Health Services Research Unit, Lørenskog, Norway ; Department of Pulmonary Medicine, Akershus University Hospital, Lørenskog, Norway ; Faculty of Medicine, University of Oslo, Lørenskog, Norway.
Health Services Research Unit, Lørenskog, Norway.
Int J Chron Obstruct Pulmon Dis. 2014 Jan 20;9:99-105. doi: 10.2147/COPD.S51467. eCollection 2014.
Early identification of patients with a prolonged stay due to acute exacerbation of chronic obstructive pulmonary disease (COPD) may reduce risk of adverse event and treatment costs. This study aimed to identify predictors of prolonged stay after acute exacerbation of COPD based on variables on admission; the study also looked to establish a prediction model for length of stay (LOS).
We extracted demographic and clinical data from the medical records of 599 patients discharged after an acute exacerbation of COPD between March 2006 and December 2008 at Oslo University Hospital, Aker. We used logistic regression analyses to assess predictors of a length of stay above the 75th percentile and assessed the area under the receiving operating characteristic curve to evaluate the model's performance.
We included 590 patients (54% women) aged 73.2±10.8 years (mean ± standard deviation) in the analyses. Median LOS was 6.0 days (interquartile range [IQR] 3.5-11.0). In multivariate analysis, admission between Thursday and Saturday (odds ratio [OR] 2.24 [95% CI 1.60-3.51], P<0.001), heart failure (OR 2.26, 95% CI 1.34-3.80), diabetes (OR 1.90, 95% CI 1.07-3.37), stroke (OR 1.83, 95% CI 1.04-3.21), high arterial PCO2 (OR 1.26 [95% CI 1.13-1.41], P<0.001), and low serum albumin level (OR 0.92 [95% CI 0.87-0.97], P=0.001) were associated with a LOS >11 days. The statistical model had an area under the receiver operating characteristic curve of 0.73.
Admission between Thursday and Saturday, heart failure, diabetes, stroke, high arterial PCO2, and low serum albumin level were associated with a prolonged LOS. These findings may help physicians to identify patients that will need a prolonged LOS in the early stages of admission. However, the predictive model exhibited suboptimal performance and hence is not ready for clinical use.
早期识别因慢性阻塞性肺疾病（COPD）急性加重而住院时间延长的患者，可能降低不良事件和治疗费用的风险。本研究旨在基于入院时的变量，确定 COPD 急性加重后住院时间延长的预测因素；同时，还建立了一个住院时间（LOS）预测模型。
我们从 2006 年 3 月至 2008 年 12 月在奥斯陆大学医院 Aker 出院的 599 例 COPD 急性加重患者的病历中提取了人口统计学和临床数据。我们使用逻辑回归分析评估了住院时间超过第 75 百分位数的预测因素，并评估了接收者操作特征曲线下的面积，以评估模型的性能。
我们将 590 例（54%为女性）年龄为 73.2±10.8 岁（平均值±标准差）的患者纳入分析。中位 LOS 为 6.0 天（四分位间距[IQR] 3.5-11.0）。多变量分析显示，周四至周六入院（比值比[OR] 2.24[95%CI 1.60-3.51]，P<0.001）、心力衰竭（OR 2.26，95%CI 1.34-3.80）、糖尿病（OR 1.90，95%CI 1.07-3.37）、中风（OR 1.83，95%CI 1.04-3.21）、高动脉 PCO2（OR 1.26[95%CI 1.13-1.41]，P<0.001）和低血清白蛋白水平（OR 0.92[95%CI 0.87-0.97]，P=0.001）与 LOS>11 天相关。该统计模型的受试者工作特征曲线下面积为 0.73。
周四至周六入院、心力衰竭、糖尿病、中风、高动脉 PCO2 和低血清白蛋白水平与延长 LOS 相关。这些发现可能有助于医生在入院早期识别需要延长 LOS 的患者。然而，预测模型的性能并不理想，因此还不能用于临床。
