Background and purpose Delayed cerebral ischemia (DCI) is a severe complication in patients with aneurysmal subarachnoid hemorrhage. Several associated predictors have been previously identified. However, their predictive value is generally low. We hypothesize that Machine Learning (ML) algorithms for the prediction of DCI using a combination of clinical and image data lead to higher predictive accuracy than previously applied logistic regressions.
Materials and methods Clinical and baseline CT image data from 317 patients with aneurysmal subarachnoid hemorrhage were included. Three types of analysis were performed to predict DCI. First, the prognostic value of known predictors was assessed with logistic regression models. Second, ML models were created using all clinical variables. Third, image features were extracted from the CT images using an auto-encoder and combined with clinical data to create ML models. Accuracy was evaluated based on the area under the curve (AUC), sensitivity and specificity with 95% CI.
Results The best AUC of the logistic regression models for known predictors was 0.63 (95% CI 0.62 to 0.63). For the ML algorithms with clinical data there was a small but statistically significant improvement in the AUC to 0.68 (95% CI 0.65 to 0.69). Notably, aneurysm width and height were included in many of the ML models. The AUC was highest for ML models that also included image features: 0.74 (95% CI 0.72 to 0.75).
Conclusion ML algorithms significantly improve the prediction of DCI in patients with aneurysmal subarachnoid hemorrhage, particularly when image features are also included. Our experiments suggest that aneurysm characteristics are also associated with the development of DCI.
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Contributors LAR: Machine learning, deep learning, programming, data pre-processing, statistical analysis, study design, literature review and manuscript writing and review. WEvdS: Study design, data pre-processing, literature and manuscript review. RSB: Machine learning, deep learning, programming and manuscript and literature review. CBLMM, RvdB, IJAZ: Study design, data collection, and manuscript review. DV: Data management, study design, manuscript review. WPV: Study design and manuscript review. AHZ: Machine learning, deep learning, statistical analysis, study design and supervision and manuscript review. GJS: Study design, supervision and manuscript review. SDO: Machine learning, deep learning, data analysis, study design and supervision, manuscript writing and review. HAM: Machine learning, deep learning, data analysis, study design and supervision, manuscript writing and review.
Funding This work was supported by ITEA3 grant number 14003 Medolution.
Competing interests None declared.
Patient consent Not required.
Ethics approval The medical ethics committee of Academic Medical Center.
Provenance and peer review Not commissioned; externally peer reviewed.
Data sharing statement Because of the sensitive nature of the data, it is available upon request to the corresponding author. All code used is publicly available at the authors Github page (https://github.com/L-Ramos/DCI_Prediction.git).
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