Naslov
Prediction of Nausea and Vomiting After Surgery under General Anesthesia: Present and Future

Autori
Šimurina, Tatjana

Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, neobjavljeni rad, ostalo

Skup
One Health Symposium

Mjesto i datum
Slavonski Brod, Hrvatska, 05.06.2014. - 07.06.2014

Vrsta sudjelovanja
Pozvano predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Postoperative nausea and vomiting

Sažetak
Postoperative nausea and vomiting (PONV) are common and harmful adverse events after general anesthesia. Despite new, less emetogenic anesthetics and modern anesthesia techniques an overall incidence in every day clinical practice still remains high, about 30%. The etiology of PONV is complex and has a multifactorial cause, including individual, anesthetic and surgical risk factors. Patient related predictors are: female gender, nonsmoking, history of PONV or motion sickness (MS). Anesthesia related predictors are: volatile anesthetics, nitrous oxide, opioids, duration of anesthesia. The influence of the type of surgery on the PONV is controversial but according to recent published data the type of surgery categorized by anatomical grouping is associated with PONV risk. Assessment of PONV risk factors helps clinicians to use appropriate antiemetic prophylaxis. Prevention of PONV may be achieved by multimodal administration of prophylactic anti-emetics and/ or use of less emetogenic anesthesia technique for high risk patients. However, pharmacological prophylaxis does not eliminate PONV completely, increases the costs and the risk of rare but well described side effects. The most benefit from antiemetic prophylaxis has patients who are at higher risk for nausea and vomiting following general anesthesia. High risk patients can be identified preoperatively by using a predictive model for PONV. Predictive models combine predictors of outcome with the relative weights of these predictors to calculate the risk of the outcome for each patient. The models calculate the risk of PONV based on individual, anesthetic and surgical factors. There are numerous predictive models for PONV in adults so far, suggesting ongoing need for a more accurate prediction model. Currently, the most used predictive PONV model is Apfel's simplified risk score which includes four predictors that are easy to memorize: female gender, previous PONV or motion sickness (MS), nonsmoking status and postoperative use of opioids. When 0, 1, 2, 3 or 4 predictors were present the risk for PONV was 10% (low risk), 20% (low risk), 40% (moderate risk), 60% (severe risk), and 80% (very severe risk), approximately. For patients with moderate, severe and very severe risk for PONV, one, two, three or more antiemetic interventions are recommended, respectively. Surgical patients signalize postoperative vomiting (POV) as most undesirable anesthesia – related outcome that is most important to avoid. So far, only two predictive models for POV in adults have been published. Koivuranta's simplified predictive model is based on logistic regression analysis and has five independent predictors for POV: female gender, previous PONV, duration of surgery over 60 minutes, nonsmoking status, history of MS. When 0, 1, 2, 3, 4 or 5 predictors were present the risk for POV was 7%, 7%, 17%, 25%, 38%, and 61%. Apfel's predictive model calculates the risk for POV according the complicated formula based on logistic regression and has five independent predictors: female gender, young age, non- smoking, history of POV or MS, and longer duration of anesthesia. Both these predictive models have not been readily implemented in clinical practice probably because of the number of predictors and the complexity of calculating. However, the predictive accuracy and general applicability of predictive models are limited. Model calibration can be influenced by the outcome incidence of the population in which the model is studied. Model discrimination can be influenced by the heterogeneity (distribution of predictor values) of the population in which the model is applied. Improvement of a PONV/ POV predictive model would require further exploration of interactions and relationships among predictive variables. For this purpose, various potential risk factors for POV were assessed and all possible relationships between predictors were included into predictive model to improve the models' power and accuracy. Proposed predictive model was developed based on risk factors data for POV in a group of 374 women who underwent general anesthesia for elective laparoscopic gynecological surgery in General Hospital Zadar, Croatia. This proposed model was based on method of multidimensional scaling (MDS) and promising approach where all interactions among the variables within the model could be visualized, called Visual CoPlot. Final predictive model based od MDS and method CoPlot comprises four predictors as follows: age more than 40 years, body mass index (BMI) ≥ 30 kg/m2, taking regular medication, and applied general anesthesia technique with nitrous oxide. CoPlot Visualization of proposed model reveals similar observations located on map close to one another, highly correlated variables described by vectors close together as well as negatively correlated variables by vectors oriented in opposite directions. Antiemetic prophylaxis and/ or modification of the anesthetic technique should be considered if two or more predictors are present in the final score. This proposed model needs validation and comparison to Koivuranta's simplified predictive model and Apfel's predictive model for POV.

Izvorni jezik
Engleski

POVEZANOST RADA