A potential method of identifying stroke and other intracranial lesions in a prehospital setting

Design and validation of a new scale for prehospital evaluation of stroke and large vessel occlusion

Background

Rapid recognition of acute stroke and large vessel occlusion (LVO) is essential in prehospital triage for timely reperfusion treatment.

Objective

This study aimed to develop and validate a new screening tool for both stroke and LVO in an urban Chinese population.

Methods

This study included patients with suspected stroke who were transferred to our hospital by emergency medical services between July 2017 and June 2021. The population was randomly partitioned into training (70%) and validation (30%) groups. The Staring-Hypertension-atrIal fibrillation-sPeech-weakneSs (SHIPS) scale, consisting of both clinical and medical history information, was generated based on multivariate logistic models. The predictive ability of the SHIPS scale was evaluated and compared with other scales using receiver operating characteristic (ROC) curve comparison analysis.

Results

A total of 400 patients were included in this analysis. In the training group (n = 280), the SHIPS scale showed a sensitivity of 90.4% and specificity of 60.8% in predicting stroke and a sensitivity of 75% and specificity of 61.5% in predicting LVO. In the validation group (n = 120), the SHIPS scale was not inferior to Stroke 1-2-0 (p = 0.301) in predicting stroke and was significantly better than the Cincinnati Stroke Triage Assessment Tool (C-STAT; formerly CPSSS) and the Prehospital Acute Stroke Severity scale (PASS) (all p < 0.05) in predicting LVO. In addition, including medical history in the scale was significantly better than using symptoms alone in detecting stroke (training group, 0.853 versus 0.818; validation group, 0.814 versus 0.764) and LVO (training group, 0.748 versus 0.722; validation group, 0.825 versus 0.778).

Conclusion

The SHIPS scale may serve as a superior screening tool for stroke and LVO identification in prehospital triage. Including medical history in the SHIPS scale improves the predictive value compared with clinical symptoms alone.

Standardised data collection in prehospital critical care: a comparison of medical problem categories and discharge diagnoses

Background

Prehospital medical problem reporting is essential in the management of helicopter emergency medical services (HEMS) operations. The consensus-based template for reporting and documenting in physician-staffed prehospital services exists and the classification of medical problems presented in the template is widely used in research and quality improvement. However, validation of the reported prehospital medical problem is lacking. This study aimed to describe the in-hospital diagnoses, patient characteristics and medical interventions in different categories of medical problems.

Methods

This retrospective, observational registry study examined the 10 most common in-hospital International Statistical Classification of Disease (ICD-10) diagnoseswithin different prehospital medical problem categories, defined by the HEMS physician/paramedic immediately after the mission was completed. Data were gathered from a national HEMS quality registry and a national hospital discharge registry. Patient characteristics and medical interventions related to different medical problem categories are also described.

Results

A total of 33,844 patients were included in the analyses. All the medical problem categories included a broad spectrum of ICD-10 diagnoses (the number of diagnosis classes per medical problem category ranged from 73 to 403). The most frequent diagnoses were mainly consistent with the reported medical problems. Overlapping of ICD-10 diagnoses was mostly seen in two medical problem categories: stroke and acute neurology excluding stroke. Additionally, typical patient characteristics and disturbances in vital signs were related to adequate medical problem categories.

Conclusions

Medical problems reported by HEMS personnel have adequate correspondence to hospital discharge diagnoses. However, the classification of cerebrovascular accidents remains challenging.