Abstract: SA-PO0072
Identification of AKI Subtypes Using Deep Learning-Based Clustering of Serum Creatinine Trajectories and Clinical Features in Critically Ill Patients
Session Information
- AKI: Clinical Diagnostics and Biomarkers
November 08, 2025 | Location: Exhibit Hall, Convention Center
Abstract Time: 10:00 AM - 12:00 PM
Category: Acute Kidney Injury
- 102 AKI: Clinical, Outcomes, and Trials
Author
- Kang, Min Woo, Korea University Guro Hospital, Guro-gu, Seoul, Korea (the Republic of)
Background
This study aimed to identify subtypes of ICU patients with AKI by clustering serum creatinine trajectories and clinical features, then compare outcomes across clusters.
Methods
In this retrospective MIMIC-IV cohort, ICU patients with KDIGO-defined AKI were included; those with end-stage kidney disease or prior KRT were excluded. We gathered creatinine measurements (7 days before to 4 days after AKI onset), demographics, vitals, comorbidities, nephrotoxin and vasopressor use, sepsis status, cardiac surgery history, and labs. Missing values were imputed. Autoencoders combined with Deep Embedded K-Means yielded ten clusters, optimized via the Davies–Bouldin index. We compared 7-, 14-, and 30-day mortality and KRT initiation rates across clusters.
Results
A total of 30,096 ICU patients with AKI were included, and ten clusters were identified. Baseline creatinine differed significantly among clusters (p<0.001), highest in cluster 6 (2.90 ± 2.62 mg/dL) and lowest in cluster 2 (1.43 ± 1.10 mg/dL). Nephrotoxic medication use was highest in cluster 7 (46.8%) and lowest in cluster 8 (15.1%). Sepsis incidence was highest in cluster 5 (85.4%) and lowest in cluster 1 (14.1%). Regarding creatinine trajectories, clusters 5 and 7 exhibited abrupt elevations, while other clusters showed gradual increases. Clinical outcomes varied significantly across clusters (all p<0.001). The 7-day mortality rate was highest in cluster 3 (2.44%), followed by cluster 6 (0.53%), while the 7-day KRT initiation rate was highest in cluster 7 (25.00%), followed by cluster 8 (3.45%).
Conclusion
Deep learning–based clustering delineated distinct AKI subtypes with divergent trajectories and outcomes, offering potential for tailored risk stratification and targeted research.