What question did this study set out to answer?

To develop and validate a clinical triage model that identifies patients at high risk of death following discharge from the intensive care unit.

May 26, 2001BMJOpen Access

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

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Why the study?

Does a logistic regression triage model accurately identify patients at risk of mortality after discharge from the intensive care unit?

Population

13,924 patients discharged from intensive care units across 20 UK intensive care units from 1989 to 1998.

Design

Cohort

Authors

KDKathleen DalyLaidlaw College

Discussion

Member takes

Implication

May aid post-ICU risk stratification; leaves open whether implementation reduces mortality in prospective trials.

Key Points

To develop and validate a clinical triage model that identifies patients at high risk of death following discharge from the intensive care unit.
Analyzed clinical data from 20 UK intensive care units collected between 1989 and 1998 using logistic regression modeling.
Trained the predictive model on a development cohort of N=5,475 patients and tested it on an independent validation cohort of N=8,449 patients.
The triage model included age, end-stage disease, ICU length of stay, cardiothoracic surgery, and physiology variables, achieving an area under the ROC curve of 0.86 with 65.5% sensitivity and 87.6% specificity.
In the validation cohort, 34% of patients classified as at-risk had a post-discharge mortality rate of 25% compared to 4% in those not at risk, indicating an additional 48 hours in the ICU could reduce discharge mortality by 39%.

Structured PICO

Does a logistic regression triage model accurately identify patients at risk of mortality after discharge from the intensive care unit?

Population

13,924 patients discharged from intensive care units (5,475 for model development and 8,449 for validation) across 20 UK intensive care units from 1989 to 1998.

Intervention

Logistic regression triage model using variables including age, end stage disease, length of stay in unit, cardiothoracic surgery, and physiology to identify patients at risk of death after discharge.

Outcome

Mortality after discharge and power of triage modelhard clinical

A logistic regression triage model can accurately identify ICU patients at high risk of post-discharge mortality, potentially guiding decisions to delay discharge and reduce deaths.

Cite This Study

Kathleen Daly (2001) studied this question.

synapsesocial.com/papers/6a8930ae58d99da5144d3d44 https://doi.org/10.1136/bmj.322.7297.1274

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

Discussion

Member takes

Implication

Key Points

Structured PICO

Cite This Study

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

Discussion

Member takes

Implication

Key Points

Structured PICO

Cite This Study

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Also Consider