Aneurysmal subarachnoid hemorrhage (aSAH) carries significant mortality and disability rates, with rebleeding posing a grave risk, particularly in ante- rior communicating artery (AcoA) aneurysms. This retrospective study aims to analyze preoperative and intraoperative variables of patients with ruptured AcoA aneurysms, evaluating the association of these variables with patient outcomes using machine learn- ing techniques, proposing a prognostic score. Materials and Methods: A retrospective study was conducted on 50 patients who underwent microsurgical clipping for a ruptured AcoA aneurysm at San Giovanni Bosco Hospital, Turin, Italy. The clinical and aneurysmal data—including clinical evaluations, risk factors, aneurysmal characteristics, and intra- and postoperative details—were examined. The study population was analyzed using machine learning techniques such as the MRMR algorithm for feature selection, and the LASSO method was employed to construct linear predictive models based on these fea- tures. Results: The study cohort had a mean age of 54 years, with 26 female and 24 male patients. Temporary clipping of main vessels was performed in 96% of procedures, with a mean duration of 3.74 min. Postoperatively, the mean Intensive Care Unit (ICU) stay was 7.28 days, with 14% mortality at 30 days and 4% within the first week. At the six-month follow-up, 63% of discharged patients had a Glasgow outcome scale (GOS) of 5, with radio- logical confirmation of complete aneurysm exclusion in 98% of cases. Machine learning techniques identified the significant predictors of patient outcomes, with LASSO algo- rithms generating linear models to predict the GOS at discharge and at 6 months follow-up. Conclusions: Preoperative factors like the BNI score, Vasograde, and preoperative cerebral edema demonstrate significant correlations with patient outcomes post-clipping. Notably, intraoperative bleeding and extended temporary clipping durations (over 3 min) emerge as pivotal intraoperative considerations. Moreover, the AcoA prognostic score shows promise in predicting patient outcomes, discharge plans, and ICU duration

Proposal of a Machine Learning Based Prognostic Score for Ruptured Microsurgically Treated Anterior Communicating Artery Aneurysms / Minardi, Massimiliano; Bianconi, Andrea; Mesin, Luca; Salvati, Luca Francesco; Griva, Federico; Narducci, Alessandro. - In: JOURNAL OF CLINICAL MEDICINE. - ISSN 2077-0383. - 14:2(2025), pp. 1-14. [10.3390/jcm14020578]

Proposal of a Machine Learning Based Prognostic Score for Ruptured Microsurgically Treated Anterior Communicating Artery Aneurysms

Mesin, Luca;
2025

Abstract

Aneurysmal subarachnoid hemorrhage (aSAH) carries significant mortality and disability rates, with rebleeding posing a grave risk, particularly in ante- rior communicating artery (AcoA) aneurysms. This retrospective study aims to analyze preoperative and intraoperative variables of patients with ruptured AcoA aneurysms, evaluating the association of these variables with patient outcomes using machine learn- ing techniques, proposing a prognostic score. Materials and Methods: A retrospective study was conducted on 50 patients who underwent microsurgical clipping for a ruptured AcoA aneurysm at San Giovanni Bosco Hospital, Turin, Italy. The clinical and aneurysmal data—including clinical evaluations, risk factors, aneurysmal characteristics, and intra- and postoperative details—were examined. The study population was analyzed using machine learning techniques such as the MRMR algorithm for feature selection, and the LASSO method was employed to construct linear predictive models based on these fea- tures. Results: The study cohort had a mean age of 54 years, with 26 female and 24 male patients. Temporary clipping of main vessels was performed in 96% of procedures, with a mean duration of 3.74 min. Postoperatively, the mean Intensive Care Unit (ICU) stay was 7.28 days, with 14% mortality at 30 days and 4% within the first week. At the six-month follow-up, 63% of discharged patients had a Glasgow outcome scale (GOS) of 5, with radio- logical confirmation of complete aneurysm exclusion in 98% of cases. Machine learning techniques identified the significant predictors of patient outcomes, with LASSO algo- rithms generating linear models to predict the GOS at discharge and at 6 months follow-up. Conclusions: Preoperative factors like the BNI score, Vasograde, and preoperative cerebral edema demonstrate significant correlations with patient outcomes post-clipping. Notably, intraoperative bleeding and extended temporary clipping durations (over 3 min) emerge as pivotal intraoperative considerations. Moreover, the AcoA prognostic score shows promise in predicting patient outcomes, discharge plans, and ICU duration
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2996718