AKI in the very elderly patients without preexisting chronic kidney disease: a comparison of 48-hour window and 7-day window for diagnosing AKI using the KDIGO criteria

OBJECTIVES: To compare the differences between the Kidney Disease Improving Global Outcomes (KDIGO) criteria of the 48-hour window and the 7-day window in the diagnosis of acute kidney injury (AKI) in very elderly patients, as well as the relationship between the 48-hour and 7-day windows for diagnosis and 90-day mortality. PATIENTS AND METHODS: We retrospectively enrolled very elderly patients (≥75 years old) from the geriatrics department of the Chinese PLA General Hospital between January 2007 and December 2015. AKI patients were divided into 48-hour and 7-day groups by their diagnosis criteria. AKI patients were divided into survivor and nonsurvivor groups by their outcomes within 90 days after diagnosis of AKI. RESULTS: In total, 652 patients were included in the final analysis. The median age of the cohort was 87 (84–91) years, the majority (623, 95.6%) of whom were male. Of the 652 AKI patients, 334 cases (51.2%) were diagnosed with AKI by the 48-hour window for diagnosis, while 318 cases (48.8%) were by the 7-day window for diagnosis. The 90-day mortality was 42.5% in patients with 48-hour window AKI and 24.2% in patients with 7-day window AKI. Kaplan–Meier curves showed that 90-day mortality was lower in the 7-day window AKI group than in the 48-hour window AKI group (log rank: P<0.001). Multivariate analysis by the Cox model revealed that 48-hour window for diagnosis hazard ratio (HR=1.818; 95% CI: 1.256–2.631; P=0.002) was associated with higher 90-day mortality. CONCLUSION: The 90-day mortality was higher in 48-hour window AKI than in 7-day window AKI in very elderly patients. The 48-hour KDIGO window definition may be less sensitive. The 48-hour KDIGO window definition is significantly better correlated with subsequent mortality and is, therefore, still appropriate for clinical use. Finding early, sensitive biomarkers of kidney damage is a future direction of research.