Outcome prediction in aneurysmal subarachnoid hemorrhage: a comparison of machine learning methods and established clinico-radiological scores
Open Access
- 20 January 2021
- journal article
- research article
- Published by Springer Science and Business Media LLC in Neurosurgical Review
- Vol. 44 (5), 2837-2846
- https://doi.org/10.1007/s10143-020-01453-6
Abstract
Reliable prediction of outcomes of aneurysmal subarachnoid hemorrhage (aSAH) based on factors available at patient admission may support responsible allocation of resources as well as treatment decisions. Radiographic and clinical scoring systems may help clinicians estimate disease severity, but their predictive value is limited, especially in devising treatment strategies. In this study, we aimed to examine whether a machine learning (ML) approach using variables available on admission may improve outcome prediction in aSAH compared to established scoring systems. Combined clinical and radiographic features as well as standard scores (Hunt & Hess, WFNS, BNI, Fisher, and VASOGRADE) available on patient admission were analyzed using a consecutive single-center database of patients that presented with aSAH (n = 388). Different ML models (seven algorithms including three types of traditional generalized linear models, as well as a tree bosting algorithm, a support vector machine classifier (SVMC), a Naive Bayes (NB) classifier, and a multilayer perceptron (MLP) artificial neural net) were trained for single features, scores, and combined features with a random split into training and test sets (4:1 ratio), ten-fold cross-validation, and 50 shuffles. For combined features, feature importance was calculated. There was no difference in performance between traditional and other ML applications using traditional clinico-radiographic features. Also, no relevant difference was identified between a combined set of clinico-radiological features available on admission (highest AUC 0.78, tree boosting) and the best performing clinical score GCS (highest AUC 0.76, tree boosting). GCS and age were the most important variables for the feature combination. In this cohort of patients with aSAH, the performance of functional outcome prediction by machine learning techniques was comparable to traditional methods and established clinical scores. Future work is necessary to examine input variables other than traditional clinico-radiographic features and to evaluate whether a higher performance for outcome prediction in aSAH can be achieved.Keywords
Funding Information
- BMBF (PREDICTioN2020)
- European Commission (777107)
This publication has 39 references indexed in Scilit:
- Clinical Outcome Prediction in Aneurysmal Subarachnoid Hemorrhage Using Bayesian Neural Networks with Fuzzy Logic InferencesComputational and Mathematical Methods in Medicine, 2013
- Subarachnoid haemorrhage WFNS grade V: is maximal treatment worthwhile?Acta Neurochirurgica, 2013
- A Simple and Quantitative Method to Predict Symptomatic Vasospasm After Subarachnoid Hemorrhage Based on Computed TomographyNeurosurgery, 2012
- Guidelines for the Management of Aneurysmal Subarachnoid HemorrhageStroke, 2012
- Prediction of Symptomatic Vasospasmafter Subarachnoid Hemorrhage: The Modified Fisher ScaleNeurosurgery, 2006
- A universal subarachnoid hemorrhage scale: report of a committee of the World Federation of Neurosurgical Societies.Journal of Neurology, Neurosurgery & Psychiatry, 1988
- Interobserver agreement for the assessment of handicap in stroke patients.Stroke, 1988
- Relation of Cerebral Vasospasm to Subarachnoid Hemorrhage Visualized by Computerized Tomographic ScanningNeurosurgery, 1980
- SCORE FOR RESPIRATORY-DISTRESS SYNDROMEThe Lancet, 1969
- Surgical Risk as Related to Time of Intervention in the Repair of Intracranial AneurysmsJournal of Neurosurgery, 1968