Predicting outcomes in emergency medical admissions - role of laboratory data and co-morbidity.
BACKGROUND
The utility of risk stratification following an emergency medical admission has been debated. We have examined the predictability of outcomes, from a database of all emergency admissions to St James' Hospital, Dublin, over a six year period (2005-2010).


METHODS
Analysis was performed using the hospital in-patient enquiry system, linked to the patient administration system and laboratory data. The utility of a fractional polynomial laboratory only model to predict 30-day in-hospital mortality was determined.


RESULTS
The AUROC for the laboratory parameters to predict a 30 day death was 0.90 ( 95% CI 0.89, 0.90) in the 2002 - 2010 derivation dataset and was 0.88 (95% CI 0.86, 0.90) in the 2011 validation set. The addition of co-morbidity measures did not improve the model prediction (0.89 : 95% CI 0.88 - 0.89).


CONCLUSIONS
A fractional polynomial laboratory only model can reliably predict 30-day hospital mortality following an emergency medical admission, potentially allowing resources to be risk focused and patients to be prioritised.
Department of Internal Medicine, St James's Hospital, Dublin 8 ,Ireland.
E
E
O'Sullivan
E
E
Callely
D
D
O'Riordan
K
K
Bennett
B
B
Silke
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D019411:Clinical Laboratory Techniques / Q000706:statistics & numerical data*; D015897:Comorbidity; D003955:Diagnostic Tests, Routine / Q000706:statistics & numerical data; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D005260:Female; D017052:Hospital Mortality; D006773:Hospitals, Municipal / Q000706:statistics & numerical data; D006801:Humans; D008297:Male; D008875:Middle Aged; D017063:Outcome Assessment, Health Care / Q000706:statistics & numerical data*; D010343:Patient Admission / Q000706:statistics & numerical data*; D011237:Predictive Value of Tests; D018570:Risk Assessment