Straney Lahn D, Udy Andrew A, Burrell Aidan, Bergmeir Christoph, Huckson Sue, Cooper D James, Pilcher David V
School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
Department of Intensive Care and Hyperbaric Medicine, The Alfred Hospital Melbourne, Victoria, Australia.
PLoS One. 2017 May 2;12(5):e0176570. doi: 10.1371/journal.pone.0176570. eCollection 2017.
Comparisons between institutions of intensive care unit (ICU) length of stay (LOS) are significantly confounded by individual patient characteristics, and currently there is a paucity of methods available to calculate risk-adjusted metrics.
We extracted de-identified data from the Australian and New Zealand Intensive Care Society (ANZICS) Adult Patient Database for admissions between January 1 2011 and December 31 2015. We used a mixed-effects log-normal regression model to predict LOS using patient and admission characteristics. We calculated a risk-adjusted LOS ratio (RALOSR) by dividing the geometric mean observed LOS by the exponent of the expected Ln-LOS for each site and year. The RALOSR is scaled such that values <1 indicate a LOS shorter than expected, while values >1 indicate a LOS longer than expected. Secondary mixed effects regression modelling was used to assess the stability of the estimate in units over time.
During the study there were a total of 662,525 admissions to 168 units (median annual admissions = 767, IQR:426-1121). The mean observed LOS was 3.21 days (median = 1.79 IQR = 0.92-3.52) over the entire period, and declined on average 1.97 hours per year (95%CI:1.76-2.18) from 2011 to 2015. The RALOSR varied considerably between units, ranging from 0.35 to 2.34 indicating large differences after accounting for case-mix.
There are large disparities in risk-adjusted LOS among Australian and New Zealand ICUs which may reflect differences in resource utilization.
重症监护病房(ICU)住院时长(LOS)在不同机构之间的比较因个体患者特征而存在显著混淆,目前可用的计算风险调整指标的方法较少。
我们从澳大利亚和新西兰重症监护学会(ANZICS)成人患者数据库中提取了2011年1月1日至2015年12月31日期间入院患者的匿名数据。我们使用混合效应对数正态回归模型,根据患者和入院特征预测住院时长。我们通过将每个地点和年份观察到的几何平均住院时长除以预期自然对数住院时长的指数,计算出风险调整住院时长比率(RALOSR)。RALOSR经过缩放,使得值<1表示住院时长低于预期,而值>1表示住院时长高于预期。使用二次混合效应回归模型评估各单位估计值随时间的稳定性。
在研究期间,共有168个单位接收了662,525例入院患者(年入院中位数 = 767,四分位间距:426 - 1121)。在整个期间,观察到的平均住院时长为3.21天(中位数 = 1.79,四分位间距 = 0.92 - 3.52),从2011年到2015年平均每年下降1.97小时(95%置信区间:1.76 - 2.18)。各单位之间的RALOSR差异很大,范围从0.35到2.34,这表明在考虑病例组合后存在很大差异。
澳大利亚和新西兰的ICU在风险调整后的住院时长方面存在很大差异,这可能反映了资源利用的差异。