Ali Pay Intelligent Navigation Applet-aided Pre-hospital Triage for Non-emergency Medical Service Patients With Acute Ischemic Stroke
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Ali Pay intelligent navigation applet.
- Who it may be relevant to
- Registry conditions: Acute Ischemic Stroke. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Ali Pay Intelligent Navigation Applet-aided Pre-hospital Triage for Non-emergency Medical Service Patients With Acute Ischemic Stroke: A Step-wedge Cluster Randomized Controlled Trial
Overview
According to the Bigdata Observatory platform for Stroke of China (BOSC), the proportion of patients with acute ischemic stroke (AIS) receiving intravenous thrombolysis or endovascular treatment in China is 5.64% and 1.45% respectively. One of the important reasons for the low treatment rate is the prolonged pre-hospital and in-hospital delay. Besides, for patients receiving reperfusion therapy, the prolonged pre-treatment delay is associated with unfavorable functional outcomes. Although tons of efforts have been made to improve the efficiency of emergency medical system in the transportation of patients with AIS, little attention has been paid to patients who arrived at hospitals on their owns, which occupying approximately 2/3 of emergency patients. This leaves a huge gap in the pre-hospital management of patietns with AIS. Therefore, the investigators plan to develop an intelligent navigation system for patients with AIS. For the convenience of public use, this system was carried on the applet of Ali Pay, which has over 1.1 billion users in China. This system comprises of three functional modules, namely stroke knowledge education, stroke recognition and hospital recommendation. The investigators aim to explore whether this intelligent navigatino system could shorten pre-hospital delay and improve functional outcomes of patients with AIS undergoing reperfusion therapy.
Detailed description
According to the Bigdata Observatory platform for Stroke of China (BOSC), the proportion of patients with acute ischemic stroke (AIS) receiving intravenous thrombolysis or endovascular treatment in China is 5.64% and 1.45% respectively. One of the important reasons for the low treatment rate is the prolonged pre-hospital and in-hospital delay. Besides, for patients receiving reperfusion therapy, the prolonged pre-treatment delay is associated with unfavorable functional outcomes.
Although tons of efforts have been made to improve the efficiency of emergency medical system in the transportation of patients with AIS, little attention has been paid to patients who arrived at hospitals on their owns, which occupying approximately 2/3 of emergency patients. This leaves a huge gap in the pre-hospital management of patietns with AIS.
Therefore, the investigators plan to develop an intelligent navigation system for patients with AIS. For the convenience of public use, this system was carried on the applet of Ali Pay, which has over 1.1 billion users in China. This system comprises of three functional modules, namely stroke knowledge education, stroke recognition and hospital recommendation.The investigators aim to explore whether this intelligent navigatino system could shorten pre-hospital delay and improve functional outcomes of patients with AIS undergoing reperfusion therapy.
Interventions
- Device Ali Pay intelligent navigation applet
The intelligent navigation applet comprises of three function modules: 1. Stroke knowledge public education: information regarding prevention and emergency treatment of stroke would be push to users\' mobile phones regularly; 2. Stroke recognition: questionaires, voice interaction, and facial recognition are employed to identify patients with AIS and large vessel occlusion; 3. Hospital recommendation: this module combines real-time traffic and average in-hopital delay of each stroke center
Primary outcome measures
- Modified rankin scale (mRS) scores of 0-2 at 90 days after reperfusion therapy [Time frame: 90 days]
Secondary outcome measures (7)
- Modified rankin scale (mRS) scores of 0-1 at 90 days after reperfusion therapy [Time frame: 90 days]
- Modified rankin scale (mRS) scores of 0-3 at 90 days after reperfusion therapy [Time frame: 90 days]
- Ordinal analysis of modified rankin scale (mRS) scores at 90 days after reperfusion therapy [Time frame: 90 days]
- Time interval between onset to treatment [Time frame: 1 day]
- Time interval between onset to hospital [Time frame: 1 day]
- Proportion of patients receiving reperfusion beyond time window [Time frame: 1 day]
- Proportion of patients receiving mechanical thrombectomy [Time frame: 1 day]
Eligibility criteria
Inclusion criteria
- Patients diagnosed as acute ischemic stroke undergoing reperfusion therapy within 24 hours of onset
Exclusion criteria
- Patients transported to hospitals via emergency medical service
- Patients with in-hospital stroke
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Allocation
- Randomized
- Model
- Parallel assignment
- Masking
- Single blind
- Primary purpose
- Health services research
Study locations
China · 1 center
- Shaoxing People's Hospital — Shaoxing
Identifiers
NCT: NCT06613074 · i-Path