Identification Of Acute Ischemic Stroke Research Articles

Multiphase CTA Collateral Score to Identify Intracranial Atherosclerotic Stenosis-Related Large Vessel Occlusion

Objective: Identification of acute ischemic stroke with large vessel occlusion (AIS-LVO) etiology is crucial for effective revascularization therapy. As collaterals are pivotal in maintaining cerebral perfusion in intracranial atherosclerotic stenosis (ICAS), we investigated whether multiphase CT angiography (mCTA) collateral score can be a diagnosis marker of ICAS-related LVO. Methods: We reviewed clinical and imaging data from 92 patients who presented with AIS-LVO and underwent mCTA (57 ICAS-related LVO and 35 embolic LVO). Logistic regression was used to identify ICAS-related LVO. The diagnostic accuracy of the mCTA collateral score for identifying ICAS-related LVO was determined using receiver operating characteristic (ROC) analysis. Results: Compared with patients with embolic LVO, those with ICAS-related LVO had a high median mCTA collateral score (4 vs. 3; P<0.0001). The multinomial logistic regression analysis revealed a significant increase in the mCTA collateral score (OR: 3.717, 95% CI: 2.009-6.876, P<0.0001) in patients with ICAS-related LVO. ROC analysis revealed that the optimal cutoff point of the mCTA collateral score to diagnosis the ICAS-related LVO was 3.5, the area under the curve (AUC) was 0.817 (95% CI: 0.736-0.899; P<0.0001), sensitivity was 80.7%, and specificity was 74.3%. Further analysis revealed that patients with a 4 to 5 mCTA collateral score exhibited a significantly higher median modified Rankin Scale (mRS) at discharge compared with those with a 0 to 3 score (P=0.0464). Conclusions: The mCTA collateral score may be associated with ICAS-related LVO and could be beneficial in identifying the etiology of AIS-LVO.

SDS-Net: A Synchronized Dual-Stage Network for Predicting Patients Within 4.5-h Thrombolytic Treatment Window Using MRI.

Timely and precise identification of acute ischemic stroke (AIS) within 4.5h is imperative for effective treatment decision-making. This study aims to construct a novel network that utilizes limited datasets to recognize AIS patients within this critical window. We conducted a retrospective analysis of 265 AIS patients who underwent both fluid attenuation inversion recovery (FLAIR) and diffusion-weighted imaging (DWI) within 24h of symptom onset. Patients were categorized based on the time since stroke onset (TSS) into two groups: TSS ≤ 4.5 h and TSS > 4.5 h. The TSS was calculated as the time from stroke onset to MRI completion. We proposed a synchronized dual-stage network (SDS-Net) and a sequential dual-stage network (Dual-stage Net), which were comprised of infarct voxel identification and TSS classification stages. The models were trained on 181 patients and validated on an independent external cohort of 84 patients using metrics of area under the curve (AUC), sensitivity, specificity, and accuracy. A DeLong test was used to statistically compare the performance of the two models. SDS-Net achieved an accuracy of 0.844 with an AUC of 0.914 in the validation dataset, outperforming the Dual-stage Net, which had an accuracy of 0.822 and an AUC of 0.846. In the external test dataset, SDS-Net further demonstrated superior performance with an accuracy of 0.800 and an AUC of 0.879, compared to the accuracy of 0.694 and AUC of 0.744 of Dual-stage Net (P = 0.049). SDS-Net is a robust and reliable tool for identifying AIS patients within a 4.5-h treatment window using MRI. This model can assist clinicians in making timely treatment decisions, potentially improving patient outcomes.

Abstract TP119: Predicting Posterior Circulation Stroke During Emergency Triage: Dizzy-7 Project

Introduction: “Stroke code” activation is a uniform process to summon healthcare stakeholders to orchestrate the rapid sequential steps for urgent management in patients with possible stroke. Acute dizziness, a common emergency department (ED) presentation, frequently leads to stroke code activation; however, cancellation rates remain high and process improvement strategies are warranted. Methods: We analyzed stroke identification accuracy, cancellation rates, and clinical characteristics in patients presenting with an acute vestibular syndrome to inform process improvement strategies for stroke code activation. We developed an ED triage diagnostic algorithm termed, “Dizzy-7”, based on evidence incorporating key history and physical examination elements derived in patients with acute posterior circulation ischemia. Subsequent analyses were performed on algorithmic utilization and its ability to predict stroke. Results: 1599 patients presented with acute dizziness over a 4-month period (2022-2023). Stroke code activation occurred in nearly one in four (366/1599) these presentations. 20% (319/1599) were ultimately diagnosed with stroke and of these, 49% (319/366) were identified following stroke code activation. The Dizzy-7 algorithm was used infrequently (4%) among all acute dizziness presentations. Of the 65 cases that incorporated the algorithm, 16 resultant stroke code activations occurred with 1 confirmed diagnosis (6%). We analyzed stroke alert cancellation and found an increased frequency in cancellation for those without use of the protocol at 22% (77/350) compared to those who did use it at 6% (1/16). Discussion: Accurate and reliable identification of acute ischemic stroke among patients presenting with acute dizziness remains complex. Implementation of a triage diagnostic algorithm based on contemporary evidence was infrequently implemented. Process improvement efforts targeted at greater algorithmic use, interprofessional collaboration, and education may improve stroke code utilization efficiency. Although based on limited sample size, a trend towards reduced stroke code cancellation rates were seen throughout the pilot Dizzy-7 algorithm use.

Acute ischemic stroke identification using mean and reorder resample, synthetic minority oversampling technique and linear discriminant analysis

Acute ischemic stroke identification using mean and reorder resample, synthetic minority oversampling technique and linear discriminant analysis

Abstract 19: Prehospital Identification of Acute Ischemic Stroke is Associated With Faster and More Frequent Thrombolysis

Background: Functional outcomes are improved when AIS patients receive faster treatment. The first medical contact for many AIS patients is with emergency medical services (EMS) providers. We hypothesize that AIS treatment is faster when EMS providers suspect stroke. Methods: We performed a retrospective analysis of the Greater Cincinnati/Northern Kentucky Stroke Study, a comprehensive study of stroke patients in a large geographical area with 1.3 million inhabitants whose demographics are representative of the United States. We compared AIS patients age ≥18 years transported by EMS in 2015 with an EMS impression of “stroke” or “weakness/numbness” to those with other EMS impressions. Primary outcome was thrombolysis rate, and secondary outcomes were times from EMS scene arrival to ED arrival, CT, and treatment and times from ED arrival to CT and treatment. Chi-square and Mann-Whitney U-tests were used to compare treatment rates and times, respectively. Logistic regression (for rates) and median regression (for times) adjusted for NIHSS, GCS, age, sex, race, and prior stroke history. Results: Among 2,486 confirmed AIS patients from 1/1/2015-12/31/2015, 868 were transported by EMS, including 595 (69%) with EMS suspected stroke. Compared to EMS non-suspected strokes, patients with EMS suspected stroke patients were more likely to receive thrombolysis (18% vs 8%; OR 2.67, 95% CI 1.63-4.47) and had faster prehospital transport (30 vs 32 min, p=0.02), ED arrival to CT (27 vs 46 min, p<0.01) and thrombolysis (64 vs 83 min, p=0.03), and EMS scene arrival to thrombolysis (91 vs 118 min, p=0.03) and EVT (164 vs 250 min, p=0.03). Findings were maintained in the adjusted models except for EMS arrival to EVT (Table). Conclusions: In a large population-based study, EMS stroke identification is associated with a higher rate of and faster thrombolysis. Efforts to increase accuracy of EMS stroke identification is likely to have significant clinical impact by shortening treatment times.

Comparative analysis, applications, and interpretation of electronic health record-based stroke phenotyping methods

BackgroundAccurate identification of acute ischemic stroke (AIS) patient cohorts is essential for a wide range of clinical investigations. Automated phenotyping methods that leverage electronic health records (EHRs) represent a fundamentally new approach cohort identification without current laborious and ungeneralizable generation of phenotyping algorithms. We systematically compared and evaluated the ability of machine learning algorithms and case-control combinations to phenotype acute ischemic stroke patients using data from an EHR.Materials and methodsUsing structured patient data from the EHR at a tertiary-care hospital system, we built and evaluated machine learning models to identify patients with AIS based on 75 different case-control and classifier combinations. We then estimated the prevalence of AIS patients across the EHR. Finally, we externally validated the ability of the models to detect AIS patients without AIS diagnosis codes using the UK Biobank.ResultsAcross all models, we found that the mean AUROC for detecting AIS was 0.963 ± 0.0520 and average precision score 0.790 ± 0.196 with minimal feature processing. Classifiers trained with cases with AIS diagnosis codes and controls with no cerebrovascular disease codes had the best average F1 score (0.832 ± 0.0383). In the external validation, we found that the top probabilities from a model-predicted AIS cohort were significantly enriched for AIS patients without AIS diagnosis codes (60–150 fold over expected).ConclusionsOur findings support machine learning algorithms as a generalizable way to accurately identify AIS patients without using process-intensive manual feature curation. When a set of AIS patients is unavailable, diagnosis codes may be used to train classifier models.

Performance of ICD-10-CM Diagnosis Codes for Identifying Acute Ischemic Stroke in a National Health Insurance Claims Database.

PurposeThe validity of the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) coding for the identification of acute ischemic stroke (AIS) in Taiwan’s National Health Insurance claims database has not been investigated. This study aimed to construct and validate the case definition algorithms for AIS based on ICD-10-CM diagnostic codes.Patients and MethodsThis study identified all hospitalizations with ICD-10-CM code of I63* in any position of the discharge diagnoses from the inpatient claims database and all patients with a final diagnosis of AIS from the stroke registry between Jan 2018 and Dec 2019. Hospitalizations in the claims data that could be successfully linked to those in the registry data were regarded as true episodes of AIS. Otherwise, their electronic medical records and images were manually reviewed to ascertain whether they were true episodes of AIS. Using the true episodes of AIS as the reference standard, the positive predictive value (PPV) and sensitivity of various case definition algorithms for AIS were calculated.ResultsA total of 1227 hospitalizations were successfully linked. Among the 155 hospitalizations that could not be linked, 54 were determined to be true episodes of AIS. Using ICD-10-CM code of I63* in any position of the discharge diagnoses to identify AIS yielded a PPV and sensitivity of 92.7% and 99.4%, respectively. The PPV increased to 99.8% with >12% decrease in the sensitivity when AIS was restricted to those with I63* as the primary diagnosis. When AIS was defined to be I63* as the primary, first secondary, or second secondary diagnosis, both PPV and sensitivity were greater than 97%.ConclusionThis study demonstrated the validity of various case definition algorithms for AIS based on ICD-10-CM coding and can provide a reference for future claims-based stroke research.

DNA Content in Ischemic Stroke Thrombi Can Help Identify Cardioembolic Strokes Among Strokes of Undetermined Cause.

Identification of acute ischemic stroke (AIS) cause is crucial for guidance of secondary prevention. Previous studies have yielded inconsistent results regarding possible correlations between AIS cause and thrombus composition, as assessed by semiquantitative histological analysis. Here, we performed a correlation analysis between AIS cause and AIS thrombus cellular composition and content, as assessed using quantitative biochemical assays. Homogenates of 250 patients with AIS thrombi were prepared by mechanical grinding. Platelet, red blood cell, and leukocyte content of AIS thrombi were estimated by quantification of GP (glycoprotein) VI, heme, and DNA in thrombus homogenates. AIS cause was defined as cardioembolic, noncardioembolic, or embolic stroke of undetermined source, according to the TOAST classification (Trial of ORG 10172 in Acute Stroke Treatment). Cardioembolic thrombi were richer in DNA (35.8 versus 13.8 ng/mg, P<0.001) and poorer in GPVI (0.104 versus 0.117 ng/mg, P=0.045) than noncardioembolic ones. The area under the receiver operating characteristic curve of DNA content to discriminate cardioembolic thrombi from noncardioembolic was 0.72 (95% CI, 0.63-0.81). With a threshold of 44.7 ng DNA/mg thrombus, 47% of thrombi from undetermined cause would be classified as cardioembolic with a specificity of 90%. Thrombus DNA content may provide an accurate biomarker for identification of cardioembolic thrombi in patients with AIS with embolic stroke of undetermined source. Registration: URL: https://www.clinicaltrials.gov. Unique identifier: NCT03268668.

Administrative data underestimate acute ischemic stroke events and thrombolysis treatments: Data from a multicenter validation survey in Italy.

BackgroundInforming health systems and monitoring hospital performances using administrative data sets, mainly hospital discharge data coded according to International-Classification-Diseases-9edition-Clinical-Modifiers (ICD9-CM), is now commonplace in several countries, but the reliability of diagnostic coding of acute ischemic stroke in the routine practice is uncertain. This study aimed at estimating accuracy of ICD9-CM codes for the identification of acute ischemic stroke and the use of thrombolysis treatment comparing hospital discharge data with medical record review in all the six hospitals of the Florence Area, Italy, through 2015.MethodsWe reviewed the medical records of all the 3915 potential acute stroke events during 2015 across the six hospitals of the Florence Area, Italy. We then estimated sensitivity and Positive Predictive Value of ICD9-CM code-groups 433*1, 434*1 and thrombolysis code 99.10 against medical record review with clinical adjudication. For each false-positive case we obtained the actual diagnosis. For each false-negative case we obtained the primary and secondary ICD9-CM diagnoses.ResultsThe medical record review identified 1273 acute ischemic stroke events. The hospital discharge records identified 898 among those (true-positive cases),but missed 375 events (false-negative cases), and identified 104 events that were not eventually confirmed as acute ischemic events (false-positive cases). Code-group specific Positive Predictive Value was 85.7% (95%CI,74.6–93.3) for 433*1 and 89.9% (95%CI, 87.8–91.7) for 434*1 codes. Thrombolysis treatment, as identified by ICD9-CM code 99.10, was only documented in 6.0% of acute ischemic stroke events, but was 13.6% in medical record review.ConclusionsHospital discharge data were found to be fairly specific but insensitive in the reporting of acute ischemic stroke and thrombolysis, providing misleading indications about both quantity and quality of acute ischemic stroke hospital care. Efforts to improve coding accuracy should precede the use of hospital discharge data to measure hospital performances in acute ischemic stroke care.

Abstract W P363: Improvement Door-to-Needle Time For Iv Tpa By Initiation Of Treatment On CT Table

Background: Rambam Medical Center is the biggest hospital in northern Israel, serving an estimated population of 2,000,000 people. Every year there are as much as 1000 patients hospitalized in Rambam with the diagnosis of acute ischemic stroke. Rambam hospital has facilities for giving all possible types of fibrinolytic treatment in patients with acute ischemic stroke. Methods: We use a special algorithm looking for immediate identification of acute ischemic stroke admitting to ER and then a special neurology nursing stroke team (in cooperation with stroke neurologist) provides fast and complete diagnostic work-up, including imaging battery. Since June 2013 we practice to start a thrombolytic treatment at CT department on CT table immediately after regular brain CT is completed. Results: Using the current approach the minimal time of door-to-needle was reduced to 27 minutes in 2014 as compared with minimal time of 43 minutes in 2012 and 46 minutes in 2011. Conclusions: Start of IV Tpa treatment on CT table significantly reduces door-to-needle time for acute stroke patients. Use of such protocol requires dedicated stroke neurology nursing team.

脳梗塞超急性期における単純ＣＴと拡散強調ＭＲＩの病巣検出能の比較

Non-contrast CT and diffusion-weighted MRI (DWI) are widely used for assessing patients with acute ischemic stroke including candidates for thrombolytic therapy. Early CT signs, still a gold standard as the diagnostic measure for thrombolysis, are quite subtle and strongly depend on image quality. We evaluated 76 patients (47 male, mean age 71.0 yrs) with ischemic stroke of the anterior cerebral circulation who underwent CT and DWI within 6 hours of onset. The scans were examined separately by two neurologists in a blinded fashion with knowledge of the affected hemisphere. Detection of acute ischemic changes were significantly higher on DWI (72/76, 95%) compared with that on CT (50/76, 66%)(P<0.0001), especially in cases with subcortical lesions (P<0.001). Detection of the lesion with more than 33% of MCA involvement, which should exclude from the thrombolitic therapy, was somewhat higher for DWI (26/26, 100%) compared with CT (22/ 26.85%).DWI is more sensitive than CT in the identification of acute ischemic stroke and can visualize major ischemia more easily than CT. Additional studies are required to determine whether these advantages of DWI are clinically relevant in the management of patients with acute stroke.