Daly K, Beale R, Chang R W
St Thomas's Hospital, London SE1 7EH.
BMJ. 2001 May 26;322(7297):1274-6. doi: 10.1136/bmj.322.7297.1274.
To develop a predictive model to triage patients for discharge from intensive care units to reduce mortality after discharge.
Logistic regression analyses and modelling of data from patients who were discharged from intensive care units.
Guy's hospital intensive care unit and 19 other UK intensive care units from 1989 to 1998.
5475 patients for the development of the model and 8449 for validation.
Mortality after discharge and power of triage model.
Mortality after discharge from intensive care was up to 12.4%. The triage model identified patients at risk from death on the ward with a sensitivity of 65.5% and specificity of 87.6%, and an area under the receiver operating curve of 0.86. Variables in the model were age, end stage disease, length of stay in unit, cardiothoracic surgery, and physiology. In the validation dataset the 34% of the patients identified as at risk had a discharge mortality of 25% compared with a 4% mortality among those not at risk.
The discharge mortality of at risk patients may be reduced by 39% if they remain in intensive care units for another 48 hours. The discharge triage model to identify patients at risk from too early and inappropriate discharge from intensive care may help doctors to make the difficult clinical decision of whom to discharge to make room for a patient requiring urgent admission to the unit. If confirmed, this study has implications on the provision of resources.
开发一种预测模型，用于对重症监护病房（ICU）患者进行出院分流，以降低出院后的死亡率。
对从ICU出院的患者数据进行逻辑回归分析和建模。
1989年至1998年期间，盖伊医院重症监护病房以及英国其他19个重症监护病房。
5475例患者用于模型开发，8449例用于验证。
出院后的死亡率和分流模型的效能。
ICU出院后的死亡率高达12.4%。该分流模型识别出病房中有死亡风险的患者，其敏感度为65.5%，特异度为87.6%，受试者工作特征曲线下面积为0.86。模型中的变量包括年龄、终末期疾病、在ICU的住院时长、心胸外科手术以及生理指标。在验证数据集中，被识别为有风险的患者中有34%的出院死亡率为25%，而无风险患者的死亡率为4%。
如果有风险的患者在重症监护病房再留观48小时，其出院死亡率可能降低39%。该出院分流模型用于识别因过早和不适当出院而有风险的患者，可能有助于医生做出艰难的临床决策，即决定让哪些患者出院，以便为需要紧急入住该病房的患者腾出空间。如果得到证实，本研究对资源配置具有重要意义。
