Modeling administrative outcomes in fever and neutropenia: clinical variables significantly influence length of stay and hospital charges.
BACKGROUND
Administrative outcomes such as length of stay and charges are used to compare the quality of care across institutions and among individual providers. Clinical variables representing disease severity may explain some of the variability in these outcomes.


OBJECTIVE
To determine the extent to which readily available clinical data can explain the variability in length of stay and charges for children with cancer admitted to the hospital for fever and neutropenia, and to assess the appropriateness of using a time-efficient electronic case-finding strategy for the development of administrative outcome models.


METHODS
A retrospective cohort of 157 fever and neutropenia encounters in a single institution during 11 months in 1997 was identified using a largely automated case-finding strategy followed by independent, blinded review of the selected discharge summaries. Models of admission variables predicting log length of stay and log charges were developed using multiple linear regression. The "smearing" technique of Duan adjusted for logarithmic retransformation was used in calculating each subject's predicted length of stay and charges. R2 values were calculated. There were two secondary analyses. In one, the result of admission blood culture was entered as a potential covariate. In the second, to evaluate the appropriateness of basing models on automated case-finding without discharge summary review, the authors rederived the models using all of the encounters (n = 160) identified by the algorithm, which had included three false-positive cases.


RESULTS
Mean length of stay was 6.45 days. Mean charges were $11,967. Absolute monocyte count at admission was a significant, independent negative predictor of length of stay and charges. Underlying cancer diagnosis also was significant. Charges were highest for acute myeloid leukemia, followed by central nervous system tumors, other solid tumors, and acute lymphoblastic leukemia and lymphomas. Length of stay was highest for acute myeloid leukemia, followed by central nervous system tumors, acute lymphoblastic leukemia and lymphomas, and other solid tumors. Absolute monocyte count and tumor type were the major components of the model, but admission temperature (for both administrative outcomes) and the presence of localized infection (for length of stay) also were significant predictors. R2 values were 35.3% (charges) and 38.5% (length of stay), with validation R2 values of 26.6% and 29.2%, respectively. Entering bacteremia as a covariate improved the models. Inclusion of the three false-positive cases generated models with only a modest loss of accuracy; it introduced over-and underreporting of some of the less significant predictors but did not disrupt the ability to identify the major predictors, absolute monocyte count and tumor type.


CONCLUSIONS
The clinical variables that were significant in this study account, in validation R2 estimates, for more than 25% of the variability in administrative outcomes for encounters of fever and neutropenia. Adjusting length of stay and charges for these clinical variables would allow for a fairer comparison of institutions and individual providers. The electronic case-finding algorithm served as an efficient way to identify absolute monocyte count and tumor type as the major predictors and provided a conservative estimate of R2.
Rosenman
Marc
M
Regenstrief Institute for Health Care and Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Madsen
Kristine
K
Hui
Siu
S
Breitfeld
Philip P
PP
D000293:Adolescent; D002648:Child; D002675:Child, Preschool; D015331:Cohort Studies; D005260:Female; D005334:Fever / Q000191:economics* / Q000209:etiology; D017722:Hospital Charges / Q000706:statistics & numerical data*; D006776:Hospitals, Pediatric; D006801:Humans; D007223:Infant; D007902:Length of Stay / Q000706:statistics & numerical data*; D008297:Male; D015233:Models, Statistical; D009369:Neoplasms / Q000150:complications / Q000191:economics; D009503:Neutropenia / Q000191:economics* / Q000209:etiology; D017063:Outcome Assessment, Health Care / Q000706:statistics & numerical data*; D012189:Retrospective Studies; D012307:Risk Factors