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1.
Aging (Albany NY) ; 112019 09 03.
Artigo em Inglês | MEDLINE | ID: mdl-31479421

RESUMO

Data regarding the association between subclinical thyroid dysfunction and clinical outcomes in ischemic stroke patients with intravenous thrombolysis (IVT) are limited. We aimed to investigate the predictive value of subclinical thyroid dysfunction in END, functional outcome and mortality at 3 months among IVT patients. We prospectively recruited 563 IVT patients from 5 stroke centers in China. Thyroid function status was classified as subclinical hypothyroidism, subclinical hyperthyroidism (SHyper) and euthyroidism. The primary outcome was END, defined as ≥ 4 point in the NIHSS score within 24 h after IVT. Secondary outcomes included 3-month functional outcome and mortality. Of the 563 participants, END occurred in 14.7%, poor outcome in 50.8%, and mortality in 9.4%. SHyper was an independent predictor of END [odd ratio (OR), 4.35; 95% confidence interval [CI], 1.86-9.68, P = 0.003], 3-month poor outcome (OR, 3.24; 95% CI, 1.43-7.33, P = 0.005) and mortality [hazard ratio, 2.78; 95% CI, 1.55-5.36, P = 0.003]. Subgroup analysis showed that there was no significant relationship between SHyper and clinical outcomes in IVT patients with endovascular therapy. In summary, SHyper is associated with increased risk of END, and poor outcome and mortality at 3 months in IVT patients without endovascular therapy.

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3.
Biomed Res Int ; 2019: 3252178, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31355255

RESUMO

The low cost, simple, noninvasive, and continuous measurement of cerebral blood flow velocity (CBFV) by transcranial Doppler is becoming a common clinical tool for the assessment of cerebral hemodynamics. CBFV monitoring can also help with noninvasive estimation of intracranial pressure and evaluation of mild traumatic brain injury. Reliable CBFV waveform analysis depends heavily on its accurate beat-to-beat delineation. However, CBFV is inherently contaminated with various types of noise/artifacts and has a wide range of possible pathological waveform morphologies. Thus, pulse onset detection is in general a challenging task for CBFV signal. In this paper, we conducted a comprehensive comparative analysis of three popular pulse onset detection methods using a large annotated dataset of 92,794 CBFV pulses-collected from 108 subarachnoid hemorrhage patients admitted to UCLA Medical Center. We compared these methods not only in terms of their accuracy and computational complexity, but also for their sensitivity to the selection of their parameters' values. The results of this comprehensive study revealed that using optimal values of the parameters obtained from sensitivity analysis, one method can achieve the highest accuracy for CBFV pulse onset detection with true positive rate (TPR) of 97.06% and positive predictivity value (PPV) of 96.48%, when error threshold is set to just less than 10 ms. We conclude that the high accuracy and low computational complexity of this method (average running time of 4ms/pulse) makes it a reliable algorithm for CBFV pulse onset detection.

4.
Artigo em Inglês | MEDLINE | ID: mdl-31217091

RESUMO

OBJECTIVE: Transcranial Doppler (TCD) ultrasonography measures pulsatile cerebral blood flow velocity in the arteries and veins of the head and neck. Similar to other real-time measurement modalities, especially in healthcare, the identification of high quality signals is essential for clinical interpretation. Our goal is to identify poor quality beats and remove them prior to further analysis of the TCD signal. METHODS: We selected objective features for this purpose including Euclidean distance between individual and average beat waveforms, cross-correlation between individual and average beat waveforms, ratio of the high frequency power to the total beat power, beat length, and variance of the diastolic portion of the beat waveform. We developed an iterative outlier detection algorithm to identify and remove the beats that are different from others in a recording. Finally, we tested the algorithm on a dataset consisting of more than 15 hours of TCD data recorded from 48 stroke and 34 in-hospital control subjects. RESULTS: We assessed the performance of the algorithm in the improvement of estimation of clinically important TCD parameters by comparing them to that of manual beat annotation. The results show that there is a strong correlation between the two that demonstrates the algorithm has successfully recovered the clinically important features. We obtained significant improvement in estimating the TCD parameters using the algorithm accepted beats compared to using all beats. SIGNIFICANCE: Our algorithm provides a valuable tool to clinicians for automated detection of the reliable portion of the data. Moreover, it can be used as a pre-processing tool to improve the data quality for automated diagnosis of pathologic beat waveforms using machine learning.

5.
Neurol Res ; 41(8): 681-690, 2019 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-31038007

RESUMO

Advances in predictive analytics and machine learning supported by an ever-increasing wealth of data and processing power are transforming almost every industry. Accuracy and precision of predictive analytics have significantly increased over the past few years and are evolving at an exponential pace. There have been significant breakthroughs in using Predictive Analytics in healthcare where it is held as the foundation of precision medicine. Yet, although the research in the field is expanding with the profuse volume of papers applying machine learning algorithms to medical data, very few have contributed meaningfully to clinical care. This lack of impact stands in stark contrast to the enormous relevance of machine learning to many other industries. Regardless of the status of its current contribution, the field of predictive analytics is expected to fundamentally change the way we diagnose and treat diseases, as well as the conduct of biomedical science research. In this review, we describe the main tools and techniques in predictive analytics and will analyze the trends in application of these techniques over the recent years. We will also provide examples of its application in medicine and more specifically in stroke and neurovascular research and outline current limitations.

6.
Ann Neurol ; 85(5): 752-764, 2019 May.
Artigo em Inglês | MEDLINE | ID: mdl-30840312

RESUMO

OBJECTIVE: To investigate whether hemodynamic features of symptomatic intracranial atherosclerotic stenosis (sICAS) might correlate with the risk of stroke relapse, using a computational fluid dynamics (CFD) model. METHODS: In a cohort study, we recruited patients with acute ischemic stroke attributed to 50 to 99% ICAS confirmed by computed tomographic angiography (CTA). With CTA-based CFD models, translesional pressure ratio (PR = pressurepoststenotic /pressureprestenotic ) and translesional wall shear stress ratio (WSSR = WSSstenotic - throat /WSSprestenotic ) were obtained in each sICAS lesion. Translesional PR ≤ median was defined as low PR and WSSR ≥4th quartile as high WSSR. All patients received standard medical treatment. The primary outcome was recurrent ischemic stroke in the same territory (SIT) within 1 year. RESULTS: Overall, 245 patients (median age = 61 years, 63.7% males) were analyzed. Median translesional PR was 0.94 (interquartile range [IQR] = 0.87-0.97); median translesional WSSR was 13.3 (IQR = 7.0-26.7). SIT occurred in 20 (8.2%) patients, mostly with multiple infarcts in the border zone and/or cortical regions. In multivariate Cox regression, low PR (adjusted hazard ratio [HR] = 3.16, p = 0.026) and high WSSR (adjusted HR = 3.05, p = 0.014) were independently associated with SIT. Patients with both low PR and high WSSR had significantly higher risk of SIT than those with normal PR and WSSR (risk = 17.5% vs 3.0%, adjusted HR = 7.52, p = 0.004). INTERPRETATION: This work represents a step forward in utilizing computational flow simulation techniques in studying intracranial atherosclerotic disease. It reveals a hemodynamic pattern of sICAS that is more prone to stroke relapse, and supports hypoperfusion and artery-to-artery embolism as common mechanisms of ischemic stroke in such patients. Ann Neurol 2019;85:752-764.

7.
Abdom Radiol (NY) ; 44(6): 2009-2020, 2019 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-30778739

RESUMO

PURPOSE: Currently, all solid enhancing renal masses without microscopic fat are considered malignant until proven otherwise and there is substantial overlap in the imaging findings of benign and malignant renal masses, particularly between clear cell RCC (ccRCC) and benign oncocytoma (ONC). Radiomics has attracted increased attention for its utility in pre-operative work-up on routine clinical images. Radiomics based approaches have converted medical images into mineable data and identified prognostic imaging signatures that machine learning algorithms can use to construct predictive models by learning the decision boundaries of the underlying data distribution. The TensorFlow™ framework from Google is a state-of-the-art open-source software library that can be used for training deep learning neural networks for performing machine learning tasks. The purpose of this study was to investigate the diagnostic value and feasibility of a deep learning-based renal lesion classifier using open-source Google TensorFlow™ Inception in differentiating ccRCC from ONC on routine four-phase MDCT in patients with pathologically confirmed renal masses. METHODS: With institutional review board approval for this 1996 Health Insurance Portability and Accountability Act compliant retrospective study and a waiver of informed consent, we queried our institution's pathology, clinical, and radiology databases for histologically proven cases of ccRCC and ONC obtained between January 2000 and January 2016 scanned with a an intravenous contrast-enhanced four-phase renal mass protocol (unenhanced (UN), corticomedullary (CM), nephrographic (NP), and excretory (EX) phases). To extract features to be used for the machine learning model, the entire renal mass was contoured in the axial plane in each of the four phases, resulting in a 3D volume of interest (VOI) representative of the entire renal mass. We investigated thirteen different approaches to convert the acquired VOI data into a set of images that adequately represented each tumor which was used to train the final layer of the neural network model. Training was performed over 4000 iterations. In each iteration, 90% of the data were designated as training data and the remaining 10% served as validation data and a leave-one-out cross-validation scheme was implemented. Accuracy, sensitivity, specificity, positive (PPV) and negative predictive (NPV) values, and CIs were calculated for the classification of the thirteen processing modes. RESULTS: We analyzed 179 consecutive patients with 179 lesions (128 ccRCC and 51 ONC). The ccRCC cohort had a mean size of 3.8 cm (range 0.8-14.6 cm) and the ONC cohort had a mean lesion size of 3.9 cm (range 1.0-13.1 cm). The highest specificity and PPV (52.9% and 80.3%, respectively) were achieved in the EX phase when we analyzed the single mid-slice of the tumor in the axial, coronal and sagittal plane, and when we increased the number of mid-slices of the tumor to three, with an accuracy of 75.4%, which also increased the sensitivity to 88.3% and the PPV to 79.6%. Using the entire tumor volume also showed that classification performance was best in the EX phase with an accuracy of 74.4%, a sensitivity of 85.8% and a PPV of 80.1%. When the entire tumor volume, plus mid-slices from all phases and all planes presented as tiled images, were submitted to the final layer of the neural network we achieved a PPV of 82.5%. CONCLUSIONS: The best classification result was obtained in the EX phase among the thirteen classification methods tested. Our proof of concept study is the first step towards understanding the utility of machine learning in the differentiation of ccRCC from ONC on routine CT images. We hope this could lead to future investigation into the development of a multivariate machine learning model which may augment our ability to accurately predict renal lesion histology on imaging.

8.
IEEE Trans Med Imaging ; 38(7): 1666-1676, 2019 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-30802855

RESUMO

Current clinical practice relies on clinical history to determine the time since stroke (TSS) onset. Imaging-based determination of acute stroke onset time could provide critical information to clinicians in deciding stroke treatment options, such as thrombolysis. The patients with unknown or unwitnessed TSS are usually excluded from thrombolysis, even if their symptoms began within the therapeutic window. In this paper, we demonstrate a machine learning approach for TSS classification using routinely acquired imaging sequences. We develop imaging features from the magnetic resonance (MR) images and train machine learning models to classify the TSS. We also propose a deep-learning model to extract hidden representations for the MR perfusion-weighted images and demonstrate classification improvement by incorporating these additional deep features. The cross-validation results show that our best classifier achieved an area under the curve of 0.765, with a sensitivity of 0.788 and a negative predictive value of 0.609, outperforming existing methods. We show that the features generated by our deep-learning algorithm correlate with the MR imaging features, and validate the robustness of the model on imaging parameter variations (e.g., year of imaging). This paper advances magnetic resonance imaging analysis one-step-closer to an operational decision support tool for stroke treatment guidance.

9.
Fluids Barriers CNS ; 16(1): 2, 2019 Jan 22.
Artigo em Inglês | MEDLINE | ID: mdl-30665428

RESUMO

BACKGROUND: This study investigated cerebrospinal fluid (CSF) hydrodynamics using cine phase-contrast MRI in the cerebral aqueduct and the prepontine cistern between three distinct groups: pre-shunt normal pressure hydrocephalus (NPH) patients, post-shunt NPH patients, and controls. We hypothesized that the hyperdynamic flow of CSF through the cerebral aqueduct seen in NPH patients was due to a reduction in cisternal CSF volume buffering. Both hydrodynamic (velocity, flow, stroke volume) and peak flow latency (PFL) parameters were investigated. METHODS: Scans were conducted on 30 pre-treatment patients ranging in age from 58 to 88 years along with an additional 12 controls. Twelve patients also received scans following either ventriculoatrial (VA) or ventriculoperitoneal (VP) shunt treatment (9 VP, 3 VA), ranging in age from 74 to 89 years with a mean follow up time of 6 months. RESULTS: Significant differences in area, velocity, flow, and stroke volume for the cerebral aqueduct were found between the pre-treatment NPH group and the healthy controls. Shunting caused a significant decrease in both caudal and cranial mean flow and stroke volume in the cerebral aqueduct. No significant changes were found in the prepontine cistern between the pre-treatment group and healthy controls. For the PFL, no significant differences were seen in the cerebral aqueduct between any of the three groups; however, the prepontine cistern PFL was significantly decreased in the pre-treatment NPH group when compared to the control group. CONCLUSIONS: Although several studies have quantified the changes in aqueductal flow between hydrocephalic groups and controls, few studies have investigated prepontine cistern flow. Our study was the first to investigate both regions in the same patients for NPH pre- and post- treatment. Following shunt treatment, the aqueductal CSF metrics decreased toward control values, while the prepontine cistern metrics trended up (not significantly) from the normal values established in this study. The opposing trend of the two locations suggests a redistribution of CSF pulsatility in NPH patients. Furthermore, the significantly decreased latency of the prepontine cisternal CSF flow suggests additional evidence for CSF pulsatility dysfunction.


Assuntos
Aqueduto do Mesencéfalo/fisiopatologia , Derivações do Líquido Cefalorraquidiano , Líquido Cefalorraquidiano , Hidrocefalia de Pressão Normal/fisiopatologia , Hidrocefalia de Pressão Normal/cirurgia , Hidrodinâmica , Idoso , Idoso de 80 Anos ou mais , Aqueduto do Mesencéfalo/diagnóstico por imagem , Feminino , Seguimentos , Humanos , Hidrocefalia de Pressão Normal/diagnóstico por imagem , Processamento de Imagem Assistida por Computador , Imagem por Ressonância Magnética , Masculino , Pessoa de Meia-Idade , Reconhecimento Automatizado de Padrão , Resultado do Tratamento
10.
Stroke ; 49(Suppl_1): A79, 2018 Jan 22.
Artigo em Inglês | MEDLINE | ID: mdl-30520353

RESUMO

BACKGROUND: Acute blood pressure (BP) elevation in acute ischemic stroke (AIS) is common, yet the link with collateral circulation remains elusive due to lack of longitudinal data on premorbid hypertension (HTN) and serial BP changes within an individual. Precision medicine for AIS and management of HTN requires an understanding of collateral circulation. METHODS: The Interventional Management of Stroke III angiography core lab prospectively evaluated collateral circulation utilizing the ASITN scale prior to endovascular therapy. We used these data to discern the relationship of clinical and imaging markers of premorbid HTN with acute, serial BP measures and collaterals. RESULTS: Collaterals at angiography were graded in 276/331 (83%) subjects. Higher initial BP was associated with impaired collateral status, driven by diastolic BP (ASITN 0-1: mean 88.9 ± 23.5 mmHg (n=70); 2: 82.7 ± 18.5 mmHg (n=106); 3-4: 79.4 ± 15.0 mmHg (n=95); p=0.002) but not systolic BP (ASITN 0-1: mean 153.7 ± 31.5 mmHg (n=71); 2: 147.8 ± 29.0 mmHg (n=107); 3-4: 146.8 ± 28.3 mmHg (n=95); p=0.153). Premorbid HTN was linked with worse collaterals (ASITN 0-1: 88.9% (64/72); 2: 78.7% (85/108); 3-4: 64.2% (61/95); p=0.001). Admission anti-hypertensive medications were tied to worse collaterals (ASITN 0-1: 76.4% (55/72); 2: 64.8% (70/108); 3-4: 57.3% (55/96); p=0.036). Prior infarction on baseline imaging was also a marker of worse collaterals (ASITN 0-1: 33.3% (24/72); 2: 24.3% (26/107); 3-4: 19.1% (18/94); p=0.039). Serial BP from pre-randomization to post-tPA, however, was unrelated to collateral status. Multivariate modeling to predict collateral grade revealed that history of HTN (OR 0.29 95%CI (0.13, 0.64); p=0.002) and diastolic BP measured post-stroke but pre-randomization (per 10 mm Hg) (OR 0.80 95%CI (0.69, 0.93); p=0.004) were both distinct markers of impaired collaterals. The poorest collaterals were seen in those with both history of HTN and acutely elevated BP. CONCLUSIONS: Chronic and acute HTN are both potent predictors of impaired collaterals in AIS. Understanding how HTN affects the structure and function of collaterals and response to acute BP changes is critical for future hypertensive management and collateral augmentation.

11.
Abdom Radiol (NY) ; 2018 Nov 20.
Artigo em Inglês | MEDLINE | ID: mdl-30460529

RESUMO

PURPOSE: The purpose of the study was to propose a deep transfer learning (DTL)-based model to distinguish indolent from clinically significant prostate cancer (PCa) lesions and to compare the DTL-based model with a deep learning (DL) model without transfer learning and PIRADS v2 score on 3 Tesla multi-parametric MRI (3T mp-MRI) with whole-mount histopathology (WMHP) validation. METHODS: With IRB approval, 140 patients with 3T mp-MRI and WMHP comprised the study cohort. The DTL-based model was trained on 169 lesions in 110 arbitrarily selected patients and tested on the remaining 47 lesions in 30 patients. We compared the DTL-based model with the same DL model architecture trained from scratch and the classification based on PIRADS v2 score with a threshold of 4 using accuracy, sensitivity, specificity, and area under curve (AUC). Bootstrapping with 2000 resamples was performed to estimate the 95% confidence interval (CI) for AUC. RESULTS: After training on 169 lesions in 110 patients, the AUC of discriminating indolent from clinically significant PCa lesions of the DTL-based model, DL model without transfer learning and PIRADS v2 score ≥ 4 were 0.726 (CI [0.575, 0.876]), 0.687 (CI [0.532, 0.843]), and 0.711 (CI [0.575, 0.847]), respectively, in the testing set. The DTL-based model achieved higher AUC compared to the DL model without transfer learning and PIRADS v2 score ≥ 4 in discriminating clinically significant lesions in the testing set. CONCLUSION: The DeLong test indicated that the DTL-based model achieved comparable AUC compared to the classification based on PIRADS v2 score (p = 0.89).

12.
Technol Cancer Res Treat ; 17: 1533033818811150, 2018 Jan 01.
Artigo em Inglês | MEDLINE | ID: mdl-30411666

RESUMO

PURPOSE:: The accuracy of dose prediction is essential for knowledge-based planning and automated planning techniques. We compare the dose prediction accuracy of 3 prediction methods including statistical voxel dose learning, spectral regression, and support vector regression based on limited patient training data. METHODS:: Statistical voxel dose learning, spectral regression, and support vector regression were used to predict the dose of noncoplanar intensity-modulated radiation therapy (4π) and volumetric-modulated arc therapy head and neck, 4π lung, and volumetric-modulated arc therapy prostate plans. Twenty cases of each site were used for k-fold cross-validation, with k = 4. Statistical voxel dose learning bins voxels according to their Euclidean distance to the planning target volume and uses the median to predict the dose of new voxels. Distance to the planning target volume, polynomial combinations of the distance components, planning target volume, and organ at risk volume were used as features for spectral regression and support vector regression. A total of 28 features were included. Principal component analysis was performed on the input features to test the effect of dimension reduction. For the coplanar volumetric-modulated arc therapy plans, separate models were trained for voxels within the same axial slice as planning target volume voxels and voxels outside the primary beam. The effect of training separate models for each organ at risk compared to all voxels collectively was also tested. The mean squared error was calculated to evaluate the voxel dose prediction accuracy. RESULTS:: Statistical voxel dose learning using separate models for each organ at risk had the lowest root mean squared error for all sites and modalities: 3.91 Gy (head and neck 4π), 3.21 Gy (head and neck volumetric-modulated arc therapy), 2.49 Gy (lung 4π), and 2.35 Gy (prostate volumetric-modulated arc therapy). Compared to using the original features, principal component analysis reduced the 4π prediction error for head and neck spectral regression (-43.9%) and support vector regression (-42.8%) and lung support vector regression (-24.4%) predictions. Principal component analysis was more effective in using all/most of the possible principal components. Separate organ at risk models were more accurate than training on all organ at risk voxels in all cases. CONCLUSION:: Compared with more sophisticated parametric machine learning methods with dimension reduction, statistical voxel dose learning is more robust to patient variability and provides the most accurate dose prediction method.

13.
J Biol Chem ; 2018 Oct 18.
Artigo em Inglês | MEDLINE | ID: mdl-30337368

RESUMO

Previous studies have reported that miR-27a-3p is down-regulated in the serum of patients with intracerebral hemorrhage (ICH).Yet the implication of miR-27a-3p down-regulation in post-ICH complications remains elusive. Here, we verified miR-27a-3p levels in the serum of ICH patients by real-time PCR and observed that miR-27a-3p is also significantly reduced in the serum of these patients. We then further investigated the effect of miR-27a-3p on post-ICH complications by intraventricular administration of a miR-27a-3p mimic in rats with collagenase-induced ICH. We found that the hemorrhage markedly reduced miR-27a-3p levels in the hematoma, perihematomal tissue, and serum and that intracerebroventricular administration of the miR-27a-3p mimic alleviated behavioral deficits 24 h after the ICH. Moreover, ICH-induced brain edema, vascular leakage, and leukocyte infiltration were also attenuated by this mimic. Of note, the miR-27a-3p mimic treatment also inhibited neuronal apoptosis and microglia activation in the perihematomal zone. We further observed that the miR-27a-3p mimic suppressed the up-regulation of aquaporin-11 (AQP11) in the perihematomal area and in rat brain microvascular endothelial cells (BMECs). Moreover, miR-27a-3p down-regulation increased BMEC monolayer permeability and impaired BMEC proliferation and migration. In conclusion, miR-27a-3p down-regulation contributes to brain edema, blood-brain barrier disruption, neuron loss, and neurological deficits following ICH. We conclude that application of exogenous miR-27a-3p may protect against post-ICH complications by targeting AQP11 in the capillary endothelial cells of the brain.

14.
Front Neurol ; 9: 717, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-30233482

RESUMO

Background: Dynamic susceptibility contrast (DSC) MR perfusion is a frequently-used technique for neurovascular imaging. The progress of a bolus of contrast agent through the tissue of the brain is imaged via a series of T2*-weighted MRI scans. Clinically relevant parameters such as blood flow and Tmax can be calculated by deconvolving the contrast-time curves with the bolus shape (arterial input function). In acute stroke, for instance, these parameters may help distinguish between the likely salvageable tissue and irreversibly damaged infarct core. Deconvolution typically relies on singular value decomposition (SVD): however, studies have shown that these algorithms are very sensitive to noise and artifacts present in the image and therefore may introduce distortions that influence the estimated output parameters. Methods: In this work, we present a machine learning approach to the estimation of perfusion parameters in DSC-MRI. Various machine learning models using as input the raw MR source data were trained to reproduce the output of an FDA approved commercial implementation of the SVD deconvolution algorithm. Experiments were conducted to determine the effect of training set size, optimal patch size, and the effect of using different machine-learning models for regression. Results: Model performance increased with training set size, but after 5,000 samples (voxels) this effect was minimal. Models inferring perfusion maps from a 5 by 5 voxel patch outperformed models able to use the information in a single voxel, but larger patches led to worse performance. Random Forest models produced had the lowest root mean squared error, with neural networks performing second best: however, a phantom study revealed that the random forest was highly susceptible to noise levels, while the neural network was more robust. Conclusion: The machine learning-based approach produces estimates of the perfusion parameters invariant to the noise and artifacts that commonly occur as part of MR acquisition. As a result, better robustness to noise is obtained, when evaluated against the FDA approved software on acute stroke patients and simulated phantom data.

15.
PLoS One ; 13(9): e0203535, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-30256814

RESUMO

BACKGROUND AND PURPOSE: Anemia is associated with worse outcome in stroke, but the impact of anemia with intravenous thrombolysis or endovascular therapy has hardly been delineated. The aim of this study was to analyze the role of anemia on infarct evolution and outcome after acute stroke treatment. METHODS: 1158 patients from Bern and 321 from Los Angeles were included. Baseline data and 3 months outcome assessed with the modified Rankin Scale were recorded prospectively. Baseline DWI lesion volumes were measured in 345 patients and both baseline and final infarct volumes in 180 patients using CT or MRI. Multivariable and linear regression analysis were used to determine predictors of outcome and infarct growth. RESULTS: 712 patients underwent endovascular treatment and 446 intravenous thrombolysis. Lower hemoglobin at baseline, at 24h, and nadir until day 5 predicted poor outcome (OR 1.150-1.279) and higher mortality (OR 1.131-1.237) independently of treatment. Decrease of hemoglobin after hospital arrival, mainly induced by hemodilution, predicted poor outcome and had a linear association with final infarct volumes and the amount and velocity of infarct growth. Infarcts of patients with newly observed anemia were twice as large as infarcts with normal hemoglobin levels. CONCLUSION: Anemia at hospital admission and any hemoglobin decrease during acute stroke treatment affect outcome negatively, probably by enlarging and accelerating infarct growth. Our results indicate that hemodilution has an adverse effect on penumbral evolution. Whether hemoglobin decrease in acute stroke could be avoided and whether this would improve outcome would need to be studied prospectively.

16.
PLoS One ; 13(8): e0202592, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-30142167

RESUMO

BACKGROUND: The pivotal impact of collateral circulation on outcomes in endovascular therapy has fueled the development of numerous CTA collateral scales, yet synchronized validation with conventional angiography has never occurred. We validated multiphase flat-detector CTA (mpFDCTA) for collateral imaging in patients undergoing endovascular stroke treatment. MATERIALS AND METHODS: Consecutive acute ischemic stroke patient data, including mpFDCTA shortly followed by digital subtraction angiography (DSA), in the setting of acute ICA- or MCA-occlusions were analyzed. An independent core lab scored mpFDCTA with an established collateral scale and separately graded American Society of Interventional and Therapeutic Neuroradiology (ASITN) collateral score on DSA, blind to all other data. RESULTS: 24 consecutive cases (age 76.7 ± 7.3 years; 58.3% women; baseline NIHSS median 17 (4-23)) of acute ICA- or MCA-occlusion were analyzed. Time from mpFDCTA to intracranial DSA was 23.04 ± 7.6 minutes. Median mpFDCTA collateral score was 3 (0-5) and median DSA ASITN collateral score was 2 (0-3), including the full range of potential collateral grades. mpFDCTA and ASITN collateral score were strongly correlated (r = 0.86, p<0.001). mpFDCTA provided more complete collateral data compared to selective DSA injections in cases of ICA-occlusion. ROC analyses for prediction of clinical outcomes revealed an AUC of 0.76 for mpFDCTA- and 0.70 for DSA ASITN collaterals. CONCLUSIONS: mpFDCTA in the angiography suite provides a validated measure of collaterals, offering distinct advantages over conventional angiography. Direct patient transfer to the angiography suite and mpFDCTA collateral grading provides a novel and reliable triage paradigm for acute ischemic stroke.

17.
IEEE Trans Biomed Eng ; 65(9): 2058-2065, 2018 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-29989941

RESUMO

OBJECTIVE: Hemorrhagic transformation (HT) is the most severe complication of reperfusion therapy in acute ischemic stroke (AIS) patients. Management of AIS patients could benefit from accurate prediction of upcoming HT. While prediction of HT occurrence has recently provided encouraging results, the prediction of the severity and territory of the HT could bring valuable insights that are beyond current methods. METHODS: This study tackles these issues and aims to predict the spatial occurrence of HT in AIS from perfusion-weighted magnetic resonance imaging (PWI) combined with diffusion weighted imaging. In all, 165 patients were included in this study and analyzed retrospectively from a cohort of AIS patients treated with reperfusion therapy in a single stroke center. RESULTS: Machine learning models are compared within our framework; support vector machines, linear regression, decision trees, neural networks, and kernel spectral regression were applied to the dataset. Kernel spectral regression performed best with an accuracy of $\text{83.7} \pm \text{2.6}\%$. CONCLUSION: The key contribution of our framework formalize HT prediction as a machine learning problem. Specifically, the model learns to extract imaging markers of HT directly from source PWI images rather than from pre-established metrics. SIGNIFICANCE: Predictions visualized in terms of spatial likelihood of HT in various territories of the brain were evaluated against follow-up gradient recalled echo and provide novel insights for neurointerventionalists prior to endovascular therapy.

18.
Front Neurol ; 9: 200, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-29674994

RESUMO

The microvasculature is prominently affected by traumatic brain injury (TBI), including mild TBI (concussion). Assessment of cerebral hemodynamics shows promise as biomarkers of TBI, and may help inform development of therapies aimed at promoting neurologic recovery. The objective of this study was to assess the evolution in cerebral hemodynamics observable with transcranial Doppler (TCD) ultrasound in subjects suffering from a concussion at different intervals during recovery. Pediatric subjects between the ages of 14 and 19 years clinically diagnosed with a concussion were observed at different points post-injury. Blood flow velocity in the middle cerebral artery was measured with TCD. After a baseline period, subjects participated in four breath holding challenges. Pulsatility index (PI), resistivity index (RI), the ratio of the first two pulse peaks (P2R), and the mean velocity (MV) were computed from the baseline section. The breath hold index (BHI) was computed from the challenge sections. TCD detected two phases of hemodynamic changes after concussion. Within the first 48 h, PI, RI, and P2R show a significant difference from the controls (U = -3.10; P < 0.01, U = -2.86; P < 0.01, and U = 2.62; P < 0.01, respectively). In addition, PI and P2R were not correlated (rp = -0.36; P = 0.23). After 48 h, differences in pulsatile features were no longer observable. However, BHI was significantly increased when grouped as 2-3, 4-5, and 6-7 days post-injury (U = 2.72; P < 0.01, U = 2.46; P = 0.014, and U = 2.38; P = 0.018, respectively). To our knowledge, this is the first longitudinal study of concussions using TCD. In addition, these results are the first to suggest the multiple hemodynamic changes after a concussion are observable with TCD and could ultimately lead to a better understanding of the underlying pathophysiology. In addition, the different hemodynamic responses to a concussion as compared to severe traumatic brain injuries highlight the need for specific diagnostic and therapeutic treatments of mild head injuries in adolescents.

19.
Acta Neurochir Suppl ; 126: 269-273, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-29492573

RESUMO

OBJECTIVE: To determine normal ranges for traditional transcranial Doppler (TCD) measurements for two age groups (14-19 and 20-29 years) and compare to existing literature results. The development of a normal range for TCD measurements will be required for the development of diagnostic and prognostic tests in the future. MATERIALS AND METHODS: We performed TCD on the middle cerebral artery on 147 healthy subjects aged 18.9 years (SD = 2.1) and calculated mean cerebral blood flow velocity (mCBFV) and pulsatility index (PI). The study population was divided into two age populations (14-19 and 20-29 years). RESULTS: There was a significant decrease in PI (p = 0.015) for the older age group with no difference in mCBFV. CONCLUSION: Age-related, normal data are a prerequisite for TCD to continue to gain clinical acceptance. Our correlation of age-related TCD findings with previously published results as the generally accepted "gold standard" underlines the validity and sensitivity of this ultrasound method.


Assuntos
Circulação Cerebrovascular/fisiologia , Artéria Cerebral Média/diagnóstico por imagem , Fluxo Pulsátil/fisiologia , Adolescente , Adulto , Fatores Etários , Velocidade do Fluxo Sanguíneo/fisiologia , Feminino , Voluntários Saudáveis , Humanos , Masculino , Artéria Cerebral Média/fisiologia , Valores de Referência , Ultrassonografia Doppler Transcraniana , Adulto Jovem
20.
J Clin Monit Comput ; 32(6): 977-992, 2018 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-29480385

RESUMO

Cardiac arrest (CA) is the leading cause of death and disability in the United States. Early and accurate prediction of CA outcome can help clinicians and families to make a better-informed decision for the patient's healthcare. Studies have shown that electroencephalography (EEG) may assist in early prognosis of CA outcome. However, visual EEG interpretation is subjective, labor-intensive, and requires interpretation by a medical expert, i.e., neurophysiologists. These limiting factors may hinder the applicability of such testing as the prognostic method in clinical settings. Automatic EEG pattern recognition using quantitative measures can make the EEG analysis more objective and less time consuming. It also allows to detect and display hidden patterns that may be useful for the prognosis over longer time periods of monitoring. Given these potential benefits, there have been an increasing interest over the last few years in the development and employment of EEG quantitative measures to predict CA outcome. This paper extensively reviews the definition and efficacy of various measures that have been employed for the prediction of outcome in CA subjects undergoing hypothermia (a neuroprotection method that has become a standard of care to improve the functional recovery of CA patients after resuscitation). The review details the State-of-the-Art and provides some perspectives on what seems to be promising for the early and accurate prognostication of CA outcome using the quantitative measures of EEG.

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