Population-Based Study of Capsular Warning Syndrome and Prognosis After Early Recurrent TIA Paul NN, Sinoni M, Schandratheva A, et al. Neurology 2012:79:1356 Conclusions: Capsular warning syndrome comprises 1.5% of transient ischemic attack (TIA) presentations but has a poor prognosis, with a 7-day stroke risk of 60%. With the exception of capsular warning syndrome, recurrent TIAs Յ7 days are not associated with a greater stroke risk than a single TIA.Summary: Stroke risk after TIA is highest within the first 7 days (Hill MD et al, Neurology 2004;62:2015-20). In addition, many guidelines recommend urgent evaluation for carotid stenosis for patients with more than two TIAs Յ7 days (Johnston SC et al, Ann Neurology 2006;60:301-13). Capsular warning syndrome, manifested by multiple stereotype motor TIAs, is thought to place patients at particular risk and precedes capsular infarction (Donnan GA et al, Neurology 1993;43:957-62). However, it is unclear whether patients with multiple TIAs have relatively untreatable or treatable underlying pathologic conditions such as carotid stenosis or atrial fibrillation. The authors used data from the Oxford Vascular Study (OXVASC) to delineate whether patients with multiple TIAs are at high early stroke risk and whether a treatable underlying condition is more common in patients with multiple TIAs. They studied clinical characteristics, acute STROKE treatment (TOAST) classification, and risk of stroke in 1000 consecutive patients with incident and recurrent TIAs as part of the prospective, population-based Oxford Vascular Study. Of the 1000 patients with TIAs, 170 had a further TIA Յ7 days (105 Յ24 hours). Multiple TIAs were not associated with carotid stenosis or atrial fibrillation. Much of the 10.6% (95% confidence interval [CI], 6.5%-15.9%) risk of stroke in the first 7 days after the an initial TIA was due to patients with small-vessel disease (SVD) etiology (10 of 24 vs 8 of 146; odds ratio, 12.3; 95% confidence interval, 3.7-41.9; P ϭ .0001), particularly in those with motor weakness (ie, capsular warning syndrome) compared with hemisensory events (9 of 15 [60%], 95% CI, 35.3-84.7 vs 1 of 9 [11.1%], 95% CI, 0-31.7; P ϭ .03). The 7-day risk of stroke after recurrent TIA was similar to the risk after a single TIA in patients with non-SVD TIA (8 of 146 [5.5%] vs 76 of 830 [9.2%]; odds ratio, 0.58; 95% CI, 0.25-1.3; P ϭ .20). All of the nine patients with stroke after a capsular warning syndrome had recurrent TIA Յ24 hours after the first TIA, and the subsequent stroke occurred Յ72 hours of the second TIA in eight patients. The ABCDE2 scores of all preceding TIAs were Յ4 in all nine patients with capsular warning syndrome before their stroke.Comment: The data point out that not all TIAs have the same prognosis for stroke. In particular, multiple TIAs without association with large-vessel disease may have the worst prognosis of all. The implication is that the emphasis on recurrent TIAs in many societal guidelines scores may not be justified. At least in this study, multiple T...
One of the challenges with urgent evaluation of patients with acute respiratory distress syndrome (ARDS) in the emergency room (ER) is distinguishing between cardiac vs infectious etiologies for their pulmonary findings. We conducted a retrospective study with the collected data of 171 ER patients. ER patient classification for cardiac and infection causes was evaluated with clinical data and chest X-ray image data. We show that a deep-learning model trained with an external image data set can be used to extract image features and improve the classification accuracy of a data set that does not contain enough image data to train a deep-learning model. An analysis of clinical feature importance was performed to identify the most important clinical features for ER patient classification. The current model is publicly available with an interface at the web link: http://nbttranslationalresearch.org/.
One of the challenges with urgent evaluation of patients with acute respiratory distress syndrome (ARDS) in the emergency room (ER) is distinguishing between cardiac vs infectious etiologies for their pulmonary findings. We evaluated ER patient classification for cardiac and infection causes with clinical data and chest X-ray image data. We show that a deep-learning model trained with an external image data set can be used to extract image features and improve the classification accuracy of a data set that does not contain enough image data to train a deeplearning model. We also conducted clinical feature importance analysis and identified the most important clinical features for ER patient classification. This model can be upgraded to include a SARS-CoV-2 specific classification with COVID-19 patients data. The current model is publicly available with an interface at the web link: http://nbttranslationalresearch.org/.
3221 Children with sickle cell disease (SCD) are at high risk for thrombotic stroke. Transcranial doppler ultrasound (TCD) is utilized to predict children at highest risk. Anemia and and vessel occlusion are assocated with elevated TCD velocities and risk of stroke. There is increasing evidence that a prothrombotic state contributes to the complications of SCD including stroke. We hypothesized that increased thrombin generation and platelet activation are associated with increased TCD velocities. We conducted a cross-sectional cohort study of children with SCD at Duke University Medical Center. Children had been clinically well for at least 2 weeks, had not been transfused for at least 3 months, and had TCD performed at study enrollment (n= 28) or within the prior 6 months (n=7). TCD time-averaged mean velocities (TAMV) were calculated for each patient and used for analysis. Blood was collected at the time of study entry per study protocol. Thrombin generation was measured utilizing: D-dimer; thrombin antithrombin (TAT); and calibrated automated thrombography (CAT) which evaluates phases of thrombin production including lag phase, time to peak thrombin, endogenous thrombin potential (ETP), and peak thrombin. Platelet activation was assessed by measurement of soluble glycoprotein V (sGPV). Clinical data were abstracted from the medical record including confirmation of SS genotype, use of hydroxyurea, prior sickle cell-related clinical events, and hematological data including hemoglobin, platelets and white blood cell count. SCD severity scores were calculated to classify patients as either high risk or low/moderate risk (van den Tweel et al, 2010). Linear regression analyses were conducted to assess correlation. Children with high risk severity scores were compared to children with low/moderate risk severity scores. Statistical analysis was performed using Graphpad 5.0; p<0.05 was considered significant. We evaluated 35 patients (median age 10 years, range 3 – 17). Linear regression revealed a correlation between the TCD velocities for TAT (r2=0.147, p=0.03), ETP (r2=0.13, p=0.049), and peak thrombin (r2=0.12, p=0.05). There was no significant correlation between TCD velocity and D-dimer, lag phase or time to peak thrombin. There also was no correlation between TCD velocities and hemoglobin, platelets or sGPV. Patients taking hydroxyurea were found to have lower thrombin generation (D-dimer: 882 ± 93 ng/ml vs. 1679 ± 428, p<0.0001) and TAT (6.8 ± 0.67 ng/ml vs. 10.59 ± 2.22, p<0.0001). There were 8 (23%) patients classified as high risk and 27 (77%) classified as low/moderate risk. Comparison of comparison of D-dimer, TAT, and each phase of thrombin generation between patients with low/moderate severity scores and those classified as high severity revealed no significant differences. In summary, increased TAT, ETP, and peak thrombin levels correlated with increased TCD velocities. Markers of thrombin generation were lower in children taking hydroxyurea. The lag phase and time to peak thrombin which both reflect the ‘thrombin burst’, were not predictive of TCD velocities. In addition, hematological values and platelet activation did not correlate with TCD velocities. Further studies are warranted to understand the contribution of thrombin generation to stroke risk, to determine if markers of thrombin generation are independent markers of stroke risk, and determine if hydroxyurea can ameliorate this risk. Disclosures: Shah: Adventrx: Consultancy; Eisai: Research Funding. Off Label Use: Hydroxyurea for use in pediatric sickle cell disease. Ortel:Eisai: Research Funding; Glaxo SmithKline: Research Funding; Pfizer: Research Funding; Instrumentation Laboratory, Inc: Consultancy, Research Funding; Boehringer Ingelheim: Consultancy.
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