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

• Published in: BMJ 2001-05, Vol.322 (7297), p.1274-1276
• Main Authors: Daly, Kathleen, Beale, R, Chang, R W S
• Format: Article
• Language: English
• Subjects: Anesthesia. Intensive care medicine. Transfusions. Cell therapy and gene therapy; APACHE; Biological and medical sciences; Chi-Square Distribution; Critical care; Critical Care - classification; Datasets; Discharge; Disease models; Emergency and intensive care: techniques, logistics; Health risk assessment; Heart surgery; Hospital admissions; Hospital beds; Hospital Mortality; Hospital units; Hospitals; Humans; Intensive care; Intensive care medicine; Intensive care unit. Emergency transport systems. Emergency, hospital ward; Intensive care units; Intensive Care Units - standards; Intensive Care Units - statistics & numerical data; Length of Stay; Logistic Models; Logistic regression analysis; Logistics; Medical sciences; Modeling; Mortality; Neural networks; Patient Discharge - standards; Patient Transfer - standards; Patients; Patients' Rooms; Physiology; Reduction; Risk Assessment; Risk factors; Timing; Triage; Triage - methods; United Kingdom - epidemiology; Variables; Wards
• ISSN: 0959-8138; 0959-8146; 0959-535X; 1468-5833; 1756-1833
• DOI: 10.1136/bmj.322.7297.1274

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. What is already known on this topic In the United Kingdom, the mortality of patients who die on the ward after discharge from intensive care is unacceptably high (9% to 27%) Indirect evidence has shown that this is due to too early and inappropriate discharge from intensive care that has increased over the past 10 years What this study adds A triage model identifies patients at risk from inappropriate discharge from intensive care Mortality after discharge from intensive care may be reduced by 39% if these patients were to stay in intensive care for another 48 hours An estimated 16% more beds are required if mortality after discharge from intensive care is to be reduced