Spontaneous regression of multiple flow-related aneurysms following treatment of an associated brain arteriovenous malformation: A case report.

INTRODUCTION There is no consensus in the treatment strategy of intracranial aneurysms (IAs) associated with brain arteriovenous malformation (BAVM). In particular, it is unknown if a more aggressive approach should be considered in patients harboring a BAVM, in whom multiple aneurysms or a history of aneurysmal subarachnoid hemorrhage (aSAH) is present. CASE PRESENTATION We report on an elderly woman harboring multiple aneurysms with a history of SAH due to rupture of an unrelated IA. On evaluation, she was also found to harbor a contralateral, left parietal convexity BAVM. Following resection of the latter, spontaneous regression of two large flow-related aneurysms was encountered. DISCUSSION We discuss the therapeutic decision-making, risk stratification, and functional outcome in this patient with regard to the pertinent literature on the risk of hemorrhage in IAs associated with BAVM

Machine Learning for Outcome Prediction in First-Line Surgery of Prolactinomas.

Background First-line surgery for prolactinomas has gained increasing acceptance, but the indication still remains controversial. Thus, accurate prediction of unfavorable outcomes after upfront surgery in prolactinoma patients is critical for the triage of therapy and for interdisciplinary decision-making. Objective To evaluate whether contemporary machine learning (ML) methods can facilitate this crucial prediction task in a large cohort of prolactinoma patients with first-line surgery, we investigated the performance of various classes of supervised classification algorithms. The primary endpoint was ML-applied risk prediction of long-term dopamine agonist (DA) dependency. The secondary outcome was the prediction of the early and long-term control of hyperprolactinemia. Methods By jointly examining two independent performance metrics - the area under the receiver operating characteristic (AUROC) and the Matthews correlation coefficient (MCC) - in combination with a stacked super learner, we present a novel perspective on how to assess and compare the discrimination capacity of a set of binary classifiers. Results We demonstrate that for upfront surgery in prolactinoma patients there are not a one-algorithm-fits-all solution in outcome prediction: different algorithms perform best for different time points and different outcomes parameters. In addition, ML classifiers outperform logistic regression in both performance metrics in our cohort when predicting the primary outcome at long-term follow-up and secondary outcome at early follow-up, thus provide an added benefit in risk prediction modeling. In such a setting, the stacking framework of combining the predictions of individual base learners in a so-called super learner offers great potential: the super learner exhibits very good prediction skill for the primary outcome (AUROC: mean 0.9, 95% CI: 0.92 - 1.00; MCC: 0.85, 95% CI: 0.60 - 1.00). In contrast, predicting control of hyperprolactinemia is challenging, in particular in terms of early follow-up (AUROC: 0.69, 95% CI: 0.50 - 0.83) vs. long-term follow-up (AUROC: 0.80, 95% CI: 0.58 - 0.97). It is of clinical importance that baseline prolactin levels are by far the most important outcome predictor at early follow-up, whereas remissions at 30 days dominate the ML prediction skill for DA-dependency over the long-term. Conclusions This study highlights the performance benefits of combining a diverse set of classification algorithms to predict the outcome of first-line surgery in prolactinoma patients. We demonstrate the added benefit of considering two performance metrics jointly to assess the discrimination capacity of a diverse set of classifiers

Prediction of Long-Term Restenosis After Carotid Endarterectomy Using Quantitative Magnetic Resonance Angiography.

Background To detect restenosis after carotid endarterectomy (CEA), long-term monitoring is required. However, non-selective follow-up is controversial and can be limited by costs and logistical considerations. Objective To examine the value of immediate perioperative vessel flow measurements after CEA using quantitative magnetic resonance angiography (QMRA) to detect patients at risk of long-term restenosis. Methods A prospective cohort study with long-term sonographic follow-up after CEA for symptomatic internal carotid artery stenosis (ICAs) > 50%. In all patients, vessel flow has been assessed both pre- and postoperatively using QMRA within ±3 days of surgery. Data on QMRA assessment were analyzed to identify patients at risk of restenosis for up to 10 years. Results Restenosis was recorded in 4 of 24 patients (17%) at a median follow-up of 6.8 ± 2.6 years. None of them experienced an ischemic event. Perioperative flow differences were significantly greater in patients without long-term restenosis, both for the ipsilateral ICA (p < 0.001) and MCA (p = 0.03), compared to those with restenosis (p = 0.22 and p = 0.3, respectively). The ICA mean flow ratio (p = 0.05) tended to be more effective than the MCA ratio in predicting restenosis over the long term (p = 0.35). Conclusion Our preliminary findings suggest that QMRA-based mean flow increases after CEA may be predictive of restenosis over the long term. Perioperative QMRA assessment could become an operator-independent screening tool to identify a subgroup of patients at risk for restenosis, in whom long-term monitoring is advised

Spontaneous regression of multiple flow-related aneurysms following treatment of an associated brain arteriovenous malformation: A case report

IntroductionThere is no consensus in the treatment strategy of intracranial aneurysms (IAs) associated with brain arteriovenous malformation (BAVM). In particular, it is unknown if a more aggressive approach should be considered in patients harboring a BAVM, in whom multiple aneurysms or a history of aneurysmal subarachnoid hemorrhage (aSAH) is present.Case presentationWe report on an elderly woman harboring multiple aneurysms with a history of SAH due to rupture of an unrelated IA. On evaluation, she was also found to harbor a contralateral, left parietal convexity BAVM. Following resection of the latter, spontaneous regression of two large flow-related aneurysms was encountered.DiscussionWe discuss the therapeutic decision-making, risk stratification, and functional outcome in this patient with regard to the pertinent literature on the risk of hemorrhage in IAs associated with BAVM

Decompressive craniectomy plus best medical treatment versus best medical treatment alone for spontaneous severe deep supratentorial intracerebral haemorrhage:a randomised controlled clinical trial

BACKGROUND: It is unknown whether decompressive craniectomy improves clinical outcome for people with spontaneous severe deep intracerebral haemorrhage. The SWITCH trial aimed to assess whether decompressive craniectomy plus best medical treatment in these patients improves outcome at 6 months compared to best medical treatment alone.METHODS: In this multicentre, randomised, open-label, assessor-blinded trial conducted in 42 stroke centres in Austria, Belgium, Finland, France, Germany, the Netherlands, Spain, Sweden, and Switzerland, adults (18-75 years) with a severe intracerebral haemorrhage involving the basal ganglia or thalamus were randomly assigned to receive either decompressive craniectomy plus best medical treatment or best medical treatment alone. The primary outcome was a score of 5-6 on the modified Rankin Scale (mRS) at 180 days, analysed in the intention-to-treat population. This trial is registered with ClincalTrials.gov, NCT02258919, and is completed.FINDINGS: SWITCH had to be stopped early due to lack of funding. Between Oct 6, 2014, and April 4, 2023, 201 individuals were randomly assigned and 197 gave delayed informed consent (96 decompressive craniectomy plus best medical treatment, 101 best medical treatment). 63 (32%) were women and 134 (68%) men, the median age was 61 years (IQR 51-68), and the median haematoma volume 57 mL (IQR 44-74). 42 (44%) of 95 participants assigned to decompressive craniectomy plus best medical treatment and 55 (58%) assigned to best medical treatment alone had an mRS of 5-6 at 180 days (adjusted risk ratio [aRR] 0·77, 95% CI 0·59 to 1·01, adjusted risk difference [aRD] -13%, 95% CI -26 to 0, p=0·057). In the per-protocol analysis, 36 (47%) of 77 participants in the decompressive craniectomy plus best medical treatment group and 44 (60%) of 73 in the best medical treatment alone group had an mRS of 5-6 (aRR 0·76, 95% CI 0·58 to 1·00, aRD -15%, 95% CI -28 to 0). Severe adverse events occurred in 42 (41%) of 103 participants receiving decompressive craniectomy plus best medical treatment and 41 (44%) of 94 receiving best medical treatment.INTERPRETATION: SWITCH provides weak evidence that decompressive craniectomy plus best medical treatment might be superior to best medical treatment alone in people with severe deep intracerebral haemorrhage. The results do not apply to intracerebral haemorrhage in other locations, and survival is associated with severe disability in both groups.FUNDING: Swiss National Science Foundation, Swiss Heart Foundation, Inselspital Stiftung, and Boehringer Ingelheim.</p

GWAS meta-analysis of over 29,000 people with epilepsy identifies 26 risk loci and subtype-specific genetic architecture

Epilepsy is a highly heritable disorder affecting over 50 million people worldwide, of which about one-third are resistant to current treatments. Here we report a multi-ancestry genome-wide association study including 29,944 cases, stratified into three broad categories and seven subtypes of epilepsy, and 52,538 controls. We identify 26 genome-wide significant loci, 19 of which are specific to genetic generalized epilepsy (GGE). We implicate 29 likely causal genes underlying these 26 loci. SNP-based heritability analyses show that common variants explain between 39.6% and 90% of genetic risk for GGE and its subtypes. Subtype analysis revealed markedly different genetic architectures between focal and generalized epilepsies. Gene-set analyses of GGE signals implicate synaptic processes in both excitatory and inhibitory neurons in the brain. Prioritized candidate genes overlap with monogenic epilepsy genes and with targets of current antiseizure medications. Finally, we leverage our results to identify alternate drugs with predicted efficacy if repurposed for epilepsy treatment

Gender-specific prolactin thresholds to determine prolactinoma size: a novel Bayesian approach and its clinical utility.

BACKGROUND In clinical practice, the size of adenomas is crucial for guiding prolactinoma patients towards the most suitable initial treatment. Consequently, establishing guidelines for serum prolactin level thresholds to assess prolactinoma size is essential. However, the potential impact of gender differences in prolactin levels on estimating adenoma size (micro- vs. macroadenoma) is not yet fully comprehended. OBJECTIVE To introduce a novel statistical method for deriving gender-specific prolactin thresholds to discriminate between micro- and macroadenomas and to assess their clinical utility. METHODS We present a novel, multilevel Bayesian logistic regression approach to compute observationally constrained gender-specific prolactin thresholds in a large cohort of prolactinoma patients (N = 133) with respect to dichotomized adenoma size. The robustness of the approach is examined with an ensemble machine learning approach (a so-called super learner), where the observed differences in prolactin and adenoma size between female and male patients are preserved and the initial sample size is artificially increased tenfold. RESULTS The framework results in a global prolactin threshold of 239.4 μg/L (95% credible interval: 44.0-451.2 μg/L) to discriminate between micro- and macroadenomas. We find evidence of gender-specific prolactin thresholds of 211.6 μg/L (95% credible interval: 29.0-426.2 μg/L) for women and 1,046.1 μg/L (95% credible interval: 582.2-2,325.9 μg/L) for men. Global (that is, gender-independent) thresholds result in a high sensitivity (0.97) and low specificity (0.57) when evaluated among men as most prolactin values are above the global threshold. Applying male-specific thresholds results in a slightly different scenario, with a high specificity (0.99) and moderate sensitivity (0.74). The male-dependent prolactin threshold shows large uncertainty and features some dependency on the choice of priors, in particular for small sample sizes. The augmented datasets demonstrate that future, larger cohorts are likely able to reduce the uncertainty range of the prolactin thresholds. CONCLUSIONS The proposed framework represents a significant advancement in patient-centered care for treating prolactinoma patients by introducing gender-specific thresholds. These thresholds enable tailored treatment strategies by distinguishing between micro- and macroadenomas based on gender. Specifically, in men, a negative diagnosis using a universal prolactin threshold can effectively rule out a macroadenoma, while a positive diagnosis using a male-specific prolactin threshold can indicate its presence. However, the clinical utility of a female-specific prolactin threshold in our cohort is limited. This framework can be easily adapted to various biomedical settings with two subgroups having imbalanced average biomarkers and outcomes of interest. Using machine learning techniques to expand the dataset while preserving significant observed imbalances presents a valuable method for assessing the reliability of gender-specific threshold estimates. However, external cohorts are necessary to thoroughly validate our thresholds