Publications
129 results found
Bentley P, 2020, Magnetic Resonance Imaging for Acute Minor Neurological Symptoms Good for Ruling Stroke in, Not Out, JAMA NEUROLOGY, Vol: 77, Pages: 775-+, ISSN: 2168-6149
Li K, Bentley P, Nair A, et al., 2020, Reward sensitivity predicts dopaminergic response in spatial neglect, Cortex, Vol: 122, Pages: 213-224, ISSN: 0010-9452
It has recently been revealed that spatial neglect can be modulated by motivational factors including anticipated monetary reward. A number of dopaminergic agents have been evaluated as treatments for neglect, but the results have been mixed, with no clear anatomical or cognitive predictors of dopaminergic responsiveness. Given that the effects of incentive motivation are mediated by dopaminergic pathways that are variably damaged in stroke, we tested the hypothesis that the modulatory influences of reward and dopaminergic drugs on neglect are themselves related.We employed a single-dose, double-blind, crossover design to compare the effects of Co-careldopa and placebo on a modified visual cancellation task in patients with neglect secondary to right hemisphere stroke. Whilst confirming that reward improved visual search in this group, we showed that dopaminergic stimulation only enhances visual search in the absence of reward. When patients were divided into REWARD-RESPONDERs and REWARD-NON-RESPONDERs, we found an interaction, such that only REWARD-NON-RESPONDERs showed a positive response to reward after receiving Co-careldopa, whereas REWARD-RESPONDERs were not influenced by drug. At a neuroanatomical level, responsiveness to incentive motivation was most associated with intact dorsal striatum.These findings suggest that dopaminergic modulation of neglect follows an ‘inverted U’ function, is dependent on integrity of the reward system, and can be measured as a behavioural response to anticipated reward.
Gasimova A, Seegoolam G, Chen L, et al., 2020, Spatial Semantic-Preserving Latent Space Learning for Accelerated DWI Diagnostic Report Generation, Pages: 333-342, ISSN: 0302-9743
In light of recent works exploring automated pathological diagnosis, studies have also shown that medical text reports can be generated with varying levels of efficacy. Brain diffusion-weighted MRI (DWI) has been used for the diagnosis of ischaemia in which brain death can follow in immediate hours. It is therefore of the utmost importance to obtain ischaemic brain diagnosis as soon as possible in a clinical setting. Previous studies have shown that MRI acquisition can be accelerated using variable-density Cartesian undersampling methods. In this study, we propose an accelerated DWI acquisition pipeline for the purpose of generating text reports containing diagnostic information. We demonstrate that we can learn a semantic-preserving latent space for minor as well as extremely undersampled MR images capable of achieving promising results on a diagnostic report generation task.
Ken-Dror G, Wade C, Sharma S, et al., 2020, COVID-19 outcomes in UK centre within highest health and wealth band: a prospective cohort study, BMJ OPEN, Vol: 10, ISSN: 2044-6055
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Math N, Han TS, Lubomirova I, et al., 2019, Influences of genetic variants on stroke recovery: a meta-analysis of the 31,895 cases, Neurological Sciences, Vol: 40, Pages: 2437-2445, ISSN: 1590-1874
BackgroundThe influences of genetic variants on functional clinical outcomes following stroke are unclear. In order to reliably quantify these influences, we undertook a comprehensive meta-analysis of outcomes after acute intracerebral haemorrhage (ICH) or ischaemic stroke (AIS) in relation to different genetic variants.MethodsPubMed, PsycInfo, Embase and Medline electronic databases were searched up to January 2019. Outcomes, defined as favourable or poor, were assessed by validated scales (Barthel index, modified Rankin scale, Glasgow outcome scale and National Institutes of Health stroke scale).ResultsNinety-two publications comprising 31,895 cases met our inclusion criteria. Poor outcome was observed in patients with ICH who possessed the APOE4 allele: OR =2.60 (95% CI = 1.25–5.41, p = 0.01) and in AIS patients with the GA or AA variant at the BDNF-196 locus: OR = 2.60 (95% CI = 1.25–5.41, p = 0.01) or a loss of function allele of CYP2C19: OR = 2.36 (95% CI = 1.56–3.55, p < 0.0001). Poor outcome was not associated with APOE4: OR = 1.02 (95% CI = 0.81–1.27, p = 0.90) or IL6-174 G/C: OR = 2.21 (95% CI = 0.55–8.86, p = 0.26) in patients with AIS.ConclusionsWe demonstrate that recovery of AIS was unfavourably associated with variants of BDNF and CYP2C19 genes whilst recovery of ICH was unfavourably associated with APOE4 gene.
Chen L, Bentley P, Mori K, et al., 2019, Self-supervised learning for medical image analysis using image context restoration, Medical Image Analysis, Vol: 58, Pages: 1-12, ISSN: 1361-8415
Machine learning, particularly deep learning has boosted medical image analysis over the past years. Training a good model based on deep learning requires large amount of labelled data. However, it is often difficult to obtain a sufficient number of labelled images for training. In many scenarios the dataset in question consists of more unlabelled images than labelled ones. Therefore, boosting the performance of machine learning models by using unlabelled as well as labelled data is an important but challenging problem. Self-supervised learning presents one possible solution to this problem. However, existing self-supervised learning strategies applicable to medical images cannot result in significant performance improvement. Therefore, they often lead to only marginal improvements. In this paper, we propose a novel self-supervised learning strategy based on context restoration in order to better exploit unlabelled images. The context restoration strategy has three major features: 1) it learns semantic image features; 2) these image features are useful for different types of subsequent image analysis tasks; and 3) its implementation is simple. We validate the context restoration strategy in three common problems in medical imaging: classification, localization, and segmentation. For classification, we apply and test it to scan plane detection in fetal 2D ultrasound images; to localise abdominal organs in CT images; and to segment brain tumours in multi-modal MR images. In all three cases, self-supervised learning based on context restoration learns useful semantic features and lead to improved machine learning models for the above tasks.
Chen L, Lobotesis K, Rueckert D, et al., 2019, Timing an ischaemic stroke with just plain CT (and a little deep learning), Publisher: SAGE PUBLICATIONS LTD, Pages: 29-29, ISSN: 1747-4930
Math N, Lubomirova I, Hill R, et al., 2019, Influences of genetic variants on stroke recovery: a meta-analysis of the 31,895 cases, Publisher: SAGE PUBLICATIONS LTD, Pages: 31-31, ISSN: 1747-4930
Chen L, Bentley P, Mori K, et al., 2019, Intelligent image synthesis to attack a segmentation CNN using adversarial learning, Simulation and Synthesis in Medical Imaging. SASHIMI 2019, Publisher: Springer Verlag, ISSN: 0302-9743
Deep learning approaches based on convolutional neural networks (CNNs) have been successful in solving a number of problems in medical imaging, including image segmentation. In recent years, it has been shown that CNNs are vulnerable to attacks in which the input image is perturbed by relatively small amounts of noise so that the CNN is no longer able to perform a segmentation of the perturbed image with sufficient accuracy. Therefore, exploring methods on how to attack CNN-based models as well as how to defend models against attacks have become a popular topic as this also provides insights into the performance and generalization abilities of CNNs. However, most of the existing work assumes unrealistic attack models, i.e. the resulting attacks were specified in advance. In this paper, we propose a novel approach for generating adversarial examples to attack CNN-based segmentation models for medical images. Our approach has three key features: (1) The generated adversarial examples exhibit anatomical variations (in form of deformations) as well as appearance perturbations; (2) The adversarial examples attack segmentation models so that the Dice scores decrease by a pre-specified amount; (3) The attack is not required to be specified beforehand. We have evaluated our approach on CNN-based approaches for the multi-organ segmentation problem in 2D CT images. We show that the proposed approach can be used to attack different CNN-based segmentation models.
Bentley P, Lovell B, 2019, Memorizing Medicine: Second Edition, Publisher: CRC Press, ISBN: 978-1138332690
Hill R, Han TS, Lubomirova I, et al., 2019, Prothrombin Complex Concentrates are Superior to Fresh Frozen Plasma for Emergency Reversal of Vitamin K Antagonists: A Meta-Analysis in 2606 Subjects, Drugs, ISSN: 0012-6667
Lotay R, Mace M, Rinne P, et al., 2019, optimizing self-exercise scheduling in motor stroke using Challenge Point Framework theory, 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR), Publisher: IEEE, Pages: 435-440
An important challenge for technology-assisted self-led rehabilitation is how to automate appropriate schedules of exercise that are responsive to patients’ needs, and optimal for learning. While random scheduling has been found to be superior for long-term learning relative to fixed scheduling (Contextual Interference), this method is limited by not adequately accounting for task difficulty, or skill acquisition during training. One method that combines contextual interference with adaptation of the challenge to the skill-level of the player is Challenge Point Framework (CPF) theory. In this pilot study we test whether self-led motor training based upon CPF scheduling achieves faster learning than deterministic, fixed scheduling. Training was implemented in a mobile gaming device adapted for arm disability, allowing for grip and wrist exercises. We tested 11 healthy volunteers and 12 hemiplegic stroke patients in a single-blinded no crossover controlled randomized trial. Results suggest that patients training with CPF-based adaption performed better than those training with fixed conditions. This was not seen for healthy volunteers whose performance was close to ceiling. Further data collection is required to determine the significance of the results.
Formstone L, Pucek M, Wilson S, et al., 2019, Myographic Information Enables Hand Function Classification in Automated Fugl-Meyer Assessment, 9th IEEE/EMBS International Conference on Neural Engineering (NER), Publisher: IEEE, Pages: 239-242, ISSN: 1948-3546
Mousele C, Bentley P, Tai YF, 2018, A rare presentation of orthostatic tremor as abdominal tremor, Tremor and Other Hyperkinetic Movements, Vol: 8, ISSN: 2160-8288
Background: Orthostatic tremor (OT) is a weight-bearing hyperkinetic disorder characterized by unsteadiness while standing that is relieved when sitting or walking.Case report: A 66-year-old male presented with a 5 year-history of tremor in his abdomen, but only when he stood in a stationary position. The tremor disappeared when he stood or walked. On examination, he had palpable tremor in his rectus abdominis and gastrocnemius virtually instantaneously after standing. His electromyography findings confirmed the presence of a 12-Hz tremor in the tibialis anterior while standing, with subharmonics recorded in the external obliques and rectus abdominis.Discussion: Our case illustrates an unusual presentation of OT. The diagnosis is supported by its characteristic frequency and specific appearance only during upright stance.
Bowles C, Liang C, Bentley P, et al., 2018, Gan augmentation: augmenting training data using generative adversarial networks
One of the biggest issues facing the use of machine learning in medical imaging is the lack of availability of large, labelled datasets. The annotation of medical images is not only expensive and time-consuming but also highly dependent on the availability of expert observers. The limited amount of training data can inhibit the performance of supervised machine learning algorithms which often need very large quantities of data on which to train to avoid overfitting. So far, much effort has been directed at extracting as much information as possible from what data is available. Generative Adversarial Networks (GANs) offer a novel way to unlock additional information from a dataset by generating synthetic samples with the appearance of real images. This paper demonstrates the feasibility of introducing GAN derived synthetic data to the training datasets in two brain segmentation tasks, leading to improvements in Dice Similarity Coefficient(DSC) of between 1 and 5 percentage points under different conditions, with the strongest effects seen fewer than ten training image stacks are available.
Bowles C, Chen L, Guerrero R, et al., 2018, GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks, arXiv
Chen L, Carlton Jones AL, Mair G, et al., 2018, Rapid automated quantification of cerebral leukoaraiosis on CT: a multicentre validation study, Radiology, Vol: 288, Pages: 573-581, ISSN: 0033-8419
Purpose - To validate a fully-automated, machine-learning method (random forest) for segmenting cerebral white matter lesions (WML) on computerized tomography (CT). Materials and Methods – A retrospective sample of 1082 acute ischemic stroke cases was obtained, comprising unselected patients: 1) treated with thrombolysis; or 2) undergoing contemporaneous MR imaging and CT; and 3) a subset of IST-3 trial participants. Automated (‘Auto’) WML images were validated relative to experts’ manual tracings on CT, and co-registered FLAIR-MRI; and ratings using two conventional ordinal scales. Analyses included correlations between CT and MR imaging volumes, and agreements between Auto and expert ratings.Results - Auto WML volumes correlated strongly with expert-delineated WML volumes on MR imaging and on CT (r2=0.85, 0.71 respectively; p<0.001). Spatial-similarity of Auto-maps, relative to MRI-WML, was not significantly different to that of expert CT-WML tracings. Individual expert CT-WML volumes correlated well with each other (r2=0.85), but varied widely (range: 91% of mean estimate; median 11 cc; range: 0.2 – 68 cc). Agreements between Auto and consensus-expert ratings were superior or similar to agreements between individual pairs of experts (kappa: 0.60, 0.64 vs. 0.51, 0.67 for two score systems; p<0.01 for first comparison). Accuracy was unaffected by established infarction, acute ischemic changes, or atrophy (p>0.05). Auto preprocessing failure rate was 4%; rating errors occurred in a further 4%. Total Auto processing time averaged 109s (range: 79 - 140 s). Conclusion - An automated method for quantifying CT cerebral white matter lesions achieves a similar accuracy to experts in unselected and multicenter cohorts.
Bentley P, Sharma P, 2018, Neurological disorders - epilepsy, Parkinson's disease and multiple sclerosis, Clinical pharmacology: 12th edition, Editors: Brown, Sharma, Mir, Bennett, Publisher: Elsevier, ISBN: 978-0702073281
Chen L, Bentley P, Mori K, et al., 2018, DRINet for medical image segmentation, IEEE Transactions on Medical Imaging, Vol: 37, Pages: 2453-2462, ISSN: 0278-0062
Convolutional neural networks (CNNs) have revolutionized medical image analysis over the past few years. The UNet architecture is one of the most well-known CNN architectures for semantic segmentation and has achieved remarkable successes in many different medical image segmentation applications. The U-Net architecture consists of standard convolution layers, pooling layers, and upsampling layers. These convolution layers learn representative features of input images and construct segmentations based on the features. However, the features learned by standard convolution layers are not distinctive when the differences among different categories are subtle in terms of intensity, location, shape, and size. In this paper, we propose a novel CNN architecture, called Dense-Res-Inception Net (DRINet), which addresses this challenging problem. The proposed DRINet consists of three blocks, namely a convolutional block with dense connections, a deconvolutional block with residual Inception modules, and an unpooling block. Our proposed architecture outperforms the U-Net in three different challenging applications, namely multi-class segmentation of cerebrospinal fluid (CSF) on brain CT images, multi-organ segmentation on abdominal CT images, multi-class brain tumour segmentation on MR images.
Rinne P, Hassan M, Fernandes C, et al., 2018, Motor dexterity and strength depend upon integrity of the attention-control system, Proceedings of the National Academy of Sciences, Vol: 115, Pages: E536-E545, ISSN: 0027-8424
Attention control (or executive control) is a higher cognitive function involved in response selection and inhibition, through close interactions with the motor system. Here, we tested whether influences of attention control are also seen on lower level motor functions of dexterity and strength—by examining relationships between attention control and motor performance in healthy-aged and hemiparetic-stroke subjects (n = 93 and 167, respectively). Subjects undertook simple-tracking, precision-hold, and maximum force-generation tasks, with each hand. Performance across all tasks correlated strongly with attention control (measured as distractor resistance), independently of factors such as baseline performance, hand use, lesion size, mood, fatigue, or whether distraction was tested during motor or nonmotor cognitive tasks. Critically, asymmetric dissociations occurred in all tasks, in that severe motor impairment coexisted with normal (or impaired) attention control whereas normal motor performance was never associated with impaired attention control (below a task-dependent threshold). This implies that dexterity and force generation require intact attention control. Subsequently, we examined how motor and attention-control performance mapped to lesion location and cerebral functional connectivity. One component of motor performance (common to both arms), as well as attention control, correlated with the anatomical and functional integrity of a cingulo-opercular “salience” network. Independently of this, motor performance difference between arms correlated negatively with the integrity of the primary sensorimotor network and corticospinal tract. These results suggest that the salience network, and its attention-control function, are necessary for virtually all volitional motor acts while its damage contributes significantly to the cardinal motor deficits of stroke.
Mace M, Kinany N, Rinne P, et al., 2017, Balancing the playing field: collaborative gaming for physical training., Journal of NeuroEngineering and Rehabilitation, Vol: 14, ISSN: 1743-0003
BACKGROUND: Multiplayer video games promoting exercise-based rehabilitation may facilitate motor learning, by increasing motivation through social interaction. However, a major design challenge is to enable meaningful inter-subject interaction, whilst allowing for significant skill differences between players. We present a novel motor-training paradigm that allows real-time collaboration and performance enhancement, across a wide range of inter-subject skill mismatches, including disabled vs. able-bodied partnerships. METHODS: A virtual task consisting of a dynamic ball on a beam, is controlled at each end using independent digital force-sensing handgrips. Interaction is mediated through simulated physical coupling and locally-redundant control. Game performance was measured in 16 healthy-healthy and 16 patient-expert dyads, where patients were hemiparetic stroke survivors using their impaired arm. Dual-player was compared to single-player performance, in terms of score, target tracking, stability, effort and smoothness; and questionnaires probing user-experience and engagement. RESULTS: Performance of less-able subjects (as ranked from single-player ability) was enhanced by dual-player mode, by an amount proportionate to the partnership's mismatch. The more abled partners' performances decreased by a similar amount. Such zero-sum interactions were observed for both healthy-healthy and patient-expert interactions. Dual-player was preferred by the majority of players independent of baseline ability and subject group; healthy subjects also felt more challenged, and patients more skilled. CONCLUSION: This is the first demonstration of implicit skill balancing in a truly collaborative virtual training task leading to heightened engagement, across both healthy subjects and stroke patients.
Chen L, Bentley P, Rueckert D, 2017, Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks, NeuroImage: Clinical, Vol: 15, Pages: 633-643, ISSN: 2213-1582
Stroke is an acute cerebral vascular disease, which is likely to cause long-term disabilities and death. Acute ischemic lesions occur in most stroke patients. These lesions are treatable under accurate diagnosis and treatments. Although diffusion-weighted MR imaging (DWI) is sensitive to these lesions, localizing and quantifyingthem manually is costly and challenging for clinicians. In this paper, we propose a novel framework to auto-matically segment stroke lesions in DWI. Our framework consists of two convolutional neural networks (CNNs):one is an ensemble of two DeconvNets (Noh et al., 2015), which is the EDD Net; the second CNN is the multi-scale convolutional label evaluation net (MUSCLE Net), which aims to evaluate the lesions detected by the EDD Net in order to remove potential false positives. To the best of our knowledge, it is the first attempt to solve this problem and using both CNNs achieves very good results. Furthermore, we study the network architectures and key configurations in detail to ensure the best performance. It is validated on a large dataset comprising clinical acquired DW images from 741 subjects. A mean accuracy of Dice coefficient obtained is 0.67 in total. The mean Dice scores based on subjects with only small and large lesions are 0.61 and 0.83, respectively. The lesion detection rate achieved is 0.94.
Mace M, Rinne P, Liardon J-L, et al., 2017, Elasticity improves handgrip performance and user experience during visuomotor control, Royal Society Open Science, Vol: 4, ISSN: 2054-5703
Passive rehabilitation devices, providing motivation andfeedback, potentially offer an automated and low-cost therapymethod, and can be used as simple human–machine interfaces.Here, we ask whether there is any advantage for a handtrainingdevice to be elastic, as opposed to rigid, in terms ofperformance and preference. To address this question, we havedeveloped a highly sensitive and portable digital handgrip,promoting independent and repetitive rehabilitation of graspfunction based around a novel elastic force and position sensingstructure. A usability study was performed on 66 healthysubjects to assess the effect of elastic versus rigid handgripcontrol during various visuomotor tracking tasks. The resultsindicate that, for tasks relying either on feedforward or onfeedback control, novice users perform significantly betterwith the elastic handgrip, compared with the rigid equivalent(11% relative improvement, 9–14% mean range; p < 0.01).Furthermore, there was a threefold increase in the number ofsubjects who preferred elastic compared with rigid handgripinteraction. Our results suggest that device compliance is animportant design consideration for grip training devices.
Mace M, Rinne P, Kinany N, et al., 2016, Collaborative gaming to enhance patient performance during virtual therapy, 3rd International Conference on NeuroRehabilitation (ICNR), Publisher: Springer International Publishing AG, Pages: 375-379, ISSN: 2195-3562
We present a collaborative training game, based on a novel task where the participants are virtually but dynamically coupled and require collective actions for successful task completion. This can be considered a new type of interpersonal interaction which both increases player motivation during training (compared to single-player participation) and also intrinsically balances the skill levels of the two partners without the need for an additional procedure. This is achieved by a temporary averaging, during collaboration, of the individual performance’s which leads to a more balanced playing field and challenge point being set for both partners.
Rinne P, Mace M, Nakornchai T, et al., 2016, Democratizing Neurorehabilitation: How Accessible are Low-Cost Mobile-Gaming Technologies for Self-Rehabilitation of Arm Disability in Stroke?, PLOS One, Vol: 11, ISSN: 1932-6203
Motor-training software on tablets or smartphones (Apps) offer a low-cost, widely-available solution to supplement arm physiotherapy after stroke. We assessed the proportions of hemiplegic stroke patients who, with their plegic hand, could meaningfully engage with mobile-gaming devices using a range of standard control-methods, as well as by using a novel wireless grip-controller, adapted for neurodisability. We screened all newly-diagnosed hemiplegic stroke patients presenting to a stroke centre over 6 months. Subjects were compared on their ability to control a tablet or smartphone cursor using: finger-swipe, tap, joystick, screen-tilt, and an adapted handgrip. Cursor control was graded as: no movement (0); less than full-range movement (1); full-range movement (2); directed movement (3). In total, we screened 345 patients, of which 87 satisfied recruitment criteria and completed testing. The commonest reason for exclusion was cognitive impairment. Using conventional controls, the proportion of patients able to direct cursor movement was 38-48%; and to move it full-range was 55-67% (controller comparison: p>0.1). By comparison, handgrip enabled directed control in 75%, and full-range movement in 93% (controller comparison: p<0.001). This difference between controllers was most apparent amongst severely-disabled subjects, with 0% achieving directed or full-range control with conventional controls, compared to 58% and 83% achieving these two levels of movement, respectively, with handgrip. In conclusion, hand, or arm, training Apps played on conventional mobile devices are likely to be accessible only to mildly-disabled stroke patients. Technological adaptations such as grip-control can enable more severely affected subjects to engage with self-training software.
Maier O, Menze BH, von der Gablentz J, et al., 2016, ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI, Medical Image Analysis, Vol: 35, Pages: 250-269, ISSN: 1361-8423
Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but the reported results are largely incomparable due to different datasets and evaluation schemes. We approached this urgent problem of comparability with the Ischemic Stroke Lesion Segmentation (ISLES) challenge organized in conjunction with the MICCAI 2015 conference. In this paper we propose a common evaluation framework, describe the publicly available datasets, and present the results of the two sub-challenges: Sub-Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES). A total of 16 research groups participated with a wide range of state-of-the-art automatic segmentation algorithms. A thorough analysis of the obtained data enables a critical evaluation of the current state-of-the-art, recommendations for further developments, and the identification of remaining challenges. The segmentation of acute perfusion lesions addressed in SPES was found to be feasible. However, algorithms applied to sub-acute lesion segmentation in SISS still lack accuracy. Overall, no algorithmic characteristic of any method was found to perform superior to the others. Instead, the characteristics of stroke lesion appearances, their evolution, and the observed challenges should be studied in detail. The annotated ISLES image datasets continue to be publicly available through an online evaluation system to serve as an ongoing benchmarking resource (www.isles-challenge.org).
Gunnoo T, Hasan N, Khan MS, et al., 2016, Quantifying the risk of heart disease following acute ischaemic stroke: a meta-analysis of over 50 000 participants, BMJ Open, Vol: 6, ISSN: 2044-6055
Rinne P, Hassan M, Liardon J, et al., 2015, Hand-and-brain training after motor stroke: Defining the problem and innovating a solution, INTERNATIONAL JOURNAL OF STROKE, Vol: 10, Pages: 67-67, ISSN: 1747-4930
Barrow T, Khan MS, Halse O, et al., 2015, Estimating weight of patients with acute stroke when dosing for thrombolysis, Stroke, Vol: 47, Pages: 228-231, ISSN: 1524-4628
Background and Purpose—Estimating patient weight forms an important part of emergency ischemic stroke management guiding the dose of alteplase (tissue-type plasminogen activator). Weighing patients with stroke can be logistically challenging and time consuming, potentially delaying treatment times. We aimed to assess the reliability of approximating weight to determine recombinant tissue-type plasminogen activator dose and whether potential inaccurate dosing affected patient outcomes.Methods—Two hundred forty-two consecutive patients were studied at a large tertiary stroke center. Estimated and actual measured weight, alteplase dose, and pre-and post-modified Rankin Scale/National Institute of Health Stroke Scale outcome were recorded for each patient.Results—Clinicians significantly underestimated weights by 1.13 kg (range, −43 to +18 kg; SD, 7.14; P<0.05). The difference between estimated and actual weight proved to be greatest in the heaviest third of patients (−4.51 kg; SD, 8.35; P<0.001), resulting in 19.7% of patients receiving a deviation of at least 10% from the recommended recombinant tissue-type plasminogen activator dose. On average, the heaviest third of patients received an underdose of 0.04 mg/kg and were found to have a greater baseline National Institute of Health Stroke Scale on admission (P<0.001). National Institute of Health Stroke Scale improvement by day 7 or on discharge was significantly reduced in patients weighing >78 kg (National Institute of Health Stroke Scale score difference of 4.0 points, P<0.05) than in lighter individuals.Conclusions—Clinicians are poor at approximating the weights of patients with stroke in the acute setting, especially when patients lie at the extremes of weight. Beds capable of weighing patients should be mandated in emergency rooms for patients with acute stroke.
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