A systematic review and meta-analysis to evaluate the diagnostic accuracy of recognition of stroke in the emergency department (ROSIER) scale

Han, Fei; Zuo, Chao; Zheng, Guo‐Dong

doi:10.1186/s12883-020-01841-x

Cited by 8 publications

(4 citation statements)

References 40 publications

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“…In 2019, the assessment of both cortical and motor function using the Rapid Arterial Occlusion Evaluation Scale (RACE), Field Assessment Stroke Triage for Emergency Destination (FAST-ED) and National Institute of Health stroke scale (NIHSS) showed the best diagnostic accuracy values for selecting subjects with large vessel occlusion (LVO) (22). In 2020, a systematic review and meta-analysis revealed that ROSIER was a valid scale with high clinical applicability (23). Even though numerous scales have emerged for assessing the stroke, only a few studies compared the Se and Sp between FAST and BEFAST.…”

Section: Discussionmentioning

confidence: 99%

A Systematic Review and Meta-Analysis Comparing FAST and BEFAST in Acute Stroke Patients

Chen

Zhao

et al. 2022

Front. Neurol.

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ObjectiveTo evaluate and compare the predictive value of Face, Arm, Speech Test (FAST) and Balance, Eyes, Face, Arm, Speech, Time (BEFAST) scale in the acute ischemic stroke (AIS).MethodsWe searched Medline and Ovid databases for relevant literature in the English language. There were no limitations on the date. The sensitivity, specificity, likelihood ratio, and diagnostic odds ratio were pooled for meta-analysis. The symmetric receiver operator characteristic curve and Fagan's Nomogram were drawn, and meta-regression and subgroup analysis were used to explore the source of heterogeneity.ResultsA total of 9 studies, including 6,151 participants, were analyzed. The combined sensitivity of FAST was 0.77 [95% CI (0.64–0.86)], specificity was 0.60 [95% CI (0.38–0.78)], the area under the ROC curve was 0.76, and the diagnostic ratio was 1.57, while the sensitivity of BEFAST was 0.68 [95% CI (0.23–0.93)], specificity was 0.85 [95% CI (0.72–0.92)], the area under the ROC curve was 0.86, and the diagnostic odds ratio was 2.44. No publication bias was detected in Deeks' funnel plot. For FAST, meta-regression analysis showed that the prospective design, satisfactory description of the index test, and a broad spectrum of disease contributed to the heterogeneity in sensitivity, while no sources contributed to the heterogeneity in sensitivity. When the pretest probability was set as 20%, the posterior probability in Fagan's Nomogram was 32%; however, when the pretest probability was set as 20% in BEFAST, the posterior probability in Fagan's Nomogram was 52%.ConclusionsOur findings indicated that FAST and BEFAST might be useful in the diagnosis of acute ischemic stroke. The diagnostic value of BEFAST in acute ischemic stroke was higher than in FAST; thus, it might have an important role in the fast recognition of acute ischemic stroke.

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Section: Discussionmentioning

confidence: 99%

A Systematic Review and Meta-Analysis Comparing FAST and BEFAST in Acute Stroke Patients

Chen

Zhao

et al. 2022

Front. Neurol.

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“…The Recognition of Stroke in the Emergency Room (ROSIER) scale adalah skala yang valid dengan penerapan klinis yang tinggi, yang tidak hanya memiliki akurasi diagnostik yang baik di Eropa tetapi juga menunjukkan kinerja yang sangat baik di Asia. Selain itu, skala ROSIER menunjukkan penerapan yang baik dalam pengaturan pra-rumah sakit dengan tenaga medis terlatih (Han et al, 2020). ROSIER scale memberikan manfaat dalam evaluasi stroke di lingkungan pra-rumah sakit.…”

Section: Pembahasanunclassified

Penerapan the Recognition of Stroke in the Emergency Room Meningkatkan Kemampuan Perawat Dalam Deteksi Dini Stroke

Herda Rophi,

Muspitha

2024

j. Keperawatan Tropis Papua

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Keselamatan pasien stroke sangat ditentukan dari cepat dan tepatnya penanganan awal. Tidak semua fasilitas kesehatan memiliki computed tomography scan (CT Scan) untuk penegakan diagnosis stroke. Tenaga kesehatan di puskesmas dapat menggunakan alternatif instrumen untuk screening stroke berupa sistem skoring, sehingga dapat memberikan tindakan segera untuk menurunkan kecacatan dan kematian akibat stroke. Penelitian ini bertujuan untuk mengevaluasi pengaruh penerapan the Recognition of Stroke in the Emergency Room (ROSIER) scale terhadap peningkatan kemampuan perawat dalam mendeteksi dini stroke. Penelitian ini merupakan quasi eksperiment dengan one group pre-post-test design yang dilakukan pada perawat dengan jadwal kerja di Unit Gawat Darurat (UGD) Puskesmas. Sampel penelitian berjumlah 34 perawat. Analisis data menggunakan uji Wilcoxon signed rank. Hasil penelitian menunjukkan adanya peningkatan rata-rata kemampuan deteksi dini stroke responden sebelum dan setelah penerapan ROSIER scale, dengan peningkatan sebesar 20,26 poin (8,09±5,02 dan 28,35±2,62). Pada pre-test sebanyak 34 responden (100%) berada pada kategori tingkat tidak mampu. Hasil post-test menunjukkan 32 responden (94%) pada tingkat mampu dan 2 responden (6%) pada tingkat cukup mampu. Ada pengaruh yang signifikan penerapan ROSIER scale terhadap peningkatan kemampuan perawat dalam deteksi dini stroke (p=0,000). ROSIER scale dapat dipertimbangkan penggunaannya sebagai instrumen deteksi dini stroke oleh tenaga kesehatan di setting pra-rumah sakit. The speed and accuracy of initial treatment highly determine the safety of stroke patients. Not all healthcare facilities have computed tomography scans (CT scans) to diagnose strokes. Healthcare workers at community health centers (puskesmas) can use alternative instruments for stroke screening in the form of scoring systems, allowing for immediate action to reduce disability and death due to stroke. This study aims to evaluate the impact of implementing the Recognition of Stroke in the Emergency Room (ROSIER) scale on improving nurses' ability to detect strokes early. This research is a quasi-experimental study with a one-group pre-post-test design conducted on nurses working in the Emergency Unit of a community health center (Puskesmas). The study sample consisted of 34 nurses. Data analysis was performed using the Wilcoxon signed-rank test. The results showed an increase in respondents' average early stroke detection ability before and after the implementation of the ROSIER scale, with an increase of 20.26 points (8.09±5.02 and 28.35±2.62). In the pre-test, all 34 respondents (100%) were incapable. The post-test results showed 32 respondents (94%) in the capable category and two respondents (6%) in the fairly capable category. The ROSIER scale implementation has a significant impact on improving nurses' ability to early detect strokes (p=0.000). The ROSIER scale can be considered for use as an early stroke detection instrument by healthcare workers in pre-hospital settings.

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“…Therefore, distinguishing hemorrhagic stroke from ischemia stroke has an important implication for EMS. Some prehospital stroke scales were used for EMS to identify stroke in recent studies, such as the Face Arm Speech Test (FAST), Los Angeles Prehospital Stroke Screen (LAPSS), and Recognition of Stroke in the Emergency Room (ROSIER), parts of which had moderateto-good sensitivity and the lower levels of specificity [9][10][11][12]. In addition, some studies have proved that serum markers had a certain advantage in distinguishing hemorrhagic stroke and ischemic stroke, including glial fibrillary acidic protein (GFAP), N-terminal proB-type natriuretic peptide (NT-proBNP), and retinol-binding protein 4 (RBP-4) [13,14].…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a clinical nomogram for differentiating hemorrhagic and ischemic stroke prehospital

Pan

et al. 2023

BMC Neurol

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Objectives The early detection and identification of stroke are essential to the prognosis of patients with suspected stroke symptoms out-of-hospital. We aimed to develop a risk prediction model based on the FAST score to identify the different types of strokes early for emergency medical services (EMS). Methods This retrospective observational study enrolled 394 stroke patients at a single center from January 2020 to December 2021. Demographic data, clinical characteristics, and stroke risk factors with patients were collected from the EMS record database. Univariate and multivariate logistic regression analysis was used to identify the independent risk predictors. The nomogram was developed based on the independent predictors, in which the discriminative value and calibration of the nomogram were verified by the receiver operator characteristic (ROC) curve and calibration plots. Results A total of 31.90% (88/276) of patients were diagnosed with hemorrhagic stroke in the training set, while 36.40% (43/118) in the validation set. The nomogram was developed based on the multivariate analysis, including age, systolic blood pressure, hypertension, vomiting, arm weakness, and slurred speech. The area under the curve (AUC) of the ROC with nomogram was 0.796 (95% CI: 0.740–0.852, P < 0.001) and 0.808 (95% CI:0.728–0.887, P < 0.001) in the training set and validation set, respectively. In addition, the AUC with the nomogram was superior to the FAST score in both two sets. The calibration curve showed a good agreement with the nomogram and the decision curves analysis also demonstrated that the nomogram had a wider range of threshold probabilities than the FAST score in the prediction risk of hemorrhagic stroke. Conclusions This novel noninvasive clinical nomogram shows a good performance in differentiating hemorrhagic and ischemic stroke for EMS staff prehospital. Moreover, all of the variables of nomogram are acquired in clinical practice easily and inexpensively out-of-hospital.

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A systematic review and meta-analysis to evaluate the diagnostic accuracy of recognition of stroke in the emergency department (ROSIER) scale

Cited by 8 publications

References 40 publications

A Systematic Review and Meta-Analysis Comparing FAST and BEFAST in Acute Stroke Patients

A Systematic Review and Meta-Analysis Comparing FAST and BEFAST in Acute Stroke Patients

Penerapan the Recognition of Stroke in the Emergency Room Meningkatkan Kemampuan Perawat Dalam Deteksi Dini Stroke

Development and validation of a clinical nomogram for differentiating hemorrhagic and ischemic stroke prehospital

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