Sepsis-associated acute kidney injury (SA-AKI) remains one of the most common and severe complications among critically ill patients. Traditional diagnostic indicators such as serum creatinine and urine output detect kidney injury only after substantial functional impairment has occurred. Recent advances in molecular biology and critical care medicine have introduced a range of biomarkers capable of detecting early renal stress and cellular injury before functional decline becomes clinically evident. This study proposes a biomarker-integrated precision diagnostic framework for early detection of septic acute kidney injury. The proposed framework integrates multiple biomarkers including neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), tissue inhibitor of metalloproteinase-2 (TIMP-2), and insulin-like growth factor binding protein-7 (IGFBP-7). Machine learning models are incorporated to enhance predictive accuracy using biomarker panels combined with clinical parameters. A conceptual predictive model is presented along with simulated diagnostic performance comparisons. The results demonstrate that biomarker-integrated diagnostics significantly improve early detection capability compared with traditional clinical indicators. This approach has the potential to transform early intervention strategies and improve patient outcomes in sepsis-associated kidney injury.
