The Prediction of Adverse Events After Microsurgery for Intracranial Unruptured Aneurysms (PRAEMIUM) Study

Introduction Unruptured intracranial aneurysms (UIAs) are incidentally detected at an increasing rate, mostly owing to the rise in availability of non-invasive cranial imaging. Decision-making in UIAs is complex and requires consideration of many risk factors for aneurysm growth and rupture to balance the benefits and risks of treatment versus observation. This is due to: 1) the high morbidity and case fatality inherent to aneurysmal subarachnoid hemorrhage (SAH) 2) the relatively low rupture rate of unruptured aneurysms; 3) the potential morbidity and mortality rate associated with either microsurgical or endovascular treatment.

Some consistent risk factors for rupture have been identified, including involvement of the posterior circulation, larger diameter, higher age, and some specific populations such as Japanese and Finnish patients. Many other risk factors have been suggested based on varying levels of evidence. However, it is difficult to integrate this considerable number of factors into a single risk assessment and to present a clear clinical decision making algorithm to patients. A range of scoring systems have been developed and validated to approximate the risk of rupture (PHASES) and growth (ELAPSS) or to balance the risks and benefits of microsurgical treatment versus follow-up imaging directly (UIATS) by integrating some of these risk factors. Still, these scores are focused on predicting rupture events instead of neurological outcome. In addition, they usually are focused on solely one outcome, instead of providing a wide range of objective predictive analytics that may then improve shared decision-making.

Machine learning (ML) methods have been extraordinarily effective at integrating many clinical patient variables into one holistic risk prediction tailored to each patient. A previous pilot study has been carried out to assess the feasibility of predicting surgical outcomes after surgery for UIAs in a small single-center sample, and it was found that prediction was feasible with good performance metrics, and the most important factors to be included in such models were also identified. A robust, multicenter, externally validated prediction model or predictive score for surgical outcome after microsurgery for UIAs does not yet exist.

Methods Data will be collected by a range of international centers. Overall, the model will be built and publication will be compiled according to the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines.

Each center will collect their data either retrospectively, or from a prospective registry, or from a prospective registry supplemented by retrospectively collected variables. Data from patients operated from January 1st 2010 and onwards will be eligible for inclusion. Data collection should be completed, and deidentified data should be sent to the sponsor institution.