Search Results (5)

Background and Objectives: Long COVID has been linked with persistent neurological symptoms, but data on its effects on acute stroke presentation, management, and outcomes remain unclear. This study aimed to compare the clinical profile, management, and short-term outcome of acute ischemic stroke patients with and without Long COVID. Materials and Methods: A retrospective cohort study was conducted on 132 patients who presented at admission with code stroke alert in our Emergency Department (ED). Out of those, 26 were identified to have the Long COVID condition and assigned to the Long COVID group, and 106 were without the Long COVID condition and assigned to the No Long COVID group. Baseline demographics, stroke severity by NIHSS (National Institutes of Health Stroke Scale), risk factors, admission symptoms, laboratory findings, Emergency department time targets, reperfusion treatments received, and outcomes between the two groups were compared. Results: There were no significant differences between the two groups in age, gender, baseline NIHSS scores, ED time targets, or laboratory values. The proportion of patients with Long COVID significantly increased among non-smokers (Fisher’s Exact Test chi-squared, p = 0.027). Also, patients suffering from Long COVID exhibited higher incidences of headache (19.2% compared to 5.7%, OR = 3.97, p = 0.040) and facial drooping (42.3% compared to 19.8%, OR = 2.97, p = 0.022). The mechanical thrombectomy was more frequent among the group with Long COVID (30.8% vs. 16.0%), but this difference was not statistically significant. More hemorrhagic transformations happened in the Long COVID group (26.9% vs. 14.2%, p = 0.143). Discharge rates and hospital length of stay in days were similar between groups. Conclusions: Long COVID patients did not present notable differences in emergency department time targets, baseline stroke severity, or short-term outcomes when presenting with code stroke alert. Nevertheless, specific clinical characteristics—such as elevated occurrences of headache and facial drooping—were more frequently observed in patients with Long COVID, alongside non-significant trends indicating a greater utilization of mechanical thrombectomy and increased rates of hemorrhagic transformation. These results imply that Long COVID may have a subtle impact on stroke presentation and potentially on underlying cerebrovascular susceptibility. Further prospective studies with larger sample sizes are necessary to investigate Long COVID’s long-term neurological and vascular consequences. Full article

Background: Rapid identification and treatment of stroke are essential for the patient. Our objective was to determine the diagnostic utility of glial fibrillary acidic protein (GFAP) and ubiquitin C-terminal hydrolase L1 (UCH-L1) in the emergency department to identify and differentiate acute stroke within 4.5 h of symptom onset in patients admitted with a stroke code alert. Methods: This study included 85 patients with a “code stroke alert” upon arrival at the emergency department. Individuals were grouped in two categories: patients with stroke (including 69 patients) and patients without stroke (including 16 patients). The research was conducted at the Emergency Municipal Clinical Hospital in Timișoara, Romania, the county’s second-largest hospital, which lacks a neurologist and a dedicated stroke unit. Results: No significant differences were observed between the two groups (with stroke and without stroke) regarding most demographic or admission parameters. Significant differences were observed for the biomarkers GFAP (142.91 ± 102.19 pg/mL in patients with acute stroke vs. 37.76 ± 19.92 pg/mL in patients without stroke (p < 0.001)) and UCH-L1 (1307.68 ± 967.54 pg/mL in stroke patients vs. 189.81 ± 92.69 pg/mL in patients without stroke (p < 0.001)). Within the stroke group, 37 patients had acute ischemic stroke, while 32 patients were diagnosed with hemorrhagic stroke based on brain CT imaging. GFAP achieved an accuracy of 94.2% for differentiating hemorrhagic from ischemic stroke, with a cut-off value of 77.15 pg/mL. Conclusions: GFAP excellently differentiated acute stroke from stroke mimics, with high sensitivity, perfect specificity, and strong predictive values. Integrating GFAP and UCH-L1 measurements into emergency protocols may enhance stroke diagnosis, optimize patient triage, and ultimately improve outcomes by facilitating the faster initiation of appropriate therapies. Full article

Background and objectives: To minimize stroke-related deaths and maximize the likelihood of cerebral reperfusion, medical professionals developed the “code stroke” emergency protocol, which allows for the prompt evaluation of patients with acute ischemic stroke symptoms in pre-hospital care and the emergency department (ED). This research will outline our experience in implementing the stroke code protocol for acute ischemic stroke patients and its impact on door-to-needle time (DTN) in the ED. Methods: Our study included patients with a “code stroke alert” upon arrival at the emergency department. The final sample of this study consisted of 258 patients eligible for intravenous (IV) thrombolysis with an onset-to-door time < 4.5 h. ED admissions were categorized into two distinct groups: “day shift” (from 8 a.m. to 8 p.m.) (n = 178) and “night shift” (from 8 p.m. to 8 a.m.) (n = 80) groups. Results: An analysis of ED time targets showed an increased median during the day shift for onset-to-ED door time of 310 min (IQR, 190–340 min), for door-to-physician (emergency medicine doctor) time of 5 min (IQR, 3–9 min), for door-to-physician (emergency medicine doctor) time of 5 min (IQR, 3–9 min), and for door-to-physician (neurologist) time of 7 min (IQR, 5–10 min), also during the day shift. During the night shift, an increased median was found for door-to-CT time of 21 min (IQR, 16.75–23 min), for door-to-CT results of 40 min (IQR, 38–43 min), and for door-to-needle time of 57.5 min (IQR, 46.25–60 min). Astonishingly, only 17.83% (n = 46) of these patients received intravenous thrombolysis, and the proportion of patients with thrombolysis was significantly higher during the night shift (p = 0.044). A logistic regression analysis considering the door-to-needle time (minutes) as the dependent variable demonstrated that onset-to-ED time (p < 0.001) and door-to-physician (emergency medicine physicians) time (p = 0.021) are predictors for performing thrombolysis in our study. Conclusions: This study identified higher door-to-CT and door-to-emergency medicine physician times associated with an increased DTN, highlighting further opportunities to improve acute stroke care in the emergency department. Further, door-to-CT and door-to-CT results showed statistically significant increases during the night shift. Full article

Artificial intelligence (AI) is increasingly used in electrocardiography (ECG) to assist in diagnosis, stratification, and management. AI algorithms can help clinicians in the following areas: (1) interpretation and detection of arrhythmias, ST-segment changes, QT prolongation, and other ECG abnormalities; (2) risk prediction integrated with or without clinical variables (to predict arrhythmias, sudden cardiac death, stroke, and other cardiovascular events); (3) monitoring ECG signals from cardiac implantable electronic devices and wearable devices in real time and alerting clinicians or patients when significant changes occur according to timing, duration, and situation; (4) signal processing, improving ECG quality and accuracy by removing noise/artifacts/interference, and extracting features not visible to the human eye (heart rate variability, beat-to-beat intervals, wavelet transforms, sample-level resolution, etc.); (5) therapy guidance, assisting in patient selection, optimizing treatments, improving symptom-to-treatment times, and cost effectiveness (earlier activation of code infarction in patients with ST-segment elevation, predicting the response to antiarrhythmic drugs or cardiac implantable devices therapies, reducing the risk of cardiac toxicity, etc.); (6) facilitating the integration of ECG data with other modalities (imaging, genomics, proteomics, biomarkers, etc.). In the future, AI is expected to play an increasingly important role in ECG diagnosis and management, as more data become available and more sophisticated algorithms are developed. Full article

This study evaluated the accuracy of tennis-specific stroke and movement event detection algorithms from a cervically mounted wearable sensor containing a triaxial accelerometer, gyroscope and magnetometer. Stroke and movement data from up to eight high-performance tennis players were captured in match-play and movement drills. Prototype algorithms classified stroke (i.e., forehand, backhand, serve) and movement (i.e., “Alert”, “Dynamic”, “Running”, “Low Intensity”) events. Manual coding evaluated stroke actions in three classes (i.e., forehand, backhand and serve), with additional descriptors of spin (e.g., slice). Movement data was classified according to the specific locomotion performed (e.g., lateral shuffling). The algorithm output for strokes were analysed against manual coding via absolute (n) and relative (%) error rates. Coded movements were grouped according to their frequency within the algorithm’s four movement classifications. Highest stroke accuracy was evident for serves (98%), followed by groundstrokes (94%). Backhand slice events showed 74% accuracy, while volleys remained mostly undetected (41–44%). Tennis-specific footwork patterns were predominantly grouped as “Dynamic” (63% of total events), alongside successful linear “Running” classifications (74% of running events). Concurrent stroke and movement data from wearable sensors allows detailed and long-term monitoring of tennis training for coaches and players. Improvements in movement classification sensitivity using tennis-specific language appear warranted. Full article