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Verfasst von:Neumann, Jan-Oliver [VerfasserIn]   i
 Schmidt, Stephanie [VerfasserIn]   i
 Nohman, Amin [VerfasserIn]   i
 Naser, Paul [VerfasserIn]   i
 Jakobs, Martin [VerfasserIn]   i
 Unterberg, Andreas [VerfasserIn]   i
Titel:Routine ICU surveillance after brain tumor surgery
Titelzusatz:patient selection using machine learning
Verf.angabe:Jan-Oliver Neumann, Stephanie Schmidt, Amin Nohman, Paul Naser, Martin Jakobs and Andreas Unterberg
E-Jahr:2024
Jahr:26 September 2024
Umfang:12 S.
Illustrationen:Diagramme
Fussnoten:Gesehen am 26.03.2025
Weitere Titel:Titel des special issue: Neurocritical Care: New Insights and Challenges
Titel Quelle:Enthalten in: Journal of Clinical Medicine
Ort Quelle:Basel : MDPI, 2012
Jahr Quelle:2024
Band/Heft Quelle:13(2024), 19, special issue, Artikel-ID 5747, Seite 1-12
ISSN Quelle:2077-0383
Abstract:Background/Objectives: Routine postoperative ICU admission following brain tumor surgery may not benefit selected patients. The objective of this study was to develop a risk prediction instrument for early (within 24 h) postoperative adverse events using machine learning techniques. Methods: Retrospective cohort of 1000 consecutive adult patients undergoing elective brain tumor resection. Nine events/interventions (CPR, reintubation, return to OR, mechanical ventilation, vasopressors, impaired consciousness, intracranial hypertension, swallowing disorders, and death) were chosen as target variables. Potential prognostic features (n = 27) from five categories were chosen and a gradient boosting algorithm (XGBoost) was trained and cross-validated in a 5 × 5 fashion. Prognostic performance, potential clinical impact, and relative feature importance were analyzed. Results: Adverse events requiring ICU intervention occurred in 9.2% of cases. Other events not requiring ICU treatment were more frequent (35% of cases). The boosted decision trees yielded a cross-validated ROC-AUC of 0.81 ± 0.02 (mean ± CI95) when using pre- and post-op data. Using only pre-op data (scheduling decisions), ROC-AUC was 0.76 ± 0.02. PR-AUC was 0.38 ± 0.04 and 0.27 ± 0.03 for pre- and post-op data, respectively, compared to a baseline value (random classifier) of 0.092. Targeting a NPV of at least 95% would require ICU admission in just 15% (pre- and post-op data) or 30% (only pre-op data) of cases when using the prediction algorithm. Conclusions: Adoption of a risk prediction instrument based on boosted trees can support decision-makers to optimize ICU resource utilization while maintaining adequate patient safety. This may lead to a relevant reduction in ICU admissions for surveillance purposes.
DOI:doi:10.3390/jcm13195747
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

kostenfrei: Volltext: https://doi.org/10.3390/jcm13195747
 kostenfrei: Volltext: https://www.mdpi.com/2077-0383/13/19/5747
 DOI: https://doi.org/10.3390/jcm13195747
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:complications
 craniotomy
 ICU
 machine learning
 postoperative surveillance
K10plus-PPN:1920611207
Verknüpfungen:→ Zeitschrift

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