Resolving the Smoking Paradox: No Evidence for Smoking-Induced Preconditioning in Large Vessel Occlusion Stroke.

INTRODUCTION: Smoking is an established risk factor for stroke. However, several studies have reported a better outcome after stroke for patients who smoke. According to this "smoking paradox" hypothesis, smoking might promote less severe strokes, higher collateral scores, and smaller infarct cores. METHODS: In this retrospective study, we screened data of 2,980 acute ischemic stroke patients with MCA-M1 occlusion treated with mechanical thrombectomy. Patients were categorized according to smoking status (current, former, or never). We assessed univariate associations between clinical characteristics and smoking status. Subsequently, we used adjusted regression analysis to evaluate associations of smoking with stroke severity on admission (National Institutes of Health Stroke Scale [NIHSS]; primary endpoint), infarct core volume, and collateral status (secondary endpoints). RESULTS: Out of 320 patients, 19.7% (n = 63) were current smokers and 18.8% (n = 60) were former smokers. Admission NIHSS, reperfusion success, and modified Rankin Scale (mRS) after 3-6 months were similar in all groups. Current smokers were younger, more often male and less likely to have atrial fibrillation compared to former and never smokers. In regression analyses, smoking status was neither associated with admission NIHSS (estimate 0.54, 95% confidence interval [CI]: -1.27-2.35, p = 0.557) nor with collateral status (estimate 0.79, 95% CI: 0.44-1.44, p = 0.447) or infarct core volume (estimate -0.69, 95% CI: -15.15-13.77, p = 0.925 for current vs. never smokers). CONCLUSION: We could not confirm the smoking paradox. Our results support the fact that smoking causes stroke at a younger age, highlighting the role of smoking as a modifiable vascular risk factor.

Deep Learning Versus Neurologists: Functional Outcome Prediction in LVO Stroke Patients Undergoing Mechanical Thrombectomy.

BACKGROUND: Despite evolving treatments, functional recovery in patients with large vessel occlusion stroke remains variable and outcome prediction challenging. Can we improve estimation of functional outcome with interpretable deep learning models using clinical and magnetic resonance imaging data? METHODS: In this observational study, we collected data of 222 patients with middle cerebral artery M1 segment occlusion who received mechanical thrombectomy. In a 5-fold cross validation, we evaluated interpretable deep learning models for predicting functional outcome in terms of modified Rankin scale at 3 months using clinical variables, diffusion weighted imaging and perfusion weighted imaging, and a combination thereof. Based on 50 test patients, we compared model performances to those of 5 experienced stroke neurologists. Prediction performance for ordinal (modified Rankin scale score, 0-6) and binary (modified Rankin scale score, 0-2 versus 3-6) functional outcome was assessed using discrimination and calibration measures like area under the receiver operating characteristic curve and accuracy (percentage of correctly classified patients). RESULTS: In the cross validation, the model based on clinical variables and diffusion weighted imaging achieved the highest binary prediction performance (area under the receiver operating characteristic curve, 0.766 [0.727-0.803]). Performance of models using clinical variables or diffusion weighted imaging only was lower. Adding perfusion weighted imaging did not improve outcome prediction. On the test set of 50 patients, binary prediction performance between model (accuracy, 60% [55.4%-64.4%]) and neurologists (accuracy, 60% [55.8%-64.21%]) was similar when using clinical data. However, models significantly outperformed neurologists when imaging data were provided, alone or in combination with clinical variables (accuracy, 72% [67.8%-76%] versus 64% [59.8%-68.4%] with clinical and imaging data). Prediction performance of neurologists with comparable experience varied strongly. CONCLUSIONS: We hypothesize that early prediction of functional outcome in large vessel occlusion stroke patients may be significantly improved if neurologists are supported by interpretable deep learning models.

Clinical validation of a contactless respiration rate monitor.

Respiratory rate (RR) is an often underestimated and underreported vital sign with tremendous clinical value. As a predictor of cardiopulmonary arrest, chronic obstructive pulmonary disease (COPD) exacerbation or indicator of health state for example in COVID-19 patients, respiratory rate could be especially valuable in remote long-term patient monitoring, which is challenging to implement. Contactless devices for home use aim to overcome these challenges. In this study, the contactless Sleepiz One+ respiration monitor for home use during sleep was validated against the thoracic effort belt. The agreement of instantaneous breathing rate and breathing rate statistics between the Sleepiz One+ device and the thoracic effort belt was initially evaluated during a 20-min sleep window under controlled conditions (no body movement) on a cohort of 19 participants and secondly in a more natural setting (uncontrolled for body movement) during a whole night on a cohort of 139 participants. Excellent agreement was shown for instantaneous breathing rate to be within 3 breaths per minute (Brpm) compared to thoracic effort band with an accuracy of 100% and mean absolute error (MAE) of 0.39 Brpm for the setting controlled for movement, and an accuracy of 99.5% with a MAE of 0.48 Brpm for the whole night measurement, respectively. Excellent agreement was also achieved for the respiratory rate statistics over the whole night with absolute errors of 0.43, 0.39 and 0.67 Brpm for the 10th, 50th and 90th percentiles, respectively. Based on these results we conclude that the Sleepiz One+ can estimate instantaneous respiratory rate and its summary statistics at high accuracy in a clinical setting. Further studies are required to evaluate the performance in the home environment, however, it is expected that the performance is at similar level, as the measurement conditions for the Sleepiz One+ device are better at home than in a clinical setting.

Deep transformation models for functional outcome prediction after acute ischemic stroke.

In many medical applications, interpretable models with high prediction performance are sought. Often, those models are required to handle semistructured data like tabular and image data. We show how to apply deep transformation models (DTMs) for distributional regression that fulfill these requirements. DTMs allow the data analyst to specify (deep) neural networks for different input modalities making them applicable to various research questions. Like statistical models, DTMs can provide interpretable effect estimates while achieving the state-of-the-art prediction performance of deep neural networks. In addition, the construction of ensembles of DTMs that retain model structure and interpretability allows quantifying epistemic and aleatoric uncertainty. In this study, we compare several DTMs, including baseline-adjusted models, trained on a semistructured data set of 407 stroke patients with the aim to predict ordinal functional outcome three months after stroke. We follow statistical principles of model-building to achieve an adequate trade-off between interpretability and flexibility while assessing the relative importance of the involved data modalities. We evaluate the models for an ordinal and dichotomized version of the outcome as used in clinical practice. We show that both tabular clinical and brain imaging data are useful for functional outcome prediction, whereas models based on tabular data only outperform those based on imaging data only. There is no substantial evidence for improved prediction when combining both data modalities. Overall, we highlight that DTMs provide a powerful, interpretable approach to analyzing semistructured data and that they have the potential to support clinical decision-making.

Circle of Willis variants and their association with outcome in patients with middle cerebral artery-M1-occlusion stroke.

BACKGROUND: An incomplete circle of Willis (CoW) has been associated with a higher risk of stroke and might affect collateral flow in large vessel occlusion (LVO) stroke. We aimed to investigate the distribution of CoW variants in a LVO stroke and transient ischemic attack (TIA) cohort and analyze their impact on 3-month functional outcome. METHODS: CoW anatomy was assessed with time-of-flight magnetic resonance angiography (TOF-MRA) in 193 stroke patients with acute middle cerebral artery (MCA)-M1-occlusion receiving endovascular treatment (EVT) and 73 TIA patients without LVO. The main CoW variants were categorized into four vascular models of presumed collateral flow via the CoW. RESULTS: 82.4% (n = 159) of stroke and 72.6% (n = 53) of TIA patients had an incomplete CoW. Most variants affected the posterior circulation (stroke: 77.2%, n = 149; TIA: 58.9%, n = 43; p = 0.004). Initial stroke severity defined by the National Institutes of Health Stroke Scale (NIHSS) on admission was similar for patients with and without CoW variants. CoW integrity did not differ between groups with favorable (modified Rankin Scale [mRS]): 0-2) and unfavorable (mRS: 3-6) 3-month outcome. However, we found trends towards a higher mortality in patients with any type of CoW variant (p = 0.08) and a higher frequency of incomplete CoW among patients dying within 3 months after stroke onset (p = 0.119). In a logistic regression analysis adjusted for the potential confounders age, sex and atrial fibrillation, neither the vascular models nor anterior or posterior variants were independently associated with outcome. CONCLUSION: Our data provide no evidence for an association of CoW variants with clinical outcome in LVO stroke patients receiving EVT.

Aspirin versus anticoagulation in cervical artery dissection (TREAT-CAD): an open-label, randomised, non-inferiority trial.

BACKGROUND: Cervical artery dissection is a major cause of stroke in young people (aged <50 years). Historically, clinicians have preferred using oral anticoagulation with vitamin K antagonists for patients with cervical artery dissection, although some current guidelines-based on available evidence from mostly observational studies-suggest using aspirin. If proven to be non-inferior to vitamin K antagonists, aspirin might be preferable, due to its ease of use and lower cost. We aimed to test the non-inferiority of aspirin to vitamin K antagonists in patients with cervical artery dissection. METHODS: We did a multicentre, randomised, open-label, non-inferiority trial in ten stroke centres across Switzerland, Germany, and Denmark. We randomly assigned (1:1) patients aged older than 18 years who had symptomatic, MRI-verified, cervical artery dissection within 2 weeks before enrolment, to receive either aspirin 300 mg once daily or a vitamin K antagonist (phenprocoumon, acenocoumarol, or warfarin; target international normalised ratio [INR] 2·0-3·0) for 90 days. Randomisation was computer-generated using an interactive web response system, with stratification according to participating site. Independent imaging core laboratory adjudicators were masked to treatment allocation, but investigators, patients, and clinical event adjudicators were aware of treatment allocation. The primary endpoint was a composite of clinical outcomes (stroke, major haemorrhage, or death) and MRI outcomes (new ischaemic or haemorrhagic brain lesions) in the per-protocol population, assessed at 14 days (clinical and MRI outcomes) and 90 days (clinical outcomes only) after commencing treatment. Non-inferiority of aspirin would be shown if the upper limit of the two-sided 95% CI of the absolute risk difference between groups was less than 12% (non-inferiority margin). This trial is registered with ClinicalTrials.gov, NCT02046460. FINDINGS: Between Sept 11, 2013, and Dec 21, 2018, we enrolled 194 patients; 100 (52%) were assigned to the aspirin group and 94 (48%) were assigned to the vitamin K antagonist group. The per-protocol population included 173 patients; 91 (53%) in the aspirin group and 82 (47%) in the vitamin K antagonist group. The primary endpoint occurred in 21 (23%) of 91 patients in the aspirin group and in 12 (15%) of 82 patients in the vitamin K antagonist group (absolute difference 8% [95% CI -4 to 21], non-inferiority p=0·55). Thus, non-inferiority of aspirin was not shown. Seven patients (8%) in the aspirin group and none in the vitamin K antagonist group had ischaemic strokes. One patient (1%) in the vitamin K antagonist group and none in the aspirin group had major extracranial haemorrhage. There were no deaths. Subclinical MRI outcomes were recorded in 14 patients (15%) in the aspirin group and in 11 patients (13%) in the vitamin K antagonist group. There were 19 adverse events in the aspirin group, and 26 in the vitamin K antagonist group. INTERPRETATION: Our findings did not show that aspirin was non-inferior to vitamin K antagonists in the treatment of cervical artery dissection. FUNDING: Swiss National Science Foundation, Swiss Heart Foundation, Stroke Funds Basel, University Hospital Basel, University of Basel, Academic Society Basel.

Machine-learning-based outcome prediction in stroke patients with middle cerebral artery-M1 occlusions and early thrombectomy.

BACKGROUND AND PURPOSE: Clinical outcomes vary substantially among individuals with large vessel occlusion (LVO) stroke. A small infarct core and large imaging mismatch were found to be associated with good recovery. The aim of this study was to investigate whether those imaging variables would improve individual prediction of functional outcome after early (<6 h) endovascular treatment (EVT) in LVO stroke. METHODS: We included 222 patients with acute ischemic stroke due to middle cerebral artery (MCA)-M1 occlusion who received EVT. As predictors, we used clinical variables and region of interest (ROI)-based magnetic resonance imaging features. We developed different machine-learning models and quantified their prediction performance according to the area under the receiver-operating characteristic curves and the Brier score. RESULTS: The rate of successful recanalization was 78%, with 54% patients having a favorable outcome (modified Rankin scale score 0-2). Small infarct core was associated with favorable functional outcome. Outcome prediction improved only slightly when imaging was added to patient variables. Age was the driving factor, with a sharp decrease in likelihood of favorable functional outcome above the age of 78 years. CONCLUSIONS: In patients with MCA-M1 occlusion strokes referred to EVT within 6 h of symptom onset, infarct core volume was associated with outcome. However, ROI-based imaging variables led to no significant improvement in outcome prediction at an individual patient level when added to a set of clinical predictors. Our study is in concordance with current practice, where imaging mismatch or collateral readouts are not recommended as factors for excluding patients with MCA-M1 occlusion for early EVT.

Brain structural substrates of reward dependence during behavioral performance.

Interindividual differences in the effects of reward on performance are prevalent and poorly understood, with some individuals being more dependent than others on the rewarding outcomes of their actions. The origin of this variability in reward dependence is unknown. Here, we tested the relationship between reward dependence and brain structure in healthy humans. Subjects trained on a visuomotor skill-acquisition task and received performance feedback in the presence or absence of reward. Reward dependence was defined as the statistical trial-by-trial relation between reward and subsequent performance. We report a significant relationship between reward dependence and the lateral prefrontal cortex, where regional gray-matter volume predicted reward dependence but not feedback alone. Multivoxel pattern analysis confirmed the anatomical specificity of this relationship. These results identified a likely anatomical marker for the prospective influence of reward on performance, which may be of relevance in neurorehabilitative settings.

Baseline frontostriatal-limbic connectivity predicts reward-based memory formation.

Reward mediates the acquisition and long-term retention of procedural skills in humans. Yet, learning under rewarded conditions is highly variable across individuals and the mechanisms that determine interindividual variability in rewarded learning are not known. We postulated that baseline functional connectivity in a large-scale frontostriatal-limbic network could predict subsequent interindividual variability in rewarded learning. Resting-state functional MRI was acquired in two groups of subjects (n = 30) who then trained on a visuomotor procedural learning task with or without reward feedback. We then tested whether baseline functional connectivity within the frontostriatal-limbic network predicted memory strength measured immediately, 24 h and 1 month after training in both groups. We found that connectivity in the frontostriatal-limbic network predicted interindividual variability in the rewarded but not in the unrewarded learning group. Prediction was strongest for long-term memory. Similar links between connectivity and reward-based memory were absent in two control networks, a fronto-parieto-temporal language network and the dorsal attention network. The results indicate that baseline functional connectivity within the frontostriatal-limbic network successfully predicts long-term retention of rewarded learning.