Prediction of hospital mortality rates by admission laboratory tests.
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.
Froom
Paul
P
Department of Epidemiology and Preventive Medicine, Sackler Medical School, Tel Aviv University, Tel Aviv, Israel. froom@maaganm.co.il
Shimoni
Zvi
Z
D000368:Aged; D000369:Aged, 80 and over; D015331:Cohort Studies; D003955:Diagnostic Tests, Routine / Q000706:statistics & numerical data; D005260:Female; D017052:Hospital Mortality / Q000639:trends*; D006801:Humans; D016015:Logistic Models; D008297:Male; D011237:Predictive Value of Tests