Improving the quality of post-anesthesia care: An evidence based initiative to decrease the incidence of postoperative nausea and vomiting in the post-anesthesia care unit

Author’s Accepted Manuscript Improving the quality of post-anesthesia care: An evidence based initiative to decrease the incidence of postoperative nausea and vomiting in the postanesthesia care unit Christopher A. Smith, Richard E. Haas, John C. Zepp, Michael Klein www.elsevier.com/locate/jcomm

PII: DOI: Reference:

S2405-6030(15)30036-4 http://dx.doi.org/10.1016/j.pcorm.2016.08.003 PCORM23

To appear in: Perioperative Care and Operating Room Management Received date: 2 September 2015 Revised date: 6 July 2016 Accepted date: 23 August 2016 Cite this article as: Christopher A. Smith, Richard E. Haas, John C. Zepp and Michael Klein, Improving the quality of post-anesthesia care: An evidence based initiative to decrease the incidence of postoperative nausea and vomiting in the post-anesthesia care unit, Perioperative Care and Operating Room Management, http://dx.doi.org/10.1016/j.pcorm.2016.08.003 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Improving the quality of post-anesthesia care: An evidence based initiative to decrease the incidence of postoperative nausea and vomiting in the post-anesthesia care unit Christopher A. Smith, DNP, CRNAa,e*1, Richard E. Haas, CRNA, PhD, PHRNb,e2, John C. Zepp, MDAc,e3, Michael Klein, MDAd,e4 a

Anesthesia Associates of York, and Department of Anesthesia, WellSpan Health, York Hospital, York, PA 17405, USA b

York College of Pennsylvania / WellSpan Health Nurse Anesthetist Program York, PA 17403, USA and Department of Anesthesia, WellSpan Health, York Hospital, York, PA 17405, USA c

Anesthesia Associates of York and Department of Anesthesia, WellSpan Health, York Hospital, York, PA 17405, USA d

Department of Anesthesia, WellSpan Health, York Hospital, York, PA 17405, USA

e

York College of Pennsylvania / WellSpan Health Nurse Anesthetist Program, Department of Anesthesia, WellSpan Health, York Hospital, York, PA 17403, USA, Anesthesia Associates of York [email protected] [email protected] [email protected]

1

Contribution: Author helped design the study, conduct the study, data collection, data analysis, and manuscript preparation. Attestation: Author attests to the integrity of the original data and the analysis reported in this manuscript. Author approved the final manuscript and is the archival author. Conflicts of Interest: None 2

Contribution: Author helped design the study, conduct the study, data analysis, and manuscript preparation. Attestation: Author attests to the integrity of the original data and the analysis reported in this manuscript and Author approved the final manuscript. Conflicts of Interest: None 3

Contribution: Author helped design the study, collect data and preparation of the manuscript.

Attestation: Author approved the final manuscript. Conflicts of Interest: None 4

Contribution: Author helped design the study, collect data and preparation of the manuscript.

Attestation: Author approved the final manuscript. Conflicts of Interest: None

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[email protected] *

Corresponding Author: Christopher A. Smith, Department of Anesthesia, WellSpan Health,

York Hospital, 1001 S. George Street, York, PA 17405. 717- 851-2415; 717- 8515250

Abstract Background Postoperative nausea and vomiting persists as a common experience for surgical patients, despite the pharmacological advances made over the past 150 years. Its presence decreases patient satisfaction, while increasing costs and complications. Evidence in the literature suggests preoperative risk screening and targeted prophylaxis decreases institutional incidence. Methods This project was a quality improvement, pre- and post-intervention design consisting of elective surgery patients, made up of a historical comparison group (N=3,768; random sample n=95) and an implementation group (N=1,139; random matched sample n=109). The implementation group underwent preoperative risk screening for postoperative nausea and vomiting utilizing the Apfel simplified risk scoring method and targeted prophylaxis based upon the identified risk score. The protocol was as follows: low risk (0 – 1 risk factors) received at least one antiemetic intervention; moderate risk (2 risk factors) received at least two antiemetic interventions; and high risk (3 – 4 risk factors) received at least three antiemetic interventions. Measurements consist of the Apfel simplified risk score, incidence of postoperative nausea and vomiting in the postoperative care unit and compliance to protocol. Descriptive statistics were used for

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demographic data, Z-score and Chi-square (χ2) statistics were utilized. Correlations were calculated for compliance to the protocol and incidence of PONV. Results The historical sample’s incidence of PONV was 35.8% (n=34) whereas the implementation sample’s incidence was 11.9% (n=13) (p = 0.000). Correlations were strong in both samples; the historical sample demonstrated less compliance as PONV risk increased (r=0.95) and the implementation sample demonstrated greater compliance with increased PONV risk (r=0.72). The overall compliance with the targeted prophylaxis protocol in the implementation sample was calculated to be 85.3%, whereas the historical sample was 36.9% (p=0). Conclusions Preoperative identification of postoperative nausea and vomiting risk, risk stratification, and compliant use of targeted prophylactic anti-emetic interventions reduce the incidence of postoperative nausea and vomiting in the post-anesthesia care unit. Use of proxy metrics are less reliable compared to direct measures.

Introduction Postoperative nausea and vomiting (PONV) has not been eradicated from the surgical population and persists as a daily problem. Current published incidences of PONV range from 10 to 75.9%.1-5 It is agreed that there are independent risk factors associated with PONV.1,6-9 Many predictive models have been presented in the literature.9-14 Antiemetic protocols have ranged from a single agent regimen through dual agent to multimodal prophylaxis.14-18 The Society for Ambulatory Anesthesia (SAMBA) published their most recent recommendations in 2014.17 The

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essence of these recommendations is comprised of two key interventions: risk identification and targeted (risk adaptive) antiemetic prophylaxis.19 Implementations of antiemetic prophylactic recommendations have not reached the desired goal of eliminating PONV, not due to ineffective protocols but as a result of reported poor compliance rates.2,3,18,20-22 Efficacy of antiemetic protocols cannot be compared until high compliance rates have been achieved. Kooij et al. (2010) describe compliance rates as low as 39% in high risk patients but were subsequently able to increase overall compliance to 79% using decision support software whereas Brampton et al. (2013) presented monthly compliance rates for seven years and reported 67% as their highest rate of compliance.18,20 The primary aim of this evidence based project was to decrease the incidence of PONV in the post-anesthesia care unit (PACU) through the implementation of preoperative risk screening and targeted prophylaxis. The secondary aim was to determine the compliance rate associated with protocol recommendations. Methods Project Design This project was a quality improvement, pre- and post-intervention design consisting of elective surgery patients (N=4907), made up of a historical comparison group (n=3,768) and an implementation group (n=1,139). Approval to carry out the project was obtained prior to the start of the EBP initiative through both the IRB at York College of Pennsylvania, York, PA and WellSpan York Hospital, York, PA (IRB Net ID 646065-1). The need to obtain written informed consent was waived by the IRB. A historical comparison group of consecutive surgical patients admitted to the PACU between September and December, 2013 (n=3,768) was identified. The incidence of PONV was

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determined through a retrospective query of electronic health records using a proxy metric consisting of the administration of antiemetic medications during the PACU stay. The assumption follows that the incidence of PONV equates to the administration of one or more antiemetics in the immediate postoperative period. This approach was chosen to estimate the current state of PONV within the PACU through the identification of a historical snap-shot incidence utilizing the electronic medical record system. These data informed the decision to allocate assets to proceed with an evidence based practice project with the aim of decreasing and minimizing the incidence of PONV in our surgical population. The implementation group consisted of a prospective, convenience sample of consecutive, adult, elective surgery patients admitted through the Short Stay Surgical Unit (SSU) with a stay in the PACU between September 15 and November 4, 2014 (n=1,139). Data sheets were completed prospectively during the day of surgery. Incidences of nausea were obtained through direct patient inquiry by the PACU nurse during care. An affirmative response to the question, “do you feel nauseous?” was annotated as a positive finding. Vomiting and number of episodes were annotated after direct observation of vomiting or retching. Determination of risk stratification in the historical group could only be accomplished through manual data abstraction of numerous electronic medical record fields. Due to the large data set and limited extraction assets, a random sampling technique was chosen. The reduction in patient charts was accomplished, after a-priori power analysis, with a random sampling of the historical and prospective groups. Random numbers were assigned to the historical group (N=3,768) from 0 to 9. The random number generator produced ‘1’ thereby retaining approximately ten percent (n=361). This sample was further reduced by assigning random numbers 1 to 3. The random number

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generator produced ‘2’ thereby retaining a sample of surgical patients (n=115). Upon further examination of the sample, only patients undergoing general anesthesia (GA) were retained (n=95). Data were abstracted utilizing the same format as for the prospective group. The prospective group was sampled using a matched random technique. All patients undergoing GA were separated into male and female groups and assigned random numbers 0 to 9. The random number generator produced ‘7’ thereby retaining nearly ten percent (n=109). Patient Risk Assessment The entire prospective group (N=1,139) were PONV risk assessed using the Apfel simplified risk score (SRS) upon admission to the SSU. The SRS was annotated upon the data collection form and placed on the anesthesia clipboard with the anesthesia consent form. This step in the process allowed for the immediate communication of the patient’s SRS to the anesthesia provider prior to the preoperative interview, obtaining consent and determining the anesthetic plan. Changes in anesthetic technique and antiemetic prophylaxis could be initiated during this step in the process. The historical sample (n=95) were retrospectively PONV risk assessed and demographic data were collected for comparison using the same data collection form. Targeted Prophylaxis Based upon the derived SRS the protocol was implemented as follows: low risk patients (0 – 1 risk factor) received at least one antiemetic intervention; those with moderate risk (2 risk factors) received at least two antiemetic interventions; and those at high risk (3 – 4 risk factors) received at least three antiemetic interventions. This protocol differs from the SAMBA17 recommendations in three ways: 1) by requiring prophylaxis in low risk patients, 2) by requiring a minimum of two antiemetics in moderate risk patients, and 3) by requiring a minimum of three

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antiemetics in high risk patients. These alterations were made in an attempt to maximize compliance to the protocol. Common practice was to administer a prophylactic antiemetic to patients undergoing general anesthesia therefore we built upon this institutional norm rather than reconstructing it. Many anesthesia providers in the department questioned whether a twenty percent risk of PONV in the low risk (one risk factor) population was acceptable; therefore, it was decided to prophylactically treat all low risk patients with at least one antiemetic. This approach also simplified the protocol by requiring action at every risk level. See Table 1 for antiemetic interventions included in project. Statistical Analysis Statistical analyses were conducted using the statistical software IBM SPSS 19.0.1 (IBM, Armonk, New York). Descriptive statistics were conducted to describe the characteristics of each group. Group comparisons were performed utilizing Z-score and chi-square (χ2) analysis. To determine an effect size of twenty percent between the two groups using a power analysis of 0.8 and an alpha of 0.05, each group needed to consist of no less than 92 patients. The historical sample consisted of 95 patients and the implementation sample consisted of 109 patients. Results The original EBP implementation project identified that 144 of the 1139 prospective patients experienced PONV. Therefore the implementation group realized an incidence of PONV of 12.6%. This rate was significantly (χ2= 414.5, p<0.00001) lower than the historical group incidence of PONV (n=2023 of 3768, 53.7%). Thirty four of the historical sample (HS) experienced PONV (35.8%) whereas in the matched post-implementation sample (IS) only thirteen patients experienced PONV (11.9%) (χ2= 16.3, p=0.000054). When comparing the HS to the historical group from which they were drawn,

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an 18% disparity between the historical sample incidence of PONV as compared to the historical group (χ2= 11.9, p=0.000554) was noted. Demographic comparison of sample characteristics can be seen on Table 2. Males (HS) had an incidence of 20.8% but males (IS) had an incidence of 6.1% (χ2= 4.52, p=0.03). Females (HS) experienced PONV in 51.1% of cases while females (IS) experienced PONV 16.7% of cases (χ2= 14.4, p=0.00014). Compliance to antiemetic protocols was identified as a major barrier to successful implementation. Protocol compliance rates were calculated by adding the number of instances when the patient received at least the minimum number of interventions as suggested by the protocol and dividing by the total number of patients. Compliance (HS) was calculated to be 36.9% and compliance (IS) was 85.3% (χ2= 51, p=0). Discussion The primary goals of this evidence based practice project were to implement a successful PONV risk screening tool and a targeted prophylaxis process to reduce the incidence of PONV in the post-anesthesia care. These goals were achieved. Significant reductions in PONV were realized in both genders. Due to these achievements, the Apfel SRS has been incorporate into the electronic pre-surgical assessment process. The targeted prophylaxis protocol has been incorporated into the pre-surgical ‘handoff process” and the electronic anesthesia record. Random sampling from both the historical and implementation groups was completed to demonstrate validity between the historical proxy metric and the direct measure performed in the implementation group. Although there remained a statistically significant difference between the historical sample and the implementation sample, the difference between the historical group and

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the historical sample was also statistically significant thereby discrediting the use of the proxy metric in identifying an accurate incidence of PONV in the PACU. The implemented protocol incorporated the findings of numerous authors15-17,23 with noted confirmation of the findings of Kappen et al. (2014) which demonstrated that anesthesia providers administered antiemetics more often when the patients’ risk for PONV were provided.21 Findings also validated the recommendations of Apfel et al. (2012) to utilize SRS to identify patients at risk for experiencing PONV to reduce institutional incidence of PONV.1 Pierre et al. (2004) identified the efficacy of a prophylactic strategy based upon SRS by reducing the institutional incidence of PONV from 49.5% to 14.3%.15 Mayeur et al. (2012) utilized the concept of preoperative PONV risk assessment (Apfel simplified risk score) and targeted prophylaxis (avoiding inhaled gases and administration of antiemetics) to reduce the incidence of PONV from 42.3% to 12.1%.24 Kappen et al. (2015) used computer based risk assessment and a recommended antiemetic prophylaxis protocol to reduce the incidence of PONV from 50% to 42%.2 The reduction, in this project, from an incidence of 35.8% in the historical sample to an incidence of 11.9% resembles the results of Mayer and Pierre. The findings of this project validate these authors’ findings and recommendations. Biedler et al. (2004) recommended at least two antiemetic interventions for the high risk (defined as two or more risk factors from the Apfel simplified risk score) surgical population.16 This approach reduced the incidence of PONV from 47% to 36%. The difference between the interventional group results in the Biedler16 study and this project can be explained by the expected risk reduction (see Table 4) in high risk patients (three or four risk factors) receiving two antiemetics (RRR 26% per intervention) which would result in an expected PONV incidence of 33% (three risk factors) or 44% (four risk factors). Whereas this project utilized at least three

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antiemetics prophylactically for the high risk group with an expected PONV incidence 24% or 32% for three interventions and 18% or 24% with four interventions.25 Reported compliance with antiemetic protocols varies among studies. Kooij et al. (2010) evaluated the utility of an automated decision support system (DDS) to increase compliance.18 Initial identification of protocol compliance was 39%. During DSS implementation, a compliance rate of 79% was achieved and a rapid decline to 41% was seen with the withdrawal of DSS. Brampton et al. (2013) described monthly audits for compliance to their institutional PONV prophylaxis guidelines over a seven year period and identified that 67% was the highest compliance rate achieved.20 Kappen et al. (2015) demonstrated an increase in compliance to their protocol from 20% to 66% after implementation of electronic recommendations.2 Overall compliance to the protocol in the implementation sample was identified as 85.3%. In light of the aforementioned compliance rates, a compliance rate of 85.3% appears exceptional. Explanations for the disparity between the compliance rates of the published studies and this EBP implementation project may be rooted in the process. When research is being conducted, it is the research team that is most interested in the protocol and study results. This EBP implementation project was conducted as a process change incorporating many stakeholders, from preoperative, intraoperative and postoperative areas. Individual, personal investment from these areas contributed to greater interest and engagement. The active communication of the patient’s PONV risk during preoperative hand-off (occurs at patient bedside with family members, nursing and anesthesia provider) may have had an effect on the anesthesia provider’s likelihood of administering the minimum number of recommended antiemetics. Limitations

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The most notable limitation was that of the quality improvement, before and after design. The before (historical sample) data was hand extracted retrospectively which limited: events that were not annotated in the electronic nursing notes; and inaccurate risk assessment, since history of PONV and motion sickness were rarely identified. Therefore the incidence of nausea in the historical sample represents a best interpretation of the patient records. Incidence of PONV in both the historical sample and the implementation group/sample: were monitored only in the PACU for in-patients; and monitored through discharge for out-patients (direct measure). Therefore, incidence of PONV was limited to between one and six hours after surgery. Habib et al. (2006) identified that 34% of patient who did not experience PONV in the PACU, experienced it later.26 Due to the absence of motion sickness history documentation in the historical sample, data were unavailable to accurately determine the Apfel simplified risk in patients without prior anesthesia exposure whom had previous motion sickness. These patients would obtain a lower SRS and be risk stratified with lower than actual risk which may have led to a falsely elevated compliance in the historical patients. Antiemetic medications available to the anesthesia provider during the project were limited to those in the project facility’s formulary and those medications present in the anesthesia medication cart. Conclusions Preoperative identification of PONV risk, risk stratification and targeted prophylactic anti-emetic interventions reduce the incidence of PONV in the PACU. Implementation of this EBP initiative had unintentional consequences and uncovered system issues that required time and resources that were not accounted for from the outset.

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Using the proxy metric (administration of antiemetics in the PACU) does not accurately estimate the incidence of PONV as compared to the direct measure of the PACU nurse inquiring face-to-face with the surgical patient. The Apfel simplified risk screening tool was easily adopted by the nursing and anesthesia staffs. Stratification of PONV risk into low, moderate and high risk with associated minimum recommendation number of antiemetic interventions (low - 1, moderate - 2 & high - 3) simplified the relationship between risk and minimum interventions to be administered and may have contributed to the substantial compliance rate. Incorporation of the simplified risk scoring and targeted prophylaxis into the EMR is a crucial step to weave these two concepts into the cultural fabric of the project facility. The data collected during this project demonstrate that the process was efficacious in the adult surgical population.

Funding This work was funded by: Department of Anesthesia, WellSpan Health, York Hospital 1001 S. George Street, York, PA 17405

IRB Institutional review board (IRB) exempt approval was obtained prior to the start of the EBP initiative, through the IRB at York College of Pennsylvania, York, PA and WellSpan Health,

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York Hospital, York, PA (IRB Net ID 646065-1). Written informed consent was waived by the IRB.

References 1.

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7. 8.

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10. 11.

12.

Apfel CC, Heidrich FM, Jukar-Rao S, et al. Evidence-based analysis of risk factors for postoperative nausea and vomiting. BJA: The British Journal of Anaesthesia. 2012;109(5):742-753. Kappen TH, Vergouwe Y, van Wolfswinkel L, Kalkman CJ, Moons KGM, van Klei WA. Impact of adding therapeutic recommendations to risk assessments from a prediction model for postoperative nausea and vomiting. British Journal Of Anaesthesia. 2015;114(2):252-260. Kapoor R, Hola ET, Adamson RT, Mathis AS. Comparison of two instruments for assessing risk of postoperative nausea and vomiting. Am J Health Syst Pharm. 2008;65(5):448-453. Manahan MA, Basdag B, Kalmar CL, et al. Risk of severe and refractory postoperative nausea and vomiting in patients undergoing diep flap breast reconstruction. Microsurgery. 2014;34(2):112-121. Rodseth RN, Gopalan PD, Cassimjee HM, Goga S. Reduced incidence of postoperative nausea and vomiting in black South Africans and its utility for a modified risk scoring system. Anesthesia And Analgesia. 2010;110(6):1591-1594. Eberhart LHJ, Morin AM, Guber D, et al. Applicability of risk scores for postoperative nausea and vomiting in adults to paediatric patients. BJA: The British Journal of Anaesthesia. 2004;93(3):386-392. Choi DH, Ko JS, Ahn HJ, Kim JA. A korean predictive model for postoperative nausea and vomiting. Journal Of Korean Medical Science. 2005;20(5):811-815. Morino R, Ozaki M, Nagata O, Yokota M. Incidence of and risk factors for postoperative nausea and vomiting at a Japanese Cancer Center: first large-scale study in Japan. Journal Of Anesthesia. 2013;27(1):18-24. Sarin P, Urman RD, Ohno-Machado L. An improved model for predicting postoperative nausea and vomiting in ambulatory surgery patients using physician-modifiable risk factors. Journal Of The American Medical Informatics Association: JAMIA. 2012;19(6):995-1002. Koivuranta M, Läärä E, Snåre L, Alahuhta S. A survey of postoperative nausea and vomiting. Anaesthesia. 1997;52(5):443-449. Apfel CC, Läärä E, Koivuranta M, Greim CA, Roewer N. A simplified risk score for predicting postoperative nausea and vomiting: conclusions from cross-validations between two centers. Anesthesiology. 1999;91(3):693-700. Sinclair DR, Chung F, Mezei G. Can postoperative nausea and vomiting be predicted? Anesthesiology. 1999;91(1):109-118.

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Kappen TH, Vergouwe Y, van Klei WA, van Wolfswinkel L, Kalkman CJ, Moons KGM. Adaptation of Clinical Prediction Models for Application in Local Settings. Medical Decision Making. 2012;32(3):E1-e10. Conway B. Prevention and management of postoperative nausea and vomiting in adults. AORN Journal. 2009;90(3):391. Pierre S, Corno G, Benais H, Apfel CC. A risk score-dependent antiemetic approach effectively reduces postoperative nausea and vomiting--a continuous quality improvement initiative. Canadian Journal Of Anaesthesia = Journal Canadien D'anesthésie. 2004;51(4):320-325. Biedler A, Wermelt J, Kunitz O, et al. A risk adapted approach reduces the overall institutional incidence of postoperative nausea and vomiting. Canadian Journal Of Anaesthesia = Journal Canadien D'anesthésie. 2004;51(1):13-19. Gan TJ, Diemunsch P, Habib AS, et al. Consensus guidelines for the management of postoperative nausea and vomiting. Anesthesia And Analgesia. 2014;118(1):85-113. Kooij FO, Klok T, Hollmann MW, Kal JE. Automated reminders increase adherence to guidelines for administration of prophylaxis for postoperative nausea and vomiting. European Journal Of Anaesthesiology. 2010;27(2):187-191. Smith CA, Ruth-Sahd L. Reducing the Incidence of Postoperative Nausea and Vomiting Begins With Risk Screening: An Evaluation of the Evidence. Journal of PeriAnesthesia Nursing. 2016;31(2):158-171 114p. Brampton W, Dryburgh IR, Wynn-Hebden A, Kumar A. Simplified measures of postoperative nausea and vomiting do not transfer to other populations. British Journal Of Anaesthesia. 2013;111(4):677-678. Kappen TH, Moons KGM, van Wolfswinkel L, Kalkman CJ, Vergouwe Y, van Klei WA. Impact of Risk Assessments on Prophylactic Antiemetic Prescription and the Incidence of Postoperative Nausea and Vomiting: A Cluster-randomized Trial. Anesthesiology. 2014;120(2):343-354. Kumar A, Brampton W, Watson S, Reid VL, Neilly D. Postoperative nausea and vomiting: simple risk scoring does work. European Journal Of Anaesthesiology. 2012;29(1):57-59. Sigaut S, Merckx P, Peuch C, Necib S, Pingeon F, Mantz J. Does an educational strategy based on systematic preoperative assessment of simplified Apfel's score decrease postoperative nausea and vomiting? Annales Françaises D'anesthèsie Et De Rèanimation. 2010;29(11):765-769. Mayeur C, Robin E, Kipnis E, et al. Impact of a prophylactic strategy on the incidence of nausea and vomiting after general surgery. Annales Françaises D'anesthèsie Et De Rèanimation. 2012;31(2):e53-e57. Apfel CC, Korttila K, Abdalla M, et al. A factorial trial of six interventions for the prevention of postoperative nausea and vomiting. New England Journal of Medicine. 2004;350(24):2441. Habib AS, Chen Y, Taguchi A, Hu XH, Gan TJ. Postoperative nausea and vomiting following inpatient surgeries in a teaching hospital: a retrospective database analysis. Current Medical Research & Opinion. 2006;22(6):1093-1099.

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Table 1 Antiemetic medications available to the anesthesia provider during the implementation of the project. Limited list due to formulary restrictions and medications stocked within the anesthesia cart. Total Intravenous Anesthesia

Ephedrine 0.5mgs/kg IM

(Propofol TIVA) Scopolamine patch 1.5mgs

Dexamethasone 4-8mgs

Ondansetron 4mgs

Metoclopramide 10mgs

Haloperidol 1-2mg

Diphenhydramine 12.5-50mgs

Note: Dimenhydrinate unavailable, alternative antihistamine included due to the similar central and peripheral H1 receptor antagonism.

Table 2. Demographic background for the historical (n=95) and interventional samples (n=109). Historical Sample n

%

95

100

0

0

1

Interventional Sample n

%

CFD

Z-score

p

-

109

100

-

-

-

0

0

0

0

0

-

-

18

19.0

19.0

18

19.0

19.0

0.455

0.325

2

43

45.3

64.3

46

42.2

61.2

0.440

0.330

3

31

32.6

96.9

30

27.5

88.7

0.795

0.213

4

3

3.1

100

15

11.3

100

-2.663

0.996

95

100

-

109

100

-

-

-

General

35

36.8

36.8

36

33

33

0.571

0.284

Gynecologic

5

5.3

42.1

15

13.8

46.8

-2.036

0.979

Orthopedic

14

14.7

56.8

7

6.4

53.2

1.949

0.026*

Genitourinary

4

4.2

61

16

14.7

67.9

-2.508

0.994

Ears-Nose-Throat

4

4.2

65.2

7

6.4

74.3

-0.698

0.757

Plastics

3

3.2

68.4

3

2.8

77.1

0.171

0.432

Apfel Simplified Risk Score

Surgical Service

CFD

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Vascular

9

9.5

77.9

8

7.3

84.4

0.550

0.291

Thoracic

5

5.3

83.2

7

6.4

90.8

-0.351

0.637

Neurosurgical

16

16.8

100

10

9.2

100

1.638

0.051

Note: CFD = cumulative frequency distribution; * indicates significance p<0.05

Table 3 A depiction of the incidence of PONV in both groups represented in number of patients, percent incidence and odds ratios (OR) with corresponding 95% confidence intervals (CI). Group

n

+ PONV

%

Historical Group

3768

2023

53.7

Implementation Group

1139

144

12.6

Historical Sample

95

34

35.8

Implementation Sample

109

13

11.9

OR (95% CI)

0.13 (0.10 to 0.16)

0.44 (0.22 to 0.880)

Note: n = number of patients; +PONV = number of patients experiencing postoperative nausea and vomiting.

Table 4 Expected percent incidence of PONV by Apfel Simplified Risk Score without prophylaxis, number of prophylactic interventions with corresponding expected reductions in PONV incidence based upon a relative risk reduction of 26% per intervention. Adapted from Apfel et al. 2004.25 Number of Interventions Apfel Score

% Risk

One

Two

Three

Four

0

10

7

5

4

3

1

20

15

11

8

6

2

40

29

22

16

12

3

60

44

33

24

18

4

80

59

44

32

24

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