Objective:The objectives of this study were to measure the global impact of the pandemic on the volumes for intravenous thrombolysis (IVT), IVT transfers, and stroke hospitalizations over 4 months at the height of the pandemic (March 1 to June 30, 2020) compared with two control 4-month periods.Methods:We conducted a cross-sectional, observational, retrospective study across 6 continents, 70 countries, and 457 stroke centers. Diagnoses were identified by their ICD-10 codes and/or classifications in stroke databases.Results:There were 91,373 stroke admissions in the 4 months immediately before compared to 80,894 admissions during the pandemic months, representing an 11.5% (95%CI, -11.7 to - 11.3, p<0.0001) decline. There were 13,334 IVT therapies in the 4 months preceding compared to 11,570 procedures during the pandemic, representing a 13.2% (95%CI, -13.8 to -12.7, p<0.0001) drop. Interfacility IVT transfers decreased from 1,337 to 1,178, or an 11.9% decrease (95%CI, -13.7 to -10.3, p=0.001). Recovery of stroke hospitalization volume (9.5%, 95%CI 9.2-9.8, p<0.0001) was noted over the two later (May, June) versus the two earlier (March, April) pandemic months. There was a 1.48% stroke rate across 119,967 COVID-19 hospitalizations. SARS-CoV-2 infection was noted in 3.3% (1,722/52,026) of all stroke admissions.Conclusions:The COVID-19 pandemic was associated with a global decline in the volume of stroke hospitalizations, IVT, and interfacility IVT transfers. Primary stroke centers and centers with higher COVID19 inpatient volumes experienced steeper declines. Recovery of stroke hospitalization was noted in the later pandemic months.
Background and Objectives:Declines in stroke admission, intravenous thrombolysis, and mechanical thrombectomy volumes were reported during the first wave of the COVID-19 pandemic. There is a paucity of data on the longer-term effect of the pandemic on stroke volumes over the course of a year and through the second wave of the pandemic. We sought to measure the impact of the COVID-19 pandemic on the volumes of stroke admissions, intracranial hemorrhage (ICH), intravenous thrombolysis (IVT), and mechanical thrombectomy over a one-year period at the onset of the pandemic (March 1, 2020, to February 28, 2021) compared with the immediately preceding year (March 1, 2019, to February 29, 2020).Methods:We conducted a longitudinal retrospective study across 6 continents, 56 countries, and 275 stroke centers. We collected volume data for COVID-19 admissions and 4 stroke metrics: ischemic stroke admissions, ICH admissions, intravenous thrombolysis treatments, and mechanical thrombectomy procedures. Diagnoses were identified by their ICD-10 codes or classifications in stroke databases.Results:There were 148,895 stroke admissions in the one-year immediately before compared to 138,453 admissions during the one-year pandemic, representing a 7% decline (95% confidence interval [95% CI 7.1, 6.9]; p<0.0001). ICH volumes declined from 29,585 to 28,156 (4.8%, [5.1, 4.6]; p<0.0001) and IVT volume from 24,584 to 23,077 (6.1%, [6.4, 5.8]; p<0.0001). Larger declines were observed at high volume compared to low volume centers (all p<0.0001). There was no significant change in mechanical thrombectomy volumes (0.7%, [0.6,0.9]; p=0.49). Stroke was diagnosed in 1.3% [1.31,1.38] of 406,792 COVID-19 hospitalizations. SARS-CoV-2 infection was present in 2.9% ([2.82,2.97], 5,656/195,539) of all stroke hospitalizations.Discussion:There was a global decline and shift to lower volume centers of stroke admission volumes, ICH volumes, and IVT volumes during the 1st year of the COVID-19 pandemic compared to the prior year. Mechanical thrombectomy volumes were preserved. These results suggest preservation in the stroke care of higher severity of disease through the first pandemic year.Trial Registration Information:This study is registered underNCT04934020.
Background: We developed an automated smart phone application for detection of acute stroke using machine learning (ML) algorithms for recognition of facial asymmetry, arm weakness, and speech changes. Methods: We analysed prospectively collected data from patients admitted to 4 major metropolitan stroke centers with confirmed diagnosis of acute stroke. Speech and facial data were captured via video recording and arm data was captured via device sensors. A. Face. This module extracts 68 facial landmark points that are passed through a dimensionality reduction step and an asymmetry classifier. We implemented and compared 26 classification methods with neurologists' clinical impression and determined Quadratic Discriminative Analysis as the best one in terms of accuracy and interpretability. B. Arm. Using data extracted from 3D accelerometer, gyroscope, and magnetometer , we designed a grasp agnostic classifier based on AdaBoost to process motion trajectories and detect arm weakness.C. Speech. We developed an algorithm based on frequency analysis and Mel Frequency Cepstral Coefficients (MFCC) to detect abnormal/slurred speech. All tests were conducted within 72 hours of admission. Each of the three ML outputs was correlated with neurologists’ clinical impression. Results: Among the 269 analysed patients, 41% were female, the median age was 71, % had hemorrhagic and % had ischemic stroke. Final analyses of 18311 facial images revealed 99.42% sensitivity, 93.67% specificity, and 97.11% accuracy in detection of facial asymmetry. The results for 43 arm trajectories revealed 71.42% sensitivity, 72.41% specificity, and 72.09% accuracy in detection of arm weakness. Preliminary analysis of MFCC algorithms confirmed adequate features for abnormal speech detection Conclusions: Our preliminary results confirm that smartphone enabled ML-algorithms can reliably identify acute stroke features with accuracy comparable to neurologists’ clinical impression.
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