Intracranial aneurysms and subarachnoid hemorrhage: Clinical studies on diagnosis and treatment

Computerized tomography angiography (CTA) can be performed quicker, safer and cheaper than digital subtraction angiography (DSA) in patients after aneurysmal subarachnoid hemorrhage (SAH). However, DSA is still regarded as the gold standard in the diagnosis of intracranial ruptured aneurysms. No studies have specifically addressed the value of CTA in planning of endovascular treatment of ruptured aneurysms. Mathieu van der Jagt investigates the diagnostic value of CTA for endovascular treatment compared with DSA, in cooperation with Radiology. He hypothesizes is that, at least in a subset of patients, CTA suffices and DSA can be omitted in the planning of endovascular treatment. Another project concerns a systematic review on rupture rate of unruptured intracranial aneurysms (UIAs), estimating the rupture rate of UIAs based on the available observational studies. The statistical method used will allow for correction for methodological quality per study, lea! ding to an estimate of rupture rate that is based on less biased data. The PhD project also evaluates the localizing value of blood distribution on CTA for the location of ruptured intracranial aneurysm; it includes a cohort study on the impact of early surgery on overall outcome after aneurysmal SAH

Fluid management of the neurological patient: A concise review

Maintenance fluids in critically ill brain-injured patients are part of routine critical care. Both the amounts of fluid volumes infused and the type and tonicity of maintenance fluids are relevant in understanding the impact of fluids on the pathophysiology of secondary brain injuries in these patients. In this narrative review, current evidence on routine fluid management of critically ill brain-injured patients and use of haemodynamic monitoring is summarized. Pertinent guidelines and consensus statements on fluid management for brain-injured patients are highlighted. In general, existing guidelines indicate that fluid management in these neurocritical care patients should be targeted at euvolemia using isotonic fluids. A critical appraisal is made of the available literature regarding the appropriate amount of fluids, haemodynamic monitoring and which types of fluids should be administered or avoided and a practical approach to fluid management is elaborated. Although hypovolemia is bound to contribute to secondary brain injury, some more recent data have emerged indicating the potential risks of fluid overload. However, it is acknowledged that many factors govern the relationship between fluid management and cerebral blood flow and oxygenation and more research seems warranted to optimise fluid management and improve outcomes

Airfoil optimization by using the Manifold Mapping method

In this report it is investigated if the Manifold Mapping method can be used in airfoil optimization. Before the method can be implemented, a suitable airfoil parametrization must be chosen. Furthermore a coarse and fine model must be assigned. These models are the key to success for the Manifold Mapping method. If two models are chosen that are completely different from each other, the Manifold Mapping will not work properly. Furthermore, if two models are chosen that are very similar to each other, the benefit in reducing computational cost will be only marginal. In this report the Manifold Mapping method will be explained in detail and applied to airfoil design. The approach is validated by using a test case, which will also be explained in detail. Furthermore recommendations and extensions will be given in the last chapter

Using Artificial Intelligence to Predict Intracranial Hypertension in Patients After Traumatic Brain Injury:A Systematic Review

Intracranial hypertension (IH) is a key driver of secondary brain injury in patients with traumatic brain injury. Lowering intracranial pressure (ICP) as soon as IH occurs is important, but a preemptive approach would be more beneficial. We systematically reviewed the artificial intelligence (AI) models, variables, performances, risks of bias, and clinical machine learning (ML) readiness levels of IH prediction models using AI. We conducted a systematic search until 12-03-2023 in three databases. Only studies predicting IH or ICP in patients with traumatic brain injury with a validation of the AI model were included. We extracted type of AI model, prediction variables, model performance, validation type, and prediction window length. Risk of bias was assessed with the Prediction Model Risk of Bias Assessment Tool, and we determined the clinical ML readiness level. Eleven out of 399 nonduplicate publications were included. A gaussian processes model using ICP and mean arterial pressure was most common. The maximum reported area under the receiver operating characteristic curve was 0.94. Four studies conducted external validation, and one study a prospective clinical validation. The prediction window length preceding IH varied between 30 and 60 min. Most studies (73%) had high risk of bias. The highest clinical ML readiness level was 6 of 9, indicating “real-time model testing” stage in one study. Several IH prediction models using AI performed well, were externally validated, and appeared ready to be tested in the clinical workflow (clinical ML readiness level 5 of 9). A Gaussian processes model was most used, and ICP and mean arterial pressure were frequently used variables. However, most studies showed a high risk of bias. Our findings may help position AI for IH prediction on the path to ultimate clinical integration and thereby guide researchers plan and design future studies.</p

Using Artificial Intelligence to Predict Intracranial Hypertension in Patients After Traumatic Brain Injury:A Systematic Review

Intracranial hypertension (IH) is a key driver of secondary brain injury in patients with traumatic brain injury. Lowering intracranial pressure (ICP) as soon as IH occurs is important, but a preemptive approach would be more beneficial. We systematically reviewed the artificial intelligence (AI) models, variables, performances, risks of bias, and clinical machine learning (ML) readiness levels of IH prediction models using AI. We conducted a systematic search until 12-03-2023 in three databases. Only studies predicting IH or ICP in patients with traumatic brain injury with a validation of the AI model were included. We extracted type of AI model, prediction variables, model performance, validation type, and prediction window length. Risk of bias was assessed with the Prediction Model Risk of Bias Assessment Tool, and we determined the clinical ML readiness level. Eleven out of 399 nonduplicate publications were included. A gaussian processes model using ICP and mean arterial pressure was most common. The maximum reported area under the receiver operating characteristic curve was 0.94. Four studies conducted external validation, and one study a prospective clinical validation. The prediction window length preceding IH varied between 30 and 60 min. Most studies (73%) had high risk of bias. The highest clinical ML readiness level was 6 of 9, indicating “real-time model testing” stage in one study. Several IH prediction models using AI performed well, were externally validated, and appeared ready to be tested in the clinical workflow (clinical ML readiness level 5 of 9). A Gaussian processes model was most used, and ICP and mean arterial pressure were frequently used variables. However, most studies showed a high risk of bias. Our findings may help position AI for IH prediction on the path to ultimate clinical integration and thereby guide researchers plan and design future studies.</p