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Open AccessArticle
Routine ICU Surveillance after Brain Tumor Surgery: Patient Selection Using Machine Learning
by
Jan-Oliver Neumann
Jan-Oliver Neumann *,
Stephanie Schmidt
Stephanie Schmidt ,
Amin Nohman
Amin Nohman ,
Paul Naser
Paul Naser ,
Martin Jakobs
Martin Jakobs and
Andreas Unterberg
Andreas Unterberg
Department of Neurosurgery, University Hospital Heidelberg, 69120 Heidelberg, Germany
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2024, 13(19), 5747; https://doi.org/10.3390/jcm13195747 (registering DOI)
Submission received: 16 August 2024
/
Revised: 11 September 2024
/
Accepted: 24 September 2024
/
Published: 26 September 2024
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.
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MDPI and ACS Style
Neumann, J.-O.; Schmidt, S.; Nohman, A.; Naser, P.; Jakobs, M.; Unterberg, A.
Routine ICU Surveillance after Brain Tumor Surgery: Patient Selection Using Machine Learning. J. Clin. Med. 2024, 13, 5747.
https://doi.org/10.3390/jcm13195747
AMA Style
Neumann J-O, Schmidt S, Nohman A, Naser P, Jakobs M, Unterberg A.
Routine ICU Surveillance after Brain Tumor Surgery: Patient Selection Using Machine Learning. Journal of Clinical Medicine. 2024; 13(19):5747.
https://doi.org/10.3390/jcm13195747
Chicago/Turabian Style
Neumann, Jan-Oliver, Stephanie Schmidt, Amin Nohman, Paul Naser, Martin Jakobs, and Andreas Unterberg.
2024. "Routine ICU Surveillance after Brain Tumor Surgery: Patient Selection Using Machine Learning" Journal of Clinical Medicine 13, no. 19: 5747.
https://doi.org/10.3390/jcm13195747
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