AI-driven Clinical Decision Support for Perioperative Blood Orders

NCT07223853 | Recruiting DMC

• 2 other identifiers: 202506199K23HL166880
• Study Type: interventional
• Target: 50
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Surgery

Keywords

artificial intelligence; clinical decision support; machine learning; surgery; perioperative blood management; anesthesia; pretransfusion testing

Outcome Measures

Primary Outcomes (1)

• Frequency of patients with a type and screen order placed during the preoperative clinic assessment visit

  This is a binary outcome at the patient / surgical case level, which will be aggregated across all patients in the study to produce an overall frequency. Placement of a type and screen order during the preoperative clinic assessment visit will be evaluated at the patient / surgical case level. This includes orders placed and collected during the preoperative clinic assessment visit, as well as orders signed during the preoperative clinic assessment visit or subsequent follow up care and held to be drawn on the day of surgery.

  Decision made during the preoperative assessment clinic visit

Secondary Outcomes (5)

• Frequency of a valid type and screen order at the start of surgery

  Start of surgery (1 hour after Anesthesia Start)

• Frequency of red cell transfusion during surgery

  During surgery

• Frequency of emergency release blood use during surgery

  During surgery

• Frequency of red cell transfusion during surgery without an active type and screen at the start of surgery

  During surgery

• Frequency of transfusion reaction

  From time of surgery to hospital discharge or 30 days after surgery

Other Outcomes (3)

• Viewing S-PATH predictions (Implementation outcome)

  Through study completion, an average of 1 year

• Acceptance of S-PATH recommendation (Implementation outcome)

  Through study completion, an average of 1 year

• Cost assessment

  Through study completion, an average of 1 year

Study Arms (2)

Usual care

ACTIVE COMPARATOR

Usual care for determining presurgical blood orders, including use of the institutional Maximum Surgical Blood Ordering Schedule (MSBOS)

Other: Usual care

S-PATH

EXPERIMENTAL

Access to the S-PATH clinical decision support system

Other: S-PATH clinical decision support system

Interventions

S-PATH clinical decision support system OTHER

Access to the S-PATH electronic health record (EHR)-integrated clinical decision support system

S-PATH

Usual care OTHER

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

Usual care

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may not qualify if:

• Clinician (resident physician or advanced practice provider) who works at a preoperative assessment clinic
• None
• 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
• Pregnant
• Presence or history of red cell alloantibodies

Sponsors & Collaborators

• Washington University School of Medicine: lead
• National Heart, Lung, and Blood Institute (NHLBI): collaborator

Study Sites (1)

Washington University / Barnes Jewish Hospital

St Louis, Missouri, 63110, United States

RECRUITING

Related Publications (4)

• 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.

  PMID: 39279676 BACKGROUND

• 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.

  PMID: 35147666 BACKGROUND

• 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.

  PMID: 37737660 BACKGROUND

• 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.

  PMID: 40577014 BACKGROUND

Central Study Contacts

Sunny S Lou, MD, PhD

CONTACT

314-362-5000; slou@wustl.edu

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: SCREENING
• Intervention Model: CROSSOVER
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Assistant Professor

Model Details: Stepped-wedge cluster randomized trial

Study Record Dates

• First Submitted: October 28, 2025
• First Posted: November 3, 2025
• Study Start: October 28, 2025
• Primary Completion (Estimated): December 1, 2026
• Study Completion (Estimated): January 1, 2027
• Last Updated: February 27, 2026

Record last verified: 2026-02

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, SAP, ICF, CSR, ANALYTIC CODE
• Time Frame: Data will be made available within 12 months of the grant period ending or on publication of a manuscript using the data, whichever comes first. Data will be retained in the WashU Libraries Open Scholarship Digital Research Materials Repository for at least 10 years.
• Access Criteria: The proposed clinical trial will involve a near-complete sampling of clinicians who work at the host institution's preoperative assessment clinic. Even with removal of identifiers, we believe it would be difficult to protect the identities of clinician participants given the type of demographic data collected and the very restricted setting in which recruitment will occur. In addition, even if patient data is anonymized, it may be possible to reidentify the patients using metadata alone. Therefore, access controls will be required prior to sharing of the all data, including patient and clinician data. Specifically, data will be shared only under a data-sharing agreement according to institutional guidelines that provides for: (1) a commitment to using the data only for research purposes and not to identify any individual participant; (2) a commitment to securing the data using appropriate computer technology; and (3) a commitment to destroying the data after analyses are complete.

Locations

US (1)