What can be expected from risk scores for predicting postoperative nausea and vomiting?

Several risk scores have been developed to calculate the Probability of pos toperative nausea and vomiting (PONV). However, the power to discriminate w hich individual will suffer from PONV is still limited. Thus, we wondered h ow the number of predictors in a score affects the discriminating power and how the characteristics of a population - which is needed to measure the p ower of a score - may affect the results. For ethical reasons and to be ind ependent from centre specific populations, we developed a computer model to simulate virtual populations. Four populations were created according to n umber, frequency, and odds ratio of predictors. Population I: parameters we re derived from a previously published paper to verify whether calculated a nd reported values are in accordance. Population II: a gynaecological popul ation was created to investigate the impact of the study setting. Populatio ns III and IV: to meet ideal assumptions a model with up to seven predictor s with an odds ratio of 2 and 3 was tested, respectively. The discriminatin g power of a risk score was measured by the area under a receiver operating characteristic curve (AUC) and an increase of more than 0.025 per predicto r was considered to be clinically relevant. The AUC of population I was sim ilar to those reported in clinical investigations (0.72). The study setting had a considerable impact on the discriminating power since the AUC decrea sed to 0.65 in a gynaecological setting. The AUC with the 'idealized' popul ations III and IV was at best in the range of 0.7-0.8. The inclusion of mor e than five predictors did not lead to a clinically relevant improvement. T he currently available simplified risk scores (with four or five predictors ) are useful both as a method to estimate individual risk of PONV and as a method for comparing groups of patients for antiemetic trials. They are als o superior to single predictor models which are just using the patients' hi story of PONV or female gender alone. However, our analysis suggests that t he power to discriminate which individual will suffer from PONV will remain imperfect, even when more predictors are considered.