Towards personalised intra-arterial treatment of patients with acute ischaemic stroke: a study protocol for development and validation of a clinical decision aid

Predicting outcome in acute stroke with large vessel occlusion-application and validation of MR PREDICTS in the ESCAPE-NA1 population

BACKGROUND

Predicting outcome after endovascular treatment for acute ischemic stroke is challenging. We aim to investigate differences between predicted and observed outcomes in patients with acute ischemic stroke treated with endovascular treatment and to evaluate the performance of a validated outcome prediction score.

PATIENTS AND METHODS

MR PREDICTS is an outcome prediction tool based on a logistic regression model designed to predict the treatment benefit of endovascular treatment based on the MR CLEAN and HERMES populations. ESCAPE-NA1 is a randomized trial of nerinetide vs. placebo in patients with acute stroke and large vessel occlusion. We applied MR PREDICTS to patients in the control arm of ESCAPE-NA1. Model performance was assessed by calculating its discriminative ability and calibration.

RESULTS

Overall, 556/1105 patients (50.3%) in the ESCAPE-NA1-trial were randomized to the control arm, 435/556 (78.2%) were treated within 6 h of symptom onset. Good outcome (modified Rankin scale 0-2) at 3 months was achieved in 275/435 patients (63.2%), the predicted probability of good outcome was 52.5%. Baseline characteristics were similar in the study and model derivation cohort except for age (ESCAPE-NA1: mean: 70 y vs. HERMES: 66 y), hypertension (72% vs. 57%), and collaterals (good collaterals, 15% vs. 44%). Compared to HERMES we observed higher rates of successful reperfusion (TICI 2b-3, ESCAPE-NA1: 87% vs. HERMES: 71%) and faster times from symptom onset to reperfusion (median: 201 min vs. 286 min). Model performance was good, indicated by a c-statistic of 0.76 (95%confidence interval: 0.71-0.81).

CONCLUSION

Outcome-prediction using models created from HERMES data, based on information available in the emergency department underestimated the actual outcome in patients with acute ischemic stroke and large vessel occlusion receiving endovascular treatment despite overall good model performance, which might be explained by differences in quality of and time to reperfusion. These findings underline the importance of timely and successful reperfusion for functional outcomes in acute stroke patients.

Prediction of Outcome and Endovascular Treatment Benefit: Validation and Update of the MR PREDICTS Decision Tool

Supplemental Digital Content is available in the text.

Background and Purpose:

Benefit of early endovascular treatment (EVT) for ischemic stroke varies considerably among patients. The MR PREDICTS decision tool, derived from MR CLEAN (Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands), predicts outcome and treatment benefit based on baseline characteristics. Our aim was to externally validate and update MR PREDICTS with data from international trials and daily clinical practice.

Methods:

We used individual patient data from 6 randomized controlled trials within the HERMES (Highly Effective Reperfusion Evaluated in Multiple Endovascular Stroke Trials) collaboration to validate the original model. Then, we updated the model and performed a second validation with data from the observational MR CLEAN Registry. Primary outcome was functional independence (defined as modified Rankin Scale score 0–2) 3 months after stroke. Treatment benefit was defined as the difference between the probability of functional independence with and without EVT. Discriminative performance was evaluated using a concordance (C) statistic.

Results:

We included 1242 patients from HERMES (633 assigned to EVT, 609 assigned to control) and 3156 patients from the MR CLEAN Registry (all of whom underwent EVT within 6.5 hours). The C-statistic for functional independence was 0.74 (95% CI, 0.72–0.77) in HERMES and, after model updating, 0.80 (0.78–0.82) in the Registry. Median predicted treatment benefit of routinely treated patients (Registry) was 10.3% (interquartile range, 5.8%–14.4%). Patients with low (<1%) predicted treatment benefit (n=135/3156 [4.3%]) had low rates of functional independence, irrespective of reperfusion status, suggesting potential absence of treatment benefit. The updated model was made available online for clinicians and researchers at www.mrpredicts.com.

Conclusions:

Because of the substantial treatment effect and small potential harm of EVT, most patients arriving within 6 hours at an endovascular-capable center should be treated regardless of their clinical characteristics. MR PREDICTS can be used to support clinical judgement when there is uncertainty about the treatment indication, when resources are limited, or before a patient is to be transferred to an endovascular-capable center.

Challenges of Outcome Prediction for Acute Stroke Treatment Decisions

Physicians often base their decisions to offer acute stroke therapies to patients around the question of whether the patient will benefit from treatment. This has led to a plethora of attempts at accurate outcome prediction for acute ischemic stroke treatment, which have evolved in complexity over the years. In theory, physicians could eventually use such models to make a prediction about the treatment outcome for a given patient by plugging in a combination of demographic, clinical, laboratory, and imaging variables. In this article, we highlight the importance of considering the limits and nuances of outcome prediction models and their applicability in the clinical setting. From the clinical perspective of decision-making about acute treatment, we argue that it is important to consider 4 main questions about a given prediction model: (1) what outcome is being predicted, (2) what patients contributed to the model, (3) what variables are in the model (considering their quantifiability, knowability at the time of decision-making, and modifiability), and (4) what is the intended purpose of the model? We discuss relevant aspects of these questions, accompanied by clinically relevant examples. By acknowledging the limits of outcome prediction for acute stroke therapies, we can incorporate them into our decision-making more meaningfully, critically examining their contents, outcomes, and intentions before heeding their predictions. By rigorously identifying and optimizing modifiable variables in such models, we can be empowered rather than paralyzed by them.

Preterm neonates benefit from low prophylactic platelet transfusion threshold despite varying risk of bleeding or death

The Platelets for Neonatal Thrombocytopenia (PlaNeT-2) trial reported an unexpected overall benefit of a prophylactic platelet transfusion threshold of 25 × 109/L compared with 50 × 109/L for major bleeding and/or mortality in preterm neonates (7% absolute-risk reduction). However, some neonates in the trial may have experienced little benefit or even harm from the 25 × 109/L threshold. We wanted to assess this heterogeneity of treatment effect in the PlaNet-2 trial, to investigate whether all preterm neonates benefit from the low threshold. We developed a multivariate logistic regression model in the PlaNet-2 data to predict baseline risk of major bleeding and/or mortality for all 653 neonates. We then ranked the neonates based on their predicted baseline risk and categorized them into 4 risk quartiles. Within these quartiles, we assessed absolute-risk difference between the 50 × 109/L- and 25 × 109/L-threshold groups. A total of 146 neonates died or developed major bleeding. The internally validated C-statistic of the model was 0.63 (95% confidence interval, 0.58-0.68). The 25 × 109/L threshold was associated with absolute-risk reduction in all risk groups, varying from 4.9% in the lowest risk group to 12.3% in the highest risk group. These results suggest that a 25 × 109/L prophylactic platelet count threshold can be adopted in all preterm neonates, irrespective of predicted baseline outcome risk. Future studies are needed to improve the predictive accuracy of the baseline risk model. This trial was registered at www.isrctn.com as #ISRCTN87736839.

Data-efficient deep learning of radiological image data for outcome prediction after endovascular treatment of patients with acute ischemic stroke

Treatment selection is becoming increasingly more important in acute ischemic stroke patient care. Clinical variables and radiological image biomarkers (old age, pre-stroke mRS, NIHSS, occlusion location, ASPECTS, among others) have an important role in treatment selection and prognosis. Radiological biomarkers require expert annotation and are subject to inter-observer variability. Recently, Deep Learning has been introduced to reproduce these radiological image biomarkers. Instead of reproducing these biomarkers, in this work, we investigated Deep Learning techniques for building models to directly predict good reperfusion after endovascular treatment (EVT) and good functional outcome using CT angiography images. These models do not require image annotation and are fast to compute. We compare the Deep Learning models to Machine Learning models using traditional radiological image biomarkers. We explored Residual Neural Network (ResNet) architectures, adapted them with Structured Receptive Fields (RFNN) and auto-encoders (AE) for network weight initialization. We further included model visualization techniques to provide insight into the network's decision-making process. We applied the methods on the MR CLEAN Registry dataset with 1301 patients. The Deep Learning models outperformed the models using traditional radiological image biomarkers in three out of four cross-validation folds for functional outcome (average AUC of 0.71) and for all folds for reperfusion (average AUC of 0.65). Model visualization showed that the arteries were relevant features for functional outcome prediction. The best results were obtained for the ResNet models with RFNN. Auto-encoder initialization often improved the results. We concluded that, in our dataset, automated image analysis with Deep Learning methods outperforms radiological image biomarkers for stroke outcome prediction and has the potential to improve treatment selection.

Thrombectomy for Acute Ischemic Stroke: Recent Insights and Future Directions

PURPOSE OF REVIEW

Mechanical thrombectomy has become the standard of care for acute ischemic stroke with proximal large vessel occlusions (LVO). This article reviews recent research relating to thrombectomy.

RECENT FINDINGS

Thrombectomy for anterior circulation stroke with proximal LVO was first shown to be highly efficacious within 6 h of stroke onset, but "late-window" trials have further demonstrated efficacy until 24-h postonset in select patients with salvageable tissue. However, the concept of "time is brain" remains critical. Thrombectomy trials have further stimulated worldwide efforts to develop systems of care for rapid treatment of eligible patients. Thrombectomy is cost-effective and likely to have long-term efficacy for both disability and mortality outcomes. Thrombectomy is a highly efficacious acute stroke therapy. Enduring uncertainties include efficacy in patients with premorbid disability, posterior circulation, or more distal occlusions; use of bridging thrombolysis; and optimal techniques to achieve consistent revascularization and address tandem occlusions or stenoses.

Selection of patients for intra-arterial treatment for acute ischaemic stroke: development and validation of a clinical decision tool in two randomised trials

Objective To improve the selection of patients with acute ischaemic stroke for intra-arterial treatment using a clinical decision tool to predict individual treatment benefit.Design Multivariable regression modelling with data from two randomised controlled clinical trials.Setting 16 hospitals in the Netherlands (derivation cohort) and 58 hospitals in the United States, Canada, Australia, and Europe (validation cohort).Participants 500 patients from the Multicenter Randomised Clinical Trial of Endovascular Treatment for Acute Ischaemic Stroke in the Netherlands trial (derivation cohort) and 260 patients with intracranial occlusion from the Interventional Management of Stroke III trial (validation cohort).Main outcome measures The primary outcome was the modified Rankin Scale (mRS) score at 90 days after stroke. We constructed an ordinal logistic regression model to predict outcome and treatment benefit, defined as the difference between the predicted probability of good functional outcome (mRS score 0-2) with and without intra-arterial treatment.Results 11 baseline clinical and radiological characteristics were included in the model. The externally validated C statistic was 0.69 (95% confidence interval 0.64 to 0.73) for the ordinal model and 0.73 (0.67 to 0.79) for the prediction of good functional outcome, indicating moderate discriminative ability. The mean predicted treatment benefit varied between patients in the combined derivation and validation cohort from -2.3% to 24.3%. There was benefit of intra-arterial treatment predicted for some individual patients from groups in which no treatment effect was found in previous subgroup analyses, such as those with no or poor collaterals.Conclusion The proposed clinical decision tool combines multiple baseline clinical and radiological characteristics and shows large variations in treatment benefit between patients. The tool is clinically useful as it aids in distinguishing between individual patients who may experience benefit from intra-arterial treatment for acute ischaemic stroke and those who will not.Trial registration clinicaltrials.gov NCT00359424 (IMS III) and isrctn.com ISRCTN10888758 (MR CLEAN).