A recent study investigates the factors contributing to acute kidney injury (AKI) in elderly patients with diabetic nephropathy and introduces a nomogram model to predict its occurrence. The findings highlight key risk factors and propose a clinically relevant tool for personalized patient care.
Introduction:
The objective of this study was to identify the factors influencing acute kidney injury (AKI) in elderly patients suffering from diabetic nephropathy and to develop a predictive nomogram model. This research aimed to assist clinicians in accurately assessing risk and tailoring treatments for this vulnerable population.
Methods:
The study involved 680 patients diagnosed with type 2 diabetic nephropathy at our hospital from May 2018 to August 2023. Among these, 50 patients with AKI formed the case group, while 630 patients without AKI were included as the control group. The prevalence and contributing factors of AKI were analyzed using multivariate logistic regression. Subsequently, a nomogram was constructed based on identified risk factors to predict the likelihood of AKI occurrence.
Results:
The analysis revealed that severe infections were the primary trigger for AKI in these patients, accounting for 40% of cases. Other contributing factors included the use of nephrotoxic antibiotics and severe heart failure. Key clinical parameters such as age, urine microalbumin-to-creatinine ratio (ACR), blood urea nitrogen (BUN), uric acid (UA), and cystatin C (CysC) were significantly elevated in the AKI group compared to the control group (P<0.05). Conversely, left ventricular ejection fraction (LVEF) and estimated glomerular filtration rate (eGFR) were notably lower in the AKI group (P<0.05).
Age, ACR, and CysC were identified as independent risk factors for AKI, while LVEF and eGFR emerged as protective factors. The nomogram developed for predicting AKI demonstrated a C-index of 0.768 (95% CI: 0.663–0.806) and closely aligned with the ideal calibration curve. The model showed a prediction threshold of >0.18 and provided substantial clinical net benefit, outperforming independent risk predictors in terms of clinical utility.
Conclusion:
The nomogram model, based on age, ACR, CysC, LVEF, and eGFR, offers a reliable tool for predicting acute kidney injury in elderly patients with diabetic nephropathy. This model can significantly enhance clinical decision-making, providing healthcare professionals with a valuable resource for assessing patient risk and planning individualized treatments. The results underscore the importance of early intervention and personalized care in managing diabetic nephropathy and its complications.
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