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AKIpredictor, an online prognostic calculator for acute kidney injury in adult critically ill patients: development, validation and comparison to serum neutrophil gelatinase-associated lipocalin

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Abstract

Purpose

Early diagnosis of acute kidney injury (AKI) remains a major challenge. We developed and validated AKI prediction models in adult ICU patients and made these models available via an online prognostic calculator. We compared predictive performance against serum neutrophil gelatinase-associated lipocalin (NGAL) levels at ICU admission.

Methods

Analysis of the large multicenter EPaNIC database. Model development (n = 2123) and validation (n = 2367) were based on clinical information available (1) before and (2) upon ICU admission, (3) after 1 day in ICU and (4) including additional monitoring data from the first 24 h. The primary outcome was a comparison of the predictive performance between models and NGAL for the development of any AKI (AKI-123) and AKI stages 2 or 3 (AKI-23) during the first week of ICU stay.

Results

Validation cohort prevalence was 29% for AKI-123 and 15% for AKI-23. The AKI-123 model before ICU admission included age, baseline serum creatinine, diabetes and type of admission (medical/surgical, emergency/planned) and had an AUC of 0.75 (95% CI 0.75–0.75). The AKI-23 model additionally included height and weight (AUC 0.77 (95% CI 0.77–0.77)). Performance consistently improved with progressive data availability to AUCs of 0.82 (95% CI 0.82–0.82) for AKI-123 and 0.84 (95% CI 0.83–0.84) for AKI-23 after 24 h. NGAL was less discriminant with AUCs of 0.74 (95% CI 0.74–0.74) for AKI-123 and 0.79 (95% CI 0.79–0.79) for AKI-23.

Conclusions

AKI can be predicted early with models that only use routinely collected clinical information and outperform NGAL measured at ICU admission. The AKI-123 models are available at http://akipredictor.com/.

Trial registration Clinical Trials.gov NCT00512122

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Acknowledgements

MF receives funding from the Fonds Wetenschappelijk Onderzoek (FWO) as a PhD fellow (11Y1116 N). GM receives funding from FWO as senior clinical investigator (1846113 N). MC receives funding from the UZLeuven KOF. GVdB, through the KULeuven, receives long-term research financing via the Flemish government Methusalem–program. The authors would like to thank Romain Pirson for his input during the development of the akipredictor.com website.

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    Authors and Affiliations

    1. Clinical Division and Laboratory of Intensive Care Medicine, Academic Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Herestraat 49, B-3000, Leuven, Belgium

      Marine Flechet, Fabian Güiza, Miet Schetz, Pieter Wouters, Ilse Vanhorebeek, Inge Derese, Jan Gunst, Michaël Casaer, Greet Van den Berghe & Geert Meyfroidt

    2. Pharmacy Department, Department of Pharmaceutical and Pharmacological Sciences, University Hospitals Leuven and Clinical Pharmacology and Pharmacotherapy, KU Leuven, Leuven, Belgium

      Isabel Spriet

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    Additional information

    Take-home message: AKI can be predicted early with models that only use routinely collected clinical information and outperform NGAL measured at ICU admission. The AKI-123 models are available at http://akipredictor.com/.

    M. Flechet and F. Güiza contributed equally to this work.

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    Flechet, M., Güiza, F., Schetz, M. et al. AKIpredictor, an online prognostic calculator for acute kidney injury in adult critically ill patients: development, validation and comparison to serum neutrophil gelatinase-associated lipocalin. Intensive Care Med 43, 764–773 (2017). https://doi.org/10.1007/s00134-017-4678-3

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