Comparative safety of tenecteplase vs alteplase for acute ischemic stroke

INTRODUCTION

Tenecteplase has been compared to alteplase in acute stroke randomized trials, with similar outcomes and safety measures, but higher doses of tenecteplase have been associated with higher hemorrhage rates in some studies. Limited data are available on the safety of tenecteplase outside of clinical trials.

METHODS

We examined the safety measures of intracranial hemorrhage, angioedema, and serious extracranial adverse events in a 21-hospital integrated healthcare system that switched from alteplase (0.9 mg/kg, maximum dose 90 mg) to tenecteplase (0.25 mg/kg, maximum dose 25 mg) for acute ischemic stroke.

RESULTS

Among 3,689 subjects, no significant differences were seen between tenecteplase and alteplase in the rate of intracranial hemorrhage (ICH), parenchymal hemorrhage, or volume of parenchymal hemorrhage. Symptomatic hemorrhage (sICH) was not different between the two agents: sICH by NINDS criteria was 2.0 % for alteplase vs 2.3 % for tenecteplase (P = 0.57), and sICH by SITS criteria was 0.8 % vs 1.1 % (P = 0.39). Adjusted logistic regression models also showed no differences between tenecteplase and alteplase: the odds ratio for tenecteplase (vs alteplase) modeling sICH by NINDS criteria was 0.9 (95 % CI 0.33 - 2.46, P = 0.83) and the odds ratio for tenecteplase modeling sICH by SITS criteria was 1.12 (95 % CI 0.25 - 5.07, P = 0.89). Rates of angioedema and serious extracranial adverse events were low and did not differ between tenecteplase and alteplase. Elapsed door-to-needle times showed a small improvement after the switch to tenecteplase (51.8 % treated in under 30 min with tenecteplase vs 43.5 % with alteplase, P < 0.001).

CONCLUSION

In use outside of clinical trials, complication rates are similar between tenecteplase and alteplase. In the context of a stroke telemedicine program, the rates of hemorrhage observed with either agent were lower than expected based on prior trials and registry data. The more easily prepared tenecteplase was associated with a lower door-to-needle time.

A new machine learning algorithm with high interpretability for improving the safety and efficiency of thrombolysis for stroke patients: A hospital-based pilot study

Background

Thrombolysis is the first-line treatment for patients with acute ischemic stroke. Previous studies leveraged machine learning to assist neurologists in selecting patients who could benefit the most from thrombolysis. However, when designing the algorithm, most of the previous algorithms traded interpretability for predictive power, making the algorithms hard to be trusted by neurologists and be used in real clinical practice.

Methods

Our proposed algorithm is an advanced version of classical k-nearest neighbors classification algorithm (KNN). We achieved high interpretability by changing the isotropy in feature space of classical KNN. We leveraged a cohort of 189 patients to prove that our algorithm maintains the interpretability of previous models while in the meantime improving the predictive power when compared with the existing algorithms. The predictive powers of models were assessed by area under the receiver operating characteristic curve (AUC).

Results

In terms of interpretability, only onset time, diabetes, and baseline National Institutes of Health Stroke Scale (NIHSS) were statistically significant and their contributions to the final prediction were forced to be proportional to their feature importance values by the rescaling formula we defined. In terms of predictive power, our advanced KNN (AUC 0.88) outperformed the classical KNN (AUC 0.75, p = 0.0192 ).

Conclusions

Our preliminary results show that the advanced KNN achieved high AUC and identified consistent significant clinical features as previous clinical trials/observational studies did. This model shows the potential to assist in thrombolysis patient selection for improving the successful rate of thrombolysis.

Explainable machine learning for long-term outcome prediction in two-center stroke patients after intravenous thrombolysis

Objective

Neurological outcome prediction in patients with ischemic stroke is very critical in treatment strategy and post-stroke management. Machine learning techniques with high accuracy are increasingly being developed in the medical field. We studied the application of machine learning models to predict long-term neurological outcomes in patients with after intravenous thrombolysis.

Methods

A retrospective cohort study was performed to review all stroke patients with intravenous thrombolysis. Patients with modified Rankin Score (mRs) less than two at three months post-thrombolysis were considered as good outcome. The clinical features between stroke patients with good and with poor outcomes were compared using three different machine learning models (Random Forest, Support Vector Machine and Logistic Regression) to identify which performed best. Two datasets from the other stroke center were included accordingly for external verification and performed with explainable AI models.

Results

Of the 488 patients enrolled in this study, and 374 (76.6%) patients had favorable outcomes. Patients with higher mRs at 3 months had increased systolic pressure, blood glucose, cholesterol (TC), and 7-day National Institute of Health Stroke Scale (NIHSS) score compared to those with lower mRs. The predictability and the areas under the curves (AUC) for the random forest model was relatively higher than support vector machine and LR models. These findings were further validated in the external dataset and similar results were obtained. The explainable AI model identified the risk factors as well.

Conclusion

Explainable AI model is able to identify NIHSS_Day7 is independently efficient in predicting neurological outcomes in patients with ischemic stroke after intravenous thrombolysis.

The feasibility and accuracy of machine learning in improving safety and efficiency of thrombolysis for patients with stroke: Literature review and proposed improvements

In the treatment of ischemic stroke, timely and efficient recanalization of occluded brain arteries can successfully salvage the ischemic brain. Thrombolysis is the first-line treatment for ischemic stroke. Machine learning models have the potential to select patients who could benefit the most from thrombolysis. In this study, we identified 29 related previous machine learning models, reviewed the models on the accuracy and feasibility, and proposed corresponding improvements. Regarding accuracy, lack of long-term outcome, treatment option consideration, and advanced radiological features were found in many previous studies in terms of model conceptualization. Regarding interpretability, most of the previous models chose restrictive models for high interpretability and did not mention processing time consideration. In the future, model conceptualization could be improved based on comprehensive neurological domain knowledge and feasibility needs to be achieved by elaborate computer science algorithms to increase the interpretability of flexible algorithms and shorten the processing time of the pipeline interpreting medical images.

Predicting hemorrhagic transformation in posterior circulation stroke patients not treated with reperfusion therapies

INTRODUCTION

Posterior Circulation (PC) stroke represents one-fifth of all ischemic strokes, with peculiar physiological characteristics. Hemorrhagic Transformation (HT) is a dreaded complication among stroke patients. Many predictive scores of this complication have been proposed, but none is designed specifically for PC stroke patients - therefore, patients who are not eligible for reperfusion therapies (RT) represent about 80% of hospitalized cases. We propose a scoring system to assess the HT risk in PC stroke patients not submitted to RT.

METHODS

We retrospectively evaluated data of patients diagnosed with PC stroke not treated with RT from 5 Comprehensive Stroke Centers (four in Brazil, 1 in the US) from 2015 to 2018. All patients underwent CT scan or MRI at admission and a follow-up neuroimaging within seven days. Independent variables identified in a logistic regression analysis were used to produce a predictive grading score.

RESULTS

We included 952 patients in the final analysis. The overall incidence of HT was 8.7%. Male gender (1 point), NIH Stroke Scale at admission ≥ 5 points (1), blood glucose at admission ≥ 160 mg/dL (1), and cardioembolism (2) were independently associated with HT. The AUC of the grading score (0 to 5 points) was 0.713 (95% CI 0.65-0.78). Subjects with a score ≥ 3 points had an OR of 4.8 (95% CI 2.9-7.9, p < 0.001) for HT.

CONCLUSIONS

Our score has good accuracy in identifying patients at higher risk of HT. This score may be useful for evaluating secondary prevention and stratifying patients in the context of even clinical trials.

Cohort study of THRIVE predicting adverse outcomes in acute ischemic stroke of the anterior circulation and posterior circulation after 3 months and 1 year of follow-up

PURPOSE

To evaluate the difference of Totaled Health Risks In Vascular Events (THRIVE) in predicting adverse outcomes in acute ischemic stroke (AIS) of the anterior circulation and posterior circulation at 3-month and 1-year follow-up.

METHODS

A total of 858 patients with AIS were followed up for 3 months and 1 year, and their data prospectively collected. The occurrence of death or moderate to severe disability (modified Rankin Scale ≥ 3 points) was regarded as the endpoint. MedCalc software was used to create the THRIVE receiver operating characteristic curve. The area under the curve (AUC) was calculated to compare the THRIVE scale in predicting adverse outcomes in AIS of the anterior and posterior circulation and compare the differences.

RESULTS

At 3-month follow-up, the AUC of THRIVE was 0.685 (95% CI 0.644-0.724) for AIS of the anterior circulation and 0.709 (95% CI 0.647-0.765) for AIS of the posterior circulation. The area difference between them was 0.0235 (95% CI -0.0728-0.120, P = 0.6330[>0.05]). The AUC of THRIVE for AIS in the anterior circulation at 1 year was 0.701 (95% CI 0.660-0.740), and that for AIS in the posterior circulation at 1 year was 0.747 (95% CI 0.687-0.800). The area difference between them was 0.0458 (95% CI -0.0489-0.140, P = 0.3436 [>0.05]). The difference was not statistically significant.

CONCLUSION

THRIVE can well predict the short-term and long-term adverse prognosis of AIS in the anterior and posterior circulation and has the same predictive effect.

Predictive value of the THRIVE score for outcome in patients with acute basilar artery occlusion treated with thrombectomy

BACKGROUND AND PURPOSE

A higher Totaled Health Risks in Vascular Events (THRIVE) score has been shown to predict poor functional outcome in patients with acute ischemic stroke (AIS) and anterior circulation large vessel occlusions undergoing thrombectomy treatment. We attempted to evaluate the value of the THRIVE score in predicting the outcome of thrombectomy treatment in AIS patients with basilar artery occlusion (BAO).

METHODS

A total of 68 AIS patients with BAO who underwent thrombectomy treatment from May 2014 to August 2018 were included in the present study. Multivariable logistic regression was performed to determine the predictive value of the THRIVE score for poor functional outcome (defined as modified Rankin Scale score ≥ 3), all-cause mortality, and hemorrhage transformation (HT) at 3 months.

RESULTS

A total of 42 (61.8%) participants experienced poor functional outcomes, 25 (36.8%) patients died from all causes, and 21 (30.9%) patients had HT during the 3-month follow-up. Multivariable logistic regression showed that a higher THRIVE score was significantly associated with poor functional outcome (odds ratio [OR] 5.86, 95% confidence interval [CI], 2.28-14.91, p < .001) as well as all-cause mortality (OR 2.40, 95% CI, 1.32-4.34, p = .004) but not HT (p = .607). The C-statistic of the THRIVE score was significantly larger than that of the NIHSS score for predicting poor functional outcome (AUC = 0.913; cutoff > 5; sensitivity, 88.5%; specificity, 83.3%, p = .007) and all-cause mortality (AUC = 0.768; cutoff > 5; sensitivity, 92.0%; specificity, 65.1%, p = .018).

CONCLUSIONS

A high THRIVE score was independently associated with an increased risk of poor functional outcome and all-cause mortality in AIS patients with BAO who underwent thrombectomy treatment. Moreover, the THRIVE score appeared to be a better predictor of clinical outcome than the NIHSS score.

Pretreatment Blood Pressure is a Simple Predictor of Hemorrhagic Infarction after Intravenous Recombinant Tissue Plasminogen Activator (rt-PA) Therapy

BACKGROUND

Hemorrhagic infarction (HI) is among the most severe complications that can occur following the administration of intravenous recombinant tissue plasminogen activator (rt-PA). In the present study, we aimed to determine the optimal cut-off points of blood pressure (BP) for HI after rt-PA treatment, and to compare our findings with those for other prediction models.

METHODS

We analyzed data from 109 consecutive patients with stroke treated at our hospital between 2009 and 2016. HI was confirmed via computed tomography or magnetic resonance imaging. Patients were classified into a symptomatic HI group, an asymptomatic HI group, and a non-HI group. BP was measured on admission and before rt-PA treatment. Glucose Race Age Sex Pressure Stroke Severity (GRASPS) and Totaled Health Risks in Vascular Events (THRIVE) scores were also calculated. Receiver operating characteristic (ROC) analysis was used to determine factors associated with symptomatic and asymptomatic HI.

RESULTS

Among the 109 total patients, 25 patients developed symptomatic HI, while 22 patients developed asymptomatic HI. ROC analysis for predicting symptomatic and asymptomatic HI revealed that the area under the curve for pretreatment systolic BP (SBP) was .88 (95% confidence interval[CI]: .83-.94), while those for GRASPS and THRIVE scores were .75 (95% CI: .66-.85) and .69 (95% CI: .59-.79), respectively. We identified an optimal cut-off point of 160 mm Hg (sensitivity: 82.3%; specificity: 76.6%; diagnostic accuracy: 80.0%; positive predictive value: 76.6%; negative predictive value: 82.5%).

CONCLUSIONS

Pre-treatment SBP may be a simple predictor of symptomatic and asymptomatic HI in patients with stroke undergoing rt-PA treatment.

Thinking About the Future: A Review of Prognostic Scales Used in Acute Stroke

Background: There are many prognostic scales that aim to predict functional outcome following acute stroke. Despite considerable research interest, these scales have had limited impact in routine clinical practice. This may be due to perceived problems with internal validity (quality of research), as well as external validity (generalizability of results). We set out to collate information on exemplar stroke prognosis scales, giving particular attention to the scale content, derivation, and validation.

Methods: We performed a focused literature search, designed to return high profile scales that use baseline clinical data to predict mortality or disability. We described prognostic utility and collated information on the content, development and validation of the tools. We critically appraised chosen scales based on the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modeling Studies (CHARMS).

Results: We chose 10 primary scales that met our inclusion criteria, six of which had revised/modified versions. Most primary scales used 5 input variables (range: 4–13), with substantial overlap in the variables included. All scales included age, eight included a measure of stroke severity, while five scales incorporated pre-stroke level of function (often using modified Rankin Scale), comorbidities and classification of stroke type. Through our critical appraisal, we found issues relating to excluding patients with missing data from derivation studies, and basing the selection of model variable on significance in univariable analysis (in both cases noted for six studies). We identified separate external validation studies for all primary scales but one, with a total of 60 validation studies.

Conclusions: Most acute stroke prognosis scales use similar variables to predict long-term outcomes and most have reasonable prognostic accuracy. While not all published scales followed best practice in development, most have been subsequently validated. Lack of clinical uptake may relate more to practical application of scales rather than validity. Impact studies are now necessary to investigate clinical usefulness of existing scales.

The Role of Imaging in Clinical Stroke Scales That Predict Functional Outcome: A Systematic Review

BACKGROUND AND PURPOSE

Numerous stroke scales have been developed to predict functional outcomes following acute ischemic stroke. The goal of this study was to summarize functional outcome scores in stroke that incorporate neuroimaging with those that don't incorporate neuroimaging.

METHODS

Searches were conducted in Ovid MEDLINE, Ovid Embase, and the Cochrane Library Database from inception to January 23, 2015. Additional records were identified by employing the "Cited by" and "View References" features in Scopus. We included studies that described stroke prognosis models or scoring systems that predict functional outcome based on clinical and/or imaging data available on presentation. Score performance was evaluated based on area under the receiver operating characteristic curve (AUC).

RESULTS

A total of 3300 articles were screened, yielding 14 scores that met inclusion criteria. Half (7) of the scores included neuroimaging as a predictor variable. Neuroimaging parameters included infarct size on magnetic resonance diffusion-weighted imaging, infarct size defined by computed tomography hypodensity, and hemodynamic abnormality on perfusion imaging. The modified Rankin Scale at 3 months poststroke was the most common functional outcome reported (13 of 14 scores). The AUCs ranged from 0.64 to 0.84 for scores that included neuroimaging as a predictor and 0.64 to 0.94 for scores that did not include neuroimaging. External validation has been performed for 7 scores.

CONCLUSIONS

Due to the marked heterogeneity in the scores and populations in which they were applied, it is unclear whether current imaging-based scores offer advantages over simpler approaches for predicting poststroke function.

Performance of four ischemic stroke prognostic scores in a Brazilian population

OBJECTIVE

Ischemic stroke (IS) prognostic scales may help clinicians in their clinical decisions. This study aimed to assess the performance of four IS prognostic scales in a Brazilian population.

METHOD

We evaluated data of IS patients admitted at Hospital Paulistano, a Joint Commission International certified primary stroke center. In-hospital mortality and modified Rankin score at discharge were defined as the outcome measures. The performance of National Institutes of Health Stroke Scale (NIHSS), Stroke Prognostication Using Age and NIHSS (SPAN-100), Acute Stroke Registry and Analysis of Lausanne (ASTRAL), and Totaled Health Risks in Vascular Events (THRIVE) were compared.

RESULTS

Two hundred six patients with a mean ± SD age of 67.58 ± 15.5 years, being 55.3% male, were included. The four scales were significantly and independently associated functional outcome. Only THRIVE was associated with in-hospital mortality. With area under the curve THRIVE and NIHSS were the scales with better performance for functional outcome and THRIVE had the best performance for mortality.

CONCLUSION

THRIVE showed the best performance among the four scales, being the only associated with in-hospital mortality.

Improved Ischemic Stroke Outcome Prediction Using Model Estimation of Outcome Probability: The THRIVE-c Calculation

Background and purpose

The Totaled Health Risks in Vascular Events (THRIVE) score is a previously validated ischemic stroke outcome prediction tool. Although simplified scoring systems like the THRIVE score facilitate ease-of-use, when computers or devices are available at the point of care, a more accurate and patient-specific estimation of outcome probability should be possible by computing the logistic equation with patient-specific continuous variables.

Methods

We used data from 12 207 subjects from the Virtual International Stroke Trials Archive and the Safe Implementation of Thrombolysis in Stroke – Monitoring Study to develop and validate the performance of a model-derived estimation of outcome probability, the THRIVE-c calculation. Models were built with logistic regression using the underlying predictors from the THRIVE score: age, National Institutes of Health Stroke Scale score, and the Chronic Disease Scale (presence of hypertension, diabetes mellitus, or atrial fibrillation). Receiver operator characteristics analysis was used to assess model performance and compare the THRIVE-c model to the traditional THRIVE score, using a two-tailed Chi-squared test.

Results

The THRIVE-c model performed similarly in the randomly chosen development cohort ( n = 6194, area under the curve = 0·786, 95% confidence interval 0·774–0·798) and validation cohort ( n = 6013, area under the curve = 0·784, 95% confidence interval 0·772–0·796) ( P = 0·79). Similar performance was also seen in two separate external validation cohorts. The THRIVE-c model (area under the curve = 0·785, 95% confidence interval 0·777–0·793) had superior performance when compared with the traditional THRIVE score (area under the curve = 0·746, 95% confidence interval 0·737–0·755) ( P < 0·001).

Conclusion

By computing the logistic equation with patientspecific continuous variables in the THRIVE-c calculation, outcomes at the individual patient level are more accurately estimated. Given the widespread availability of computers and devices at the point of care, such calculations can be easily performed with a simple user interface.

Pulmonary and circulatory parameter guided anesthesia in patients with ischemic stroke undergoing endovascular recanalization

Background and purpose

Endovascular recanalization in ischemic stroke is often performed under general anesthesia. Some studies have shown a detrimental effect of general anesthesia. The reasons are unknown.

Methods

This was an observational study with retrospective and prospective phases. From 2008 to 2010, 60 patients treated by endovascular recanalization due to proximal vessel occlusion were analyzed with regard to ventilation parameters, blood gas values, blood pressure, and clinical parameters (pre-protocol phase). Subsequently, a protocol with target values for end-tidal CO2 (Petco2) and systolic blood pressure (SBP) was introduced and prospectively analyzed in 64 patients in 2012 (protocol phase).

Results

In the pre-protocol phase, significant hypocapnia (<30 mm Hg), a decrease in SBP after intervention (p<0.001), and an increase in SBP after extubation (p<0.001) were observed. After implementing the protocol in 2012, 63% of Petco2 values and 55% of SBP values (median) of the duration of intervention were within the predefined range. Severe hypocapnia and hypotension (SBP <100 mm Hg) after the intervention were significantly reduced. Longer duration of Petco2 values within 40–45 mm Hg, intracerebral hemorrhage, longer door to needle time, older age, unsuccessful recanalization, longer duration of endovascular treatment, and higher cumulative dose of norepinephrine were associated with an unfavorable outcome (modified Rankin Scale score >2). Intracerebral hemorrhage (OR 0.028, p=0.001), age (OR 0.9, p=0.013), and cumulative dose of norepinephrine (OR 0.142, p=0.003) were independent predictors of an unfavorable outcome.

Conclusions

In patients receiving endovascular stroke treatment under general anesthesia, the cumulative dose of norepinephrine was an independent predictor of an unfavorable outcome. Further studies are needed to evaluate the optimal management of blood pressure in these patients, and whether avoidance of catecholamines could partly explain the improved outcomes for patients treated under conscious sedation in retrospective studies.