Search

Background and purpose: Direct transportation to a thrombectomy-capable intervention center is beneficial for patients with ischemic stroke due to large vessel occlusion (LVO), but can delay intravenous thrombolytics (IVT). The aim of this modeling study was to estimate the effect of prehospital triage strategies on treatment delays and overtriage in different regions. Methods: We used data from two prospective cohort studies in the Netherlands: the Leiden Prehospital Stroke Study and the PRESTO study. We included stroke code patients within 6 h from symptom onset. We modeled outcomes of Rapid Arterial oCclusion Evaluation (RACE) scale triage and triage with a personalized decision tool, using drip-and-ship as reference. Main outcomes were overtriage (stroke code patients incorrectly triaged to an intervention center), reduced delay to endovascular thrombectomy (EVT), and delay to IVT. Results: We included 1798 stroke code patients from four ambulance regions. Per region, overtriage ranged from 1-13% (RACE triage) and 3-15% (personalized tool). Reduction of delay to EVT varied by region between 24 ± 5 min (n = 6) to 78 ± 3 (n = 2), while IVT delay increased with 5 (n = 5) to 15 min (n = 21) for non-LVO patients. The personalized tool reduced delay to EVT for more patients (25 ± 4 min [n = 8] to 49 ± 13 [n = 5]), while delaying IVT with 3-14 min (8-24 patients). In region C, most EVT patients were treated faster (reduction of delay to EVT 31 ± 6 min (n = 35), with RACE triage and the personalized tool. Conclusions: In this modeling study, we showed that prehospital triage reduced time to EVT without disproportionate IVT delay, compared to a drip-and-ship strategy. The effect of triage strategies and the associated overtriage varied between regions. Implementation of prehospital triage should therefore be considered on a regional level.

Background: Long door-in-door-out (DIDO) times are an important cause of treatment delay in patients transferred for endovascular thrombectomy (EVT) from primary stroke centres (PSC) to an intervention centre. Insight in causes of prolonged DIDO times may facilitate process improvement interventions. We aimed to quantify different components of DIDO time and to identify determinants of DIDO time. Methods: We performed a retrospective cohort study in a Dutch ambulance region consisting of six PSCs and one intervention centre. We included consecutive adult patients with anterior circulation large vessel occlusion, transferred from a PSC for EVT between October 1, 2019 and November 31, 2020. We subdivided DIDO into several time components and quantified contribution of these components to DIDO time. We used univariable and multivariable linear regression models to explore associations between potential determinants and DIDO time. Results: We included 133 patients. Median (IQR) DIDO time was 66 (52–83) min. The longest component was CTA-to-ambulance notification time with a median (IQR) of 24 (16–37) min. DIDO time increased with age (6 min per 10 years, 95% CI: 2–9), onset-to-door time outside 6 h (20 min, 95% CI: 5–35), M2-segment occlusion (15 min, 95% CI: 4–26) and right-sided ischaemia (12 min, 95% CI: 2–21). Conclusions: The CTA-to-ambulance notification time is the largest contributor to DIDO time. Higher age, onset-to-door time longer than 6 h, M2-segment occlusion and right-sided occlusions are independently associated with a longer DIDO time. Future interventions that aim to decrease DIDO time should take these findings into account.

Objective: Awareness campaigns advise the public to call emergency medical services (EMS) directly in case of suspected stroke. We aimed to explore patient and notification characteristics that influence direct EMS notification, the time to alert, and the time to treatment. Methods: We performed a secondary analysis with data from the PRESTO study, a multi-center prospective observational cohort study that included patients with suspected stroke. We used multivariable binary logistic regression analyses to assess the association with direct EMS notification and multivariable linear regression analyses to assess the association with the onset-to-alert time, onset-to-needle time and onset-to-groin time. Results: Of 436 included patients, 208 patients (48%) contacted EMS directly. FAST scores (aOR 1.45 for every point increase, 95%CI: 1.14–1.86), alert outside office hours (aOR 1.64 [1.05–2.55]), and onset-to-alert time (aOR for every minute less [≤55 min]: 0.96 [0.95–0.97]) were independently associated with direct EMS notification. Direct EMS call was independently associated with shorter onset-to-alert times (27 min [54–0.84]) and with shorter onset-to-needle times (−30 min [−51 to −10]). The association between direct EMS call and the onset-to-groin time was almost similar to the association with onset-to-needle time, though not statistically significant (univariable analysis: 23.7 min decrease [−103.7 to 56.2]). Conclusion: More than half of all patients with suspected stroke do not call EMS directly but call their GP instead. Patients with higher FAST scores, alert outside office hours, and a rapid alert, more often call EMS directly. Patients who call EMS directly are treated with IVT 30 min faster than patients who call the GP first. Trial registration number: Netherlands Trial Register: NL7387, (www.trialregister.nl).

BACKGROUND: Machine learning algorithms hold the potential to contribute to fast and accurate detection of large vessel occlusion (LVO) in patients with suspected acute ischemic stroke. We assessed the diagnostic performance of an automated LVO detection algorithm on CT angiography (CTA). METHODS: Data from the MR CLEAN Registry and PRESTO were used including patients with and without LVO. CTA data were analyzed by the algorithm for detection and localization of LVO (intracranial internal carotid artery (ICA)/ICA terminus (ICA-T), M1, or M2). Assessments done by expert neuroradiologists were used as reference. Diagnostic performance was assessed for detection of LVO and per occlusion location by means of sensitivity, specificity, and area under the curve (AUC). RESULTS: We analyzed CTAs of 1110 patients from the MR CLEAN Registry (median age (IQR) 71 years (60-80); 584 men; 1110 with LVO) and of 646 patients from PRESTO (median age (IQR) 73 years (62-82); 358 men; 141 with and 505 without LVO). For detection of LVO, the algorithm yielded a sensitivity of 89% in the MR CLEAN Registry and a sensitivity of 72%, specificity of 78%, and AUC of 0.75 in PRESTO. Sensitivity per occlusion location was 88% for ICA/ICA-T, 94% for M1, and 72% for M2 occlusion in the MR CLEAN Registry, and 80% for ICA/ICA-T, 95% for M1, and 49% for M2 occlusion in PRESTO. CONCLUSION: The algorithm provided a high detection rate for proximal LVO, but performance varied significantly by occlusion location. Detection of M2 occlusion needs further improvement.

BACKGROUND AND PURPOSE: Prediction models for outcome of patients with acute ischemic stroke who will undergo endovascular treatment have been developed to improve patient management. The aim of the current study is to provide an overview of preintervention models for functional outcome after endovascular treatment and to validate these models with data from daily clinical practice.METHODS: We systematically searched within Medline, Embase, Cochrane, Web of Science, to include prediction models. Models identified from the search were validated in the MR CLEAN (Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands) registry, which includes all patients treated with endovascular treatment within 6.5 hours after stroke onset in the Netherlands between March 2014 and November 2017. Predictive performance was evaluated according to discrimination (area under the curve) and calibration (slope and intercept of the calibration curve). Good functional outcome was defined as a score of 0-2 or 0-3 on the modified Rankin Scale depending on the model.RESULTS: After screening 3468 publications, 19 models were included in this validation. Variables included in the models mainly addressed clinical and imaging characteristics at baseline. In the validation cohort of 3156 patients, discriminative performance ranged from 0.61 (SPAN-100 [Stroke Prognostication Using Age and NIH Stroke Scale]) to 0.80 (MR PREDICTS). Best-calibrated models were THRIVE (The Totaled Health Risks in Vascular Events; intercept -0.06 [95% CI, -0.14 to 0.02]; slope 0.84 [95% CI, 0.75-0.95]), THRIVE-c (intercept 0.08 [95% CI, -0.02 to 0.17]; slope 0.71 [95% CI, 0.65-0.77]), Stroke Checkerboard score (intercept -0.05 [95% CI, -0.13 to 0.03]; slope 0.97 [95% CI, 0.88-1.08]), and MR PREDICTS (intercept 0.43 [95% CI, 0.33-0.52]; slope 0.93 [95% CI, 0.85-1.01]).CONCLUSIONS: The THRIVE-c score and MR PREDICTS both showed a good combination

Background: Due to the time-sensitive effect of endovascular treatment, rapid prehospital identification of large-vessel occlusion in individuals with suspected stroke is essential to optimise outcome. Interhospital transfers are an important cause of delay of endovascular treatment. Prehospital stroke scales have been proposed to select patients with large-vessel occlusion for direct transport to an endovascular-capable intervention centre. We aimed to prospectively validate eight prehospital stroke scales in the field. Methods: We did a multicentre, prospective, observational cohort study of adults with suspected stroke (aged ≥18 years) who were transported by ambulance to one of eight hospitals in southwest Netherlands. Suspected stroke was defined by a positive Face-Arm-Speech-Time (FAST) test. We included individuals with blood glucose of at least 2·5 mmol/L. People who presented more than 6 h after symptom onset were excluded from the analysis. After structured training, paramedics used a mobile app to assess items from eight prehospital stroke scales: Rapid Arterial oCclusion Evaluation (RACE), Los Angeles Motor Scale (LAMS), Cincinnati Stroke Triage Assessment Tool (C-STAT), Gaze-Face-Arm-Speech-Time (G-FAST), Prehospital Acute Stroke Severity (PASS), Cincinnati Prehospital Stroke Scale (CPSS), Conveniently-Grasped Field Assessment Stroke Triage (CG-FAST), and the FAST-PLUS (Face-Arm-Speech-Time plus severe arm or leg motor deficit) test. The primary outcome was the clinical diagnosis of ischaemic stroke with a proximal intracranial large-vessel occlusion in the anterior circulation (aLVO) on CT angiography. Baseline neuroimaging was centrally assessed by neuroradiologists to validate the true occlusion status. Prehospital stroke scale performance was expressed as the area under the receiver operating characteristic curve (AUC) and was compared with National Institutes of Health Stroke Scale (NIHSS) scores assessed by clinicians at the emergency departmen

Introduction: Prehospital stroke scales have been proposed to identify stroke patients with a large vessel occlusion to allow direct transport to an intervention centre capable of endovascular treatment (EVT). It is unclear whether these scales are able to detect not only proximal, but also more distal treatable occlusions. Our aim was to assess the sensitivity of prehospital stroke scales for different EVT-eligible occlusion locations in the anterior circulation. Patients and methods: The MR CLEAN Registry is a prospective, observational study in all centres that perform EVT in the Netherlands. We included adult patients with an anterior circulation stroke treated between March 2014 and November 2017. We used National Institutes of Health Stroke Scale scores at admission to reconstruct previously published prehospital stroke scales. We compared the sensitivity of each scale for different occlusion locations. Occlusions were assessed with CT angiography by an imaging core laboratory blinded to clinical findings. Results: We included 3021 patients for the analysis of 14 scales. All scales had the highest sensitivity to detect internal carotid artery terminus occlusions (ranging from 0.21 to 0.97) and lowest for occlusions of the M2 segment (0.08 to 0.84, p-values < 0.001). Discussion and conclusion: Although prehospital stroke scales are generally sensitive for proximal large vessel occlusions, they are less sensitive to detect more distal occlusions.