Predictors of Atrial Fibrillation in Patients With Stroke Attributed to Large- or Small-Vessel Disease: A Prespecified Secondary Analysis of the STROKE AF Randomized Clinical Trial

Importance

The Stroke of Known Cause and Underlying Atrial Fibrillation (STROKE AF) trial found that approximately 1 in 8 patients with recent ischemic stroke attributed to large- or small-vessel disease had poststroke atrial fibrillation (AF) detected by an insertable cardiac monitor (ICM) at 12 months. Identifying predictors of AF could be useful when considering an ICM in routine poststroke clinical care.

Objective

To determine the association between commonly assessed risk factors and poststroke detection of new AF in the STROKE AF cohort monitored by ICM.

Design, Setting, and Participants

This was a prespecified analysis of a randomized (1:1) clinical trial that enrolled patients between April 1, 2016, and July 12, 2019, with primary follow-up through 2020 and mean (SD) duration of 11.0 (3.0) months. Eligible patients were selected from 33 clinical research sites in the US. Patients had an index stroke attributed to large- or small-vessel disease and were 60 years or older or aged 50 to 59 years with at least 1 additional stroke risk factor. A total of 496 patients were enrolled, and 492 were randomly assigned to study groups (3 did not meet inclusion criteria, and 1 withdrew consent). Patients in the ICM group had the index stroke within 10 days before insertion. Data were analyzed from October 8, 2021, to January 28, 2022.

Interventions

ICM monitoring vs site-specific usual care (short-duration external cardiac monitoring).

Main Outcomes and Measures

The ICM device automatically detects AF episodes 2 or more minutes in length; episodes were adjudicated by an expert committee. Cox regression multivariable modeling included all parameters identified in the univariate analysis having P values <.10. AF detection rates were calculated using Kaplan-Meier survival estimates.

Results

The analysis included the 242 participants randomly assigned to the ICM group in the STROKE AF study. Among 242 patients monitored with ICM, 27 developed AF (mean [SD] age, 66.6 [9.3] years; 144 men [60.0%]; 96 [40.0%] women). Two patients had missing baseline data and exited the study early. Univariate predictors of AF detection included age (per 1-year increments: hazard ratio [HR], 1.05; 95% CI, 1.01-1.09; P = .02), CHA2DS2-VASc score (per point: HR, 1.54; 95% CI, 1.15-2.06; P = .004), chronic obstructive pulmonary disease (HR, 2.49; 95% CI, 0.86-7.20; P = .09), congestive heart failure (CHF; with preserved or reduced ejection fraction: HR, 6.64; 95% CI, 2.29-19.24; P < .001), left atrial enlargement (LAE; HR, 3.63; 95% CI, 1.55-8.47; P = .003), QRS duration (HR, 1.02; 95% CI, 1.00-1.04; P = .04), and kidney dysfunction (HR, 3.58; 95% CI, 1.35-9.46; P = .01). In multivariable modeling (n = 197), only CHF (HR, 5.06; 95% CI, 1.45-17.64; P = .05) and LAE (HR, 3.32; 1.34-8.19; P = .009) remained significant predictors of AF. At 12 months, patients with CHF and/or LAE (40 of 142 patients) had an AF detection rate of 23.4% vs 5.0% for patients with neither (HR, 5.1; 95% CI, 2.0-12.8; P < .001).

Conclusions and Relevance

Among patients with ischemic stroke attributed to large- or small-vessel disease, CHF and LAE were associated with a significantly increased risk of poststroke AF detection. These patients may benefit most from the use of ICMs as part of a secondary stroke prevention strategy. However, the study was not powered for clinical predictors of AF, and therefore, other clinical characteristics may not have reached statistical significance.

Trial Registration

ClinicalTrials.gov Identifier: NCT02700945.

Abstract WP55: Predicting Malignant Edema in MCA Infarct Using MRI and MR Perfusion Parameters

Introduction: Malignant edema in large MCA infarct (MMI) is known for high rate of mortality and morbidity. Hemicraniectomy was showed to decrease mortality but not improve functional outcome. Further studies are required for better patient selection for surgery. Among several clinical biomarkers, DWI lesion volume > 82 ml within 6 hours from symptoms onset is the most used predictor for MMI. There is also evidence suggesting brain edema is correspond with severity of tissue ischemia, which could be measured by semiquantitative cerebral blood volume (CBV) and cerebral blood flow (CBF) on MRP map. We hypothesized that DWI volume combining with MRP parameters could provide more predictive value.

Methods: Patients in MMI-MRI study (a prospective, multicenter, observational MRI/MRP study of MCA ischemic stroke within 6 hours of symptom onset) and STIR (Stroke Imaging Repository) were included if they met the inclusion criteria: age ≥ 18, MCA infarct with NIHSS ≥ 15, and MRI/MRP study within 12 hours from symptoms onset. Patients underwent thrombectomy, with poor quality images or without follow up images, with bilateral strokes, or developed hemorrhagic transformation were excluded. MMI was defined as NIHSS score >18 with suppressed level of consciousness, and at least 2/3 of the MCA territory infarct on follow-up MRI or CT.

Results: Of 89 patients included, 17 (19.1%) developed MMI. A malignant edema predicting score (MEPS) of maximum three points was developed: one point for DWI lesion volume > 82 ml, one point for more than 40% of DWI volume with CBV < 40% of contralateral normal side, and one point for more than 50% DWI volume with CBF < 30% of contralateral normal side. The threshold of a DWI lesion volume > 82 ml predicted malignant edema with sensitivity (0.65, 95% CI 0.39-0.85), specificity (0.86, 95% CI 0.75-0.93), positive predictive value (0.52, 95% CI 0.30-0.74), and negative predictive value (0.91, 95% CI 0.81-0.96). MEPS ≥ 2 predicted malignant edema with sensitivity (0.65, 95% CI 0.39-0.85), specificity (0.98, 95% CI 0.91-1.00), PPV (0.92, 95% CI 0.60-1.00), and NPV (0.92, 95% CI 0.83-0.97).

Conclusions: DWI volume is reliable in predicting MMI. The addition of information on tissue ischemia in a multivariate score improves the prediction of MMI over DWI alone.

Abstract T P255: QCI-NASCAR - Quality Care Improvement with Nursing-driven Acute Stroke CARe

Objective: To observe the impact on stroke code time metrics after applying a “pit stop” model of bedside nursing for telestroke encounters.

Background: Despite the recent push for target treatment times in acute stroke codes, no guidelines exist for optimizing practices specific to stroke care via telemedicine. Effective telestroke is dependent on efficient data gathering by remote staff, and lengthy metrics for real-world telestroke often preclude timely tPA treatment. By co-opting “pit stops” as inspiration, an optimized nursing workflow for telestroke can be created on the following principles: Identification of Shared Goals; Organized Urgency with the Removal of Gatekeepers; Multi-personnel, Non-Sequential Processes; Focus on Defined Staged Roles; and Empowered Engagement/Responsibility.

Methods: The QCI-NASCAR protocol was implemented in Oct 2013, and data was collected prospectively on consecutive stroke code activations through Apr 2014 at St. Paul University Hospital (Dallas, TX), a telestroke spoke site. The nurse-driven protocol was reinforced by a paper checklist (i.e. “Driver Sheet”), which doubled as a data collection form. Timestamps were recorded in real time for: door time, MD at bedside, CT arrival, needle time, and/or code cancellation. The primary outcome was Door-to-CT (D2CT) times to reflect the portion of the stroke code most impacted by the nursing protocol.

Results: Mean D2CT times were: all cases (n=152, 33.2 min), intervention-eligible cases (n=71, 27.0 min), and thrombolytic-eligible cases (n=57, 22.2 min). A trend for lower D2CT times and standard deviations was noted in comparing the first half of the data (n=76, 38.04 ± 58.1 min) to the second (n=77, 27.8 ± 19.1 min; p<0.05). A similar pattern was noted in the subset of intervention-eligible cases: first half (n=36, 29.4 ± 37.4 min) vs. second half (n=35, 24.3 ± 18.6 min; p<0.05). IV tPA was administered 3 times, including an institutional best door-to-needle time of 32.0 min.

Conclusion: QCI-NASCAR demonstrates the feasibility of implementing a nursing-driven protocol for telestroke encounters. A larger, multi-institutional trial will demonstrate if such a protocol can significantly and reproducibly lower stroke code metrics to national guideline parameters.

Abstract 3849: The Optimal Target for Thrombolytic Trials: The Interaction of Mismatch Size and IV tPA Treatment in Predicting Good Clinical Outcome

Introduction: Because IV tPA treatment initiated within 4.5 hours is the only acute stroke therapy of proven clinical efficacy, this population offers the greatest validity for determining imaging markers that optimally would differentiate clinical outcomes in treated vs. placebo patients in later time-window clinical trials. The Perfusion Diffusion Mismatch (PDM) approximates the ischemic penumbra, but there is no consensus on the optimal definition or threshold to be used in trials. We assessed the relationship of mismatch size to clinical outcome of an IV tPA treated sample compared with an untreated cohort.

Methods: We selected patients from the NINDS Lesion Evolution of Stroke and Ischemia On Neuroimaging (LESION) database who met the following criteria: 1) treated with standard IV tPA 2) received multimodal MRI pre-treatment including diffusion (DWI) and perfusion imaging (PWI), 3) had interpretable MRI imaging showing 4) non-lacunar infarcts or lesions less than 100 ml volumes on DWI and 5) follow-up modified Rankin Score (mRS). We also selected 23 acute stroke patients who did not receive treatment but otherwise met the same criteria. Volumes were measured from the DWI and Mean Transit Time (MTT) images. PDM was defined as either a volume (MTT-DWI) or a percentage (MTT-DWI/MTT). Good outcome was defined as mRS of 0-1. Logistic regression was performed to predict good outcome with covariates of age, initial NIHSS, PDM size, tPA treatment, and tPA treatment by PDM size interaction.

Result: Ninety-six patients were treated with IV tPA, 23 patients had no treatment. The figures , showing the unadjusted proportions of patients achieving good outcome as a function of minimum PDM size, suggest greater separation of treated and untreated patients at larger PDM sizes. For percentage PDM a significant interaction of tPA treatment by PDM size was observed at ≥ 80% (p=0.029), indicating that the benefit of tPA treatment is greater for PDM ≥ 80%. For volume PDM, the interaction of tPA treatment by PDM size trended positive at ≥ 50 ml (p=0.058).

Conclusion: Greater differences in clinical outcomes with IV tPA versus untreated patients were evident with PDM ≥ 80% or ≥ 50 ml. These minimums of PDM size suggest an optimal target for thrombolytic trials.