Khairollah Asadollahi, MD, PhD, Assistant Professor in Clinical Epidemiology; Ian M Hastings, B.Sc., Ph.D, Senior Lecturer in Epidemiology and statistics; Geoffrey V Gill, MA, MSc, MD, PhD, FRCP, DTM&H, Professor of International Medicine; Nicholas J Beeching, MA, BM BCh, FRCP(Lond), FRACP, FFTM RCPS(Glasg), DCH, DTM&H, Senior Lecturer (Clinical) in Tropical and Infectious Diseases.
Prediction of hospital mortality from admission laboratory data and patient age: A simple model
Article first published online: 27 APR 2011
© 2011 The Authors. EMA © 2011 Australasian College for Emergency Medicine and Australasian Society for Emergency Medicine
Emergency Medicine Australasia
Volume 23, Issue 3, pages 354–363, June 2011
How to Cite
Asadollahi, K., Hastings, I. M., Gill, G. V. and Beeching, N. J. (2011), Prediction of hospital mortality from admission laboratory data and patient age: A simple model. Emergency Medicine Australasia, 23: 354–363. doi: 10.1111/j.1742-6723.2011.01410.x
- Issue published online: 14 JUN 2011
- Article first published online: 27 APR 2011
- Accepted 11 February 2011
- acute admission;
- laboratory data;
- scoring system
Objective: To devise a simple clinical scoring system, using age of patients and laboratory data available on admission, to predict in-hospital mortality of unselected medical and surgical patients.
Methods: All patients admitted as emergencies to a large teaching hospital in Liverpool in the 5 months July–November 2004 were reviewed retrospectively, identifying all who died in hospital and controls who survived. Laboratory data available on admission were extracted to form a derivation dataset. Factors that predicted mortality were determined using logistic regression analysis and then used to construct models tested using receiver operating characteristic curves. Models were simplified to include only seven data items, with minimal loss of predictive efficiency. The simplified model was tested in a second validation dataset of all patients admitted to the same hospital in October and November 2004.
Results: The derivation dataset included 550 patients who died and 1100 controls. After logistic regression comparisons, 22 dummy variables were given weightings in discriminant analysis and used to create a receiver operating characteristic curve with area under the curve (AUC) of 0.884. The model was simplified to include the seven most discriminant variables, which can each be assigned scores of 2, 3 or 4 to form an index predicting outcome; a validation dataset contained 4828 patients (overall mortality 4.7%), showed this simplified scoring system accurately predicted mortality with AUC 0.848, compared with an AUC of 0.861 in a model containing all 23 original variables.
Conclusion: A simple scoring system accurately predicts in-hospital mortality of unselected hospital patients, using age of patient and a small number of laboratory parameters available very soon after admission.