Validation of the RRE-90 Scale to Predict Stroke Risk after Transient Symptoms with Infarction: A Prospective Cohort Study

A nomogram for predicting the in-hospital risk of recurrence among patients with minor non-cardiac stroke

BACKGROUND

Patients with minor stroke suffer a substantial risk of further recurrences, especially in the first two weeks. We aimed to develop and validate a prognostic nomogram to predict in-hospital stroke recurrence among patients with acute minor stroke.

METHODS

A total of 1326 patients with minor non-cardiac stroke (NIHSS) ≤5) from three centers were divided into development cohort (1016 patients from two centers) and validation cohort (310 patients from another center). Recurrent stroke was defined as a new ischemic stroke. A logistic regression model was employed to develop the nomogram to predict in-hospital stroke recurrence in patients with minor stroke using demographic, medical and imaging information. We then validated the nomogram externally. The predictive discrimination and calibration of the nomogram were assessed in the development and validation cohorts by area under the curve (AUC) and calibration plots.

RESULTS

During a median length of stay of 12 days, stroke recurrence occurred in 34 patients (3.3%). Predictors of in-hospital recurrence included prior history of transient ischemic attack, baseline NIHSS score, multiple infarctions, and carotid stenosis. The clinical and imaging-based nomogram B demonstrated adequate calibration and discrimination (AUC = 0.777), which was validated among 273 patients in a separate validation cohort (AUC = 0.753). Our clinical-imaging based nomogram was determined to be superior to the clinical-based nomogram and the RRE90 score in terms of discrimination.

CONCLUSION

A prognostic nomogram that integrates clinical and imaging information to predict the in-hospital risk of stroke recurrence among patients after acute minor stroke was constructed and validated externally. The nomogram demonstrated adequate calibration and discrimination in both the development and validation cohort.

A Novel Clinical Nomogram to Predict Transient Symptomatic Associated with Infarction: The ABCD3-SLOPE Score

Background

It is hard to differentiate transient symptoms associated with infarction (TSI) from transient ischemic stroke (TIA) without MRI in the early onset. However, they have distinct clinical outcomes and respond differently to therapeutics. Therefore, we aimed to develop a risk prediction model based on the clinical features to identify TSI.

Methods

We enrolled 230 consecutive patients with transient neurologic deficit in the Department of Neurology, Tongji University Affiliated Tenth People's Hospital from March 2014 to October 2019. All the patients were assigned into TIA group (DWI-negative) or TSI group (DWI-positive) based on MRI conducted within five days of onset. We summarized the clinical characteristics of TSI by univariate and multivariate analyses. And then, we developed and validated a nomogram to identify TSI by the logistic regression equation.

Results

Of the 230 patients, 41.3% were diagnosed with TSI. According to the multivariate analysis, four independent risk factors, including smoking history, low-density lipoprotein cholesterol, brain natriuretic peptide precursor, and ABCD3 score, were incorporated into a nomogram. We developed a predictive model named ABCD3-SLOPE. The calibration curve showed good agreement between nomogram prediction and observation. The concordance index (C-index) of the nomogram for TSI prediction was 0.77 (95% confidence interval, 0.70-0.83), and it was well-calibrated.

Conclusions

Smoking history, low-density lipoprotein cholesterol, brain natriuretic peptide precursor, and ABCD3 score were reliable risk factors for TSI. ABCD3-SLOPE was a potential tool to quantify the likelihood of TSI.

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.

Dual antiplatelet therapy reduced stroke risk in high-risk patients with transient ischaemic attack assessed by ABCD3-I score

BACKGROUND AND PURPOSE

Several clinical trials have demonstrated that dual antiplatelet therapy (DAPT) benefited patients with transient ischaemic attack (TIA) with an ABCD2 score ≥4. The present study aimed to investigate whether the ABCD3-I score could be a more appropriate tool for selection of patients with TIA to receive DAPT in real-world settings.

METHODS

We derived data from the TIA database of The First Affiliated Hospital of Zhengzhou University. The predictive outcome was ischaemic stroke at 90 days. The additive interaction effect was presented by the attributable proportion due to interaction. Kaplan-Meier curves were plotted to present cumulative stroke rates in different risk categories with monotherapy and DAPT. Cox proportional hazards regression was used to determine risk factors associated with stroke.

RESULTS

Among 785 patients, the mean (SD) age was 56.95 (12.73) years and 77 patients (9.8%) had an ischaemic stroke at 90 days. A total of 55.8% of patients (attributable proportion due to interaction; 95% confidence interval, 20.8%-90.9%) were attributed to additive interaction of ABCD3-I score and antiplatelet therapy. Kaplan-Meier curves showed a significant difference between patients receiving monotherapy and DAPT in high-risk patients with TIA (P = 0.021). DAPT reduced 90-day stroke risk in high-risk patients with TIA as assessed independently by ABCD3-I score (adjusted hazard ratio, 0.43; 95% confidence interval, 0.20-0.92, P = 0.031). The benefit did not exist in low- and medium-risk patients by ABCD3-I score (patients with ABCD2 score ≥ 4 or <4).

CONCLUSIONS

High-risk patients with TIA assessed by ABCD3-I score received the most pronounced clinical benefit from early use of DAPT in real-world clinical experience.