Academic Medical Center, University of Amsterdam, Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands.
Clinical Research Unit, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands.
J Crit Care. 2018 Feb;43:114-121. doi: 10.1016/j.jcrc.2017.08.014. Epub 2017 Aug 10.
We described the association between Intensive care units (ICU) characteristics and ICU Length of stay (LoS), after correcting for patient characteristics. We also compared the predictive performances of models including either patient and ICU characteristics or only patient characteristics.
We included all admissions of 38 ICUs participating in the Dutch National Intensive Care Evaluation registry (NICE) between 2014 and 2016. We performed mixed effect regression including, one ICU characteristic in each model and a random intercept per ICU. Furthermore, we developed a prediction model containing multiple ICU characteristics and patients characteristics.
We found negative associations for the number of hospital beds; number of ICU beds; availability of fellows in training for intensivist; full-time equivalent ICU nurses; and discharged in a shift with 100% bed occupancy. Furthermore, we found a U-shaped association with the nurses to patient ratio as spline function. The performance based on R was between 0.30 and 0.32 for both the model containing only patient characteristics and the model also containing ICU characteristics.
After correcting for patient characteristics, we found statistically significant associations between ICU LoS and six ICU characteristics, mainly describing staff availability. Furthermore, we conclude that including ICU characteristics did not significantly improve ICU LoS prediction.
在纠正患者特征后,我们描述了重症监护病房(ICU)特征与 ICU 住院时间(LoS)之间的关联。我们还比较了包含患者和 ICU 特征或仅包含患者特征的模型的预测性能。
我们纳入了 2014 年至 2016 年期间参与荷兰国家重症监护评估登记处(NICE)的 38 个 ICU 的所有入院患者。我们进行了混合效应回归,每个模型包含一个 ICU 特征和一个 ICU 随机截距。此外,我们开发了一个包含多个 ICU 特征和患者特征的预测模型。
我们发现与医院床位数量、ICU 床位数量、住院医师培训研究员的可用性、全职等效 ICU 护士数量以及 100%床位占用的班次出院呈负相关。此外,我们发现护士与患者比例呈 U 形关联,作为样条函数。基于 R 的性能对于仅包含患者特征的模型和也包含 ICU 特征的模型均在 0.30 到 0.32 之间。
在纠正患者特征后,我们发现 ICU LoS 与六个 ICU 特征之间存在统计学显著关联,主要描述了人员可用性。此外,我们得出结论,包含 ICU 特征并没有显著提高 ICU LoS 的预测。