The Kinetics of Cystatin C and Serum Creatinine in Acute Kidney Injury

Background: Acute kidney injury (AKI) is a common condition affecting a significant portion of hospitalized and critically ill patients. Current AKI diagnosis relies on serum creatinine (sCr), which has several recognized limitations that impact the timely detection and response to AKI management. Serum cystatin C (sCys) has characteristics that can overcome the limitations of sCr, but head-to-head comparisons of these biomarkers is difficult to study prospectively. A quantitative assessment of the kinetics of sCys and sCr during AKI is necessary to support clinical workflow implementation for AKI diagnosis and management. Methods: A quantitative systems pharmacology (QSP) model was developed using MATLAB and Simbiology (The MathWorks, Natick, MA), to simulate the concentration-time profiles of sCr and sCys under varying degrees of AKI across a spectrum of baseline kidney function. The model incorporated parameters from existing literature and used a contemporary sCr and sCys GFR equation to assess the time to reach AKI diagnostic criteria for both biomarkers. Results: The model demonstrated that sCys achieves steady-state concentration and meets AKI diagnostic thresholds significantly faster than sCr, with an advantage of six to 48 hours, depending on CKD stage. sCys exhibited greater sensitivity in detecting GFR reductions, with the ability to detect AKI within 12-24 hours post-AKI, compared to 12-72 hours for sCr. The study also identified that for sCys, absolute value diagnostic cutoffs are more effective than percentage-based thresholds and can provide consistent detection across different CKD stages. Conclusion: sCys has superior kinetics for early AKI detection compared to sCr, making it a valuable addition to AKI diagnostic protocols, particularly in high-risk populations. Daily monitoring of sCys in patients at risk for AKI would facilitate more timely detection and potentially improve clinical outcomes. Future research should focus on validating sCys diagnostic criteria and integrating it with other biomarkers to enhance AKI management.