Johns Hopkins TREWS identifies 82% of sepsis cases early, cuts time to antibiotics by 1.85 hours
“Johns Hopkins TREWS identifies 82% of sepsis cases early, cuts time to antibiotics by 1.85 hours” documents a Clinical Decision Support deployment in Hospital & Health System at Johns Hopkins Medicine. www.mayoclinicplatform.org reports sepsis detection rate: 82%; this directory has not independently verified that result.
Evidence at a glance
- Evidence status:
- Automated evidence gate passed
- Deployment timeframe:
- Not reported by source
- Reported outcome metrics:
- 2 cited below
- Directory entry published:
- Source link checked:
The source-link check confirms reachability, not independent re-verification of every claim.
Source-reported figures — cited source: www.mayoclinicplatform.org
The Challenge
Sepsis affects nearly 49 million patients annually with ~11 million deaths worldwide. Existing detection protocols including Epic's sepsis algorithm have well-documented shortcomings, often failing to identify patients early enough for timely intervention.
The Solution
Johns Hopkins investigators developed TREWS (Targeted Real-Time Early Warning System), a machine learning model deployed across five hospitals over two years. The system monitors patients in real time and alerts providers, who can confirm or dismiss the alert within a 3-hour window before antibiotic orders are placed.
Results
Across 9,800+ retrospectively confirmed sepsis cases, TREWS identified 82% of patients early. A prospective multi-site study of 6,800+ patients showed that timely provider confirmation (within 3 hours) reduced median time to first antibiotic order by 1.85 hours and was associated with reduced in-hospital mortality.
Key Takeaways
- Provider confirmation speed is a critical mediating variable — alert value depends on clinician engagement within a narrow window.
- Both retrospective and prospective validation are needed; retrospective alone may miss confounders.
- Deploying across five hospitals in a real-world setting demonstrates generalizability beyond a single site.
Explore Related
Details
- Industry
- Hospital & Health System
- Use Case
- Clinical Decision Support
- AI Technology
- Machine Learning & Predictive Analytics
- Company Size
- Enterprise
- Company
- Johns Hopkins Medicine
- Evidence status
- Automated evidence gate passed
- Deployment timeframe
- Not reported by source
- Directory entry published
- Source link checked
Cited source
www.mayoclinicplatform.orgHave a similar implementation?
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