Evaluation of a Clinical Decision Support System for Anemia Management in Pre-operative Care (iAnemia)
Evaluation of a Clinical Decision Support System for Anemia Management in Pre-operative Care: Feasibility & Validation Study
Anemia need to be diagnosed and treated, following several guidelines. However, the complexity of these recommendations leads to low compliance and to unnecessary and harmful per- and postoperative blood transfusion.
In order to improve practices and regarding the complexity of the guidelines, the latest European Consensus Conference recommends the use of decision support systems for the management of preoperative anemia.
Study Overview
Status
Conditions
Intervention / Treatment
Detailed Description
The World Health Organization defines anemia as a hemoglobin level of less than 13 g/dL in adult men and less than 12 g/dL in women.
The cause of these anemias is most often deficient with 17% iron deficiency. This deficiency is related to age, inflammatory diseases, blood loss, and absorption disorders mainly. Anemia must be diagnosed preoperatively and treated.
To reduce transfusion in the perioperative period there are various possibilities: increase of the preoperative erythrocyte mass, reduction of losses, use of the blood that has been applied.
The impact of these preoperative strategies can be considerable. In their work, Beattie and al demonstrates that transfusion is associated with an increase in mortality at 90 days, proportional to the volume transfused, with certainly a higher risk in fragile subjects. More recently, an American registry of more than 220,000 patients showed that 30% had preoperative anemia. This anemia not only significantly increased mortality, but all causes of comorbidity were aggravated in an anemic patient.
However, the complexity of these recommendations leads to low compliance and to unnecessary and harmful per- and postoperative blood transfusion.
In order to improve practices and regarding the complexity of the guidelines, the latest European Consensus Conference recommends the use of decision support systems for the management of preoperative anemia.
Study Type
Enrollment (Anticipated)
Contacts and Locations
Participation Criteria
Eligibility Criteria
Ages Eligible for Study
- Child
- Adult
- Older Adult
Accepts Healthy Volunteers
Genders Eligible for Study
Sampling Method
Study Population
Anesthesiologist:
- Less than one year experience in anesthesia
- 2 to 5 years of experience
- 5 to 10 years of experience
- 10 to 20 years of experience
- More than 20 years of experience
Description
Inclusion Criteria:
- Anesthesiologist: anesthesiologists, resident/fellow physician anesthesiologists.
Exclusion Criteria:
- Unfinished questionnaire.
Study Plan
How is the study designed?
Design Details
Cohorts and Interventions
Group / Cohort |
Intervention / Treatment |
|---|---|
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Control Group
Anesthesiologists would have to respond to an online questionnaire about 10 hypotheticals clinical situations regarding patient blood management.
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Clinical Decision Support Group
Anesthesiologists would have to respond to an online questionnaire about 10 hypotheticals clinical situations regarding patient blood management helped by dedicated clinical decision support systems for patient blood management (iAnemia, Intelligence Anesthesia).
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iAnemia permits to display the appropriate guideline using several inputs about a patient: age, type and delay of the surgery, gender, hemoglobin level, and iron deficiency status.
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What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Average global score obtained for the two groups.
Time Frame: Up to 24 weeks
|
The mean number of correct answers (out of possible 10);
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Up to 24 weeks
|
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Average subcategories score obtained for the two groups.
Time Frame: Up to 24 weeks
|
The mean number of correct answers (out of possible 10);
|
Up to 24 weeks
|
Collaborators and Investigators
Sponsor
Publications and helpful links
General Publications
- Mueller MM, Van Remoortel H, Meybohm P, Aranko K, Aubron C, Burger R, Carson JL, Cichutek K, De Buck E, Devine D, Fergusson D, Follea G, French C, Frey KP, Gammon R, Levy JH, Murphy MF, Ozier Y, Pavenski K, So-Osman C, Tiberghien P, Volmink J, Waters JH, Wood EM, Seifried E; ICC PBM Frankfurt 2018 Group. Patient Blood Management: Recommendations From the 2018 Frankfurt Consensus Conference. JAMA. 2019 Mar 12;321(10):983-997. doi: 10.1001/jama.2019.0554.
- Rothschild JM, McGurk S, Honour M, Lu L, McClendon AA, Srivastava P, Churchill WH, Kaufman RM, Avorn J, Cook EF, Bates DW. Assessment of education and computerized decision support interventions for improving transfusion practice. Transfusion. 2007 Feb;47(2):228-39. doi: 10.1111/j.1537-2995.2007.01093.x.
- Adams ES, Longhurst CA, Pageler N, Widen E, Franzon D, Cornfield DN. Computerized physician order entry with decision support decreases blood transfusions in children. Pediatrics. 2011 May;127(5):e1112-9. doi: 10.1542/peds.2010-3252. Epub 2011 Apr 18.
Study record dates
Study Major Dates
Study Start (Anticipated)
Primary Completion (Anticipated)
Study Completion (Anticipated)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- IRB 00010254-2020-131
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
product manufactured in and exported from the U.S.
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