Results indicated the robustness and adaptability of this methodology, enhancing its potential for use in clinical laboratories with different equipment and sample types. The models could adapt to deal with variations among samples and non-biological factors, minimizing their impact on performance and reducing the need to process and measure a subset of samples in both laboratories. Results also encourage the compilation of samples from different setups into global datasets and repositories, which can help develop big data-driven diagnostic models for routine diagnosis and monitoring of albuminuria. These models could be applied at any point of care within the health system. To support this, we have provided the full dataset used, which can be leveraged to expand the dataset size and further enhance the performance of future models based on this approach.