pyAKI-An open source solution to automated acute kidney injury classification

OBJECTIVE

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.

MATERIALS AND METHODS

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.

RESULTS

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.

DISCUSSION

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.

CONCLUSION

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.

Glutamine modulates neutrophil recruitment and effector functions during sterile inflammation

During sterile inflammation, tissue damage induces excessive activation and infiltration of neutrophils into tissues, where they critically contribute to organ dysfunction. Tight regulation of neutrophil migration and their effector functions is crucial to prevent overshooting immune responses. Neutrophils utilize more glutamine, the most abundant free α-amino acid in the human blood, than other leukocytes. However, under inflammatory conditions, the body's requirements exceed its ability to produce sufficient amounts of glutamine. This study investigates the impact of glutamine on neutrophil recruitment and their key effector functions. Glutamine treatment effectively reduced neutrophil activation by modulating β2-integrin activity and chemotaxis in vitro. In a murine in vivo model of sterile inflammation induced by renal ischemia-reperfusion injury, glutamine administration significantly attenuated neutrophil recruitment into injured kidneys. Transcriptomic analysis revealed, glutamine induces transcriptomic reprograming in murine neutrophils, thus improving mitochondrial functionality and glutathione metabolism. Further, glutamine influenced key neutrophil effector functions, leading to decreased production of reactive oxygen species and formation of neutrophil extracellular traps. Mechanistically, we used a transglutaminase 2 inhibitor to identify transglutaminase 2 as a downstream mediator of glutamine effects on neutrophils. In conclusion, our findings suggest that glutamine diminishes activation and recruitment of neutrophils and thus identify glutamine as a potent means to curb overshooting neutrophil responses during sterile inflammation.