Traumatic Brain Injury: Evidence, Guidelines and Treatment Variation

Traumatic Brain Injury (TBI) is a heterogeneous disease with a high incidence, morbidity and mortality. Good treatment modalities are, especially for severe traumas, few and for most of them there is little to no evidence available in the literature. This thesis aims to analyze and improve the evidence generation process and its translation to guidelines. From our analyses we conclude that the recommendations in the past were unjustly highly graded. Moreover, the chance that a certain recommendation still appears in a subsequent edition of the guidelines (“survival”) is around 30 percent. However, methodologically sound evidence is being published and the quality of research in TBI is improving, but very high quality evidence to guide care is still lacking.We also show that despite the improvement in evidence base, the number of recommendation decreases. This leads to highly significant variation in clinical practice, which means the guidelines suffer from low adherence and the treatment a patient receives in one city is very different than the treatment the same patient would receive in a city just 50 km further. To illustrate new evidence generation methods we used a "comparative effectiveness research" approach, in which we used these differences in patient care in an attempt to determine what the best way is to monitor intracranial pressure after severe head trauma. We have shown that choosing one of these monitors is associated with less need for surgery

Contemporary frameless intracranial biopsy techniques: Might variation in safety and efficacy be expected?

Background: Frameless stereotactic neuronavigation has proven to be a feasible technology to acquire brain biopsies with good accuracy and little morbidity and mortality. New systems are constantly i

The membrane of Liliequist—a safe haven in the middle of the brain. A narrative review

Background: The membrane of Liliequist is one of the best-known inner arachnoid membranes and an essential intraoperative landmark when approaching the interpeduncular cistern but also an obstacle in the growth of lesions in the sellar and parasellar regions. The limits and exact anatomical description of this membrane are still unclear, as it blends into surr

A roadmap to fair and trustworthy prediction model validation in healthcare

A prediction model is most useful if it generalizes beyond the development data with external validations, but to what extent should it generalize remains unclear. In practice, prediction models are externally validated using data from very different settings, including populations from other health systems or countries, with predictably poor results. This may not be a fair reflection of the performance of the model which was designed for a specific target population or setting, and may be stretching the expected model generalizability. To address this, we suggest to externally validate a model using new data from the target population to ensure clear implications of validation performance on model reliability, whereas model generalizability to broader settings should be carefully investigated during model development instead of explored post-hoc. Based on this perspective, we propose a roadmap that facilitates the development and application of reliable, fair, and trustworthy artificial intelligence prediction models.Comment: 12 pages, 2 figure