Background: Acute kidney injury (AKI) may transition into acute kidney disease (AKD) or chronic kidney disease (CKD), leading to subacute and chronic deterioration, respectively. Despite extensive research on AKI, a significant gap exists in understanding the specific biomarkers and development of individualized treatments prior to progression to AKD and CKD.
Methods: As a consortium linking health medical records, biospecimens, and biosignals, eight Korean tertiary hospitals participated in the establishment of a retrospective and prospective cohort, each comprising approximately 1,500 patients with AKI receiving continuous kidney replacement therapy (CKRT). Other information included AKI-related information, CKRT prescriptions, and patient outcomes. Follow-up timeframes were set at baseline, 1 week, 3 months, and 1 year after the initiation of CKRT. Human biospecimens will be collected from the prospective cohort. An artificial intelligence model was developed using the retrospective cohort to predict the prognosis of AKD and its subsequent sequelae and to formulate patient-individualized treatments, with validation planned in a prospective cohort. Follow-up studies are scheduled to identify biomarkers related to outcomes using biospecimens. Finally, based on the results and literature review, decision-making on the prevention and management of diseases, as well as the development of treatment guidelines, are being planned.
Conclusion: This study will provide scientific evidence on clinical insights and appropriate management targets for AKI and AKD, which will form the basis for relevant treatment guidelines. Additionally, these findings may facilitate a more personalized approach to patient care, enabling clinicians to tailor treatments based on individual biomarker profiles and predictive models.
Keywords: Acute kidney disease; Acute kidney injury; Artificial intelligence; Continuous kidney replacement therapy; Multicenter cohort.