like neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), and cell cycle arrest markers ([TIMP-2] •[IGFBP7]) demonstrate capability for very early AKI prediction and accurate risk stratification. Their incorporation has potential to facilitate timely targeted interventions and personalized management. However, factors influencing biomarker performance, optimal cutoffs, cost-effectiveness, and impact on patient outcomes require robust validation across diverse settings before widespread implementation. Addressing these limitations through ongoing research can help translate novel biomarkers into improved detection, prognosis, and management of AKI in clinical practice.

Biomarkers in cardiorenal syndrome, a potential use in precision medicine

Cardiorenal syndrome refers to the interrelated dysfunction of the heart or kidney resulting in a cascade of feedback mechanisms, hemodynamic, neurohormonal, and immunological and/or biochemical feedback pathways causing damage in the other organ. Cardiorenal syndrome is categorized into five clinical subtypes depending on the perceived primary precipitant of organ injury and is associated with high morbidity and mortality. Therefore, the development of tools for the earliest identification of cardiorenal syndrome in hospitalized patients is of extremely high significance to ameliorate the prognosis and outcome of these patients. There is increasing interest in identifying molecules serving as biomarkers, reflecting hemodynamic changes, heart and kidney damage and/or dysfunction and oxidative stress-induced cell damage or changes in the extracellular matrix of both the heart and kidneys. Biomarkers provide important insights into the pathophysiology of cardiorenal syndrome and are invaluable tools to predict the decrease in renal function during cardiac dysfunction and vice versa. Based on the pathophysiological mechanisms of cardiorenal syndrome, we reviewed and evaluated the available literature on serum and urinary biomarkers as predictors of kidney and/or heart injury. In addition, heart- and kidney-specific biomarkers were also evaluated based on their reference to kidney and cardiac (dys)function respectively, and whether they would provide any prediction and prognostication of cardiorenal syndrome. In this article, we discuss the current knowledge on the pathophysiology of different types of cardiorenal syndrome, examine the clinical utility of candidate biomarkers in the early diagnosis of cardiorenal syndrome, and guide treatment by evaluating the respective roles of the involved pathophysiological pathways.

Early Diagnosis of Pediatric Acute Kidney Injury: An Achievable Goal?

How to cite this article: Solomon R. Early Diagnosis of Pediatric Acute Kidney Injury: An Achievable Goal? Indian J Crit Care Med 2024;28(11):1002-1004.

Biomarkers in acute kidney injury

Acute kidney injury (AKI) is a multifactorial syndrome with a high risk of short- and long-term complications as well as increased health care costs. The traditional biomarkers of AKI, serum creatinine and urine output, have important limitations. The discovery of new functional and damage/stress biomarkers has enabled a more precise delineation of the aetiology, pathophysiology, site, mechanisms, and severity of injury. This has allowed earlier diagnosis, better prognostication, and the identification of AKI sub-phenotypes. In this review, we summarize the roles and challenges of these new biomarkers in clinical practice and research.

Prognostic value of systemic inflammatory response index for acute kidney injury and the prognosis of pediatric patients in critical care units

BACKGROUND

We proposed a link between the first systemic inflammatory response index (SIRI) and acute kidney injury (AKI), as well as the prognosis of pediatric patients in intensive care units (PICU).

METHODS

This study comprised 5114 children from the pediatric-specific intensive care (PIC) database. SIRI was estimated as a neutrophil monocyte lymphocyte ratio. All patients were arbitrarily allocated to the training set (n = 3593) and the validation cohort (n = 1521) and divided into two groups depending on their SIRI levels. The diagnostic value of SIRI for pediatric ICU patients was subsequently determined using LASSO regression models.

RESULTS

After controlling for additional confounding variables in the training set, the higher SIRI value (≥ 0.59) had a greater risk of AKI (adjusted odds ratio, OR, 3.95, 95% confidence interval, 95%CI, 2.91-5.36, P<0.001) and in-hospital mortality (hazard ratio, HR, 5.01, 95%CI 2.09-12.03, P<0.001). Similar findings were discovered in the validation set. Furthermore, the suggested nomogram derived from SIRI and other clinical metrics showed outstanding calibration capability as well as therapeutic usefulness in both groups.

CONCLUSIONS

SIRI is a reliable and useful factor for AKI and fatality in pediatric ICU patients, and the proposed nomogram based on SIRI yields an appropriate prediction value for critically sick pediatric patients.

Tissue Inhibitor of Metalloproteinases-2 (TIMP-2) as a Prognostic Biomarker in Acute Kidney Injury: A Narrative Review

Acute kidney damage (AKI) is a serious and common consequence among critically unwell individuals. Traditional biomarkers, such as serum creatinine, frequently fail to detect AKI in its early stages, necessitating the development of new accurate early biomarkers. Tissue inhibitor of metalloproteinases 2 (TIMP-2) has emerged as a promising biomarker for predicting early AKI. The present narrative review investigates the role of TIMP-2 in AKI prediction in a variety of clinical scenarios. In the NephroCheck® test, TIMP-2 exceeds established biomarkers for the early identification of AKI in terms of sensitivity and specificity when combined with insulin-like growth factor-binding protein 7 (IGFBP-7). Elevated levels of these biomarkers can provide a warning signal for AKI two to three days before clinical symptoms appear. TIMP-2 and IGFBP-7 have high predictive values, with an area under the curve (AUC) typically above 0.8, indicating good predictive capacity. For example, the [TIMP-2] × [IGFBP-7] product produced an AUC of 0.85 in surgical patients at high risk. In critically ill patients, a threshold of 0.3 (ng/mL)2/1000 demonstrated 92% sensitivity and 72% specificity. Elevated TIMP-2 levels have been correlated with higher mortality rates and the need for renal replacement therapy (RRT). In sepsis-associated AKI (SA-AKI), TIMP-2 levels combined with clinical prognostic models improved predictive accuracy (AUC: 0.822). Furthermore, elevated urine TIMP-2 levels were good predictors of AKI in pediatric patients after cardiac surgery, with AUC-ROC values of up to 0.848. Urine output and the presence of concomitant disorders may influence the prognostic accuracy of these biomarkers; therefore, more research is needed to fully understand their utility. The predictive value of TIMP-2 could be strengthened by combining it with other clinical parameters, reinforcing its role in the early detection and treatment of AKI.

Machine learning for prediction of acute kidney injury in patients diagnosed with sepsis in critical care

BACKGROUND AND OBJECTIVE

Acute Kidney Injury (AKI) is a common and severe complication in patients diagnosed with sepsis. It is associated with higher mortality rates, prolonged hospital stays, increased utilization of medical resources, and financial burden on patients' families. This study aimed to establish and validate predictive models using machine learning algorithms to accurately predict the occurrence of AKI in patients diagnosed with sepsis.

METHODS

This retrospective study utilized real observational data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. It included patients aged 18 to 90 years diagnosed with sepsis who were admitted to the ICU for the first time and had hospital stays exceeding 48 hours. Predictive models, employing various machine learning algorithms including Light Gradient Boosting Machine (LightGBM), EXtreme Gradient Boosting (XGBoost), Random Forest (RF), Decision Tree (DT), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Logistic Regression (LR), were developed. The dataset was randomly divided into training and test sets at a ratio of 4:1.

RESULTS

A total of 10,575 sepsis patients were included in the analysis, of whom 8,575 (81.1%) developed AKI during hospitalization. A selection of 47 variables was utilized for model construction. The models derived from LightGBM, XGBoost, RF, DT, ANN, SVM, and LR achieved AUCs of 0.801, 0.773, 0.772, 0.737, 0.720, 0.765, and 0.776, respectively. Among these models, LightGBM demonstrated the most superior predictive performance.

CONCLUSIONS

These machine learning models offer valuable predictive capabilities for identifying AKI in patients diagnosed with sepsis. The LightGBM model, with its superior predictive capability, could aid clinicians in early identification of high-risk patients.