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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.

Objectives To assess the stability of lipid profiles throughout childhood and evaluate their onset and dynamic. Materials and methods Lipid markers were longitudinally measured in more than 1300 healthy children from the LIFE Child study (Germany) and categorized into normal , at-risk , or adverse . Year-to-year intra-person persistence of the categories during follow-ups was examined and Pearson’s correlation coefficient was calculated. Results We found strong positive correlations for TC, LDL-C and ApoB ( r  > 0.75, p  < 0.001) from the age of four years. Correlations were lowest during the first two years of life. Most children with normal levels also had normal levels the following year. Children with at-risk levels showed a tendency towards normal levels at the follow-up visit. Adverse levels of TC, LDL-C, ApoB (all ages), and HDL-C (from age 15) persisted in more than half of the affected children. Age-dependent patterns of stability were most pronounced and similar for TC, LDL-C, and ApoB. Conclusions Normal levels of serum lipids show high stability and adverse levels stabilized in early childhood for TC, LDL, and ApoB. At-risk and adverse levels of TC, LDL-C or ApoB may warrant further or repeated diagnostic measurements with regards to preventing CVD in the long run.

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.