Abstract
BackgroundWe explored the use of routine blood tests and national early warning scores (NEWS) reported within ±24 hours of admission to predict in-hospital mortality in emergency admissions, using empirical decision Tree models because they are intuitive and may ultimately be used to support clinical decision making.MethodologyA retrospective analysis of adult emergency admissions to a large acute hospital during April 2009 to March 2010 in the West Midlands, England, with a full set of index blood tests results (albumin, creatinine, haemoglobin, potassium, sodium, urea, white cell count and an index NEWS undertaken within ±24 hours of admission). We developed a Tree model by randomly splitting the admissions into a training (50%) and validation dataset (50%) and assessed its accuracy using the concordance (c-) statistic. Emergency admissions (about 30%) did not have a full set of index blood tests and/or NEWS and so were not included in our analysis.ResultsThere were 23248 emergency admissions with a full set of blood tests and NEWS with an in-hospital mortality of 5.69%. The Tree model identified age, NEWS, albumin, sodium, white cell count and urea as significant (p<0.001) predictors of death, which described 17 homogeneous subgroups of admissions with mortality ranging from 0.2% to 60%. The c-statistic for the training model was 0.864 (95%CI 0.852 to 0.87) and when applied to the testing data set this was 0.853 (95%CI 0.840 to 0.866).ConclusionsAn easy to interpret validated risk adjustment Tree model using blood test and NEWS taken within ±24 hours of admission provides good discrimination and offers a novel approach to risk adjustment which may potentially support clinical decision making. Given the nature of the clinical data, the results are likely to be generalisable but further research is required to investigate this promising approach.
Highlights
There is considerable interest in developing statistical models which can adjust for patient case-mix and predict the risk of death in hospital
The Tree model identified age, national early warning scores (NEWS), albumin, sodium, white cell count and urea as significant (p,0.001) predictors of death, which described 17 homogeneous subgroups of admissions with mortality ranging from 0.2% to 60%
Notwithstanding the use of such data to produce hospital mortality ratios, our primary focus is on developing tools which may support clinical decision making in real time
Summary
There is considerable interest in developing statistical models which can adjust for patient case-mix and predict the risk of death in hospital. As most deaths in hospital occur in patients admitted as emergencies, we focused on these and noted that very early into their care, almost all of these patients will have a routine blood test and, at least in the National Health Service (NHS), an early warning score (EWS) derived from a range of vital signs (e.g. respiration rate, blood pressure, heart rate, temperature) measurements [14]. These data are not subject to many of the shortcomings of administrative clinically coded data. We explored the use of routine blood tests and national early warning scores (NEWS) reported within 624 hours of admission to predict in-hospital mortality in emergency admissions, using empirical decision Tree models because they are intuitive and may be used to support clinical decision making
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