Ambulatory Surgery Unit, Hospital Universitario Dr. Peset, Valencia, Spain.
BMC Health Serv Res. 2011 Oct 13;11:269. doi: 10.1186/1472-6963-11-269.
The correct implementation of Ambulatory Surgery must be accompanied by an accurate monitoring of the patient post-discharge state. We fit different statistical models to predict the first hours postoperative status of a discharged patient. We will also be able to predict, for any discharged patient, the probability of needing a closer follow-up, or of having a normal progress at home.
The status of a discharged patient is predicted during the first 48 hours after discharge by using variables routinely used in Ambulatory Surgery. The models fitted will provide the physician with an insight into the post-discharge progress. These models will provide valuable information to assist in educating the patient and their carers about what to expect after discharge as well as to improve their overall level of satisfaction.
A total of 922 patients from the Ambulatory Surgery Unit of the Dr. Peset University Hospital (Valencia, Spain) were selected for this study. Their post-discharge status was evaluated through a phone questionnaire. We pretend to predict four variables which were self-reported via phone interviews with the discharged patient: sleep, pain, oral tolerance of fluid/food and bleeding status. A fifth variable called phone score will be built as the sum of these four ordinal variables. The number of phone interviews varies between patients, depending on the evolution. The proportional odds model was used. The predictors were age, sex, ASA status, surgical time, discharge time, type of anaesthesia, surgical specialty and ambulatory surgical incapacity (ASI). This last variable reflects, before the operation, the state of incapacity and severity of symptoms in the discharged patient.
Age, ambulatory surgical incapacity and the surgical specialty are significant to explain the level of pain at the first call. For the first two phone calls, ambulatory surgical incapacity is significant as a predictor for all responses except for sleep at the first call.
The variable ambulatory surgical incapacity proved to be a good predictor of the patient's status at home. These predictions could be used to assist in educating patients and their carers about what to expect after discharge, as well as to improve their overall level of satisfaction.
门诊手术的正确实施必须伴随着对出院后患者状态的准确监测。我们拟合了不同的统计模型来预测出院患者术后最初几小时的状态。我们还将能够预测任何出院患者需要更密切随访的概率，或者在家中正常进展的概率。
通过在门诊手术中常规使用的变量来预测出院后 48 小时内患者的状态。拟合的模型将为医生提供对出院后进展的深入了解。这些模型将提供有价值的信息，以帮助教育患者及其照顾者出院后预期的情况，并提高他们的整体满意度。
从西班牙瓦伦西亚的 Dr. Peset 大学医院的门诊外科病房选择了 922 名患者进行这项研究。通过电话问卷评估他们的出院后状态。我们试图通过与出院患者的电话访谈来预测四个自报告变量：睡眠、疼痛、液体/食物的口服耐受性和出血状况。将第五个称为电话评分的变量构建为这四个有序变量的总和。根据患者的病情变化，电话访谈的次数也有所不同。使用比例优势模型。预测因子为年龄、性别、ASA 状态、手术时间、出院时间、麻醉类型、手术专业和门诊手术能力丧失（ASI）。这个最后一个变量反映了手术前出院患者的能力丧失和症状严重程度。
年龄、门诊手术能力丧失和手术专业对解释第一次电话访问时的疼痛程度有重要意义。对于前两次电话访问，除了第一次电话访问的睡眠外，门诊手术能力丧失是所有反应的重要预测因素。
门诊手术能力丧失这一变量被证明是预测患者在家中状态的良好指标。这些预测结果可以帮助教育患者及其照顾者出院后的预期情况，提高他们的整体满意度。
