Comparison of Prehospital Assessment by Paramedics and In-Hospital Assessment by Physicians in Suspected Stroke Patients: Results From 2 Prospective Cohort Studies.

BACKGROUND: It is unknown if ambulance paramedics adequately assess neurological deficits used for prehospital stroke scales to detect anterior large-vessel occlusions. We aimed to compare prehospital assessment of these stroke-related deficits by paramedics with in-hospital assessment by physicians. METHODS: We used data from 2 prospective cohort studies: the LPSS (Leiden Prehospital Stroke Study) and PRESTO study (Prehospital Triage of Patients With Suspected Stroke). In both studies, paramedics scored 9 neurological deficits in stroke code patients in the field. Trained physicians scored the National Institutes of Health Stroke Scale (NIHSS) at hospital presentation. Patients with transient ischemic attack were excluded because of the transient nature of symptoms. Spearman rank correlation coefficient (rs) was used to assess correlation between the total prehospital assessment score, defined as the sum of all prehospital items, and the total NIHSS score. Correlation, sensitivity and specificity were calculated for each prehospital item with the corresponding NIHSS item as reference. RESULTS: We included 2850 stroke code patients. Of these, 1528 had ischemic stroke, 243 intracranial hemorrhage, and 1079 stroke mimics. Correlation between the total prehospital assessment score and NIHSS score was strong (rs=0.70 [95% CI, 0.68-0.72]). Concerning individual items, prehospital assessment of arm (rs=0.68) and leg (rs=0.64) motor function correlated strongest with corresponding NIHSS items, and had highest sensitivity (arm 95%, leg 93%) and moderate specificity (arm 71%, leg 70%). Neglect (rs=0.31), abnormal speech (rs=0.50), and gaze deviation (rs=0.51) had weakest correlations. Neglect and gaze deviation had lowest sensitivity (52% and 66%) but high specificity (84% and 89%), while abnormal speech had high sensitivity (85%) but lowest specificity (65%). CONCLUSIONS: The overall prehospital assessment of stroke code patients correlates strongly with in-hospital assessment. Prehospital assessment of neglect, abnormal speech, and gaze deviation differed most from in-hospital assessment. Focused training on these deficits may improve prehospital triage.

Clinical prediction of thrombectomy eligibility: A systematic review and 4-item decision tree.

BACKGROUND: A clinical large anterior vessel occlusion (LAVO)-prediction scale could reduce treatment delays by allocating intra-arterial thrombectomy (IAT)-eligible patients directly to a comprehensive stroke center. AIM: To subtract, validate and compare existing LAVO-prediction scales, and develop a straightforward decision support tool to assess IAT-eligibility. METHODS: We performed a systematic literature search to identify LAVO-prediction scales. Performance was compared in a prospective, multicenter validation cohort of the Dutch acute Stroke study (DUST) by calculating area under the receiver operating curves (AUROC). With group lasso regression analysis, we constructed a prediction model, incorporating patient characteristics next to National Institutes of Health Stroke Scale (NIHSS) items. Finally, we developed a decision tree algorithm based on dichotomized NIHSS items. RESULTS: We identified seven LAVO-prediction scales. From DUST, 1316 patients (35.8% LAVO-rate) from 14 centers were available for validation. FAST-ED and RACE had the highest AUROC (both >0.81, p < 0.01 for comparison with other scales). Group lasso analysis revealed a LAVO-prediction model containing seven NIHSS items (AUROC 0.84). With the GACE (Gaze, facial Asymmetry, level of Consciousness, Extinction/inattention) decision tree, LAVO is predicted (AUROC 0.76) for 61% of patients with assessment of only two dichotomized NIHSS items, and for all patients with four items. CONCLUSION: External validation of seven LAVO-prediction scales showed AUROCs between 0.75 and 0.83. Most scales, however, appear too complex for Emergency Medical Services use with prehospital validation generally lacking. GACE is the first LAVO-prediction scale using a simple decision tree as such increasing feasibility, while maintaining high accuracy. Prehospital prospective validation is planned.