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eBook details

• Title: Prediction of Hospital Mortality Rates by Admission Laboratory Tests (Technical Briefs)
• Author : Clinical Chemistry
• Release Date : January 01, 2006
• Genre: Chemistry,Books,Science & Nature,
• Pages : * pages
• Size : 175 KB

Description

Monitoring hospital mortality rates is used to evaluate and improve the quality of healthcare (1) and is one measure of the performance monitoring systems required by hospital-accrediting organizations for quality control (2). This process requires adjustment for differences in severity of illness and other risk factors to ensure a fair comparison. Many attempts worldwide have used readily available independent variables to predict in-hospital mortality (3-5). Among these variables are the diagnostic codes listed in the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM), which are often used for billing. These codes can only be applied retrospectively because they are based on the final discharge diagnosis. This method, however, has been criticized because, although widely available and inexpensive, it lacks clinical details necessary to permit adequate adjustment for each patient's underlying medical condition (6,7) and does not differentiate between diseases on admission and hospital-acquired complications, such as shock, that often precede death. Risk-adjustment models that include hospital-acquired complications could therefore overestimate the predictive value of the model and could mask inadequate care by increasing the measured risk of patients whose health deteriorated during hospitalization. In fact, Pine et al. (1), using ICD codes after excluding diagnoses that may have been hospital-acquired, found that the mean areas under the ROC curves in a logistic regression model decreased from 0.87 to 0.75. Electronically retrieved laboratory data might be useful in predicting mortality because the tests are done routinely on admission, are unbiased by clinical evaluation, and reflect, at least to some extent, disease severity. We explored whether electronically retrieved laboratory data not requiring any data abstraction could predict mortality in internal medicine departments in a regional hospital.

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