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Official websites use. Share sensitive information only on official, secure websites. Correspondence: olga. Our study provides valuable insight into potential quality and outcome improvements and lower costs, considering elderly patients, women and patients with acute-on-chronic KD as the most vulnerable. Keywords: data-driven diagnosis, electronic health records, digital markers, rule engine, precision medicine, real-world data, kidney disease, KDIGO staging.
Global priorities are changing to an increased focus on non-communicable diseases NCD. Kidney disease KD is one of the ten most important fastest-growing drivers of burden [ 1 ] and a significant contributor to mortality and disability. In , According to the Burden of Disease scenarios for countries and territories, a forecasting analysis for the Global Burden of Disease Study , diabetes and kidney disease are the two most extensive Level 2 causes of the increase in the number of disability-adjusted life years DALYs , from Historically, more than 35 definitions of KD have been proposed, reflecting the difficulty of capturing the disease pattern; the uncertainty of obtaining a baseline and the mostly asymptomatic progression complicates the staging process [ 5 ].
Considering the underlying physiological processes of acute kidney injury AKI and chronic kidney disease CKD as a unified and bidirectional clinical syndrome facilitates effective prevention and treatment strategies [ 6 , 7 , 8 , 9 , 10 ]. If conducted manually, the calculation is prone to errors and time-consuming, often aggravated by the baseline uncertainty. Although it is possible to detect KD using routine laboratory data, many clinicians currently fail to assess KD correctly.
Why might it be essential to assess the KD stage correctly? The treatment approach for kidney disease varies significantly depending on the stage; correctly assessing the staging helps monitor and prevent complications and disease progression.
