A predictive model for the early identification of patients at risk for a prolonged intensive care unit length of stay.
A predictive model for the early identification of patients at risk for a prolonged intensive care unit length of stay.
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DOI：
10.1186/1472-6947-10-27
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发表时间：
2010-05-13
影响因子：
3.5
通讯作者：
Zimmerman JE
中科院分区：
文献类型：
--
作者：
Kramer AA;Zimmerman JE
重症监护室（ICU）住院时间延长的患者占资源使用量不成比例。早期识别患者存在延长住院时间的风险，可以提高质量，减少ICU住院时间。这项研究开发并验证了一个模型，该模型可以识别出有长期ICU住院风险的患者。我们对2002-2007年美国31家医院83个ICU的343，555例住院患者进行了回顾性队列研究。我们检查了ICU住院时间的分布，以确定临床医生可能担心延长住院时间的阈值;这导致选择5天的临界点。从第5天留在ICU的患者，我们开发了一个多变量回归模型，预测剩余的ICU停留时间。预测变量包括入院时、第1天和ICU第5天收集的信息。2002-2005年期间12，640名入院者的数据用于开发该模型，其余12，904名入院者的数据用于内部验证该模型。最后，我们使用2006-2007年期间11，903例入院的数据来外部验证模型。对ICU剩余住院时间影响最大的变量是第5天测量的变量，而不是入院时或第1天。机械通气，PaO 2：第5天的FiO 2比值、其他生理成分和镇静作用占预计剩余ICU住院时间变化的81.6%。在外部验证集中，观察到的ICU住院时间为11.99天，预测的总ICU住院时间（5天+第5天预测的剩余住院时间）为11.62天，差异为8.7小时。对于相同的患者，使用APACHE第1天模型，观察到的平均ICU住院时间与预测的平均ICU住院时间之间的差异为149.3小时。新模型的r2在个体之间为20.2%，在单位之间为44.3%。使用ICU第1天和第5天的患者数据的模型准确预测了ICU住院时间的延长。这些预测比仅基于ICU第1天数据的预测更准确。该模型可用于衡量ICU的表现，并提醒医生探索旨在减少ICU住院时间的护理替代方案。
Patients with a prolonged intensive care unit (ICU) length of stay account for a disproportionate amount of resource use. Early identification of patients at risk for a prolonged length of stay can lead to quality enhancements that reduce ICU stay. This study developed and validated a model that identifies patients at risk for a prolonged ICU stay. We performed a retrospective cohort study of 343,555 admissions to 83 ICUs in 31 U.S. hospitals from 2002-2007. We examined the distribution of ICU length of stay to identify a threshold where clinicians might be concerned about a prolonged stay; this resulted in choosing a 5-day cut-point. From patients remaining in the ICU on day 5 we developed a multivariable regression model that predicted remaining ICU stay. Predictor variables included information gathered at admission, day 1, and ICU day 5. Data from 12,640 admissions during 2002-2005 were used to develop the model, and the remaining 12,904 admissions to internally validate the model. Finally, we used data on 11,903 admissions during 2006-2007 to externally validate the model. The variables that had the greatest impact on remaining ICU length of stay were those measured on day 5, not at admission or during day 1. Mechanical ventilation, PaO2: FiO2 ratio, other physiologic components, and sedation on day 5 accounted for 81.6% of the variation in predicted remaining ICU stay. In the external validation set observed ICU stay was 11.99 days and predicted total ICU stay (5 days + day 5 predicted remaining stay) was 11.62 days, a difference of 8.7 hours. For the same patients, the difference between mean observed and mean predicted ICU stay using the APACHE day 1 model was 149.3 hours. The new model's r2 was 20.2% across individuals and 44.3% across units. A model that uses patient data from ICU days 1 and 5 accurately predicts a prolonged ICU stay. These predictions are more accurate than those based on ICU day 1 data alone. The model can be used to benchmark ICU performance and to alert physicians to explore care alternatives aimed at reducing ICU stay.
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影响因子：
39.2
作者：
KNAUS, WA;WAGNER, DP;DRAPER, EA
通讯作者：
DRAPER, EA
影响因子：
8.8
作者：
Dimick, JB;Pronovost, PJ;Lipsett, PA
通讯作者：
Lipsett, PA
影响因子：
8.8
作者：
Bakhshi-Raiez, Ferishta;Peek, Niels;de Keizer, Nicolette F.
通讯作者：
de Keizer, Nicolette F.
影响因子：
8.8
作者：
Marshall, John;Finn, Christine A.;Theodore, Arthur C.
通讯作者：
Theodore, Arthur C.
影响因子：
38.9
作者：
Niskanen, Minna;Reinikainen, Matti;Pettila, Ville
通讯作者：
Pettila, Ville