MERWACS: Development and external validation of a non-invasive machine learning tool for identifying subjects to be...

Early detection of chronic kidney disease (CKD) is a critical public health priority. However, a gap exists for non-invasive tools to guide screening selection in the general adult population, leaving many at-risk individuals undiagnosed. We developed and validated MERWACS (Machineborne Early Renal Warning And Control System), a machine learning model designed to identify which individuals should be prioritized for definitive laboratory testing. We used 30 years of data from the U.S. National Health and Nutrition Examination Survey (NHANES; training set n = 9,534) to train MERWACS using a final set of 12 non-invasive parameters derived from demographics, anthropometrics, and medical history. The model predicts a composite outcome of prevalent reduced kidney function, defined as a urine albumin-to-creatinine ratio ≥ 30 mg/g or an estimated glomerular filtration rate (eGFR) below the age- and sex-specific 2.5th percentile, thereby accounting for the natural eGFR decline in healthy aging. To ensure robustness, we developed parallel models for three major eGFR equations. The final XGBoost-based model was validated on an internal test set (n = 4,085) and a separate external dataset from the Korea NHANES (KNHANES; n = 6,454). MERWACS demonstrated moderate and consistent discrimination across all three eGFR equation-based models, achieving ROCAUCs ranging from 0.68 to 0.70 on internal validation and 0.71 to 0.73 on external validation. MERWACS is a robust, externally validated, non-invasive tool that identifies adults and elderly individuals with a high probability of currently having reduced kidney health, helping prioritize them for definitive CKD screening. Its moderate performance is an intentional trade-off for accessibility, as it deliberately excludes laboratory data. By providing a personalized, numerical predicted probability through an open-access online application, MERWACS can empower individuals and support clinicians in identifying at-risk patients, prompting timely conversations and crucial evaluations to improve kidney health outcomes.