Intensive care medicine, 2015-03-20, Vol.41 (6), p.1029-1036
Rapid assessment and intervention is important for the prognosis of acutely ill patients admitted to the emergency department (ED). The aim of this study was to prospectively develop and validate a model predicting the risk of in-hospital death based on all available information available at the time of ED admission and to compare its discriminative performance with a non-systematic risk estimate by the triaging first health-care provider.
Prospective cohort analysis based on a multivariable logistic regression for the probability of death.
A total of 8,607 consecutive admissions of 7,680 patients admitted to the ED of a tertiary care hospital were analysed. Most frequent APACHE II diagnostic categories at the time of admission were neurological (2,052, 24 %), trauma (1,522, 18 %), infection categories [1,328, 15 %; including sepsis (357, 4.1 %), severe sepsis (249, 2.9 %), septic shock (27, 0.3 %)], cardiovascular (1,022, 12 %), gastrointestinal (848, 10 %) and respiratory (449, 5 %). The predictors of the final model were age, prolonged capillary refill time, blood pressure, mechanical ventilation, oxygen saturation index, Glasgow coma score and APACHE II diagnostic category. The model showed good discriminative ability, with an area under the receiver operating characteristic curve of 0.92 and good internal validity. The model performed significantly better than non-systematic triaging of the patient.
The use of the prediction model can facilitate the identification of ED patients with higher mortality risk. The model performs better than a non-systematic assessment and may facilitate more rapid identification and commencement of treatment of patients at risk of an unfavourable outcome.
Adolescent ; Adult ; Aged ; Aged, 80 and over ; Anesthesiology ; Care and treatment ; Critical Care Medicine ; Critical Illness - mortality ; Critically ill ; Decision Support Techniques ; Emergency department ; Emergency Medicine ; Emergency Service, Hospital - statistics & numerical data ; Female ; Health risk assessment ; Hospital Mortality ; Humans ; Intensive ; Intensive / Critical Care Medicine ; Logistic Models ; Male ; Medicine ; Medicine & Public Health ; Methods ; Middle Aged ; Mortality ; Original ; Pain Medicine ; Patients ; Pediatrics ; Pneumology/Respiratory System ; Prognostic model ; Prospective Studies ; Risk ; Risk factors ; Triage ; Triage - methods ; Vital sign instability ; Vital Signs - physiology ; Young Adult
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