Relationships and Clinical Utility of Creatinine, Cystatin C, eGFRs, GFRs, and Clearances

Developing a Label-Free Infrared Spectroscopic Analysis with Chemometrics and Computational Enhancement for Assessing Lupus Nephritis Activity

Patterns of disease and therapeutic responses vary widely among patients with autoimmune glomerulonephritis. This study introduces groundbreaking personalized infrared (IR)-based diagnostics for real-time monitoring of disease status and treatment responses in lupus nephritis (LN). We have established a relative absorption difference (RAD) equation to assess characteristic spectral indices based on the temporal peak heights (PHs) of two characteristic serum absorption bands: ν1 as the target signal and ν2 as the PH reference for the ν1 absorption band, measured at each dehydration time (t) during dehydration. The RAD gap (Ψ), defined as the difference in the RAD values between the initial and final stages of serum dehydration, enables the measurement of serum levels of IgG glycosylation (ν1 (1030 cm-1), ν2 (1171 cm-1)), serum lactate (ν1 (1021 cm-1), ν2 (1171 cm-1)), serum hydrophobicity (ν1 (2930 cm-1), ν2 (2960 cm-1)), serum hydrophilicity (ν1 (1550 cm-1), ν2 (1650 cm-1)), and albumin (ν1 (1400 cm-1), ν2 (1450 cm-1)). Furthermore, this IR-based assay incorporates an innovative algorithm and our proprietary iPath software (ver. 1.0), which calculates the prognosis prediction function (PPF, Φ) from the RAD gaps of five spectral markers and correlates these with conventional clinical renal biomarkers. We propose that this algorithm-assisted, IR-based approach can augment the patient-centric care of LN patients, particularly by focusing on changes in serum IgG glycosylation.

Deciphering AKI in Burn Patients: Correlations between Clinical Clusters and Biomarkers

Acute kidney injury (AKI) is a significant complication in burn patients, impacting outcomes substantially. This study explores the heterogeneity of AKI in burn patients by analyzing creatinine time-series data to identify distinct AKI clusters and evaluating routine biomarkers' predictive values. A retrospective cohort analysis was performed on 2608 adult burn patients admitted to Hangang Sacred Heart Hospital's Burn Intensive Care Unit (BICU) from July 2010 to December 2022. Patients were divided into four clusters based on creatinine trajectories, ranging from high-risk, severe cases to lower-risk, short-term care cases. Cluster A, characterized by high-risk, severe cases, showed the highest mortality and severity, with significant predictors being PT and TB. Cluster B, representing intermediate recovery cases, highlighted PT and albumin as useful predictors. Cluster C, a low-risk, high-resilience group, demonstrated predictive values for cystatin C and eGFR cys. Cluster D, comprising lower-risk, short-term care patients, indicated the importance of PT and lactate. Key biomarkers, including albumin, prothrombin time (PT), cystatin C, eGFR cys, and total bilirubin (TB), were identified as significant predictors of AKI development, varying across clusters. Diagnostic accuracy was assessed using area under the curve (AUC) metrics, reclassification metrics (NRI and IDI), and decision curve analysis. Cystatin C and eGFR cys consistently provided significant predictive value over creatinine, with AUC values significantly higher (p < 0.05) in each cluster. This study highlights the need for a tailored, biomarker-driven approach to AKI management in burn patients, advocating for the integration of diverse biomarkers in clinical practice to facilitate personalized treatment strategies. Future research should validate these biomarkers prospectively to confirm their clinical utility.

Glomerular filtration rate correlation and agreement between common predictive equations and standard 24-hour urinary creatinine clearance in medical critically ill patients

Background

Determining kidney function in critically ill patients is paramount for the dose adjustment of several medications. When assessing kidney function, the glomerular filtration rate (GFR) is generally estimated either by calculating urine creatinine clearance (UCrCl) or using a predictive equation. Unfortunately, all predictive equations have been derived for medical outpatients. Therefore, the validity of predictive equations is of concern when compared with that of the UCrCl method, particularly in medical critically ill patients. Therefore, we conducted this study to assess the agreement of the estimated GFR (eGFR) using common predictive equations and UCrCl in medical critical care setting.

Methods

This was the secondary analysis of a nutrition therapy study. Urine was collected from participating patients over 24 h for urine creatinine, urine nitrogen, urine volume, and serum creatinine measurements on days 1, 3, 5, and 14 of the study. Subsequently, we calculated UCrCl and eGFR using four predictive equations, the Cockcroft-Gault (CG) formula, the four and six-variable Modification of Diet in Renal Disease Study (MDRD-4 and MDRD-6) equations, and the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. The correlation and agreement between eGFR and UCrCl were determined using the Spearman rank correlation coefficient and Bland-Altman plot with multiple measurements per subject, respectively. The performance of each predictive equation for estimating GFR was reported as bias, precision, and absolute percentage error (APE).

Results

A total of 49 patients with 170 urine samples were included in the final analysis. Of 49 patients, the median age was 74 (21-92) years-old and 49% was male. All patients were hemodynamically stable with mean arterial blood pressure of 82 (65-108) mmHg. Baseline serum creatinine was 0.93 (0.3-4.84) mg/dL and baseline UCrCl was 46.69 (3.40-165.53) mL/min. The eGFR from all the predictive equations showed modest correlation with UCrCl (r: 0.692 to 0.759). However, the performance of all the predictive equations in estimating GFR compared to that of UCrCl was poor, demonstrating bias ranged from -8.36 to -31.95 mL/min, precision ranged from 92.02 to 166.43 mL/min, and an unacceptable APE (23.01% to 47.18%). Nevertheless, the CG formula showed the best performance in estimating GFR, with a small bias (-2.30 (-9.46 to 4.86) mL/min) and an acceptable APE (14.72% (10.87% to 23.80%)), especially in patients with normal UCrCl.

Conclusion

From our finding, CG formula was the best eGFR formula in the medical critically ill patients, which demonstrated the least bias and acceptable APE, especially in normal UCrCl patients. However, the predictive equation commonly used to estimate GFR in critically ill patients must be cautiously applied due to its large bias, wide precision, and unacceptable error, particularly in renal function impairment.