Machine Learning-Based Prediction of Clinical Outcomes in Microsurgical Clipping Treatments of Cerebral Aneurysms

Toader, Corneliu; Brehar, Felix-Mircea; Radoi, Mugurel Petrinel; Covache-Busuioc, Razvan-Adrian; Glavan, Luca-Andrei; Grama, Matei; Corlatescu, Antonio-Daniel; Costin, Horia Petre; Bratu, Bogdan-Gabriel; Popa, Andrei Adrian; Serban, Matei; Ciurea, Alexandru Vladimir

doi:10.3390/diagnostics14192156

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Open AccessArticle

Machine Learning-Based Prediction of Clinical Outcomes in Microsurgical Clipping Treatments of Cerebral Aneurysms

by

Corneliu Toader

^1,2,

Felix-Mircea Brehar

^1,3,*,

Mugurel Petrinel Radoi

^1,2,*,

Razvan-Adrian Covache-Busuioc

¹,

Luca-Andrei Glavan

¹,

Matei Grama

⁴

,

Antonio-Daniel Corlatescu

¹

,

Horia Petre Costin

¹

,

Bogdan-Gabriel Bratu

¹,

Andrei Adrian Popa

¹,

Matei Serban

¹ and

Alexandru Vladimir Ciurea

^1,5

¹

Department of Neurosurgery “Carol Davila”, University of Medicine and Pharmacy, 030167 Bucharest, Romania

²

Department of Vascular Neurosurgery, National Institute of Neurology and Neurovascular Diseases, 077160 Bucharest, Romania

³

Department of Neurosurgery, Clinical Emergency Hospital “Bagdasar-Arseni”, 041915 Bucharest, Romania

⁴

Department of Research and Development, Syndical.io, street Icoanei 29A, 020452 Bucharest, Romania

⁵

Neurosurgery Department, Sanador Clinical Hospital, 010991 Bucharest, Romania

^*

Authors to whom correspondence should be addressed.

Diagnostics 2024, 14(19), 2156; https://doi.org/10.3390/diagnostics14192156

Submission received: 2 August 2024 / Revised: 19 September 2024 / Accepted: 19 September 2024 / Published: 27 September 2024

(This article belongs to the Special Issue Machine Learning and Deep Learning for Healthcare Data Processing and Analyzing)

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Abstract

Background: This study investigates the application of Machine Learning techniques to predict clinical outcomes in microsurgical clipping treatments of cerebral aneurysms, aiming to enhance healthcare processes through informed clinical decision making. Methods: Relying on a dataset of 344 patients’ preoperative characteristics, various ML classifiers were trained to predict outcomes measured by the Glasgow Outcome Scale (GOS). The study’s results were reported through the means of ROC-AUC scores for outcome prediction and the identification of key predictors using SHAP analysis. Results: The trained models achieved ROC-AUC scores of 0.72 ± 0.03 for specific GOS outcome prediction and 0.78 ± 0.02 for binary classification of outcomes. The SHAP explanation analysis identified intubation as the most impactful factor influencing treatment outcomes’ predictions for the trained models. Conclusions: The study demonstrates the potential of ML for predicting surgical outcomes of ruptured cerebral aneurysm treatments. It acknowledged the need for high-quality datasets and external validation to enhance model accuracy and generalizability.

Keywords: machine learning; ruptured intracranial aneurysm; treatment outcome; microsurgical clipping

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MDPI and ACS Style

Toader, C.; Brehar, F.-M.; Radoi, M.P.; Covache-Busuioc, R.-A.; Glavan, L.-A.; Grama, M.; Corlatescu, A.-D.; Costin, H.P.; Bratu, B.-G.; Popa, A.A.; et al. Machine Learning-Based Prediction of Clinical Outcomes in Microsurgical Clipping Treatments of Cerebral Aneurysms. Diagnostics 2024, 14, 2156. https://doi.org/10.3390/diagnostics14192156

AMA Style

Toader C, Brehar F-M, Radoi MP, Covache-Busuioc R-A, Glavan L-A, Grama M, Corlatescu A-D, Costin HP, Bratu B-G, Popa AA, et al. Machine Learning-Based Prediction of Clinical Outcomes in Microsurgical Clipping Treatments of Cerebral Aneurysms. Diagnostics. 2024; 14(19):2156. https://doi.org/10.3390/diagnostics14192156

Chicago/Turabian Style

Toader, Corneliu, Felix-Mircea Brehar, Mugurel Petrinel Radoi, Razvan-Adrian Covache-Busuioc, Luca-Andrei Glavan, Matei Grama, Antonio-Daniel Corlatescu, Horia Petre Costin, Bogdan-Gabriel Bratu, Andrei Adrian Popa, and et al. 2024. "Machine Learning-Based Prediction of Clinical Outcomes in Microsurgical Clipping Treatments of Cerebral Aneurysms" Diagnostics 14, no. 19: 2156. https://doi.org/10.3390/diagnostics14192156

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Machine Learning-Based Prediction of Clinical Outcomes in Microsurgical Clipping Treatments of Cerebral Aneurysms

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