The Prediction of Functional Outcome After Microsurgical Treatment of Unruptured Intracranial Aneurysm Based on Machine Learning

Ishankulov, Timur; Grebenev, Fyodor; Strunina, Yu V; Shekhtman, O D; Eliava, Shalva; Szostek, Małgorzata

doi:10.3233/shti220503

Cited by 1 publication

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Staartjes et al addressed this issue in their pilot study and were able to demonstrate the feasibility of such predictive models for functional outcomes and postoperative complications 15 . Moreover, Ishankulov et al published promising predictive models for a functional outcome (mRS) after the treatment of UIAs in a pilot study 16 . However, both studies randomly assigned their patients to either the train or test group (random train-test split) 49 .…”

Section: Discussionmentioning

confidence: 99%

“…Fuse et al published an external validation of their preoperative prediction model for postoperative outcomes after chronic subdural hematoma evacuation and external validation revealed an excellent ROC-AUC of 0.860 56 . However, no external validation of a preoperative prediction model for microsurgically treated UIAs has been published so far 15 , 16 , 46 .…”

Section: Discussionmentioning

confidence: 99%

“…Several ML-based prediction tools for the complication- and treatment-aware outcomes of patients with aneurysmal subarachnoid hemorrhage (aSAH) have been published 11 – 14 . However, very few studies have been published on prediction models for UIAs 15 , 16 .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Machine learning based outcome prediction of microsurgically treated unruptured intracranial aneurysms

Stroh,

Stefanits,

Maletzky

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Machine learning (ML) has revolutionized data processing in recent years. This study presents the results of the first prediction models based on a long-term monocentric data registry of patients with microsurgically treated unruptured intracranial aneurysms (UIAs) using a temporal train-test split. Temporal train-test splits allow to simulate prospective validation, and therefore provide more accurate estimations of a model’s predictive quality when applied to future patients. ML models for the prediction of the Glasgow outcome scale, modified Rankin Scale (mRS), and new transient or permanent neurological deficits (output variables) were created from all UIA patients that underwent microsurgery at the Kepler University Hospital Linz (Austria) between 2002 and 2020 (n = 466), based on 18 patient- and 10 aneurysm-specific preoperative parameters (input variables). Train-test splitting was performed with a temporal split for outcome prediction in microsurgical therapy of UIA. Moreover, an external validation was conducted on an independent external data set (n = 256) of the Department of Neurosurgery, University Medical Centre Hamburg-Eppendorf. In total, 722 aneurysms were included in this study. A postoperative mRS > 2 was best predicted by a quadratic discriminant analysis (QDA) estimator in the internal test set, with an area under the receiver operating characteristic curve (ROC-AUC) of 0.87 ± 0.03 and a sensitivity and specificity of 0.83 ± 0.08 and 0.71 ± 0.07, respectively. A Multilayer Perceptron predicted the post- to preoperative mRS difference > 1 with a ROC-AUC of 0.70 ± 0.02 and a sensitivity and specificity of 0.74 ± 0.07 and 0.50 ± 0.04, respectively. The QDA was the best model for predicting a permanent new neurological deficit with a ROC-AUC of 0.71 ± 0.04 and a sensitivity and specificity of 0.65 ± 0.24 and 0.60 ± 0.12, respectively. Furthermore, these models performed significantly better than the classic logistic regression models (p < 0.0001). The present results showed good performance in predicting functional and clinical outcomes after microsurgical therapy of UIAs in the internal data set, especially for the main outcome parameters, mRS and permanent neurological deficit. The external validation showed poor discrimination with ROC-AUC values of 0.61, 0.53 and 0.58 respectively for predicting a postoperative mRS > 2, a pre- and postoperative difference in mRS > 1 point and a GOS < 5. Therefore, generalizability of the models could not be demonstrated in the external validation. A SHapley Additive exPlanations (SHAP) analysis revealed that this is due to the most important features being distributed quite differently in the internal and external data sets. The implementation of newly available data and the merging of larger databases to form more broad-based predictive models is imperative in the future.

show abstract

Section: Discussionmentioning

confidence: 99%