Identifying patients at high risk of emergency hospital admissions: a logistic regression analysis.
OBJECTIVE
To use routine data to identify patients at high risk of future emergency hospital admissions.


METHODS
Descriptive analysis of inpatient hospital episode statistics. Predictive model developed using multiple logistic regression.


METHODS
National Health Service hospital trusts in England.


METHODS
All patients with an emergency admission to an NHS hospital between 1 April 2000 and 31 March 2001.


METHODS
'High-impact users' were defined as patients who had at least one emergency inpatient admission and who then went on to have at least two further emergency hospital admissions in the 12 months following the start date of that index admission.


RESULTS
2,895,234 patients were admitted as emergencies in 2000/2001, of whom 147,725 (5.1%) did not survive their first spell. Of the 2,747,509 surviving patients, 269,686 (9.8%) subsequently had at least two or more emergency admissions within 365 days of the index date of admission. A further 236,779 (8.6%) died during this period. Risk factors for becoming a high-impact user included the number of emergencies in the 36 months before index spell, comorbidity, age, an admission for an ambulatory care sensitive condition, ethnicity, area-level socioeconomic data, local admission rates, the number of episodes in the index spell, sex and the source of admission. The predictive model based on all emergency admissions produced a receiver operating characteristic curve score of 0.72.


CONCLUSIONS
Routine hospital episode statistics can be used to identify patients who are at high risk of suffering future multiple emergency hospital admissions. The potential cost savings in preventing a proportion of these subsequent admissions need to be compared with the costs of case management of these patients.
Bottle
Alex
A
Department of Primary Care and Social Medicine, Imperial College London, London W6 8RP, UK. robert.bottle@imperial.ac.uk
Aylin
Paul
P
Majeed
Azeem
A
D000293:Adolescent; D000328:Adult; D000368:Aged; D002648:Child; D002675:Child, Preschool; D004630:Emergencies / Q000453:epidemiology*; D005500:Follow-Up Studies; D006760:Hospitalization / Q000706:statistics & numerical data*; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008875:Middle Aged; D012044:Regression Analysis; D018570:Risk Assessment; D012307:Risk Factors