Prediction of hospital mortality rates by admission laboratory tests

Clin Chem. 2006 Feb;52(2):325-8. doi: 10.1373/clinchem.2005.059030.

Abstract

Background: The aim of this study was to explore whether electronically retrieved laboratory data can predict mortality in internal medicine departments in a regional hospital.

Methods: All 10,308 patients hospitalized in internal medicine departments over a 1-year period were included in the cohort. Nearly all patients had a complete blood count and basic clinical chemistries on admission. We used logistic regression analysis to predict the 573 deaths (5.6%), including all variables that added significantly to the model.

Results: Eight laboratory variables and age significantly and independently contributed to a logistic regression model (area under the ROC curve, 88.7%). The odds ratio for the final model per quartile of risk was 6.44 (95% confidence interval, 5.42-7.64), whereas for age alone, the odds ratio per quartile was 2.01 (95% confidence interval, 1.84-2.19).

Conclusions: A logistic regression model including only age and electronically retrieved laboratory data highly predicted mortality in internal medicine departments in a regional hospital, suggesting that age and routine admission laboratory tests might be used to ensure a fair comparison when using mortality monitoring for hospital quality control.

MeSH terms

  • Aged
  • Aged, 80 and over
  • Cohort Studies
  • Diagnostic Tests, Routine* / statistics & numerical data
  • Female
  • Hospital Mortality / trends*
  • Humans
  • Logistic Models
  • Male
  • Predictive Value of Tests