AI-driven Clinical Decision Support for Perioperative Blood Orders

Официальное название

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

Обзор

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.

Вмешательства

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

Первичные конечные точки

• Frequency of patients with a type and screen order placed during the preoperative clinic assessment visit [Срок оценки: Decision made during the preoperative assessment clinic visit]

Вторичные конечные точки (5)

• Frequency of a valid type and screen order at the start of surgery [Срок оценки: Start of surgery (1 hour after Anesthesia Start)]
• Frequency of red cell transfusion during surgery [Срок оценки: During surgery]
• Frequency of emergency release blood use during surgery [Срок оценки: During surgery]
• Frequency of red cell transfusion during surgery without an active type and screen at the start of surgery [Срок оценки: During surgery]
• Frequency of transfusion reaction [Срок оценки: From time of surgery to hospital discharge or 30 days after surgery]

Критерии участия

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

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Нет

Дизайн исследования

Распределение

Рандомизированное

Модель

Перекрёстный дизайн

Маскирование

Открытое

Основная цель

Скрининг

Центры проведения

США · 1 центр

• Washington University / Barnes Jewish Hospital — St Louis

Публикации

• 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

Идентификаторы

NCT: NCT07223853 · 202506199 · K23HL166880

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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