Routine ICU surveillance after brain tumor surgery: patient selection using machine learning
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: Retrosp...
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| Main Authors: | , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
26 September 2024
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| In: |
Journal of Clinical Medicine
Year: 2024, Volume: 13, Issue: 19, Pages: 1-12 |
| ISSN: | 2077-0383 |
| DOI: | 10.3390/jcm13195747 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.3390/jcm13195747 Verlag, kostenfrei, Volltext: https://www.mdpi.com/2077-0383/13/19/5747 
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| Author Notes: | Jan-Oliver Neumann, Stephanie Schmidt, Amin Nohman, Paul Naser, Martin Jakobs and Andreas Unterberg |
| Summary: | 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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| Item Description: | Gesehen am 26.03.2025 |
| Physical Description: | Online Resource |
| ISSN: | 2077-0383 |
| DOI: | 10.3390/jcm13195747 |