None listed
Conditions
Brief summary
Pre-treatment rebleeding following aneurysmal subarachnoid hemorrhage (aSAH) independently increases the risk of death and a poor neurological outcome. Over three-quarters of re-bleeds occur within 12 hours so it is recommended that aneurysm treatment occur as ‘soon as feasible’. However, additional factors such as aneurysm location, size, clinical grade, radiological grade, systemic arterial hypertension, systolic blood pressure over 160mmHg, hydrocephalus, intracerebral hematoma (ICH), subdural haematoma (SDH) and intraventricular hemorrhage (IVH) have also been associated with rebleeding. There is likely to be a complex interplay between the time-to-treatment and these other factors as even with reduced treatment times, re-bleeding still affects between 2 to 10% of patients. Several models have been recently described attempting to estimate the risk of pre-treatment rebleeding but none demonstrate a low risk of bias, high clinical applicability and useability. Even the most rigorously formulated model suffers bias from inconsistent predictor ascertainment and the use of historical patient data for model formulation not reflective of current practice which aim to treat ruptured aneurysms within 24 hours termed ultra-early treatment using predominantly endovascular methods. The model also uses as predictors age, sex, aneurysm irregularity and pre-treatment cerebrospinal fluid diversion despite the lack of evidence for an association between these factors and rebleeding whilst it neglects other validated factors such as ICH, IVH and SDH at the time of diagnosis. The model also calculates probability but without elucidating the threshold for altering patient management. In this study we seek to address these limitations using machine learning techniques, referring to types of artificial intelligence which can be trained to automatically detect complex nonlinear relationships between multiple competing factors in the prediction of outcomes of interest. In this study we aim to train a supervised machine learning model using our 14-year cohort of consecutive aSAH patients with ruptured saccular aneurysms managed using an endovascular-first, ultra-early paradigm in accordance with current best-practice guidelines. We will use as inputs a number of routinely available, individually validated clinical and radiological predictor parameters together with the time to re-bleeding or treatment as an outcome to develop this predictive model. This model will be capable of resolving the complex interaction between the time-to-treatment and other re-bleed predictors. Ultimately, it will provide clinicians with actionable information in the form of an optimal time period for the treatment of any individual patient with a ruptured saccular aneurysm to minimise their risk of pre-treatment re-bleeding.
Interventions
This study will use retrospective machine learning analysis of prospectively collected, de-identified data from the Sir Charles Gairdner Hospital saccular aneurysmal subarachnoid haemorrhage database from the 1st of January 2009 to the 31st of December 2022 inclusive. The database has been formulated from the amalgamation of two independently compiled, prospective institutional databases of Sir Charles Gairdner Hospital in Nedlands, Perth, Western Australia. This included the Department of Neuro
This study will use retrospective machine learning analysis of prospectively collected, de-identified data from the Sir Charles Gairdner Hospital saccular aneurysmal subarachnoid haemorrhage database from the 1st of January 2009 to the 31st of December 2022 inclusive. The database has been formulated from the amalgamation of two independently compiled, prospective institutional databases of Sir Charles Gairdner Hospital in Nedlands, Perth, Western Australia. This included the Department of Neurosurgery database composed of hospital admissions, clinical management software entries and Business Intelligence Unit data used for funding allocation together with the Department of Neuroradiology Transcranial Doppler (TCD) ultrasonography database composed of all non-palliated patients admitted to SCGH with subarachnoid haemorrhage of any aetiology. All non-duplicated patients entered into either of these two independently compiled databases within the study period with aneurysmal subarachnoid haemorrhage due to a singular culprit saccular aneurysm identified at the time of admission using either CT-angiography (CT-A), Magnetic Resonance Angiography (MRA) and/or Digital Subtraction Catheter Angiography (DSA) were included. Patients were excluded if they had SAH from a non-saccular aneurysmal source or recurrent SAH from a previously treated saccular aneurysm. The age and sex of the patient at the time of admission will be retrospectively collected. We will retrospectively collect data regarding the following evidence based predictors of pre-treatment re-bleeding: the World Federation of Neurosurgical Societies clinical grade at presentation, the Fisher radiological grade at presentation, the maximal dome size of the ruptured aneurysm in millimetres, the parent vessel location of the ruptured aneurysm, the presence of hydrocephalus at the time of diagnosis, the presence of an intracerebral haematoma at diagnosis, the presence of a subdural haematoma at the time of diagnosis and the presence of intraventricular hemorrhage at diagnosis. We will also retrospectively determine for each patient whether they were managed with an intention for acute aneurysm treatment, an intention for delayed treatment pending neurological improvement and the time from imaging diagnosis of subarachnoid haemorrhage to the time of aneurysm re-bleeding or treatment. No additional prospective information will be collected from patients and all demographic, predictor and outcome information above outlined will be determined through retrospective assessment of each patient's existing medical records, blood test results and radiological imaging including request forms. During the entire 14-year study period, our institutional management protocol for aSAH from a rupture saccular aneurysm was treatment within 24 hours (ultra-early) using an 'endovascular-first' approach in accordance with the results of the 2002 International Subarachnoid Aneurysm Trial (ISAT), the 2002 publication 'Ultra-early surgery for aneurysmal subarachnoid hemorrhage: outcomes for a consecutive series of 391 patients not selected by grade or age' published in the Journal of Neurosurgery as well as both the 2012 and 2023 American Heart Association/American Stroke Association Guideline for the Management of Patients With Aneurysmal Subarachnoid Hemorrhage.
Sponsors
Department of Neurosurgery
Eligibility
Sex/Gender
All
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
The Sir Charles Gairdner Hospital (SCGH) saccular aneurysmal subarachnoid haemorrhage database contains prospectively collected data for patients 18 years or over admitted to SCGH in Nedlands, Western Australia (WA) between 1st January 2009 and 31st December 2022 inclusive with aneurysmal SAH confirmed using either non-contrast computerised tomography (CT) brain scanning and/or cerebrospinal fluid spectroscopic analysis due to the rupture of a singular culprit saccular cerebral aneurysm as identified on CT-angiography, magnetic resonance angiography and/or digital subtraction catheter angiography.
Exclusion criteria
Patients were excluded from the study population if they experienced cryptogenic subarachnoid haemorrhage (SAH), peri-mesencephalic SAH, SAH due to arterial dissection, fusiform, mycotic or blister type aneurysm, recurrent SAH from a previously treated saccular aneurysm or SAH due to a ruptured arteriovenous malformation.
Outcome results
None listed