ArticleBMJ (Clinical research ed.)2001
Reduction in mortality after inappropriate early discharge from intensive care unit: logistic regression triage model.
Article in BMJ (Clinical research ed.), 2001. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 3 of them syntheses that pooled it.
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Who cites it
52 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Association between time of discharge from ICU and hospital mortality: a systematic review and meta-analysis.Critical care (London, England) · 2016Pooled it
- A meta-analysis to derive literature-based benchmarks for readmission and hospital mortality after patient discharge from intensive care.Critical care (London, England) · 2014Pooled it
- A systematic review of tools for predicting severe adverse events following patient discharge from intensive care units.Critical care (London, England) · 2013Pooled it
- Effects of Implementing an ICU Discharge Readiness Checklist onHealthcare (Basel, Switzerland) · 2025Article
- Development of a Simple Scoring System for Predicting Discharge Safety from the Medical ICU to Low-Acuity Wards: The Role of the Sequential Organ Failure Assessment Score, Albumin, and Red Blood Cell Distribution Width.Journal of personalized medicine · 2024Article
- Article
- Status and Risk Factors in Patients Requiring Unplanned Intensive Care Unit Readmission Within 48 Hours: A Retrospective Propensity-Matched Study in China.Risk management and healthcare policy · 2023Article
- State of the Art of Machine Learning-Enabled Clinical Decision Support in Intensive Care Units: Literature Review.JMIR medical informatics · 2022Review
- Explainable Machine Learning on AmsterdamUMCdb for ICU Discharge Decision Support: Uniting Intensivists and Data Scientists.Critical care explorations · 2021Article
- A continuous-time Markov model for estimating readmission risk for hospital inpatients.Journal of applied statistics · 2021Article
- Serious sports-related injury in England and Wales from 2012-2017: a study protocol.Injury epidemiology · 2020Article
- The Pros and Cons of the Prediction Game: The Never-ending Debate of Mortality in the Intensive Care Unit.International journal of environmental research and public health · 2019Observational
- Using the National Early Warning Score (NEWS/NEWS 2) in different Intensive Care Units (ICUs) to predict the discharge location of patients.BMC public health · 2019Article
- Hidden hospital mortality in patients with sepsis discharged from the intensive care unit.Revista Brasileira de terapia intensiva · 2019Observational
- Protocol for a mixed-methods exploratory investigation of care following intensive care discharge: the REFLECT study.BMJ open · 2019Article
- The Critical Care Society of Southern Africa Consensus Statement on ICU Triage and Rationing (ConICTri).The Southern African journal of critical care : the official journal of the Critical Care Society · 2019Article
- Risk Factors for Intensive Care Unit Readmission After Liver Transplantation: A Retrospective Cohort Study.Annals of transplantation · 2018Article
- Predicting Intensive Care Unit Readmission with Machine Learning Using Electronic Health Record Data.Annals of the American Thoracic Society · 2018Observational
- Article
- Using an intervention mapping approach to develop a discharge protocol for intensive care patients.BMC health services research · 2017Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo develop a predictive model to triage patients for discharge from intensive care units to reduce mortality after discharge.
designLogistic regression analyses and modelling of data from patients who were discharged from intensive care units.
settingGuy's hospital intensive care unit and 19 other UK intensive care units from 1989 to 1998.
participants5475 patients for the development of the model and 8449 for validation.
main outcome measuresMortality after discharge and power of triage model.
resultsMortality 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.
conclusionsThe 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.
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