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A supervised, externally validated machine learning model for artifact and drainage detection in high-resolution intracranial pressure monitoring data.
Huo, Shufan; Nelde, Alexander; Meisel, Christian; Scheibe, Franziska; Meisel, Andreas; Endres, Matthias; Vajkoczy, Peter; Wolf, Stefan; Willms, Jan F; Boss, Jens M; Keller, Emanuela.
Afiliação
  • Huo S; 1Department of Neurology with Experimental Neurology, Charité - Universitätsmedizin Berlin, Germany.
  • Nelde A; 2Neurocritical Care Unit, Department of Neurosurgery and Institute of Intensive Care Medicine, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Switzerland.
  • Meisel C; 3Center for Stroke Research Berlin, Charité - Universitätsmedizin Berlin, Germany.
  • Scheibe F; 4Computational Neurology, Department of Neurology, Charité - Universitätsmedizin Berlin, Germany.
  • Meisel A; 3Center for Stroke Research Berlin, Charité - Universitätsmedizin Berlin, Germany.
  • Endres M; 4Computational Neurology, Department of Neurology, Charité - Universitätsmedizin Berlin, Germany.
  • Vajkoczy P; 5Berlin Institute of Health (BIH), Berlin, Germany.
  • Wolf S; 1Department of Neurology with Experimental Neurology, Charité - Universitätsmedizin Berlin, Germany.
  • Willms JF; 6NeuroCure Cluster of Excellence, Charité - Universitätsmedizin Berlin, Germany.
  • Boss JM; 1Department of Neurology with Experimental Neurology, Charité - Universitätsmedizin Berlin, Germany.
  • Keller E; 3Center for Stroke Research Berlin, Charité - Universitätsmedizin Berlin, Germany.
J Neurosurg ; 141(2): 509-517, 2024 Aug 01.
Article em En | MEDLINE | ID: mdl-38489814
ABSTRACT

OBJECTIVE:

In neurocritical care, data from multiple biosensors are continuously measured, but only sporadically acknowledged by the attending physicians. In contrast, machine learning (ML) tools can analyze large amounts of data continuously, taking advantage of underlying information. However, the performance of such ML-based solutions is limited by different factors, for example, by patient motion, manipulation, or, as in the case of external ventricular drains (EVDs), the drainage of CSF to control intracranial pressure (ICP). The authors aimed to develop an ML-based algorithm that automatically classifies normal signals, artifacts, and drainages in high-resolution ICP monitoring data from EVDs, making the data suitable for real-time artifact removal and for future ML applications.

METHODS:

In their 2-center retrospective cohort study, the authors used labeled ICP data from 40 patients in the first neurocritical care unit (University Hospital Zurich) for model development. The authors created 94 descriptive features that were used to train the model. They compared histogram-based gradient boosting with extremely randomized trees after building pipelines with principal component analysis, hyperparameter optimization via grid search, and sequential feature selection. Performance was measured with nested 5-fold cross-validation and multiclass area under the receiver operating characteristic curve (AUROC). Data from 20 patients in a second, independent neurocritical care unit (Charité - Universitätsmedizin Berlin) were used for external validation with bootstrapping technique and AUROC.

RESULTS:

In cross-validation, the best-performing model achieved a mean AUROC of 0.945 (95% CI 0.92-0.969) on the development dataset. On the external validation dataset, the model performed with a mean AUROC of 0.928 (95% CI 0.908-0.946) in 100 bootstrapping validation cycles to classify normal signals, artifacts, and drainages.

CONCLUSIONS:

Here, the authors developed a well-performing supervised model with external validation that can detect normal signals, artifacts, and drainages in ICP signals from patients in neurocritical care units. For future analyses, this is a powerful tool to discard artifacts or to detect drainage events in ICP monitoring signals.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Pressão Intracraniana / Artefatos Limite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Revista: J Neurosurg Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Alemanha

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Pressão Intracraniana / Artefatos Limite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Revista: J Neurosurg Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Alemanha