From the Department of Anesthesiology, Division of Anesthesiology, Intensive Care and Emergency Medicine (T.H.K., L.v.W., C.J.K., W.A.v.K., K.G.M.M.) and Julius Center for Health Sciences and Primary Care (K.G.M.M., Y.V.), University Medical Center Utrecht, Utrecht, The Netherlands.
Anesthesiology. 2014 Feb;120(2):343-54. doi: 10.1097/ALN.0000000000000009.
Clinical prediction models have been shown to have moderate sensitivity and specificity, yet their use will depend on implementation in clinical practice. The authors hypothesized that implementation of a prediction model for postoperative nausea and vomiting (PONV) would lower the PONV incidence by stimulating anesthesiologists to administer more "risk-tailored" prophylaxis to patients.
A single-center, cluster-randomized trial was performed in 12,032 elective surgical patients receiving anesthesia from 79 anesthesiologists. Anesthesiologists were randomized to either exposure or nonexposure to automated risk calculations for PONV (without patient-specific recommendations on prophylactic antiemetics). Anesthesiologists who treated less than 50 enrolled patients were excluded during the analysis to avoid too small clusters, yielding 11,613 patients and 57 anesthesiologists (intervention group: 5,471 and 31; care-as-usual group: 6,142 and 26). The 24-h incidence of PONV (primary outcome) and the number of prophylactic antiemetics administered per patient were studied for risk-dependent differences between allocation groups.
There were no differences in PONV incidence between allocation groups (crude incidence intervention group 41%, care-as-usual group 43%; odds ratio, 0.97; 95% CI, 0.87-1.1; risk-dependent odds ratio, 0.92; 95% CI, 0.80-1.1). Nevertheless, intervention-group anesthesiologists administered more prophylactic antiemetics (rate ratio, 2.0; 95% CI, 1.6-2.4) and more risk-tailored than care-as-usual-group anesthesiologists (risk-dependent rate ratio, 1.6; 95% CI, 1.3-2.0).
Implementation of a PONV prediction model did not reduce the PONV incidence despite increased antiemetic prescription in high-risk patients by anesthesiologists. Before implementing prediction models into clinical practice, implementation studies that include patient outcomes as an endpoint are needed.
临床预测模型的灵敏度和特异度均为中等，但其应用将取决于在临床实践中的实施情况。作者假设，实施术后恶心和呕吐（PONV）预测模型将通过刺激麻醉师向更多高危患者给予更“个体化”的预防措施，从而降低 PONV 的发生率。
这是一项在 12032 名接受 79 名麻醉师麻醉的择期手术患者中进行的单中心、整群随机试验。麻醉师被随机分为接受或不接受 PONV 自动风险计算（无针对预防止吐药物的患者特定建议）。在分析过程中，排除了治疗少于 50 名入组患者的麻醉师，以避免太小的群组，从而得到 11613 名患者和 57 名麻醉师（干预组：5471 名和 31 名；常规护理组：6142 名和 26 名）。研究了分配组之间风险依赖性差异的 24 小时 PONV 发生率（主要结局）和每位患者给予的预防止吐药物数量。
两组间 PONV 发生率无差异（干预组粗发生率为 41%，常规护理组为 43%；比值比，0.97；95%CI，0.87-1.1；风险依赖性比值比，0.92；95%CI，0.80-1.1）。然而，干预组麻醉师给予了更多的预防止吐药物（比率比，2.0；95%CI，1.6-2.4），并且比常规护理组麻醉师给予了更多的个体化预防措施（风险依赖性比率比，1.6；95%CI，1.3-2.0）。
尽管麻醉师对高危患者的止吐药物处方增加，但实施 PONV 预测模型并未降低 PONV 的发生率。在将预测模型应用于临床实践之前，需要进行包括患者结局作为终点的实施研究。
