AI-driven Clinical Decision Support for Perioperative Blood Orders
NCT ID: NCT07223853
Last Updated: 2025-11-04
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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RECRUITING
NA
50 participants
INTERVENTIONAL
2025-10-28
2027-01-01
Brief Summary
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Detailed Description
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Conditions
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Study Design
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RANDOMIZED
CROSSOVER
SCREENING
NONE
Study Groups
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Usual care
Usual care for determining presurgical blood orders, including use of the institutional Maximum Surgical Blood Ordering Schedule (MSBOS)
Usual care
Including use of the conventional Maximum Surgical Blood Ordering Schedule (MSBOS)
S-PATH
Access to the S-PATH clinical decision support system
S-PATH clinical decision support system
Access to the S-PATH electronic health record (EHR)-integrated clinical decision support system
Interventions
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S-PATH clinical decision support system
Access to the S-PATH electronic health record (EHR)-integrated clinical decision support system
Usual care
Including use of the conventional Maximum Surgical Blood Ordering Schedule (MSBOS)
Eligibility Criteria
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Inclusion Criteria
* Evaluated in-person at one of the preoperative assessment clinics affiliated with BJC Healthcare
* Have a valid S-PATH model prediction prior to their preoperative assessment clinic visit
Exclusion Criteria
* None
* Pregnant
* Presence or history of red cell alloantibodies
18 Years
ALL
No
Sponsors
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National Heart, Lung, and Blood Institute (NHLBI)
NIH
Washington University School of Medicine
OTHER
Responsible Party
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Sunny Lou
Assistant Professor
Locations
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Washington University / Barnes Jewish Hospital
St Louis, Missouri, United States
Countries
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Central Contacts
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Facility Contacts
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References
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Yang P, Zijlstra EP, Hall BL, Gregory SH, Jackups R Jr, Li J, Abraham J, Lou SS. Challenges in reliable preoperative blood ordering: A qualitative interview study. Transfusion. 2024 Oct;64(10):1889-1898. doi: 10.1111/trf.18012. Epub 2024 Sep 16.
Lou SS, Liu H, Lu C, Wildes TS, Hall BL, Kannampallil T. Personalized Surgical Transfusion Risk Prediction Using Machine Learning to Guide Preoperative Type and Screen Orders. Anesthesiology. 2022 Jul 1;137(1):55-66. doi: 10.1097/ALN.0000000000004139.
Lou SS, Liu Y, Cohen ME, Ko CY, Hall BL, Kannampallil T. National Multi-Institutional Validation of a Surgical Transfusion Risk Prediction Model. J Am Coll Surg. 2024 Jan 1;238(1):99-105. doi: 10.1097/XCS.0000000000000874. Epub 2023 Sep 22.
Lou SS, Kumar S, Goss CW, Avidan MS, Kheterpal S, Kannampallil T; Multicenter Perioperative Outcomes Group. Multicenter Validation of a Machine Learning Model for Surgical Transfusion Risk at 45 US Hospitals. JAMA Netw Open. 2025 Jun 2;8(6):e2517760. doi: 10.1001/jamanetworkopen.2025.17760.
Provided Documents
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Document Type: Study Protocol and Statistical Analysis Plan
Document Type: Informed Consent Form
Other Identifiers
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202506199
Identifier Type: -
Identifier Source: org_study_id
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