AI-driven Clinical Decision Support for Perioperative Blood Orders

NCT ID: NCT07223853

Last Updated: 2025-11-04

Study Results

Results pending

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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Recruitment Status

RECRUITING

Clinical Phase

NA

Total Enrollment

50 participants

Study Classification

INTERVENTIONAL

Study Start Date

2025-10-28

Study Completion Date

2027-01-01

Brief Summary

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20 million patients have surgery in the United States every year, with approximately 1 million of those patients requiring life-saving blood transfusion. Presurgical preparation for transfusion is important to allow for safe and timely transfusion during surgery; however, excessive preparation is unfortunately common, costly, and contributes to blood waste. This study aims to evaluate an intelligent clinical decision support system that helps clinicians prepare blood for patients who are likely to need it, while avoiding excessive preparation for patients who don't, potentially improving patient safety while reducing blood waste and healthcare costs.

Detailed Description

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Conditions

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Surgery

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

CROSSOVER

Stepped-wedge cluster randomized trial
Primary Study Purpose

SCREENING

Blinding Strategy

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)

Group Type ACTIVE_COMPARATOR

Usual care

Intervention Type OTHER

Including use of the conventional Maximum Surgical Blood Ordering Schedule (MSBOS)

S-PATH

Access to the S-PATH clinical decision support system

Group Type EXPERIMENTAL

S-PATH clinical decision support system

Intervention Type OTHER

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

Intervention Type OTHER

Usual care

Including use of the conventional Maximum Surgical Blood Ordering Schedule (MSBOS)

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

* Scheduled for surgery in one of the main operating room areas (non-remote) at Barnes Jewish Hospital
* 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

* Clinician (resident physician or advanced practice provider) who works at a preoperative assessment clinic


* None


* Pregnant
* Presence or history of red cell alloantibodies
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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National Heart, Lung, and Blood Institute (NHLBI)

NIH

Sponsor Role collaborator

Washington University School of Medicine

OTHER

Sponsor Role lead

Responsible Party

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Sunny Lou

Assistant Professor

Responsibility Role PRINCIPAL_INVESTIGATOR

Locations

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Washington University / Barnes Jewish Hospital

St Louis, Missouri, United States

Site Status RECRUITING

Countries

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United States

Central Contacts

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Sunny S Lou, MD, PhD

Role: CONTACT

314-362-5000

Facility Contacts

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Sunny S Lou, MD, PhD

Role: primary

314-362-5000

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.

Reference Type BACKGROUND
PMID: 39279676 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 35147666 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 37737660 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 40577014 (View on PubMed)

Provided Documents

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Document Type: Study Protocol and Statistical Analysis Plan

View Document

Document Type: Informed Consent Form

View Document

Other Identifiers

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K23HL166880

Identifier Type: NIH

Identifier Source: secondary_id

View Link

202506199

Identifier Type: -

Identifier Source: org_study_id

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