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An empirical study on KDIGO-defined acute kidney injury prediction in the intensive care unit.
Lyu, Xinrui; Fan, Bowen; Hüser, Matthias; Hartout, Philip; Gumbsch, Thomas; Faltys, Martin; Merz, Tobias M; Rätsch, Gunnar; Borgwardt, Karsten.
Afiliação
  • Lyu X; Department of Computer Science, ETH Zürich, Zürich, 8092, Switzerland.
  • Fan B; NEXUS Personalized Health Technologies, ETH Zürich, Schlieren, 8952, Switzerland.
  • Hüser M; Swiss Institute for Bioinformatics, Lausanne, 1015, Switzerland.
  • Hartout P; Swiss Institute for Bioinformatics, Lausanne, 1015, Switzerland.
  • Gumbsch T; Department of Biosystems Science and Engineering, ETH Zürich, Basel, 4056, Switzerland.
  • Faltys M; Department of Computer Science, ETH Zürich, Zürich, 8092, Switzerland.
  • Merz TM; Swiss Institute for Bioinformatics, Lausanne, 1015, Switzerland.
  • Rätsch G; Department of Biosystems Science and Engineering, ETH Zürich, Basel, 4056, Switzerland.
  • Borgwardt K; Department of Machine Learning and Systems Biology, Max Planck Institute of Biochemistry, Martinsried, 82152, Germany.
Bioinformatics ; 40(Suppl 1): i247-i256, 2024 06 28.
Article em En | MEDLINE | ID: mdl-38940165
ABSTRACT
MOTIVATION Acute kidney injury (AKI) is a syndrome that affects a large fraction of all critically ill patients, and early diagnosis to receive adequate treatment is as imperative as it is challenging to make early. Consequently, machine learning approaches have been developed to predict AKI ahead of time. However, the prevalence of AKI is often underestimated in state-of-the-art approaches, as they rely on an AKI event annotation solely based on creatinine, ignoring urine output.We construct and evaluate early warning systems for AKI in a multi-disciplinary ICU setting, using the complete KDIGO definition of AKI. We propose several variants of gradient-boosted decision tree (GBDT)-based models, including a novel time-stacking based approach. A state-of-the-art LSTM-based model previously proposed for AKI prediction is used as a comparison, which was not specifically evaluated in ICU settings yet.

RESULTS:

We find that optimal performance is achieved by using GBDT with the time-based stacking technique (AUPRC = 65.7%, compared with the LSTM-based model's AUPRC = 62.6%), which is motivated by the high relevance of time since ICU admission for this task. Both models show mildly reduced performance in the limited training data setting, perform fairly across different subcohorts, and exhibit no issues in gender transfer.Following the official KDIGO definition substantially increases the number of annotated AKI events. In our study GBDTs outperform LSTM models for AKI prediction. Generally, we find that both model types are robust in a variety of challenging settings arising for ICU data. AVAILABILITY AND IMPLEMENTATION The code to reproduce the findings of our manuscript can be found at https//github.com/ratschlab/AKI-EWS.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Injúria Renal Aguda / Unidades de Terapia Intensiva Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Injúria Renal Aguda / Unidades de Terapia Intensiva Idioma: En Ano de publicação: 2024 Tipo de documento: Article