pyAKI - An Open Source Solution to Automated KDIGO classification

• URL: http://arxiv.org/abs/2401.12930v1
• Date: Tue, 23 Jan 2024 17:33:41 GMT
• Title: pyAKI - An Open Source Solution to Automated KDIGO classification
• Authors: Christian Porschen, Jan Ernsting, Paul Brauckmann, Raphael Weiss, Till W\"urdemann, Hendrik Booke, Wida Amini, Ludwig Maidowski, Benjamin Risse, Tim Hahn, Thilo von Groote
• Abstract summary: Acute Kidney Injury (AKI) is a frequent complication in critically ill patients, affecting up to 50% of patients in the intensive care units. The lack of standardized and open-source tools for applying the Kidney Disease Improving Global Outcomes (KDIGO) criteria to time series data has a negative impact on workload and study quality. This project introduces pyAKI, an open-source pipeline addressing this gap by providing a comprehensive solution for consistent KDIGO criteria implementation.
• Score: 0.40125518029941076
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Acute Kidney Injury (AKI) is a frequent complication in critically ill patients, affecting up to 50% of patients in the intensive care units. The lack of standardized and open-source tools for applying the Kidney Disease Improving Global Outcomes (KDIGO) criteria to time series data has a negative impact on workload and study quality. This project introduces pyAKI, an open-source pipeline addressing this gap by providing a comprehensive solution for consistent KDIGO criteria implementation. The pyAKI pipeline was developed and validated using a subset of the Medical Information Mart for Intensive Care (MIMIC)-IV database, a commonly used database in critical care research. We defined a standardized data model in order to ensure reproducibility. Validation against expert annotations demonstrated pyAKI's robust performance in implementing KDIGO criteria. Comparative analysis revealed its ability to surpass the quality of human labels. This work introduces pyAKI as an open-source solution for implementing the KDIGO criteria for AKI diagnosis using time series data with high accuracy and performance.

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