Impact of Prehospital Triage Protocol on Outcomes of Spontaneous Intracerebral Hemorrhage Patients
Fan, T.; Lawrence, M.; Badillo Goicoechea, E.; Wick, A.; Prabhakaran, S.
Show abstract
BackgroundWhile prehospital triage protocols for suspected large vessel occlusion (LVO) improve ischemic stroke outcomes, their impact on spontaneous intracerebral hemorrhage (sICH) remains uncertain. We evaluated whether a regional LVO-focused emergency medical service (EMS) transport protocol affected care efficiency and outcomes in sICH patients. MethodsWe conducted a multicenter pre-post implementation cohort study using the Get-With-The-Guidelines-Stroke database in Chicago (April 2017-January 2020). Included were EMS-transported sICH patients arriving [≤]6 hours from last known normal at 8 comprehensive stroke centers (CSCs) and 15 primary stroke centers (PSCs). Primary outcomes were in-hospital mortality and favorable discharge disposition (home/acute rehabilitation). Secondary outcomes included good neurologic outcome (independent ambulation) at discharge and time metrics (symptom-to-arrival, door-to-CT). Interrupted time series (ITS) analysis assessed changes while accounting for temporal trends. ResultsAmong 311 sICH patients (111 pre-, 192 post-implementation), there was no difference in in-hospital mortality (12% vs. 9%, p=0.4; ITS level change: -5% [95% CI: -31% to 21%], p=0.68; trend change: 1% [95% CI: -1% to 2%], p=0.34), favorable discharge disposition (58% vs. 64%, p=0.3; ITS level change: -20% [-77% to 38%], p=0.49; trend change: 1% [95% CI: -2% to 4%], p=0.47) or good neurologic outcomes (13% vs. 19%, p=0.4; ITS level change: 11% [-25% to 48%], p=0.53; trend change: -1% [95% CI: -3% to 1%], p=0.37) between pre-post implementation periods. Time metrics (door-to-CT, symptom-to-arrival, symptom-to-CT) showed no significant changes in unadjusted or ITS analyses. The protocol also did not impact CSC admissions rate and inter-hospital transfers in ITS analyses. ConclusionImplementation of an LVO-focused EMS transport protocol did not improve outcomes or care efficiency among sICH patients, nor did it affect CSC admission or transfer rates. These findings highlight the need for dedicated prehospital triage strategies specific to sICH, distinct from ischemic stroke pathways.
Matching journals
The top 1 journal accounts for 50% of the predicted probability mass.
Similar papers in this journal
- Modified Rankin Scale Disability Status at Day 4 Poststroke is an Informative Predictor of Long-Term Day 90 Outcome 96%
- White matter lesions as a prognostic marker of recurrence in cryptogenic stroke with high-risk patent foramen ovale 95%
- Impact of Fludrocortisone on the Outcomes of Subarachnoid Hemorrhage Patients: A Retrospective Analysis 94%
Similar papers in this journal
- Prospective Observational Cohort Study Of Tenecteplase Versus Alteplase In Routine Clinical Practice 97%
- Safety Outcomes of Mechanical Thrombectomy Versus Combined Thrombectomy and Intravenous Thrombolysis in Tandem Lesions 96%
- Improvement in Delivery of Ischemic Stroke Treatments but Stagnation of Clinical Outcomes in Young Adults in South Korea 96%
Similar papers in this journal
- COVID-19 Infection Is Associated with Poor Outcomes in Patients with Intracerebral Hemorrhage 97%
- Tenecteplase 0.4 mg/kg in moderate and severe acute ischemic stroke: A pooled analysis of NOR-TEST & NOR-TEST 2A 95%
- Disparities in Access to Vascular Stroke Imaging and Carotid Revascularization: A Population Study 95%
Similar papers in this journal
- Predicting factors for long-term survival in patients with out-of-hospital cardiac arrest - a propensity score-matched analysis 93%
- Leveraging Machine Learning for Enhanced and Interpretable Risk Prediction of Venous Thromboembolism in Acute Ischemic Stroke Care 92%
- Intravenous Thrombolysis for Acute Central Retinal Artery Occlusion: Protocol For a Systematic Review and Individual Participant Data Meta-Analysis of Randomized Controlled Trials 92%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.