PREDICTORS OF POSTOPERATIVE PULMONARY COMPLICATIONS

术后肺部并发症(PPC)经常导致增加 患者发病率和死亡率、住院时间和资源 利用率。与PPC相关的多达35个风险因素已被 已确认身份。然而，在系统地制定清单方面，几乎没有做什么工作 将风险因素转化为实用的预测模型，供临床使用。一位模特 建筑研究将用于前瞻性地检查个人风险。 受试者PPC发生的相关因素 腹部外科手术。可接触到的风险因素 术前、术中、术后即刻的护理 将使用设置。这些风险因素将在开发中使用 PPC的预测模型，该模型将在 独立样本。将使用三个机构收集数据 目标人群是将接受预定腹部手术的成年人 外科手术。预计540人的样本规模将在 14个月。将使用PPC的多标准结果和16个风险 评估的因素包括：年龄、性别、吸烟史、体重指数、 活动能力、意识水平、手术前住院时间、病史 肺部疾病史、其他疾病史(S)、术前呼吸 治疗、麻醉风险、麻醉持续时间、位置和类型 手术方式、切开方向和鼻胃管。物流 将使用回归分析来检验单变量的危险因素 对购买力平价的影响，并建立多元模型。分开的样本 将使用技术来测试预测模型。敏感度和 将对该模型的特异性进行评估。

Postoperative pulmonary complications (PPCs) often lead to increased patient morbidity and mortality, hospital length of stay and resource utilization. As many as 35 risk factors associated with PPCs have been identified. Yet little has been done to systematically develop the list of risk factors into a practical predictive model for clinical use. A model building study will be used to prospectively examine individual risk factors associated with the development of a PPC in subjects undergoing an abdominal surgical procedure. Risk factors which are accessible to the nurse in the preoperative, intraoperative and immediate postoperative setting will be used. These risk factors will be used in the development of a predictive model for PPCs and the model will be tested in an independent sample. Three institutions will be used for data collection and the target population is adults who will undergo a scheduled abdominal surgical procedure. A sample size of 540 is projected over the course of 14 months. A multi-criteria outcome of PPC will be used and 16 risk factors will be assessed:age, sex, smoking history, body mass index, mobility, level of consciousness, length of stay prior to surgery, history of lung disease, history of other disease(s), preoperative respiratory therapy, anesthesia risk, duration of anesthesia, location and type of surgical procedure, incision direction and nasogastric tube. Logistic regression analysis will be used to examine the risk factors for univariate effects on PPC and to develop the multivariate model. A split sample technique will be used to test the predictive model. The sensitivity and specificity of the model will be assessed.