Stallings-Welden Lois M, Doerner Mary, Ketchem Elizabeth Libby, Benkert Laura, Alka Susan, Stallings Jonathan D
J Perianesth Nurs. 2018 Apr;33(2):116-128. doi: 10.1016/j.jopan.2016.09.001. Epub 2017 Mar 16.
To determine effectiveness of aromatherapy (AT) compared with standard care (SC) for postoperative and postdischarge nausea and vomiting (PONV/PDNV) in ambulatory surgical patients.
Prospective randomized study.
Patients (n = 254) received either SC or AT for PONV and interviewed for effectiveness of PDNV. Machine learning methods (eight algorithms) were used to evaluate.
Of patients (64 of 221) that experienced PONV, 52% were in the AT group and 48% in the SC group. The majority were satisfied with treatment (timely, P = .60; effectiveness, P = .86). Of patients that experienced PDNV, treatment was 100% effective in the AT group and 67% in the SC group. The cforest algorithm was used to develop a model for predicting PONV with literature-based risk factors (0.69 area under the curve).
AT is an effective way to manage PONV/PDNV. Gender and age were the most important predictors of PONV.
确定与标准护理（SC）相比，芳香疗法（AT）对门诊手术患者术后及出院后恶心呕吐（PONV/PDNV）的疗效。
前瞻性随机研究。
254例患者因PONV接受SC或AT治疗，并就PDNV的疗效进行访谈。使用机器学习方法（八种算法）进行评估。
在经历PONV的患者（221例中的64例）中，52%在AT组，48%在SC组。大多数患者对治疗满意（及时性，P = 0.60；有效性，P = 0.86）。在经历PDNV的患者中，AT组的治疗有效率为100%，SC组为67%。使用cforest算法建立了一个基于文献风险因素预测PONV的模型（曲线下面积为0.69）。
AT是管理PONV/PDNV的有效方法。性别和年龄是PONV最重要的预测因素。
