Use of a novel smartphone-based application tool for enrolment and randomisation in pre-hospital clinical trials.

The effective recruitment and randomisation of patients in pre-hospital clinical trials presents unique challenges. Owing to the time critical nature of many pre-hospital emergencies and limited resourcing, the use of traditional methods of randomisation that may include centralised telephone or web-based systems are often not practicable or feasible. Previous technological limitations have necessitated that pre-hospital trialists strike a compromise between implementing pragmatic, deliverable study designs, with robust enrolment and randomisation methodologies. In this commentary piece, we present a novel smartphone-based solution that has the potential to align pre-hospital clinical trial recruitment processes to that of best-in-practice in-hospital and ambulatory care based studies.

Determining the sensitivity of emergency dispatcher and paramedic diagnosis of stroke: statewide registry linkage study.

Introduction: Correctly identifying people with suspected stroke is essential for ensuring rapid treatment. Our aims were to determine the sensitivity of emergency dispatcher and paramedic identification of patients with stroke, the factors associated with correct identification, and whether there were any implications for hospital arrival times. Methods: Observational study using patient-level data from the Australian Stroke Clinical Registry (2015-2017) linked with ambulance and emergency department records for the state of Victoria. The registry diagnosis was the reference standard to compare with the provisional diagnoses made by emergency services personnel classified as "suspected" and "not suspected" stroke/transient ischemic attack (TIA). Multivariable logistic and quintile regressions were used to determine factors associated with correct identification and timely arrival to hospital. Results: Overall, 4717 (64%) were matched to ambulance transport records (median age: 73 years, 43% female). Stroke/TIA was suspected in 56% of registrants by call-takers and 69% by paramedics. Older patients (75+ years) (adjusted odds ratio [aOR]: 0.61; 95% confidence interval [CI]: 0.49-0.75), females (aOR: 0.86; 95% CI: 0.75-0.99), those with severe stroke or intracerebral hemorrhage were less often suspected as stroke. Cases identified as stroke had a shorter arrival time to hospital (unadjusted median minutes: stroke, 54 [43, 72] vs not stroke, 66 [51, 89]). Conclusions: Emergency dispatchers and paramedics identified over half of patients with stroke in the prehospital setting. Important patient characteristics, such as being female and those having a severe stroke, were found that may enable refinement of prehospital ambulance protocols and dispatcher/paramedic education. Those correctly identified as stroke, arrived earlier to hospital optimizing their chances of receiving time-critical treatments.

Linking Data From the Australian Stroke Clinical Registry With Ambulance and Emergency Administrative Data in Victoria.

Objective: In Australia, approximately 3 in 4 people with acute stroke use an ambulance. Few examples of merging ambulance clinical records, hospital government data, and national registry data for stroke exist. We sought to understand the advantages of using linked datasets for describing the full clinical journey of people with stroke and the possibility of investigating their long-term outcomes based on pre-hospital management of stroke. Method: Patient-level data from the Australian Stroke Clinical Registry (AuSCR) (January 2013-October 2017) were linked with Ambulance Victoria (AV) records and Victorian Emergency Minimum Dataset (VEMD). Probabilistic iterative matching on personal identifiers were used and records merged with a project specific identification number. Results: Of the 7,373 episodes in the AuSCR and 6,001 in the AV dataset; 4,569 (62%) were matched. Unmatched records that were positive for "arrival by ambulance" in the AuSCR and VEMD (no corresponding record in AV) were submitted to AV. AV were able to identify 148/435 additional records related to these episodes. The final cohort included 4,717 records (median age: 73 years, female 42%, ischemic stroke 66%). Conclusion: The results of the data linkage provides greater confidence for use of these data for future research related to pre-hospital management of stroke.

Factors associated with arrival by ambulance for patients with stroke: a multicentre, national data linkage study.

BACKGROUND: Hospital arrival via ambulance influences treatment of acute stroke. We aimed to determine the factors associated with use of ambulance and access to evidence-based care among patients with stroke. METHODS: Patients with first-ever strokes from the Australian Stroke Clinical Registry (2010-2013) were linked with administrative data (emergency, hospital admissions). Multilevel, multivariable regression models were used to determine patient, clinical and system factors associated with arrival by ambulance. RESULTS: Among the 6,262 patients with first-ever stroke, 4,737 (76%) arrived by ambulance (52% male; 80% ischaemic). Patients who were older, frailer, with comorbidities or were unable to walk on admission (stroke severity) were more likely to arrive by ambulance to hospital. Compared to those using other means of transport, those who used ambulances arrived to hospital sooner after stroke onset (minutes, 124 vs 397) and were more likely to receive reperfusion therapy (adjusted odds ratio, 1.57, 95% CI: 1.09, 2.27). CONCLUSION: Patients with stroke who use ambulances arrived faster and were more likely to receive reperfusion therapy compared to those using personal transport. Further public education about using ambulance services at all times, instead of personal transport when stroke is suspected is needed to optimise access to time critical care.