Reduction in mortality after inappropriate early discharge from intensive care unit: logistic regression triage model

Abstract

Objective: To develop a predictive model to triage patients for discharge from intensive care units to reduce mortality after discharge.

Design: Logistic regression analyses and modelling of data from patients who were discharged from intensive care units.

Setting: Guy's hospital intensive care unit and 19 other UK intensive care units from 1989 to 1998.

Participants: 5475 patients for the development of the model and 8449 for validation.

Main outcome measures: Mortality after discharge and power of triage model.

Results: 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.

Conclusions: 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.

Figure 1

Receiver operator characteristic curve of discharge triage model

Figure 2

Receiver operator characteristic curve for validation dataset