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During the congress, E-Posters will be accessible to all participants on the congress website 24/7, as well as in the E-poster stations in the congress center.
Preparing your E-Poster
Please review the E-Poster format requirements carefully when preparing your E-Poster. Should your E-Poster not meet the mentioned requirements, it may not be displayed as described above.
E-Poster Submission Deadline
Please prepare and upload your E-Poster no later than March 14, 2026 11.59PM CET. After this date, you will no longer be able to prepare and upload your E-poster and it will not be displayed and accessible on the congress website.
Please follow the instructions below to input your abstract title.
Abstract titles should be brief and reflect the content of the abstract.
Chronic Kidney Disease (CKD) represents a significant global health burden. Understanding its prevalence and associated risk factors is essential for developing effective public health strategies and clinical interventions. This study aims to estimate the prevalence of CKD in the adult Panamanian population aged 18 years and older, leveraging advanced analytical techniques.
This observational study utilizes a large dataset of laboratory results from individuals aged 18 and above in Panama, spanning from 2022 to 2024. Artificial Intelligence (AI) algorithms were employed for the comprehensive review and analysis of these extensive laboratory data, specifically focusing on serum creatinine and albuminuria measurements from the MODULAB system. Estimated Glomerular Filtration Rate (eGFR) was calculated using the CKD-EPI 2021 equation, adjusted for sex and age. CKD progression was defined as a sustained eGFR decline exceeding 5 mL/min/year, confirmed by at least two measurements over three months. Chronicity of CKD was assessed using a Q0-Q4 scale based on KDIGO criteria, categorizing diagnostic reliability. Patients were classified into KDIGO G (eGFR) and A (albuminuria) stages (G1-G5, A1-A3) to determine severity and progression risk. Finally, a KDIGO risk stratification matrix (Green, Yellow, Orange, Red) was applied to assign risk levels based on combined G and A stages.
The initial dataset comprised 3,506,777 laboratory test records containing serum creatinine and Albumin-to-Creatinine Ratio (ACR) results corresponding to 1,232,878 unique individuals. After excluding individuals under 18 years of age (n = 207,314) and filtering out encounters associated with obstetric or neonatal care (n = 14,560), a total of 1,011,004 adult patients remained. Among these, 921,786 individuals (3,005,077 tests) had at least one serum creatinine measurement, and 98,165 patients (554,821 tests) had at least one valid ACR measurement (excluding tests with error flags) the distribution of eGFR (in mL/min/1.73m²) stages is described in Figure 1. Approximately 30.0% of individuals had moderately or severely increased albuminuria (stages A2–A3). Overall, 18.0% of participants (17673) met criteria for CKD of having 2 serum creatinine exams over a 3-month period.
The application of AI in analyzing large-scale laboratory data is instrumental in accurately estimating the prevalence of CKD. This methodology offers a robust and efficient approach for epidemiological studies, facilitating timely identification of at-risk populations and informing targeted public health interventions to mitigate the burden of CKD. Prevalence of CKD using KDIGO criteria is 18% for Panamenian adults over 18. Albuminuria is a present in more than 30% of participants, which is an important risk factor for cardiovascular morbidity and mortality. The results underscore the potential of AI for enhanced clinical decision support and discovery research in nephrology, that can provide results that before were very difficult to obtain.
