BACKGROUND: As electronic @entity1 data from automated hospital databases become increasingly available, it is important to explore the ways in which these data could be used for the purposes other than @entity1 care, such as quality assurance and improvement. OBJECTIVE: To determine if information from automated @entity1 databases can be used to derive a model that can predict @entity1 ' daily risk of @entity204 in hospital. Such a model could be used to improve the ability to risk-adjust hospital mortality rates. STUDY DESIGN AND SETTING: Retrospective cohort study of 159,794 hospitalizations at The Ottawa Hospital between April 1, 2004 and March 31, 2009. The model was derived using time-dependent Cox regression methods on a random two-thirds of admissions. The model was validated by applying the coefficients to the other third of admissions. RESULTS: Inpatient mortality was 5%. The final model included: @entity1 age; admission type; intensive care unit status; alternative level of care status; and separate scores for @entity1 comorbidity, in-hospital procedures, and @entity697 (using information from 14 laboratory tests). In the validation set, the model had excellent discrimination (c-statistic 0.879, 95% confidence interval: 0.872-0.886) and calibration in all risk strata over all admission days. CONCLUSION: We found that information from our hospital's automated @entity1 databases could be used to accurately predict @entity1 ' daily risk of @entity204 in hospital. The predictions from this model could be used in quality of care analyses to more accurately risk-adjust hospital mortality rates and by hospitals to improve triage processes and @entity1 flow.

Derivation and validation of a model to predict daily risk of XXXX in hospital.

@entity1 :: ('9606', 'Species') :: ['patients', 'patient'] @entity204 :: ('MESH:D003643', 'Disease') :: ['death'] @entity697 :: ('MESH:D000208', 'Disease') :: ['acute illness']
@entity204 :: (MESH:D003643,Disease) :: ['death']
