Journal article
Development of machine learning-based preoperative predictive analytics for unruptured intracranial aneurysm surgery: a pilot study.
- Staartjes VE Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland.
- Sebök M Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland.
- Blum PG Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland.
- Serra C Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland.
- Germans MR Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland.
- Krayenbühl N Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland.
- Regli L Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland.
- Esposito G Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland. giuseppe.esposito@usz.ch.
- 2020-05-03
Published in:
- Acta neurochirurgica. - 2020
Clipping
Intracranial unruptured aneurysm
Machine learning
Outcome prediction
Predictive analytics
Prognostic modeling
English
BACKGROUND
The decision to treat unruptured intracranial aneurysms (UIAs) or not is complex and requires balancing of risk factors and scores. Machine learning (ML) algorithms have previously been effective at generating highly accurate and comprehensive individualized preoperative predictive analytics in transsphenoidal pituitary and open tumor surgery. In this pilot study, we evaluate whether ML-based prediction of clinical endpoints is feasible for microsurgical management of UIAs.
METHODS
Based on data from a prospective registry, we developed and internally validated ML models to predict neurological outcome at discharge, as well as presence of new neurological deficits and any complication at discharge. Favorable neurological outcome was defined as modified Rankin scale (mRS) 0 to 2. According to the Clavien-Dindo grading (CDG), every adverse event during the post-operative course (surgery and not surgery related) is recorded as a complication. Input variables included age; gender; aneurysm complexity, diameter, location, number, and prior treatment; prior subarachnoid hemorrhage (SAH); presence of anticoagulation, antiplatelet therapy, and hypertension; microsurgical technique and approach; and various unruptured aneurysm scoring systems (PHASES, ELAPSS, UIATS).
RESULTS
We included 156 patients (26.3% male; mean [SD] age, 51.7 [11.0] years) with UIAs: 37 (24%) of them were treated for multiple aneurysm and 39 (25%) were treated for a complex aneurysm. Poor neurological outcome (mRS ≥ 3) was seen in 12 patients (7.7%) at discharge. New neurological deficits were seen in 10 (6.4%), and any kind of complication occurred in 20 (12.8%) patients. In the internal validation cohort, area under the curve (AUC) and accuracy values of 0.63-0.77 and 0.78-0.91 were observed, respectively.
CONCLUSIONS
Application of ML enables prediction of early clinical endpoints after microsurgery for UIAs. Our pilot study lays the groundwork for development of an externally validated multicenter clinical prediction model.
The decision to treat unruptured intracranial aneurysms (UIAs) or not is complex and requires balancing of risk factors and scores. Machine learning (ML) algorithms have previously been effective at generating highly accurate and comprehensive individualized preoperative predictive analytics in transsphenoidal pituitary and open tumor surgery. In this pilot study, we evaluate whether ML-based prediction of clinical endpoints is feasible for microsurgical management of UIAs.
METHODS
Based on data from a prospective registry, we developed and internally validated ML models to predict neurological outcome at discharge, as well as presence of new neurological deficits and any complication at discharge. Favorable neurological outcome was defined as modified Rankin scale (mRS) 0 to 2. According to the Clavien-Dindo grading (CDG), every adverse event during the post-operative course (surgery and not surgery related) is recorded as a complication. Input variables included age; gender; aneurysm complexity, diameter, location, number, and prior treatment; prior subarachnoid hemorrhage (SAH); presence of anticoagulation, antiplatelet therapy, and hypertension; microsurgical technique and approach; and various unruptured aneurysm scoring systems (PHASES, ELAPSS, UIATS).
RESULTS
We included 156 patients (26.3% male; mean [SD] age, 51.7 [11.0] years) with UIAs: 37 (24%) of them were treated for multiple aneurysm and 39 (25%) were treated for a complex aneurysm. Poor neurological outcome (mRS ≥ 3) was seen in 12 patients (7.7%) at discharge. New neurological deficits were seen in 10 (6.4%), and any kind of complication occurred in 20 (12.8%) patients. In the internal validation cohort, area under the curve (AUC) and accuracy values of 0.63-0.77 and 0.78-0.91 were observed, respectively.
CONCLUSIONS
Application of ML enables prediction of early clinical endpoints after microsurgery for UIAs. Our pilot study lays the groundwork for development of an externally validated multicenter clinical prediction model.
- Language
-
- English
- Open access status
- closed
- Identifiers
-
- DOI 10.1007/s00701-020-04355-0
- PMID 32358656
- Persistent URL
- https://folia.unifr.ch/global/documents/127493
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