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Recruiting NCT07223853

AI-driven Clinical Decision Support for Perioperative Blood Orders

No phase Interventional Surgery

For patients and families

In plain language

An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.

What is being studied
The protocol lists: S-PATH clinical decision support system, Usual care.
Who it may be relevant to
Registry conditions: Surgery. Basic parameters: from 18 years · All.
What needs checking
Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
Where it takes place
United States
Next step
Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →

Unsure about the terms? Read our patient guide →

Official title

Intelligent Clinical Decision Support for Preoperative Blood Management: A Cluster-Randomized Clinical Trial

Overview

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.

Interventions

  • Other S-PATH clinical decision support system
    Access to the S-PATH electronic health record (EHR)-integrated clinical decision support system
  • Other Usual care
    Including use of the conventional Maximum Surgical Blood Ordering Schedule (MSBOS)

Primary outcome measures

  • Frequency of patients with a type and screen order placed during the preoperative clinic assessment visit [Time frame: Decision made during the preoperative assessment clinic visit]
Secondary outcome measures (5)
  • Frequency of a valid type and screen order at the start of surgery [Time frame: Start of surgery (1 hour after Anesthesia Start)]
  • Frequency of red cell transfusion during surgery [Time frame: During surgery]
  • Frequency of emergency release blood use during surgery [Time frame: During surgery]
  • Frequency of red cell transfusion during surgery without an active type and screen at the start of surgery [Time frame: During surgery]
  • Frequency of transfusion reaction [Time frame: From time of surgery to hospital discharge or 30 days after surgery]

Eligibility criteria

Clinician Level Exclusion Criteria:

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

Clinician Level Exclusion Criteria:

  • None

Patient 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

Patient Exclusion Criteria:

  • Pregnant
  • Presence or history of red cell alloantibodies

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Allocation
Randomized
Model
Crossover
Masking
Open label
Primary purpose
Screening

Study locations

United States · 1 center
  • Washington University / Barnes Jewish Hospital — St Louis

Publications

  • 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
  • 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
  • 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
  • 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

Identifiers

NCT: NCT07223853 · 202506199 · K23HL166880

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗