Real-time Decision Support for Postoperative Nausea and Vomiting (PONV) Prophylaxis
Phase: NA
Conditions: Postoperative Nausea and Vomiting
Sponsor: Vanderbilt University

 
Real-time Decision Support for  
Postoperative Nausea and Vomiting (PONV) Prophylaxis 
 
 
 
Version: October 28th, 2018 
NCT02625181 
 
PI: Jonathan Wanderer, MD, MPhil 
Associate Professor, Departments of Anesthesiology and Biomedical Informatics 
Vanderbilt University Medical Center 
615-936-5194 
Jon.wanderer@vanderbilt.edu 
 
 

Background 
Nausea and vomiting after surgery (PONV) is a common side effect of the surgical procedure, general 
anesthesia and opioid use occurring in about one third of patients. In addition to being very unpleasant 
for patients, it is associated with longer recovery room stays and increased costs.[1] Specific 
prophylactic interventions may be applied during general anesthesia with the aim to prevent PONV in 
individual patients. These interventions mainly consist of administering one or more prophylactic drugs 
either at the start or the end of the procedure, and/or selecting a different type of anesthetic - i.e. total 
intravenous anesthesia rather than inhalational anesthesia. The efficacy of these interventions have 
been extensively studied in several clinical trials and meta-analyses.[2,3]  
Current national guidelines as well as departmental guidelines recommend that patients receive 
prophylactic interventions for PONV according to their risk of PONV as predicted by a formal risk 
score.[1] A risk score predicts the PONV risk for each individual patient (Tables 1 and 2), based on 
specific characteristics of that patient and the scheduled procedure. However, in a busy operating room 
where the anesthesia provider performs multiple patient care tasks, closely following the 
recommendations to minimize the risk of PONV is often difficult.[4,5] 
A clinical decision support system (CDSS) may help anesthesia providers to adhere to best 
practices for PONV prevention. Such a CDSS automatically calculates the risk of PONV for an individual 
patient and presents this predicted risk to the anesthesia provider on the computer screen that is being 
used by the anesthesia team for record keeping. In recent studies, such decision support systems have 
been demonstrated to improve adherence to PONV guidelines, especially when a recommendation on 
the number of interventions is added to the predicted risk.[6–8] Despite that improvement, there was 
still quite some room for improvement of the adherence to PONV guidelines in these studies.  
Current literature on decisions support systems suggests that the design may have a great 
influence on what impact a CDSS has on medical decision making.[9] However, we are still only 
beginning to understand which CDSS design features will have the largest impact on the behavior of 
healthcare providers. The context of the clinical problem may be an important factor in what design for 
a decision support tool is the most appropriate.[5] The proposed study aims to improve the adherence 
to PONV guidelines by implementing multiple decision support elements with different design features. 
Research question and hypotheses 
Research question 
Does the adherence to the recommendations of a clinical decision support system for postoperative 
nausea and vomiting depend on the proximity of the advice to specific decisions? 
Hypotheses 
The research question will be addressed in this study by testing the following hypotheses: 
1. An automated recommendation on PONV prophylaxis that is based on a patient-specific PONV 
risk that is sent by email to individual anesthesia providers the day before the scheduled 
procedure increases the adherence to PONV guidelines. 

2. An automated recommendation on PONV prophylaxis that is based on a patient-specific PONV 
risk that is presented at the start of anesthesia increases the adherence to PONV guidelines (and 
has an added value to the automated email recommendation). 
Study Design and Participants 
Study design 
The time series consists of three phases. The first phase is the preintervention phase – i.e. before the 
decision support has been implemented. The second phase is the first intervention phase in which the 
email with recommendations is being sent (Figure 1). The third phase is the second intervention phase 
in which the real time decision support guidance (Figure 2) is being displayed along with the email 
intervention. 
Study population 
All adult patients (18 years and older) that are scheduled for an elective surgical procedure under 
general anesthesia within the study period will be considered eligible. We will exclude patients 
undergoing emergency surgery, and patients who will not go to the PACU to recover (i.e. patients that 
are transferred directly to the Intensive Care Unit or die intraoperatively). Procedures that only require a 
sedative level of anesthesia will be excluded from the analysis. 
 
 
Figure 1. Screenshot example of email recommendations. 

 
Figure 2. A screenshot mockup of the real-time decision support within our perioperative information 
management system. 
 
Table 1 - Apfel's simplified risk score 
Factor 
Risk Score 
Gender – Female 
1 
History of PONV/Motion sickness 
1 
Expected postoperative use of opioids 
1 
Non-smoker 
1 
 
 
Table 2 - Relation between predicted risks and recommendations 
Number of 
PONV risk factors 
Predicted 
PONV risk 
Recommended 
number of interventions 
0 
0% 
0 
1 
20% 
1 
2 
40% 
2 
3 
60% 
3 
4 
80% 
4 
 

Outcome 
Primary outcome 
The adherence to PONV guidelines will be used as the primary outcome. This is defined as an exact 
match between the recommended and applied numbers of interventions. Either a lower or a higher 
number of interventions will be considered as noncompliant to the guidelines. For the primary analysis 
the overall change in adherence as well as the added values of the different decision support elements 
to that adherence will be quantified. Medications with antiemetic properties will be considered 
prophylactic administrations, if they occur before the documented anesthesia stop time. We will 
consider scopolamine, dexamethasone, ondansetron, haloperidol, droperidol as prophylactic 
medications. 
Secondary outcomes 
The occurrence of PONV within the patient’s PACU admission. PONV will be defined as the 
administration of rescue antiemetics. The list of rescue antiemetics is similar to the list of prophylactic 
interventions, excluding the pre-anesthesia interventions, but complemented by the use of 
promethazine and compazine. Other secondary process outcome variables are: the absolute number of 
prophylactic interventions applied and time to discharge readiness at the PACU.  
Data collection 
We plan to use the Perioperative Data Warehouse (PDW), an IRB-approved data repository, as the data 
source to study the effects of the decision support on the application of prophylactic interventions for 
PONV and the occurrence of PONV within the PACU. 
Data safety monitoring 
The principal investigator (PI) will be responsible for data safety and monitoring within the VUMC. 
Quality control will include regular data verification and protocol compliance checks. Protocol 
adherence at the VUMC will be monitored by the PI throughout the study. Events determined by the PI 
to be unanticipated problems involving risks to subjects will be reported by the PI to the VUMC IRB 
within 10 working days of the Investigator's knowledge of the problem. 
Statistical analysis plan 
A two-sided alpha of 0.05 will be considered to be statistically significant. Continuous variables will be 
visually assessed for a normal distribution using histograms and QQ-plots. In descriptive statistics, 
parametric variables will be expressed as means with standard deviations, nonparametric variables will 
be expressed as medians with interquartile ranges, and discrete variables will be expressed as numbers 
with percentages. The primary analysis will be performed under the intention-to-treat principle. We will 
compare the incidence of the primary and secondary outcomes between the study groups utilizing 
analysis of variances to compare the means of multiple groups. 
 

References 
[1] 
Gan TJ, Diemunsch P, Habib AS, Kovac A, Kranke P, Meyer T a, et al. Consensus guidelines for the 
management of postoperative nausea and vomiting. Anesth Analg 2014;118:85–113. 
doi:10.1213/ANE.0000000000000002. 
[2] 
Apfel CC, Korttila K, Abdalla M, Kerger H, Turan A, Vedder I, et al. A factorial trial of six 
interventions for the prevention of postoperative nausea and vomiting. N Engl J Med 
2004;350:2441–51. doi:10.1056/NEJMoa032196. 
[3] 
Carlisle JB, Stevenson CA. Drugs for preventing postoperative nausea and vomiting. Cochrane 
Database Syst Rev 2006;3:CD004125. doi:10.1002/14651858.CD004125.pub2. 
[4] 
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and vomiting: a cluster-randomized trial. Anesthesiology 2014;120:343–54. 
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[5] 
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[6] 
Kooij FO, Klok T, Hollmann MW, Kal JE. Automated reminders increase adherence to guidelines 
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[7] 
Kooij FO, Vos N, Siebenga P, Klok T, Hollmann MW, Kal JE. Automated reminders decrease 
postoperative nausea and vomiting incidence in a general surgical population. Br J Anaesth 
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[8] 
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adding therapeutic recommendations to risk assessments from a prediction model for 
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[9] 
Lobach D, Sanders GD, Bright TJ, Wong A, Dhurjati R, Bristow E, et al. Enabling health care 
decisionmaking through clinical decision support and knowledge management. Evid Rep Technol 
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[10] 
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