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Development and validation of clinical prediction models for acute kidney injury recovery at hospital discharge in critically ill adults

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Abstract

Purpose

Acute kidney injury (AKI) recovery prediction remains challenging. The purpose of the present study is to develop and validate prediction models for AKI recovery at hospital discharge in critically ill patients with ICU-acquired AKI stage 3 (AKI-3).

Methods

Models were developed and validated in a development cohort (n = 229) and a matched validation cohort (n = 244) from the multicenter EPaNIC database to create prediction models with the least absolute shrinkage and selection operator (Lasso) machine-learning algorithm. We evaluated the discrimination and calibration of the models and compared their performance with plasma neutrophil gelatinase-associated lipocalin (NGAL) measured on first AKI-3 day (NGAL_AKI3) and reference model that only based on age.

Results

Complete recovery and complete or partial recovery occurred in 33.20% and 51.23% of the validation cohort patients respectively. The prediction model for complete recovery based on age, need for renal replacement therapy (RRT), diagnostic group (cardiac/surgical/trauma/others), and sepsis on admission had an area under the receiver operating characteristics curve (AUROC) of 0.53. The prediction model for complete or partial recovery based on age, need for RRT, platelet count, urea, and white blood cell count had an AUROC of 0.61. NGAL_AKI3 showed AUROCs of 0.55 and 0.53 respectively. In cardiac patients, the models had higher AUROCs of 0.60 and 0.71 than NGAL_AKI3’s AUROCs of 0.52 and 0.54. The developed models demonstrated a better performance over the reference models (only based on age) for cardiac surgery patients, but not for patients with sepsis and for a general ICU population.

Conclusion

Models to predict AKI recovery upon hospital discharge in critically ill patients with AKI-3 showed poor performance in the general ICU population, similar to the biomarker NGAL. In cardiac surgery patients, discrimination was acceptable, and better than NGAL. These findings demonstrate the difficulty of predicting non-reversible AKI early.

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Availability of data and material

The data for the current study are not publicly available due to no prior agreement with the local ethical committee. Upon reasonable request, amendment can be requested to the corresponding author to share the necessary data.

Code availability

All analyses and modeling were done in python 3.7.4 with the open-source scikit-learn library 0.23.1.

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Acknowledgements

The authors would like to thank the members of the EPaNIC research group for helpfully providing the EPaNIC database, which was used to develop the AKI recovery prediction models.

Funding

C-YH receives a grant from the Taiwan-KU Leuven scholarship. JG holds a postdoctoral research fellowship supported by the University Hospitals Leuven. GDV receives a clinical fellowship grant of the Flanders Research Foundation (1701719 N). GM received a grant of the Flanders Research Foundation as senior clinical investigator. This work was supported by the Methusalem program of the Flemish government (through the University of Leuven to GVdB, METH/08/07 and to GVdB and IV, METH14/06).

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Author notes

  1. Chao-Yuan Huang and Fabian Güiza have contributed equally to this work.

Authors and Affiliations

  1. Laboratory of Intensive Care Medicine, Academic Department of Cellular and Molecular Medicine, Katholieke Universiteit Leuven, Louvain, Belgium

    Chao-Yuan Huang, Greet De Vlieger, Jan Gunst, Michael Casaer, Ilse Vanhorebeek, Inge Derese, Greet Van den Berghe & Geert Meyfroidt

  2. Department of Intensive Care Medicine, University Hospitals Leuven, Louvain, Belgium

    Fabian Güiza, Greet De Vlieger, Pieter Wouters, Jan Gunst, Michael Casaer, Greet Van den Berghe & Geert Meyfroidt

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  1. Chao-Yuan Huang
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Contributions

Conceptualization: FG and GM. Data curation: C-YH, FG, PW, JG, MC, IV, ID, and GVdB. Formal analysis: C-YH. Funding acquisition: IV and GVdB. Investigation: C-YH, FG, and GM. Methodology: C-YH, FG, and GM. Project administration: FG and GM. Resources: FG, PW, JG, MC, IV, ID, GVdB, and GM. Software: C-YH. Supervision: FG and GM. Validation: C-YH, FG, and GM. Visualization: C-YH, FG, and GM. Writing—original draft preparation: C-YH, FG, GDV, and GM. writing—review and editing: C-YH, FG, PW, JG, MC, IV, GDV, GVdB, and GM.

Corresponding author

Correspondence to Geert Meyfroidt.

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All the authors declared no competing interests that are relevant to the content of this article.

Ethical approval

EPaNIC data collection has ethical committee (EC) approval from the institutional review board of the participating centers and by the Belgian authorities.

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Written informed consent was obtained from all patients participating in the EPaNIC study or their designated representatives.

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Informed consent was obtained from all individual participants included in the EPaNIC study or their designated representatives.

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Huang, CY., Güiza, F., De Vlieger, G. et al. Development and validation of clinical prediction models for acute kidney injury recovery at hospital discharge in critically ill adults. J Clin Monit Comput 37, 113–125 (2023). https://doi.org/10.1007/s10877-022-00865-7

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