Implementation Study of the PostOperative Nausea and Vomiting Prediction Rule
|Postoperative Nausea and Vomiting||Device: Automatic Risk Presentation in the operating room Other: Education Other: Feedback|
|Study Design:||Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: None (Open Label)
Primary Purpose: Prevention
|Official Title:||IMplementation of a Prediction Rule in Anesthesia Practice to Improve Cost-Effectiveness of Treatment of Postoperative Nausea and Vomiting|
- the incidence of PONV within the first 24 hours [ Time Frame: within 24 hours after surgery ]
- Behaviour of the anaesthesiologist regarding PONV-prophylaxis [ Time Frame: Perioperative ]
- Cost-effectiveness risk-based prophylaxis compared to standard care [ Time Frame: Within 24 hours after surgery ]
- Attitude of anesthesiologists to use risk estimations from a prediction rule [ Time Frame: At the start and end of the study ]
|Study Start Date:||March 2006|
|Study Completion Date:||January 2008|
|Primary Completion Date:||December 2007 (Final data collection date for primary outcome measure)|
Arm of anesthesiologists and senior residents who receive a patient's individual predicted PONV risk intraoperatively
Device: Automatic Risk Presentation in the operating room
Automatic calculation and presentation of a patient's individual predicted PONV risk by the anesthesia information management system during the entire procedureOther: Education
Specific information is provided to the intervention group: about PONV, about the prediction model. While the Usual Care group only receives information about the study purposesOther: Feedback
Feedback about the physician's personal performance on prevention of PONV
Active Comparator: Usual Care
Anesthesiologists and senior residents who provide usual care: they provide PONV prophylaxis as they always have
Specific information is provided to the intervention group: about PONV, about the prediction model. While the Usual Care group only receives information about the study purposes
Background and objectives. So-called prediction rules (risk scores) have become increasingly popular in all medical disciplines. This will only rise with the introduction of electronic patient records as these will enhance their use. However, effects of implementation of such rules in daily care has hardly been studied. Also not in anesthesiology. We developed and validated an accurate rule to preoperatively predict the risk of postoperative nausea and vomiting (PONV) in surgical inpatients. PONV causes extreme patient discomfort and occurs in even 30%-50% of all surgical inpatients. As routine administration of PONV prophylaxis is not cost-effective, a risk-tailored approach using an accurate prediction rule is widely advocated. Before large-scale implementation, we aim to study whether such implementation indeed changes physician behavior and improves patient outcome. Given the increase interest in prediction rules, another aim is to study general causes of successful/poor implementation of prediction rules in health care. Design. Cluster, randomized study in which 60 anesthesiologists and senior residents of the UMC Utrecht will be randomized to either the intervention or usual care group.
Study population. Adult,elective,non-ambulatory,surgical patients undergoing general anesthesia of UMC Utrecht.
Intervention. Implementation of risk-tailored PONV strategy (use of the PONV prediction rule with suggested anti-emetic strategies per risk group) in current care.
Outcomes. Primary:incidence of PONV in first 24 hours. Secondary:change in anesthesiologists' behavior in terms of administered anti-emetic management, cost-effectiveness of intervention, attitudes of physicians towards prediction rules in general.
Sample size. 11,000
Economic evaluation. Estimation of incremental costs per prevented PONV case.
Please refer to this study by its ClinicalTrials.gov identifier: NCT00293618
|Utrecht, Netherlands, 3508 GA|
|Study Chair:||Cor J Kalkman, M.D. PhD||UMC Utrecht|
|Principal Investigator:||Karel G Moons, PhD||UMC Utrecht|