The rationale of performing surgery for acute subdural hematoma (ASDH) to reduce mortality is often compared with the self-evident effectiveness of a parachute when skydiving. Nevertheless, it is... Show moreThe rationale of performing surgery for acute subdural hematoma (ASDH) to reduce mortality is often compared with the self-evident effectiveness of a parachute when skydiving. Nevertheless, it is of clinical relevance to estimate the magnitude of the effectiveness of surgery. The aim of this study is to determine whether surgery reduces mortality in traumatic ASDH compared with initial conservative treatment. A systematic search was performed in the databases IndexCAT, PubMed, Embase, Web of Science, Cochrane library, CENTRAL, Academic Search Premier, Google Scholar, ScienceDirect, and CINAHL for studies investigating ASDH treated conservatively and surgically, without restriction to publication date, describing the mortality. Cohort studies or trials with at least five patients with ASDH, clearly describing surgical, conservative treatment, or both, with the mortality at discharge, reported in English or Dutch, were eligible. The search yielded 2025 reports of which 282 were considered for full-text review. After risk of bias assessment, we included 102 studies comprising 12,287 patients. The data were synthesized using meta-analysis of absolute risks; this was conducted in random-effects models, with dramatic effect estimation in subgroups.Overall mortality in surgically treated ASDH is 48% (95% confidence interval [CI] 44-53%). Mortality after surgery for comatose patients (Glasgow Coma Scale <= 8) is 41% (95% CI 31-51%) in contemporary series (after 2000). Mortality after surgery for non-comatose ASDH is 12% (95% CI 4-23%). Conservative treatment is associated with an overall mortality of 35% (95% CI 22-48%) and 81% (95% CI 56-98%) when restricting to comatose patients. The absolute risk reduction is 40% (95% CI 35-45%), with a number needed to treat of 2.5 (95% CI 2.2-2.9) to prevent one death in comatose ASDH. Thus, surgery is effective to reduce mortality among comatose patients with ASDH. The magnitude of the effect is large, although the effect size may not be sufficient to overcome any bias. Show less
As the world's population continues to age over the decades ahead, medical educators and researchers in every adult medical and surgical specialty will need to 'geriatricise' their clinical science... Show moreAs the world's population continues to age over the decades ahead, medical educators and researchers in every adult medical and surgical specialty will need to 'geriatricise' their clinical science. Many have already engaged with geriatrics. Here we describe the progress that has been made and the opportunities ahead in the field of Geriatric Emergency Medicine (GEM), a field that has taken large steps in integrating holistic care. Future opportunities exist in the three domains of evidence-based medicine: including patient preferences and needs, generating scientific evidence, and improving physician knowledge and expertise. Implementation requires new innovations also in the organisation of care. Similar strategies may be useful in other fields of medicine, in making holistic care the standard for older people. Show less
Jong, Y. de; Ramspek, C.L.; Zoccali, C.; Jager, K.J.; Dekker, F.W.; Diepen, M. van 2021
Over the past few years, a large number of prediction models have been published, often of poor methodological quality. Seemingly objective and straightforward, prediction models provide a risk... Show moreOver the past few years, a large number of prediction models have been published, often of poor methodological quality. Seemingly objective and straightforward, prediction models provide a risk estimate for the outcome of interest, usually based on readily available clinical information. Yet, using models of substandard methodological rigour, especially without external validation, may result in incorrect risk estimates and consequently misclassification. To assess and combat bias in prediction research the prediction model risk of bias assessment tool (PROBAST) was published in 2019. This risk of bias (ROB) tool includes four domains and 20 signalling questions highlighting methodological flaws, and provides guidance in assessing the applicability of the model. In this paper, the PROBAST will be discussed, along with an in-depth review of two commonly encountered pitfalls in prediction modelling that may induce bias: overfitting and composite endpoints. We illustrate the prevalence of potential bias in prediction models with a meta-review of 50 systematic reviews that used the PROBAST to appraise their included studies, thus including 1510 different studies on 2104 prediction models. All domains showed an unclear or high ROB; these results were markedly stable over time, highlighting the urgent need for attention on bias in prediction research. This article aims to do just that by providing (1) the clinician with tools to evaluate the (methodological) quality of a clinical prediction model, (2) the researcher working on a review with methods to appraise the included models, and (3) the researcher developing a model with suggestions to improve model quality. Show less
Neurosurgical guidelines are fundamental for evidence-based practice and have considerably increased both in number and content over the last decades. Yet, guidelines in neurosurgery are not... Show moreNeurosurgical guidelines are fundamental for evidence-based practice and have considerably increased both in number and content over the last decades. Yet, guidelines in neurosurgery are not without limitations, as they are overwhelmingly based on low-level evidence. Such recommendations have in the past been occasionally overturned by well-designed randomized controlled trials (RCTs), demonstrating the volatility of poorly underpinned evidence. Furthermore, even RCTs in surgery come with several limitations; most notably, interventions are often insufficiently standardized and assume a homogeneous patient population, which is not always applicable to neurosurgery. Lastly, guidelines are often outdated by the time they are published and smaller fields such as neurosurgery may lack a sufficient workforce to provide regular updates. These limitations raise the question of whether it is ethical to use low-level evidence for guideline recommendations, and if so, how strictly guidelines should be adhered to from an ethical and legal perspective. This article aims to offer a critical approach to the ethical and legal status of guidelines in neurosurgery. To this aim, the authors discuss: 1) the current state of neurosurgical guidelines and the evidence they are based on; 2) the degree of implementation of these guidelines; 3) the legal status of guidelines in medical disciplinary cases; and 4) the ethical balance between confident and critical use of guidelines. Ultimately, guidelines are neither laws that should always be followed nor purely academic efforts with little practical use. Every patient is unique, and tailored treatment defined by the surgeon will ensure optimal care; guidelines play an important role in creating a solid base that can be adhered to or deviated from, depending on the situation. From a research perspective, it is inevitable to rely on weaker evidence initially in order to generate more robust evidence later, and clinician-researchers have an ethical duty to contribute to generating and improving neurosurgical guidelines. Show less
Heuvel, L. van den; Dorsey, R.R.; Prainsack, B.; Post, B.; Stiggelbout, A.M.; Meinders, M.J.; Bloem, B.R. 2020
Clinical decision making for Parkinson's disease patients is supported by a combination of three distinct information resources: best available scientific evidence, professional expertise, and the... Show moreClinical decision making for Parkinson's disease patients is supported by a combination of three distinct information resources: best available scientific evidence, professional expertise, and the personal needs and preferences of patients. All three sources have clear value but also share several important limitations, mainly regarding subjectivity, generalizability and variability. For example, current scientific evidence, especially from controlled clinical trials, is often based on selected study populations, making it difficult to translate the outcome to the care for individual patients in everyday clinical practice. Big data, including data from real-life unselected Parkinson populations, can help to bridge this information gap. Fine-grained patient profiles created from big data have the potential to aid in identifying therapeutic approaches that will be most effective given each patient's individual characteristics, which is particularly important for a disorder characterized by such tremendous interindividual variability as Parkinson's disease. In this viewpoint, we argue that big data approaches should be acknowledged and harnessed, not to replace existing information resources, but rather as a fourth and complimentary source of information in clinical decision making, helping to represent the full complexity of individual patients. We introduce the `quadruple decision making' model and illustrate its mode of action by showing how this can be used to pursue precision medicine for persons living with Parkinson's disease. Show less
Boer, M.C. den; Houtlosser, M.; Foglia, E.E.; Davis, P.G.; Kaam, A.H. van; Kamlin, C.O.F.; ... ; Pas, A.B. te 2019
