What can be expected from risk scores for predicting postoperative nausea and vomiting?
What can be expected from risk scores for predicting postoperative nausea and vomiting?
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DOI：
10.1093/bja/86.6.822
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发表时间：
2001-06-01
影响因子：
9.8
通讯作者：
Roewer, N
中科院分区：
文献类型：
--
作者：
Apfel, CC;Kranke, P;Roewer, N
已经开发了几种风险评分来计算术后恶心和呕吐（PONV）的概率。然而，辨别哪个个体将患有PONV的能力仍然有限。因此，我们想知道分数中预测因子的数量如何影响区分能力，以及人口的特征-测量分数的能力所需的特征-如何影响结果。出于道德原因，并独立于中心特定人群，我们开发了一个计算机模型来模拟虚拟人群。根据预测因子的数量、频率和比值比创建了四个人群。群体I：参数来自先前发表的论文，以验证计算值和报告值是否一致。人群II：创建妇科人群以研究研究环境的影响。人群III和IV：为了满足理想假设，分别测试了具有多达7个预测因子的模型，优势比为2和3。通过受试者工作特征曲线下面积（AUC）测量风险评分的区分能力，每个预测因子增加超过0.025被认为具有临床相关性。人群I的AUC与临床研究中报告的AUC相似（0.72）。研究环境对鉴别力有相当大的影响，因为在妇科环境中，AUC降至0.65。“理想化”群体III和IV的AUC最好在0.7-0.8的范围内。纳入超过5个预测因子并没有导致临床相关的改善。目前可用的简化风险评分（有四个或五个预测因子）既可作为估计PONV个体风险的方法，也可作为比较止吐试验患者组的方法。它们也优于仅使用患者的PONV病史或女性性别的单一预测模型的上级。然而，我们的分析表明，即使考虑了更多的预测因子，区分哪个个体将患有PONV的能力仍然是不完美的。
Several risk scores have been developed to calculate the Probability of postoperative nausea and vomiting (PONV). However, the power to discriminate which individual will suffer from PONV is still limited. Thus, we wondered how the number of predictors in a score affects the discriminating power and how the characteristics of a population - which is needed to measure the power of a score - may affect the results. For ethical reasons and to be independent from centre specific populations, we developed a computer model to simulate virtual populations. Four populations were created according to number, frequency, and odds ratio of predictors. Population I: parameters were derived from a previously published paper to verify whether calculated and reported values are in accordance. Population II: a gynaecological population was created to investigate the impact of the study setting. Populations III and IV: to meet ideal assumptions a model with up to seven predictors with an odds ratio of 2 and 3 was tested, respectively. The discriminating 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 predictor was considered to be clinically relevant. The AUC of population I was similar to those reported in clinical investigations (0.72). The study setting had a considerable impact on the discriminating power since the AUC decreased to 0.65 in a gynaecological setting. The AUC with the 'idealized' populations III and IV was at best in the range of 0.7-0.8. The inclusion of more than five predictors did not lead to a clinically relevant improvement. The 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 also superior to single predictor models which are just using the patients' history of PONV or female gender alone. However, our analysis suggests that the power to discriminate which individual will suffer from PONV will remain imperfect, even when more predictors are considered.