Postoperative nausea and vomiting and associated factors among adult elective surgical patients at Jimma Medical Center, South West Ethiopia

doi:10.21203/rs.3.rs-1596261/v1

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Postoperative nausea and vomiting and associated factors among adult elective surgical patients at Jimma Medical Center, South West Ethiopia

https://doi.org/10.21203/rs.3.rs-1596261/v1

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Background

Postoperative nausea and vomiting (PONV) is the most frequent side effect of anesthesia. It affects 20–30% of all post-operative and 70%-80% of higher-risk patients. Consequently, it is one of the most frequently observed adverse events associated with the provision of anesthesia. Thus, this study is aimed to assess the incidence and associated factors of postoperative nausea and vomiting.

Methods

This cross-sectional study was conducted using a consecutive sampling method. Regular supervision and follow-up were made. Data was entered into Epi info version 7 software and transported to SPSS version 20 for analysis. The odd ratio and 95% confidence interval were computed. The findings of the study were reported using tables, figures, and narration. Variables that were found to be a candidate (p-value < 0.25) on binary logistic regression were entered into a multiple logistic regression analysis to identify independent predictors of postoperative nausea and vomiting.

Results

The results of this study indicated that the incidence of postoperative nausea and vomiting was 27.4%. Output of multiple logistic regression revealed that female sex (AOR = 4.065 (2.090–7.906), history of motion sickness (AOR = 2.836 (1.582–5.083), Gynecologic type of surgery (AOR = 3.782 (1.156–12.373), long duration of anesthesia (> 60 min) (AOR = 2.974 (1.491–5.933) and administration of post-operative opioids (AOR = 2.333 (1.221–4.457) were considered as independent predictors of postoperative nausea and vomiting at P-value < 0.05.

Conclusion

The present finding has shown that the overall incidence of postoperative nausea and vomiting was relatively high. Therefore, prevention of PONV was strongly recommended.

Magnitude

post-operative nausea

vomiting

Jimma University Medical Center

Worldwide, more than 310 million major surgical procedures are performed each year.^{(1, 2)} Consequently, surgery and anesthesia exposure have their own risks which can occur preoperatively, intraoperatively and postoperatively.⁽³⁾ While anesthesia related mortality and morbidity have fallen dramatically in recent decades ⁽⁴⁾, postoperative nausea and vomiting (PONV) remains one of the most frequent and distressing complications following anesthetic and surgical procedures.^(5–7) It affects 20–30% of all postoperative surgical and 70%-80% higher risk patients.^{(8, 9)}

Nausea is a subjective sensation of the urge to vomit, whereas vomiting as a clinical symptom is a forceful, dynamic expulsion of gastric contents through the mouth or nose. Dry-retching is a vomit-like feeling with contractions of the stomach without vomiting or substance.⁽⁹⁾ Postoperative nausea and vomiting (PONV) is a clinical manifestation that exists when a total score of nausea, dry retching and vomiting are greater than or equal to fifty depending on a linear postoperative nausea and vomiting intensity scale.^{(10, 11)} These activities are associated with increased heart rate, breathing and sweating.⁽¹²⁾ Although, it can occur in postoperative period during any time, the prevalence is large within 24 hrs postoperatively.⁽¹³⁾

It started as an emesis from an emetic center, triggered by multiple factors which can be summarized as a patient, anesthesia, and surgical factors. Patient background factors such as patients’ age, sex, history of PONV and motion sickness, full stomach and anxiety are important predictors of PONV. Anesthesia and surgery related factors such as use of volatile agents, type and duration of anesthesia, maintenance and reversal agents, and perioperative use of opioids are important predictors.^{(7, 14)}

Because PONV has a multifactorial etiology, strategies to prevent and treat are also multifactorial. They fall into: risk-reduction strategies, pharmacological interventions and non-pharmacological interventions.^{(13, 15)} Firstly, the risk reduction strategies, management of PONV should involve preventing maneuvers that provoke PONV and universal multimodal pharmacologic prophylaxis.⁽¹⁶⁾ Secondly, pharmacological interventions such as, H1 and H3 antagonists, Steroids and Phenothiazine are advocated as treatment.^{(13, 15)} Lastly, acupuncture and acupressure has been postulated as non-pharmacological therapies prophylactic measure to prevent or reduce PONV.⁽¹⁷⁾

Although the available antiemetic drugs used for prevention and management of PONV have been proven safe in clinical trials, no agent is without its side effects. Some of the most commonly described side effects of these prophylaxes are headaches, hypotension, sedation, insomnia and QT interval prolongation. As a result, it has been advocated that antiemetic prophylactic regimens should be tailored to the patients most likely to experience PONV.^{(6, 18)}

Because of this, an identification of independent PONV risk factors would make it easier to select specific patients for effective antiemetic therapy.⁽¹⁹⁾

A cross-sectional study was conducted from August 1, 2019, to September 30, 2019. The Source of population consisted of all adult elective surgical patients who were operated under anesthesia. The study population included all adult elective surgical patients who were operated under anesthesia during the study period. Inclusion criteria were: adult elective patients who were operated under general, spinal or combined anesthesia and those who were willing to give informed written consent. On the other hand, operations that were performed in the outpatient department, patients who were unconscious postoperatively, patients who were re-operated, patients with medical and surgical conditions that could lead to nausea and vomiting, patients discharged before 24 hours postoperatively were excluded. The formula for calculating sample size (estimation of population proportion) is:

n = \(\frac{{\left(Z\alpha /2\right)}^{2}\times p\left(1-p\right)}{{d}^{2}}\) = \(\frac{{\left(1.96\right)}^{2} \times 0.35\left(1-0.35\right)}{{0.05}^{2}}\) = 350. Where, n = sample size = 350, C.I = 95%, a = 0.05, D = Precision = 0.05, P = 35% (prevalence of PONV from previous study at Gondar University Specialized Hospital), Non-respondent rate (10%) = 35, Therefore, total sample size = 385. The consecutive sampling technique was used. The study was conducted by interviewing patients using a structured questionnaire, which was prepared in English and then translated into Amharic and Afan Oromo. The checklist was also used to obtain the patient’s information from the chart. Four data collectors were selected, and one day training was given on how to complete data collection, and supervised by the investigator during data collection. Patients were asked by data collectors to self-report on sociodemographic characteristics (sex, age, BMI) and patient related associated factors of PONV such as: previous history of motion sickness and migraine headache, smoking history and pre-operative anxiety. Other secondary data of intraoperative and post-operative factors such as, type of anesthesia, use of volatile anesthetics, intraoperative and post-operative use of opioids, duration and type of surgery, incidence of hypotension and use of reversal were obtained from the anesthetic record sheet, patients’ chart, and operation registration books.

In this study, a Linear PONV Intensity Scale was used to measure the incidence of PONV (Annex 1). It is believed to be a valid, reliable, and responsive measure of clinically important PONV. Accordingly, clinically important PONV was defined as a total score ≥ 50 at any time throughout the study period.⁽²⁰⁾ Count, distinct episodes are defined as several vomits or retching events occurring over a short time frame (5 min) should be counted as one vomiting/dry-retching episode; multiple episodes require distinct time periods without vomiting/dry-retching. Clinically important PONV is defined as a total score ≥ 50 at any time throughout the study period.⁽²⁰⁾

To assess preoperative anxiety, the Amsterdam Preoperative Anxiety and Information Scale (APAI) was used (Annex 2). The anxiety scale consists of four items (questions 1,2, 4, 5), each of which could be scored from 1 to 5. The Anxiety score is calculated as the sum of items 1, 2, 4 and 5 and the need for information score is the sum of items 3 and 6. The score above 11 for the Anxiety subscale is a sign that the patient is experiencing anxiety. ⁽²²⁾ The intensity of pain was evaluated using a verbal rating scale. It is a subjective measure in which individuals rate their pain as no pain; mild pain; moderate pain; and severe or intense pain as an average during the 24-hour assessment period. Assessment of pain was done by asking patients to rate their intensity of pain after surgery, by indicating which of the four words read aloud gave the best description of their pain. ⁽²¹⁾

Training was given for data collectors and supervisors. During data collection, regular supervisions and follow-ups were made by the researcher and cross-checked for completeness and consistency of collecting data on a daily basis. Once the data was collected and checked for completeness, consistency and accuracy, it was sorted and categorized. Then, the data entered into the computer using a prepared format. Data was summarized in the form of proportions and frequency tables for categorical variables. In the binary logistic regression analysis, Odds ratio together with the 95% confidence interval was calculated to test for the association between the possible predictors and outcome variables (PONV). A P-value of less than 0.25 was considered as potential candidates for the final model and entered into a multiple logistic regression to determine independent predictors of PONV. All variables that were found to be statistically significant (P-value < 0.05) on multiple logistic regression analysis were considered as independent predictors of PONV.

Description Of Sociodemographic And Preoperative Factors

Out of a total of 385 participants, 368 included in the analysis (17 patients excluded due to incomplete documents and refusal) with a response rate of 95%. Of the total patients included in the analysis, 55.7% were female and 44.3% were male. The ages ranged from 20 to 88 years. Most of the patients (44%) were aged between 20 and 30 years. The body mass index (BMI) was obtained by dividing weight (in kilograms) over the square of the height (in meters). Forty (10.9%) of the study patients were obese, with a BMI of over 30. (Table 1)

Table :1 Summary of description of pre-operative factors among surgical elective patients at JUMC, from August 1, 2019, to September 30, 2019

Variables		Frequency	Percentages
Sex of patient	Male	163	44.3
Sex of patient	Female	205	55.7
Age of patient	20–30	162	44
	31–40	129	35.1
	41–50	45	12.2
	> 50	32	8.7
BMI of the patient	< 20 (underweight)	44	12.0
	20–25 (normal weight)	224	60.9
	26–30 (overweight)	60	16.3
	> 30 (obese)	40	10.9

(BMI = Body mass index, PONV = Postoperative nausea and vomiting)

Perioperative Factors That Are Candidate For Final Mode

Output of logistic regression analysis identifies the following preoperative factors as a potential candidate for the final model (with p value 0.25): Female gender, age, previous history of PONV and migraine headache, BMI and non-administration of antiemetic prophylaxis (Table 2)

Table 2

Results of binary logistic regression analysis showing pre-operative factors that are candidate for final analysis model among surgical elective patients at JUMC, from August 1, 2019, to September 30, 2019
Variables		Incidence of PONV		COR (95% CI)	P-value
Variables		Present (%)	Absent (%)	COR (95% CI)	P-value
Sex	Male	18 (11.0)	145 (89.0)	1
Sex	Female	83 (40.5)	122 (59.5)	5.480 (3.119–9.629) *	0.000
Age	20–30	38 (23.5)	124 (76.5)	0.448 (0.203–0.990) *	0.047
	31–40	30 (23.3)	99 (76.7)	0.443 (0.196–1.001)	0.050
	41–50	20 (44.4)	25 (55.6)	1.169 (0.467–2.929)	0.739
	> 50	13 (40.6)	19 (59.4)	1
BMI	< 20	7 (15.9)	37 (84.1)	1
	20–25	59 (26.3)	165 (73.7)	1.890 (0.799–4.470)	0.147
	26–30	19 (31.7)	41 (68.3)	2.449 (0.925–6.487)	0.071
	> 30	16 (40.0)	24 (60.0)	3.524 (1.263–9.831) *	0.016
History of PONV	Yes	57 (42.9)	76 (57.1)	3.256 (2.025–5.234) *	0.000
History of PONV	No	44 (18.7)	191 (81.3)	1
Smoking history	Smoker	12 (32.4)	25 (67.8)	1
Smoking history	Non smoker	89 (26.9)	242 (73.1)	0.766 (0.369–1.590) *	0.024
Migraine headache	Yes	56 (31.8)	120 (68.2)	1.524 (0.962–2.416) *	0.073
Migraine headache	No	45 (23.4)	147 (76.6)	1
Antiemetic prophylaxis	Yes	12 (14.0)	74 (86.0)	1
Antiemetic prophylaxis	No	89 (31.6)	193 (68.4)	2.844 (1.470–5.500) *	0.002
Presence of anxiety	Yes	35 (34.0)	68 (66.0)	1.552 (0.947–2.543) *	0.810
Presence of anxiety	No	66 (24.9)	199 (75.1)	1
(PONV – Postoperative nausea and vomiting, 1 - Reference category, COR – Crude Odds Ratio, CI - Confidence Interval, * - significantly associated variables that are candidate for final analysis model at P value less than 0.25

Intraoperative factors that were potential candidate for final model

Intraoperative factors that were potential candidate for the final model (with p value < 0.25) after binary logistic regression analysis included type of anesthesia used, use of intraoperative opioids, type of surgery, duration of anesthesia, administration of neostigmine and incidence of intraoperative hypotension. Conversely, type of induction and maintenance agents were not candidates for the final model after binary logistic regression analysis (Table 3).

Table 3

Results of binary logistic regression analysis showing intraoperative factors that are candidate for final analysis model among surgical elective patients at JUMC, from August 1, 2019, to September 30, 2019
Variables		Incidence of PONV		COR (95% CI)	P- value
Variables		Present (%)	Absent (%)	COR (95% CI)	P- value
Types of anesthesia used	GA ETT	59 (35.5)	107 (64.5)	0.551 (0.240–1.267)	0.161
	GA sedation	6 (16.7)	30 (83.3)	0.200 (0.062–0.642) *	0.007
	SA	23 (16.4)	117 (83.6)	0.197 (0.081–0.478) *	0.000
	GA and SA	13 (50.0)	13 (50.0)	1
Type of induction agent for GA	Ketamine	38 (39.6)	58 (60.4)	1
	Thiopentone	14 (34.1)	27 (65.9)	0.791 (0.369–1.699)	0.618
	Propofol	20 (26.3)	56 (73.7)	0.545 (0.283–1.049)	0.952
	Combination	6 (33.3)	12 (66.7)	0.763 (0.264–2.207)	0.551
Maintenance agent	Inhal. agents	60 (33.0)	122 (67.0)	1.059 (0.512–2.191)	0.877
Maintenance agent	IV agents	13 (31.7)	28 (68.3)	1
Intraoperative opioids	Used	75 (34.6)	142 (65.4)	2.336 (1.404–3.888) *	0.001
Intraoperative opioids	Not used	26 (18.4)	115 (81.6)	1
Neostigmine	Used	53 (39.0)	83 (61.0)	2.554 (1.585–4.117) *	0.000
Neostigmine	Not used	44 (20.0)	176 (80.0)	1
Intraoperative hypotension	Present	18 (43.9)	23 (56.1)	2.301 (1.183–4.474) *	0.014
Intraoperative hypotension	Not present	83 (25.4)	244 (74.6)	1
Type of surgery performed	Thyroid	4 (20.0)	16 (80.0)	1.750 (0.384–7.969)	0.469
	Gynecologic	68(46.3)	79(53.7)	6.025 (2.012–8.041) *	0.001
	Abdominal	5 (20.8)	19 (79.2)	1.842 (0.437–7.760)	0.405
	Orthopedic	7 (8.3)	77 (91.7)	0.636 (0.173–2.341)	0.496
	Plastic	3 (13.0)	20 (87.0)	1.050 (0.211–5.217)	0.952
	Maxillofacial	3 (11.1)	24 (88.9)	0.875 (0.178–4.307)	0.870
	Urologic	4 (12.5)	28 (87.5)	1
Duration of anesthesia	≤ 60 min	20 (14.7)	116 (85.3)	1
Duration of anesthesia	> 60 min	81 (34.4)	151 (65.1)	3.111 (1.802–5.371) *	0.000
(PONV – Postoperative nausea and vomiting, 1 - Reference category, COR – Crude Odds Ratio, CI - Confidence Interval, GA – General anesthesia, SA – Spinal anesthesia, IV – intravenous anesthesia, ETT – Endotracheal intubation, inhal. – inhalational, * - significantly associated variables that are candidate for final analysis model at P value less than 0.25)

Post-operative factors that were potential candidate for final model

Postoperative factors such as, administration of post-operative opioid and severity of postoperative pain were identified as potential candidates for multiple logistic regression. However, the time of first oral intake was not a candidate for the final model. (Table 4)

Table 4

Results of binary logistic regression analysis showing post-operative factors that are candidate for final analysis model among surgical elective patients at JUMC, from August 1, 2019, to September 30, 2019
Variables		Incidence of PONV		COR (95% CI)	P-value
Variables		Present (%)	Absent (%)	COR (95% CI)	P-value
Timing of first oral intake	0–6	6 (27.3)	16 (72.7)	1
	7–12	8 (15.4)	44 (84.6)	0.485 (0.146–1.615)	0.288
	13–24	53 (27.6)	139 (72.4)	1.017 (0.378–2.737)	0.974
	> 24	34 (33.3)	68 (66.7)	1.333 (0.479–3.715)	0.583
Severity of postoperative pain	Less pain	32 (19.2)	135 (80.8)	1
Severity of postoperative pain	More pain	69 (34.3)	132 (65.7)	2.205 (1.361–3.575) *	0.001
Post-operative use of opioids	Used	77 (37.7)	127 (62.3)	3.537 (2.108–5.933 *
Post-operative use of opioids	Not used	24 (14.6)	140 (85.4)	1	0.000
(PONV – Postoperative nausea and vomiting, 1 - Reference category, COR – Crude Odds Ratio, CI - Confidence Interval, * - significantly associated variables that are candidate for final analysis model at P value less than 0.25)

5.5 Independent Predictors of PONV

Risk factors associated with PONV were analyzed further. All factors that turned out with a significant level (p value < 0.25) after binary logistic regression analysis were used to model for final analysis. Consequently, backward multiple logistic regression analysis was used to determine the association of pre-operative factors with the incidence of PONV.

Female patients were identified as the strongest pre-operative independent predictor with (AOR = 4.065, CI = 2.090 - 7.906). Whereas, previous history of PONV or motion sickness was also one of independent predictors of PONV with (AOR = 2.836, CI = 1.582 - 5.083). Among intraoperative factors, only duration of anesthesia greater than sixty minutes (AOR = 2.974, CI = 1.491 - 5.933) and Gynecologic type of surgery (AOR = 3.782, CI = 1.156 - 12.373) were identified as independent predictor. Whereas, only postoperative opioid administration (AOR = 2.333, CI = 1.221 - 4.457) was identified as independent factors among post-operative factors. (Table 5)

Table 5: Output of multiple logistic regression analysis model showing independent predictors of PONV among surgical elective patients at JUMC, from August 1, 2019, to September 30, 2019

Variables		Incidence of PONV		AOR (95% CI)	P value
Variables		Present (%)	Absent (%)	AOR (95% CI)	P value
Sex	Male	18 (11.0)	145 (89.0)	1
Sex	Female	83(40.5)	122 (59.5)	4.065 (2.090 - 7.906) *	0.000
History of PONV	Yes	57 (42.9)	76 (57.1)	2.836 (1.582 - 5.083) *	0.000
History of PONV	No	44 (18.7)	191 (81.3)	1
Duration of Anesthesia	≤ 60 min	20 (14.7)	116 (85.3)	1
Duration of Anesthesia	> 60 min	81 (34.4)	151 (65.1)	3.089 (1.467 – 6.505) *	0.002
Postoperative opioids	Used	77 (37.7)	127 (62.3)	2.333 (1.221 - 4.457) *	0.010
Postoperative opioids	Not used	24 (14.6)	140 (85.4)	1
Type of surgery	Thyroid	4 (20.0)	16 (80.0)	0.835 (0.161 - 4.323)
	Gynecology	68 (46.3)	79 (53.7)	3.782(1.156 - 12.373) *	0.028
	Abdominal	5 (20.8)	19 (79.2)	3.133 (0.632 - 15.531)
	Orthopedic	7 (8.3)	77 (91.7)	0 .460 (0.114 - 1.859)
	Plastic	3 (13.0)	20 (87.0)	1.677 (0.298 - 9.423)
	Maxillofacial	3 (11.1)	24 (88.9)	0 .688 (0.125 - 3.789)
	Urologic	4 (12.5)	28 (87.5)	1

(PONV – Postoperative nausea and vomiting, 1 - Reference category, AOR – Adjusted Odds Ratio, COR – Crude Odds Ratio CI - Confidence Interval, * - independent predictors of PONV at P value less than 0.05)

This study was designed to investigate the incidence of postoperative nausea and vomiting to ascertain the degree of discomfort after everyday surgery with modern anesthetic and surgical techniques. Our finding has shown that the incidence of PONV is 27.4% at JUMC, which indicates that the occurrence of PONV in this setup is high. Out of 368 study patients, 267(72.6%) were free of the complication. These findings are in line with studies done in South Africa which showed the incidence of PONV among African groups was 27%. ⁽²³⁾ This does not differ much from what studies done in Malawi which showed the incidence of PONV was 29%. ⁽²⁴⁾

This finding was low when compared with other studies done in Africa and Asia. Consequently, studies conducted in Nigeria ⁽²⁵⁾, Uganda ⁽²⁶⁾, Tanzania ⁽²⁷⁾ and Ghana ⁽²⁸⁾, found the incidence of PONV 41%, 40.7%, 41.4%, and 34% respectively. In India ⁽⁷⁾ and Singapore ⁽²⁹⁾, it was found to be 33.3% and 34.1%, respectively. This might be due to the techniques used in their studies to measure PONV. They used traditional methods (absolute counts, Likert scales, visual analogue scales (VAS), and treatment response). They just defined PONV as having at least one episode of nausea, vomiting or both within 24 hours postoperatively. Due to this, they might be included in the PONV condition, which was not clinically significant. However, this study has used a recently developed PONV Intensity Scale, which is believed to be a valid, reliable, and responsive measure of clinically important PONV. ⁽²⁰⁾ On the other hand, this study finding is high when compared with the studies in developed countries. A recent study in South Korea by J.Choi found the incidence of PONV was 23%.⁽³⁰⁾ Similarly, in Turkey it was found to be 25%.⁽³¹⁾ This difference in incidence might be due to anesthesia and surgical technique differences and study design.

In this study, the incidence of PONV was higher in the female patients. Among the females, 40.5% had PONV compared to 11% in males. The female gender increased the likelihood of PONV by four times, and it was the strongest predictor of PONV with AOR of 4.065 (2.090–7.906). This finding was consistent with what has previously been reported from studies done elsewhere. Morino, in Japan found that women were significantly more likely to develop PONV (four times) than men and suggested that women be considered for prophylactic antiemetic therapy.⁽³²⁾ Similarly, N. Rodseth, in South Africa found that women patients were more likely to develop PONV than men and suggested for prophylactic antiemetic therapy.⁽²³⁾ A descriptive study conducted at Toronto Hospital, Canada, found that women had a nearly two fold higher rate of PONV than male.⁽³³⁾ In the same way, a 2012 systematic review study of PONV from San Francisco (USA), identified being a female as strongest patient specific predictor.⁽³⁴⁾ This is due to vomiting center in females is influenced by the endocrine or hormonal environment.⁽³⁵⁾

A previous history of PONV or motion sickness was also strongly associated with PONV. Among the seventeen six patients who had PONV previously, 42.9% had an incidence of PONV while only 18.7% had developed in the group with no history of PONV. This group of patients was three times more likely to have PONV with AOR of 2.836 (1.582–5.083). Similar findings have been noted in other studies. In the study done by Morino, C. C. Apfel ⁽³²⁾ and N. Rodseth, in South Africa ⁽²³⁾, previous history of PONV or motion sickness was a strong risk factor with AOR of 3.65 (2.52–5.30) and 2.6 (1.8–3.7), respectively. Similarly, it was the second strongest predictor of PONV in a Korean predictive model for post-operative nausea and vomiting study with an odds ratio of 2.4.⁽³⁶⁾ This is because of genetic factors to determine sensitivity to motion sickness ⁽³⁷⁾ and because individuals with a history of these factors have already established reflex arch for vomiting.^{(9, 38)} Other preoperative factors such as, age of patient, BMI, pre-operative anxiety, history of migraine headache and smoking history is reported to affect the prevalence of PONV, but in this study these factors were not found to be independent predictors of PONV after multiple logistic regression.

Consequently, the duration of anesthesia was one of intraoperative independent predictors of PONV. It was found patients who had a duration of anesthesia greater than sixty minutes were three times more likely developed PONV in comparison than those durations of less than sixty minutes, with an adjusted odds ratio of 2.974 (1.491–5.933). This result was in agreement with what has previously been reported from different studies.^{(8, 33, 34)} One study indicated that for every 30-minute increase in surgical time, there was a 59% rise in the baseline risk of PONV.⁽³³⁾ Similarly, in a Korean Predictive Model for postoperative nausea and vomiting study, duration of anesthesia was a predictor of PONV with the odds ratio of 1.9 for the anesthesia time longer than 1 hour.⁽³⁶⁾ This is due to prolonged surgery increases exposure to anesthetic agents, use of intraoperative opioids, and increased exposure to hemodynamic instability which increases incidence of PONV.⁽⁹⁾

Output of multiple logistic regression analysis also revealed that among the type of surgery, only gynecologic surgery was significantly associated with PONV as an independent predictor of PONV. In patients who underwent gynecologic surgery, the incidence of PONV was four times more likely occurred than those with non-gynecologic surgeries with AOR of 3.782(1.156–12.373). This result was in agreement with what has previously been reported from other studies. In a 2012 cross-sectional study at the university of Gondar teaching hospital, northwest, Ethiopia, gynecology operation was identified as independent predictor of PONV with AOR of 2.58 (1.24–5.39).⁽³⁹⁾ This might be due to stimulation of vagal afferents nerves during gynecologic surgery manipulation that result in activation of vomiting center.^(4,48) Moreover, it might be because of patients are all females.⁽³⁴⁾ In contrast to this, a 2012 systematic review, were stated that “there is no or insufficient evidence for type of surgery to be an independent risk factor for PONV.⁽³⁴⁾ This is possibly due to differences in study design and study population. On the other hand, despite intraoperative factors such as, type of anesthesia, incidence of intraoperative hypotension, administration of neostigmine and intraoperative use of opioids were included in the final model, they were not independent predictors of PONV after multiple logistic regression.

Postoperative administration of opioid was the only post-operative factor that was identified as an independent predictor of PONV after multiple logistic regressions. Among 204 patients who had taken postoperative opioids, 37.7% had PONV compared to 14.6% of those who hadn’t taken. In patients who had received postoperative opioids, the incidence of PONV was two times more likely developed when compared with those who hadn’t received postoperative opioids, with AOR of 2.333 (1.221–4.457). This was in line with findings of studies done elsewhere. Roberts et al, in Australia found postoperative opioid use to significantly (p = 0.025) affect the incidence of PONV, stating that postoperative opioid administration increases the incidence of PONV in dose dependent manner.⁽⁴⁰⁾ Similarly, in a 2012 systematic review study of PONV from San Francisco (USA), it was the strongest anesthesia-related predictor of PONV.⁽³⁴⁾ The reason for this is due to the fact that opioids directly stimulates the Chemoreceptor Trigger Zone and vestibular apparatus, and also decreases motility of the gut increasing the incidence of PONV.⁽⁴⁾

The strength of this study was that we have used a recently developed technique (linear PONV Intensity Scale), a reliable technique which is used to identify clinically significant PONV. Limitations of the Study include some perioperative factors were not well documented

The overall incidence of postoperative nausea and vomiting in the surgical patients at Jimma University Medical Center is high. The following group of surgical patients is at an increased risk for PONV: Female patients, patients with previous history of PONV or motion sickness, long duration of anesthesia, patients who underwent gynecologic type of surgery and received postoperative opioids.

Ethical approval and consent to participate

The study proposal was submitted for consideration, comment, guidance and approval to the ethical review committee of Jimma university medical center before the study begins. After critical appraisal and evaluation of the study compatibility with laws and regulations of the institution (Jimma University) and country (Ethiopia) in which the research was performed as well as in relation with international norms and standards, ethical approval was successfully obtained from the ethical review committee of Jimma University. Finally, official permission letter was obtained from Jimma University medical center.

Moreover, informed written consent was obtained from every participant and the purpose of the study was explained to all participants of the study. The information obtained was kept confidential and the name of any participant was not written so that the collected data remained anonymous. We interviewed women in a private room to maintain participants’ privacy. The participants had the right to interrupt their participation at any time without any cost.

Consent for publication:

Not applicable

Funding:

Not applicable

Availability of data and materials:

Data used for analysis was uploaded as a supplementary file.

Competing interests:

We declared that we had no competing interests.

Author’s contribution:

DC.E and MS. O were involved in the conception, study design, execution, acquisition of data, analysis and interpretation of data, took part in drafting the article or revising it critically for important intellectual content. GT.M, ZM. D, NG. F, SM. T, and AH. G were involved in study design, execution, acquisition of data, analysis, interpretation, drafted and final manuscript writing. All authors reviewed and agreed on all versions of the manuscript before submission, agreed to submit to the current journal; gave final approval of the version to be published; and agreed to be accountable for all aspects of the work.

Acknowledgements:

We would like to extend our deepest thanks to Jimma University for material support.

Weiser TG, Haynes AB, Molina G, Lipsitz SR, Esquivel MM, Uribe-leitz T, et al. Size and distribution of the global volume of surgery in 2012 Global volume of surgery. 2016;i(March):201–9.
Weiser TG, Haynes AB, Molina G, Lipsitz SR, Esquivel MM, Uribe-leitz T, et al. Surgical Services: Access and Coverage Estimate of the global volume of surgery in 2012 : an. 2012;94305.
Merry AF, Mitchell SJ. Complications of anaesthesia. 2018;73:7–11.
John Doyle D, Dahaba A, LeManach Y. Advances in anesthesia technology are improving patient care, but many challenges remain. BMC Anesthesiol. 2018;18(1):1–5.
For PM, Factor R, Ponv P. PONV Prophylaxis Guidelines. 2015;(October):1–4.
Gan TJ, Diemunsch P, Habib AS, Kovac A, Kranke P, Meyer TA, et al. Consensus Guidelines for the Management of Postoperative Nausea and Vomiting. 2014;118(1):85–113.
Manoharan AK, Mallick NA, Thangavelu S. A Cross Sectional Study on Postoperative Nausea and Vomiting – Risk Prediction and Assessment in Patients Undergoing Elective Surgical Patients. 2017;4(3):640–4.
Srilata M, Jayaram K. Nausea and Vomiting. 2016;345–57.
Balaban CD, Yates BJ. Autonomic Neuroscience: Basic and Clinical What is nausea ? A historical analysis of changing views. Auton Neurosci Basic Clin. 2017;202:5–17.
Myles PS, Wengritzky R. Simplified postoperative nausea and vomiting impact scale for audit and post-discharge review. 2012;108(January):423–9.
Wengritzky R, Mettho T, Myles PS, Burke J, Kakos A. Development and validation of a postoperative nausea and vomiting intensity scale. Br J Anaesth. 2010;104(2):158–66.
Golembiewski J. Antiemetics: Focus on Pharmacology. J PeriAnesthesia Nurs. 2014;29(6):514–8.
Blackburn J. Postoperative nausea and vomiting. Anaesth Intensive Care Med. 2015;16(9):452–6.
Parra-sanchez I, Abdallah R, You J, Fu AZ, Grady M, Iii KC, et al. A time-motion economic analysis of postoperative nausea and vomiting in ambulatory surgery ´ conomique des temps et mouvements pour les Une analyse e ´ es et vomissements postope ´ ratoires en chirurgie d ’ un jour nause. 2012;366–75.
Whelan R. Nausea and vomiting after surgery. 2018;13(1):28–32.
Views E. me Cost-eflective Management of Postoperative Nausea and Vomiting. 2019;(4):4–6.
Noroozinia H, Mahoori A, Hasani E. The Effect of Acupressure on Nausea and Vomiting after Cesarean Section Under Spinal Anesthesia. 2013;(9).
Obrink E, Jildenstål P, Oddby E, Jakobsson JG. Post-operative nausea and vomiting: Update on predicting the probability and ways to minimize its occurrence, with focus on ambulatory surgery. 2015;15:100–6.
Gan TJ, Diemunsch P, Habib AS, Kovac A, Kranke P, Meyer TA, et al. Consensus guidelines for the management of postoperative nausea and vomiting. Anesth Analg. 2014;118(1):85–113.
Wengritzky R, Mettho T, Myles PS, Burke J, Kakos A. CLINICAL PRACTICE Development and validation of a postoperative nausea and vomiting intensity scale. 2010;104(2):158–66.
Williamson A, Hoggart B. Pain: a review of three commonly used pain rating scales. 2005;1994(Spence 2000):798–804.
Moerman N, Oosting H. The Amsterdam Scale (APAIS). 1996;
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. Anesth Analg. 2010;110(6):1591–4.
Mndolo S, Jung K, Pollach G. Postoperative Nausea & Vomiting in Malawi. 2014;(April):447–51.
Nze P. A survey of postoperative nausea and vomiting in Enugu, Nigeria. Orient J Med. 2011;17(3):443–9.
Ssebuufu R, Kakande I, Okello M. Post-operative Nausea and Vomiting at Mulago Hospital. East Cent Afr.j.surg(online). 2009;14(2):50–7.
Sciences-bugando A. Postoperative nausea and vomiting at a tertiary care hospital in north- western Tanzania. 2015;17(3):1–9.
Amponsah G. POSTOPERATIVE NAUSEA AND VOMITING IN KORLE BU TEACH- ING HOSPITAL. 2007;(December):181–5.
Yu X, Leong A, Wen X, Kwa C, Hui S, Ng E, et al. Singapore General Hospital Experience on Ethnicity and the Incidence of Postoperative Nausea and Vomiting after Elective Orthopaedic Surgeries. 2015;2015.
Choi JB, Shim YH, Lee Y, Lee JS, Choi J, Chang CH. Incidence and Risk Factors of Postoperative Nausea and Vomiting in Patients with Fentanyl-Based Intravenous Patient-Controlled Analgesia and Single Antiemetic Prophylaxis. 2019;55(5):1430–5.
Article O. Risk Factors for Postoperative Nausea and Vomiting in Pediatric Patients Undergoing Ambulatory Dental Treatment. 2018;597–602.
Morino R, Ozaki M, Nagata O. Incidence of and risk factors for postoperative nausea and vomiting at a Japanese Cancer Center: first large-scale study in Japan. 2013;18–24.
Sinclair DR, Chung F, Mezei G, Ph D. Can Postoperative Nausea and Vomiting Be Predicted? 2019;(1):109–18.
Apfel CC, Heidrich FM, Jalota L, Hornuss C, Whelan RP, Zhang K, et al. Evidence-based analysis of risk factors for postoperative. Br J Anaesth. 2012;109(5):742–53.
Buchanan FF, Myles PS, Cicuttini F. Effect of patient sex on general anaesthesia and recovery. Br J Anaesth. 2011;106(6):832–9.
Duck HC, Justin SK, Hyun JA, Jie AK. A Korean predictive model for postoperative nausea and vomiting. J Korean Med Sci. 2005;20(5):811–5.
Horn CC, Meyers K, Oberlies N. Physiology & Behavior Musk shrews selectively bred for motion sickness display increased anesthesia-induced vomiting. Physiol Behav. 2014;124:129–37.
Murphy MJ, Hooper VD, Sullivan E, Clifford T, Apfel CC. Identification of Risk Factors for Postoperative Nausea and Vomiting in the Perianesthesia Adult Patient. 2006;
Prevalence and factors associated with post-operative nausea and vomiting at University of Gondar Hospital, Northwest Ethiopia, 2012 : Across-sectional study. PREVALENCE AND FACTORS ASSOCIATED WITH POSTOPERATIVE NAUSEA AND VOMITING AT THE UNIVERSITY OF. 2015;(September 2014).
Roberts GW, Bekker TB, Carlsen HH, Moffatt CH, Slattery PJ, McClure F. Postoperative nausea and vomiting are strongly influenced by postoperative opioid use in dose-related a manner. Anesth Analg. 2005;101(5):1343–8.

No competing interests reported.

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Postoperative nausea and vomiting and associated factors among adult elective surgical patients at Jimma Medical Center, South West Ethiopia

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Abstract

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Background

Methods And Materials

Results

Description Of Sociodemographic And Preoperative Factors

Perioperative Factors That Are Candidate For Final Mode

Intraoperative factors that were potential candidate for final model

Post-operative factors that were potential candidate for final model

5.5 Independent Predictors of PONV

Discussion

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