Generalizability of a simple approach for predicting hospital admission from an emergency department.
OBJECTIVE
The objective was to test the generalizability, across a range of hospital sizes and demographics, of a previously developed method for predicting and aggregating, in real time, the probabilities that emergency department (ED) patients will be admitted to a hospital inpatient unit.


METHODS
Logistic regression models were developed that estimate inpatient admission probabilities of each patient upon entering an ED. The models were based on retrospective development (n = 4,000 to 5,000 ED visits) and validation (n = 1,000 to 2,000 ED visits) data sets from four heterogeneous hospitals. Model performance was evaluated using retrospective test data sets (n = 1,000 to 2,000 ED visits). For one hospital the developed model also was applied prospectively to a test data set (n = 910 ED visits) coded by triage nurses in real time, to compare results to those from the retrospective single investigator-coded test data set.


RESULTS
The prediction models for each hospital performed reasonably well and typically involved just a few simple-to-collect variables, which differed for each hospital. Areas under receiver operating characteristic curves (AUC) ranged from 0.80 to 0.89, R(2) correlation coefficients between predicted and actual daily admissions ranged from 0.58 to 0.90, and Hosmer-Lemeshow goodness-of-fit statistics of model accuracy had p > 0.01 with one exception. Data coded prospectively by triage nurses produced comparable results.


CONCLUSIONS
The accuracy of regression models to predict ED patient admission likelihood was shown to be generalizable across hospitals of different sizes, populations, and administrative structures. Each hospital used a unique combination of predictive factors that may reflect these differences. This approach performed equally well when hospital staff coded patient data in real time versus the research team retrospectively.
New England Veterans Engineering Resource Center, Boston Veterans Health Administration, Boston, MA; Engineering Systems Division, Massachusetts Institute of Technology, Cambridge, MA.
Jordan S
JS
Peck
Stephan A
SA
Gaehde
Deborah J
DJ
Nightingale
David Y
DY
Gelman
David S
DS
Huckins
Mark F
MF
Lemons
Eric W
EW
Dickson
James C
JC
Benneyan
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D005260:Female; D006760:Hospitalization / Q000706:statistics & numerical data*; D006801:Humans; D008297:Male; D008875:Middle Aged; D010343:Patient Admission / Q000706:statistics & numerical data; D011237:Predictive Value of Tests; D012189:Retrospective Studies; D014218:Triage; D014481:United States