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Cited by 169Acute Kidney Injury ResearchChronic Kidney Disease and DiabetesSepsis Diagnosis and Treatment

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

Flechet, Marine, Güiza, Fabian, Schetz, Miet, Wouters, Pieter, Vanhorebeek, Ilse, Derese, Inge, Gunst, Jan, Spriet, Isabel +3 more·2017·Intensive Care Medicine
DOI 10.1007/s00134-017-4678-3
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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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  • Many research groups have tackled early prediction of AKI using electronic health records data, but no model so far explains the rationale behind specific predictions despite a clear need for explainable and actionable predictions.

    “Many research groups have tackled early prediction of AKI using electronic health records (EHR) data [8–11], but no model so far explains the rationale behind specific predictions despite a clear need for explainable and actionable predictions [6, 12].”

    From Machine learning model for early prediction of acute kidney injury (AKI) in pediatric critical care (2021) · cites this paper as [11]

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Authors
Flechet, M., Güiza, F., Schetz, M., Wouters, P., Vanhorebeek, I., Derese, I., Gunst, J., Spriet, I., Casaer, M., Van den Berghe, G., & Meyfroidt, G.
Affiliations
KU Leuven (BE)
ORCID
Venue
Intensive Care Medicine
Publisher
Springer Science and Business Media LLC
Published
2017-01-27
Volume
43
Issue
6
Pages
764-773
ISSN
  • 0342-4642 (ISSN-L)
  • 1432-1238
DOI
10.1007/s00134-017-4678-3
OpenAlex
W2582793731
Cited by
169
References
50
Citations by year
2026: 7 · 2025: 11 · 2024: 16 · 2023: 18 · 2022: 33 · 2021: 21 · 2020: 29 · 2019: 15 · 2018: 12 · 2017: 7
FWCI
15.41
Type
journal article
Language
en
Open access
closed
Licence
www.springer.com/tdm (unspecified)
Locations
  • Intensive Care Medicine (publishedVersion)
  • PubMed (publishedVersion)
  • Lirias (submittedVersion)
Funders
Fonds Wetenschappelijk Onderzoek (11Y1116N, 1846113N)
Topics
  • Health Sciences › Medicine › Nephrology › Acute Kidney Injury Research
  • Health Sciences › Medicine › Nephrology › Chronic Kidney Disease and Diabetes
  • Health Sciences › Medicine › Epidemiology › Sepsis Diagnosis and Treatment
Keywords
acute kidney injury, AKI prediction model, neutrophil gelatinase-associated lipocalin, critically ill adults, intensive care unit, early diagnosis, serum creatinine, model validation, AKI severity staging, clinical risk factors
MeSH terms
Lipocalin-2, Adult, Aged, Aged, 80 and over, Critical Care, Female, Humans, Male, Middle Aged, Predictive Value of Tests, Prognosis, Cohort Studies, Biomarkers, Critical Illness, Internet, Early Diagnosis, Acute Kidney Injury

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