Rate and associated factors of transient ischemic attack misdiagnosis

Care settings of transient ischemic attack in the United States: A cohort study from the TriNetX health research network

BACKGROUND

Evaluation and hospitalization rates after a transient ischemic attack (TIA)-like presentation vary widely in clinical practice. This study aimed to examine variations in care settings at initial TIA diagnosis in the United States.

METHODS

We retrospectively analyzed an adult cohort with a first TIA principal diagnosis between January 1, 2015, and December 31, 2019, from TriNetX Diamond Network. Care settings at TIA diagnosis were defined as hospital care (including inpatient services and observation unit care without admission) and outpatient care (including any outpatient or emergency department visits). We estimated the distribution of care settings at TIA diagnosis and examined the associations of the hospital care setting with baseline age, sex, race, ethnicity, region, and stroke history.

RESULTS

Among the 554,315 included patients, 38.8% received hospital care at their initial TIA diagnosis. A higher percentage of hospital care was observed in the age group of 50-64 years (40.3%), Black (46.0%), Hispanic (41.2%), South (40.9%), and Midwest (43.0%) Regions, and with a history of stroke (39.6%). Multivariable logistic regression consistently showed patients who were aged 50-64 years (Odds Ratio=1.09, 95% CI: [1.07, 1.11]), Black (1.28, [1.24, 1.32]), Hispanic (1.13, [1.09, 1.18]), from South (1.20, [1.18, 1.22]) and Midwest Region (1.33, [1.30, 1.35]), and had a history of stroke (1.02, [1.00, 1.04]) to more likely receive hospital care.

CONCLUSIONS

Although there are TIA care disparities based on demographics, most patients with initial TIA received acute care in outpatient settings. It is imperative to ensure primary providers can risk-stratify TIA patients and provide rapid and proper management.

Transient Ischemic Attack Outpatient Clinic: Past Journey and Future Adventure

A transient ischemic attack (TIA), a constellation of temporary neurological symptoms, precedes stroke in one-fifth of patients. Thus far, many clinical models have been introduced to optimize the quality, time to treatment, and cost of acute TIA care, either in an inpatient or outpatient setting. In this article, we aim to review the characteristics and outcomes of outpatient TIA clinics across the globe. In addition, we discussed the main challenges for outpatient management of TIA, including triage and diagnosis, and the system dynamics of the clinics. We further reviewed the potential developments in TIA care, such as telemedicine, predictive analytics, personalized medicine, and advanced imaging.

Neuroimaging for differential diagnosis of transient neurological attacks

BACKGROUND

Rapid yet comprehensive neuroimaging protocols are required for patients with suspected acute stroke. However, stroke mimics can account for approximately one in five clinically diagnosed acute ischemic strokes and the rate of thrombolyzed mimics can be as high as 17%. Therefore, to accurately determine the diagnosis and differentiate mimics from true transient ischemic attacks, acute ischemic stroke is a challenge to every clinician.

DISCUSSION

Medical history and neurological examination, noncontract head computed tomography, and routine magnetic resonance imaging play important roles in the assessment and management of patients with transient neurological attacks in the emergency department. This review attempts to summarize how neuroimaging can be utilized to help differentiate the most common mimics from transient ischemic attack and acute ischemic stroke.

CONCLUSION

Although imaging can help direct critical triage decisions for intravenous thrombolysis or endovascular therapy, more detailed medical history and neurological examination are crucial for making a prompt and accurate diagnosis for transient neurological attack patients.

Low-to-Moderate Risk Transient Ischemic Attack Patients Can Be Safely Discharged From the Emergency Department to a Nurse Practitioner-Led Clinic

ABSTRACT

BACKGROUND: Unnecessary admissions fuel rising healthcare costs and take away resources from higher acuity patients without evidence of increased safety. The purpose of this quality improvement project was to determine whether the care diversion for transient ischemic attack (TIA), from inpatient to a nurse practitioner (NP)-led specialty clinic, resulted in no increase in stroke incidence at 90 days. METHODS: The sample included all adults presenting to the emergency department with TIA at a low-to-moderate risk for stroke. Risks were defined by the ABCD2 score and noninvasive vessel imaging. Patients who met the criteria were discharged and evaluated by a stroke NP at the TIA clinic within 7 days. These patients were compared with those who were admitted before clinic launch. Medical record reviews were conducted to determine stroke incidence at 90 days post TIA. Descriptive statistics were used to evaluate clinical variables, and Fisher exact test was used to assess difference in stroke rates. Patient satisfaction score was collected using the existing institutional survey. RESULTS: Eighty-one participants were included, 40 in the clinic group and 41 in the admission group. The mean ages in the clinic and admission groups were 72.8 and 75.2 years, respectively (P = .37). Women comprised 45% of patients in the clinic group, compared with 51.2% in the admission group (P = .58). The mean ABCD2 scores were 4.08 and 3.95 in the clinic and admission groups, respectively (P = .63). The median clinic follow-up time was 6 days. There was no stroke incidence in the clinic group and 1 in the admission group within 90 days post TIA. Patient satisfaction score metrics for the NP exceeded the institutional benchmark of 90%. CONCLUSION: Referral to an NP-led clinic in patients with low- to moderate-risk TIA was equally safe as hospital admission.

Biomarkers for Transient Ischemic Attack: A Brief Perspective of Current Reports and Future Horizons

Cerebrovascular disease is the leading cause of long-term disability in the world and the third-leading cause of death in the United States. The early diagnosis of transient ischemic attack (TIA) is of great importance for reducing the mortality and morbidity of cerebrovascular diseases. Patients with TIA have a high risk of early subsequent ischemic stroke and the development of permanent nervous system lesions. The diagnosis of TIA remains a clinical diagnosis that highly relies on the patient's medical history assessment. There is a growing list of biomarkers associated with different components of the ischemic cascade in the brain. In this review, we take a closer look at the biomarkers of TIA and their validity with a focus on the more clinically important ones using recent evidence of their reliability for practical usage.

Risk of Subsequent Stroke Among Patients Receiving Outpatient vs Inpatient Care for Transient Ischemic Attack: A Systematic Review and Meta-analysis

IMPORTANCE

Transient ischemic attack (TIA) often indicates a high risk of subsequent cerebral ischemic events. Timely preventive measures improve the outcome.

OBJECTIVE

To estimate and compare the risk of subsequent ischemic stroke among patients with TIA or minor ischemic stroke (mIS) by care setting.

DATA SOURCES

MEDLINE, Web of Science, Scopus, Embase, International Clinical Trials Registry Platform, ClinicalTrials.gov, Trip Medical Database, CINAHL, and all Evidence-Based Medicine review series were searched from the inception of each database until October 1, 2020.

STUDY SELECTION

Studies evaluating the occurrence of ischemic stroke after TIA or mIS were included. Cohorts without data on evaluation time for reporting subsequent stroke, with retrospective diagnosis of the index event after stroke occurrence, and with a report of outcomes that were not limited to patients with TIA or mIS were excluded. Two authors independently screened the titles and abstracts and provided the list of candidate studies for full-text review; discrepancies and disagreements in all steps of the review were addressed by input from a third reviewer.

DATA EXTRACTION AND SYNTHESIS

The study was prepared and reported following the Preferred Reporting Items for Systematic Reviews and Meta-analyses, Meta-analysis of Observational Studies in Epidemiology, Methodological Expectations of Cochrane Intervention Reviews, and Enhancing the Quality and Transparency of Health Research guidelines. The Risk of Bias in Nonrandomized Studies-of Exposures (ROBINS-E) tool was used for critical appraisal of cohorts, and funnel plots, Begg-Mazumdar rank correlation, Kendall τ2, and the Egger bias test were used for evaluating the publication bias. All meta-analyses were conducted under random-effects models.

MAIN OUTCOMES AND MEASURES

Risk of subsequent ischemic stroke among patients with TIA or mIS who received care at rapid-access TIA or neurology clinics, inpatient units, emergency departments (EDs), and unspecified or multiple settings within 4 evaluation intervals (ie, 2, 7, 30, and 90 days).

RESULTS

The analysis included 226 683 patients from 71 articles recruited between 1981 and 2018; 5636 patients received care at TIA clinics (mean [SD] age, 65.7 [3.9] years; 2291 of 4513 [50.8%] men), 130 139 as inpatients (mean [SD] age, 78.3 [4.0] years; 49 458 of 128 745 [38.4%] men), 3605 at EDs (mean [SD] age, 68.9 [3.9] years; 1596 of 3046 [52.4%] men), and 87 303 patients received care in an unspecified setting (mean [SD] age, 70.8 [3.8] years, 43 495 of 87 303 [49.8%] men). Among the patients who were treated at a TIA clinic, the risk of subsequent stroke following a TIA or mIS was 0.3% (95% CI, 0.0%-1.2%) within 2 days, 1.0% (95% CI, 0.3%-2.0%) within 7 days, 1.3% (95% CI, 0.4%-2.6%) within 30 days, and 2.1% (95% CI, 1.4%-2.8%) within 90 days. Among the patients who were treated as inpatients, the risk of subsequent stroke was to 0.5% (95% CI, 0.1%-1.1%) within 2 days, 1.2% (95% CI, 0.4%-2.2%) within 7 days, 1.6% (95% CI, 0.6%-3.1%) within 30 days, and 2.8% (95% CI, 2.1%-3.5%) within 90 days. The risk of stroke among patients treated at TIA clinics was not significantly different from those hospitalized. Compared with the inpatient cohort, TIA clinic patients were younger and had had lower ABCD2 (age, blood pressure, clinical features, duration of TIA, diabetes) scores (inpatients with ABCD2 score >3, 1101 of 1806 [61.0%]; TIA clinic patients with ABCD2 score >3, 1933 of 3703 [52.2%]).

CONCLUSIONS AND RELEVANCE

In this systematic review and meta-analysis, the risk of subsequent stroke among patients who were evaluated in a TIA clinic was not higher than those hospitalized. Patients who received treatment in EDs without further follow-up had a higher risk of subsequent stroke. These findings suggest that TIA clinics can be an effective component of the TIA care component pathway.

A meta-analysis of clinical risk factors for stroke in anticoagulant-naïve patients with atrial fibrillation

AIMS

The aim of this study is to summarize data from prospective cohort studies on clinical predictors of stroke and systemic embolism in anticoagulant-naïve atrial fibrillation (AF) patients.

METHODS AND RESULTS

EMBASE, MEDLINE, Global Index Medicus, and Web of Science were searched to identify all studies published by 28 November 2019. Forty-seven studies reporting data from 1 756 984 participants in 15 countries were included. The pooled incidence of stroke in anticoagulant-naïve AF patients was 23.8 per 1000 person-years (95% CI 19.7-28.2). Older age was associated with incident stroke or systemic embolism, with a pooled hazard ratio (HR) of 2.14 (95% CI 1.85-2.47), 2.83 (95% CI 2.27-3.51), and 6.87 (95% CI 6.33-7.44) for age 65-75, ≥75, and ≥85 years, respectively. Other predictors of stroke or systemic embolism included history of stroke or TIA (HR 2.84, 95% CI 2.19-3.67), hypertension (HR 1.60, 95% CI 1.37-1.86), diabetes (HR 1.28, 95% CI 1.20-1.37), heart failure (HR 1.25, 95% CI 1.11-1.40), peripheral artery disease (pooled HR 1.35, 95% CI 1.04-1.75), vascular disease (pooled HR 1.21, 95% CI 1.06-1.39), and prior myocardial infarction (pooled HR 1.08, 95% CI 1.03-1.14). Female sex was a predictor of thromboembolism in studies outside Asia (HR 1.33, 95% CI 1.15-1.55), but not in those done in Asia (HR 0.95, 95% CI 0.81-1.10).

CONCLUSION

This study confirms age and prior stroke as the strongest predictors of stroke or systemic embolism in anticoagulant-naive AF patients. Other predictors include hypertension, diabetes, heart failure, and vascular disease. Female sex seems not to be universally associated with stroke or systemic embolism.

SARS-CoV-2 and Stroke Characteristics: A Report From the Multinational COVID-19 Stroke Study Group

Supplemental Digital Content is available in the text.

Background and Purpose:

Stroke is reported as a consequence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection in several reports. However, data are sparse regarding the details of these patients in a multinational and large scale.

Methods:

We conducted a multinational observational study on features of consecutive acute ischemic stroke, intracranial hemorrhage, and cerebral venous or sinus thrombosis among SARS-CoV-2–infected patients. We further investigated the risk of large vessel occlusion, stroke severity as measured by the National Institutes of Health Stroke Scale, and stroke subtype as measured by the TOAST (Trial of ORG 10172 in Acute Stroke Treatment) criteria among patients with acute ischemic stroke. In addition, we explored the neuroimaging findings, features of patients who were asymptomatic for SARS-CoV-2 infection at stroke onset, and the impact of geographic regions and countries’ health expenditure on outcomes.

Results:

Among the 136 tertiary centers of 32 countries who participated in this study, 71 centers from 17 countries had at least 1 eligible stroke patient. Of 432 patients included, 323 (74.8%) had acute ischemic stroke, 91 (21.1%) intracranial hemorrhage, and 18 (4.2%) cerebral venous or sinus thrombosis. A total of 183 (42.4%) patients were women, 104 (24.1%) patients were <55 years of age, and 105 (24.4%) patients had no identifiable vascular risk factors. Among acute ischemic stroke patients, 44.5% (126 of 283 patients) had large vessel occlusion; 10% had small artery occlusion according to the TOAST criteria. We observed a lower median National Institutes of Health Stroke Scale (8 [3–17] versus 11 [5–17]; P=0.02) and higher rate of mechanical thrombectomy (12.4% versus 2%; P<0.001) in countries with middle-to-high health expenditure when compared with countries with lower health expenditure. Among 380 patients who had known interval onset of the SARS-CoV-2 and stroke, 144 (37.8%) were asymptomatic at the time of admission for SARS-CoV-2 infection.

Conclusions:

We observed a considerably higher rate of large vessel occlusions, a much lower rate of small vessel occlusion and lacunar infarction, and a considerable number of young stroke when compared with the population studies before the pandemic. The rate of mechanical thrombectomy was significantly lower in countries with lower health expenditures.

Machine Learning-Enabled 30-Day Readmission Model for Stroke Patients

Background and Purpose: Hospital readmissions impose a substantial burden on the healthcare system. Reducing readmissions after stroke could lead to improved quality of care especially since stroke is associated with a high rate of readmission. The goal of this study is to enhance our understanding of the predictors of 30-day readmission after ischemic stroke and develop models to identify high-risk individuals for targeted interventions. Methods: We used patient-level data from electronic health records (EHR), five machine learning algorithms (random forest, gradient boosting machine, extreme gradient boosting-XGBoost, support vector machine, and logistic regression-LR), data-driven feature selection strategy, and adaptive sampling to develop 15 models of 30-day readmission after ischemic stroke. We further identified important clinical variables. Results: We included 3,184 patients with ischemic stroke (mean age: 71 ± 13.90 years, men: 51.06%). Among the 61 clinical variables included in the model, the National Institutes of Health Stroke Scale score above 24, insert indwelling urinary catheter, hypercoagulable state, and percutaneous gastrostomy had the highest importance score. The Model's AUC (area under the curve) for predicting 30-day readmission was 0.74 (95%CI: 0.64-0.78) with PPV of 0.43 when the XGBoost algorithm was used with ROSE-sampling. The balance between specificity and sensitivity improved through the sampling strategy. The best sensitivity was achieved with LR when optimized with feature selection and ROSE-sampling (AUC: 0.64, sensitivity: 0.53, specificity: 0.69). Conclusions: Machine learning-based models can be designed to predict 30-day readmission after stroke using structured data from EHR. Among the algorithms analyzed, XGBoost with ROSE-sampling had the best performance in terms of AUC while LR with ROSE-sampling and feature selection had the best sensitivity. Clinical variables highly associated with 30-day readmission could be targeted for personalized interventions. Depending on healthcare systems' resources and criteria, models with optimized performance metrics can be implemented to improve outcomes.

Adherence to anticoagulant guideline for atrial fibrillation: A large care gap among stroke patients in a rural population

OBJECTIVE

This study aimed to investigate the prevalence and factors associated with oral anticoagulant undertreatment of atrial fibrillation (AF) among a cohort of rural patients with stroke outcomes and examine how undertreatment may influence a patient's one-year survival after stroke.

METHODS

This retrospective cohort study examined ischemic stroke patients with pre-stroke AF diagnosis from September 2003 to May 2019 and divided them into proper treatment and undertreatment group. Analysis included chi-square test, variance analysis, Kruskal-Wallis test, logistic regression, Kaplan-Meier estimator, and Cox proportional-hazards model.

RESULTS

Out of 1062 ischemic stroke patients with a pre-stroke AF diagnosis, 1015 patients had a CHA₂DS₂-VASc score ≥2, and 532 (52.4%) of those were undertreated. Median time from AF diagnosis to index stroke was significantly lower among undertreated patients (1.9 years vs. 3.6 years, p < 0.001). Other thromboembolism, excluding stroke, TIA, and myocardial infarction (OR 0.41, p < 0.001), the number of encounters per year (OR 0.90, p < 0.001), and the median time between AF diagnosis and stroke event (OR 0.86, p < 0.001) were negatively associated with undertreatment. Kaplan-Meier estimator showed no statistical difference in the one-year survival probability between groups (log-rank test, p = 0.29), while the Cox-Hazard model showed that age (HR 1.05, p < 0.001) and history of congestive heart failure (HR 1.88, p < 0.001) increased the risk of mortality.

CONCLUSIONS

More than half of our rural stroke patients with a pre-index AF diagnosis were not on guideline-recommended treatment. The study highlights a large care gap and an opportunity to improve AF management.

Prediction of Long-Term Stroke Recurrence Using Machine Learning Models

Background: The long-term risk of recurrent ischemic stroke, estimated to be between 17% and 30%, cannot be reliably assessed at an individual level. Our goal was to study whether machine-learning can be trained to predict stroke recurrence and identify key clinical variables and assess whether performance metrics can be optimized. Methods: We used patient-level data from electronic health records, six interpretable algorithms (Logistic Regression, Extreme Gradient Boosting, Gradient Boosting Machine, Random Forest, Support Vector Machine, Decision Tree), four feature selection strategies, five prediction windows, and two sampling strategies to develop 288 models for up to 5-year stroke recurrence prediction. We further identified important clinical features and different optimization strategies. Results: We included 2091 ischemic stroke patients. Model area under the receiver operating characteristic (AUROC) curve was stable for prediction windows of 1, 2, 3, 4, and 5 years, with the highest score for the 1-year (0.79) and the lowest score for the 5-year prediction window (0.69). A total of 21 (7%) models reached an AUROC above 0.73 while 110 (38%) models reached an AUROC greater than 0.7. Among the 53 features analyzed, age, body mass index, and laboratory-based features (such as high-density lipoprotein, hemoglobin A1c, and creatinine) had the highest overall importance scores. The balance between specificity and sensitivity improved through sampling strategies. Conclusion: All of the selected six algorithms could be trained to predict the long-term stroke recurrence and laboratory-based variables were highly associated with stroke recurrence. The latter could be targeted for personalized interventions. Model performance metrics could be optimized, and models can be implemented in the same healthcare system as intelligent decision support for targeted intervention.

Specialized Outpatient Clinic vs Stroke Unit for TIA and Minor Stroke: A Cohort Study

OBJECTIVE

To evaluate the effects of an outpatient clinic setup for minor stroke/TIA using subsequent admission of patients at high risk of recurrent stroke.

METHODS

We performed a cohort study of all patients with suspected minor stroke/TIA seen in an outpatient clinic at Aarhus University Hospital, Denmark, between September 2013 and August 2014. Patients with stroke were compared to historic (same hospital) and contemporary (another comparable hospital) matched, hospitalized controls on nonprioritized outcomes: length of stay, readmissions, care quality (10 process-performance measures), and mortality. Patients with TIA were compared to contemporary matched, hospitalized controls. Following complete diagnostic workup, patients with stroke/TIA were classified into low/high risk of recurrent stroke ≤7 days.

RESULTS

We analyzed 1,076 consecutive patients, of whom 253 (23.5%) were subsequently admitted to the stroke ward. Stroke/TIA was diagnosed in 215/171 patients, respectively. Fifty-six percent (121/215) of the patients with stroke were subsequently admitted to the stroke ward. Comparison with the historic stroke cohort (n = 191) showed a shorter acute hospital stay for the strokes (median 1 vs 3 days; adjusted length of stay ratio 0.49; 95% confidence interval 0.33-0.71). Thirty-day readmission rate was 3.2% vs 11.6% (adjusted hazard ratio 0.23 [0.09-0.59]), and care quality was higher, with a risk ratio of 1.30 (1.15-1.47). The comparison of stroke and TIAs to contemporary controls showed similar results. Only one patient in the low risk category and not admitted experienced stroke within 7 days (0.6%).

CONCLUSIONS

An outpatient clinic setup for patients with minor stroke/TIA yields shorter acute hospital stay, lower readmission rates, and better quality than hospitalization in stroke units.

CLASSIFICATION OF EVIDENCE

This study provides Class III evidence that a neurovascular specialist-driven outpatient clinic for patients with minor stroke/TIA with the ability of subsequent admission is safe and yields shorter acute hospital stay, lower readmission rates, and better quality than hospitalization in stroke units.

Sex disparity in long-term stroke recurrence and mortality in a rural population in the United States

BACKGROUND

Several studies suggest women may be disproportionately affected by poorer stroke outcomes than men. This study aims to investigate whether women have a higher risk of all-cause mortality and recurrence after an ischemic stroke than men in a rural population in central Pennsylvania, United States.

METHODS

We analyzed consecutive ischemic stroke patients captured in the Geisinger NeuroScience Ischemic Stroke research database from 2004 to 2019. Kaplan-Meier (KM) estimator curves stratified by gender and age were used to plot survival probabilities and Cox Proportional Hazards Ratios were used to analyze outcomes of all-cause mortality and the composite outcome of ischemic stroke recurrence or death. Fine-Gray Competing Risk models were used for the outcome of recurrent ischemic stroke, with death as the competing risk. Two models were generated; Model 1 was adjusted by data-driven associated health factors, and Model 2 was adjusted by traditional vascular risk factors.

RESULTS

Among 8900 adult ischemic stroke patients [median age of 71.6 (interquartile range: 61.1-81.2) years and 48% women], women had a higher crude all-cause mortality. The KM curves demonstrated a 63.3% survival in women compared with a 65.7% survival in men (p = 0.003) at 5 years; however, the survival difference was not present after controlling for covariates, including age, atrial fibrillation or flutter, myocardial infarction, diabetes mellitus, dyslipidemia, heart failure, chronic lung diseases, rheumatic disease, chronic kidney disease, neoplasm, peripheral vascular disease, past ischemic stroke, past hemorrhagic stroke, and depression. There was no adjusted or unadjusted sex difference in terms of recurrent ischemic stroke or composite outcome.

CONCLUSION

Sex was not an independent risk factor for all-cause mortality and ischemic stroke recurrence in the rural population in central Pennsylvania.

Risk of stroke in hospitalized SARS-CoV-2 infected patients: A multinational study

BACKGROUND

There is an increased attention to stroke following SARS-CoV-2. The goal of this study was to better depict the short-term risk of stroke and its associated factors among SARS-CoV-2 hospitalized patients.

METHODS

This multicentre, multinational observational study includes hospitalized SARS-CoV-2 patients from North and South America (United States, Canada, and Brazil), Europe (Greece, Italy, Finland, and Turkey), Asia (Lebanon, Iran, and India), and Oceania (New Zealand). The outcome was the risk of subsequent stroke. Centres were included by non-probability sampling. The counts and clinical characteristics including laboratory findings and imaging of the patients with and without a subsequent stroke were recorded according to a predefined protocol. Quality, risk of bias, and heterogeneity assessments were conducted according to ROBINS-E and Cochrane Q-test. The risk of subsequent stroke was estimated through meta-analyses with random effect models. Bivariate logistic regression was used to determine the parameters with predictive outcome value. The study was reported according to the STROBE, MOOSE, and EQUATOR guidelines.

FINDINGS

We received data from 26,175 hospitalized SARS-CoV-2 patients from 99 tertiary centres in 65 regions of 11 countries until May 1st, 2020. A total of 17,799 patients were included in meta-analyses. Among them, 156(0.9%) patients had a stroke-123(79%) ischaemic stroke, 27(17%) intracerebral/subarachnoid hemorrhage, and 6(4%) cerebral sinus thrombosis. Subsequent stroke risks calculated with meta-analyses, under low to moderate heterogeneity, were 0.5% among all centres in all countries, and 0.7% among countries with higher health expenditures. The need for mechanical ventilation (OR: 1.9, 95% CI:1.1-3.5, p = 0.03) and the presence of ischaemic heart disease (OR: 2.5, 95% CI:1.4-4.7, p = 0.006) were predictive of stroke.

INTERPRETATION

The results of this multi-national study on hospitalized patients with SARS-CoV-2 infection indicated an overall stroke risk of 0.5%(pooled risk: 0.9%). The need for mechanical ventilation and the history of ischaemic heart disease are the independent predictors of stroke among SARS-CoV-2 patients.

FUNDING

None.

A predictive analytics model for differentiating between transient ischemic attacks (TIA) and its mimics

Background

Transient ischemic attack (TIA) is a brief episode of neurological dysfunction resulting from cerebral ischemia not associated with permanent cerebral infarction. TIA is associated with high diagnostic errors because of the subjective nature of findings and the lack of clinical and imaging biomarkers. The goal of this study was to design and evaluate a novel multinomial classification model, based on a combination of feature selection mechanisms coupled with logistic regression, to predict the likelihood of TIA, TIA mimics, and minor stroke.

Methods

We conducted our modeling on consecutive patients who were evaluated in our health system with an initial diagnosis of TIA in a 9-month period. We established the final diagnoses after the clinical evaluation by independent verification from two stroke neurologists. We used Recursive Feature Elimination (RFE) and Least Absolute Shrinkage and Selection Operator (LASSO) for prediction modeling.

Results

The RFE-based classifier correctly predicts 78% of the overall observations. In particular, the classifier correctly identifies 68% of the cases labeled as “TIA mimic” and 83% of the “TIA” discharge diagnosis. The LASSO classifier had an overall accuracy of 74%. Both the RFE and LASSO-based classifiers tied or outperformed the ABCD2 score and the Diagnosis of TIA (DOT) score. With respect to predicting TIA, the RFE-based classifier has 61.1% accuracy, the LASSO-based classifier has 79.5% accuracy, whereas the DOT score applied to the dataset yields an accuracy of 63.1%.

Conclusion

The results of this pilot study indicate that a multinomial classification model, based on a combination of feature selection mechanisms coupled with logistic regression, can be used to effectively differentiate between TIA, TIA mimics, and minor stroke.

Clinical Risk Score for Predicting Recurrence Following a Cerebral Ischemic Event

Introduction: Recurrent stroke has a higher rate of death and disability. A number of risk scores have been developed to predict short-term and long-term risk of stroke following an initial episode of stroke or transient ischemic attack (TIA) with limited clinical utilities. In this paper, we review different risk score models and discuss their validity and clinical utilities.

Methods: The PubMed bibliographic database was searched for original research articles on the various risk scores for risk of stroke following an initial episode of stroke or TIA. The validation of the models was evaluated by examining the internal and external validation process as well as statistical methodology, the study power, as well as the accuracy and metrics such as sensitivity and specificity.

Results: Different risk score models have been derived from different study populations. Validation studies for these risk scores have produced conflicting results. Currently, ABCD² score with diffusion weighted imaging (DWI) and Recurrence Risk Estimator at 90 days (RRE-90) are the two acceptable models for short-term risk prediction whereas Essen Stroke Risk Score (ESRS) and Stroke Prognosis Instrument-II (SPI-II) can be useful for prediction of long-term risk.

Conclusion: The clinical risk scores that currently exist for predicting short-term and long-term risk of recurrent cerebral ischemia are limited in their performance and clinical utilities. There is a need for a better predictive tool which can overcome the limitations of current predictive models. Application of machine learning methods in combination with electronic health records may provide platform for development of new-generation predictive tools.