Porschen Christian, Ernsting Jan, Brauckmann Paul, Weiss Raphael, Würdemann Till, Booke Hendrik, Amini Wida, Maidowski Ludwig, Risse Benjamin, Hahn Tim, von Groote Thilo
Department of Anaesthesiology, Intensive Care and Pain Medicine, University Hospital Müunster, Müunster, Germany.
Institute for Geoinformatics, University of Münster, Münster, Germany.
PLoS One. 2025 Jan 3;20(1):e0315325. doi: 10.1371/journal.pone.0315325. eCollection 2025.
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, requires researchers to implement classification algorithms of their own which is resource intensive and might impact study quality by introducing different interpretations of edge cases. 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 constructed a standardized data model in order to ensure reproducibility. PyAKI implements the Kidney Disease: Improving Global Outcomes (KDIGO) guideline on AKI diagnosis. After implementation of the diagnostic algorithm, using both serum creatinine and urinary output data, pyAKI was tested on a subset of patients and diagnostic accuracy was compared in a comparative analysis against annotations by physicians.
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 with an accuracy of 1.0 in all categories.
The pyAKI pipeline is the first open-source solution for implementing KDIGO criteria in time series data. It provides a standardized data model and a comprehensive solution for consistent AKI classification in research applications for clinicians and data scientists working with AKI data. The pipeline's high accuracy make it a valuable tool for clinical research and decision support systems.
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.
急性肾损伤（AKI）是危重症患者常见的并发症，在重症监护病房中影响高达50%的患者。缺乏将改善全球肾脏病预后（KDIGO）标准应用于时间序列的标准化和开源工具，这要求研究人员自行实施分类算法，这既耗费资源，又可能因对边缘情况的不同解读而影响研究质量。本项目引入了pyAKI，这是一个开源流程，通过提供一致实施KDIGO标准的全面解决方案来填补这一空白。
使用重症监护医学信息数据库（MIMIC）-IV数据库的一个子集开发并验证了pyAKI流程，该数据库是重症监护研究中常用的数据库。我们构建了一个标准化数据模型以确保可重复性。PyAKI实施了关于AKI诊断的《肾脏病：改善全球预后》（KDIGO）指南。在实施诊断算法后，使用血清肌酐和尿量数据，在一部分患者中对pyAKI进行了测试，并在对比分析中与医生的注释进行比较，以评估诊断准确性。
与专家注释进行的验证表明pyAKI在实施KDIGO标准方面具有强大性能。对比分析显示其能够在所有类别中以1.0的准确率超越人工标注的质量。
pyAKI流程是首个在时间序列数据中实施KDIGO标准的开源解决方案。它为处理AKI数据的临床医生和数据科学家在研究应用中进行一致的AKI分类提供了标准化数据模型和全面解决方案。该流程的高准确率使其成为临床研究和决策支持系统的宝贵工具。
本研究介绍了pyAKI，它是一种使用时间序列数据以高精度和高性能实施KDIGO AKI诊断标准的开源解决方案。
