The relationship between severity of illness and hospital length of stay and mortality.
To address the question of quantification of severity of illness on a wide scale, the Computerized Severity Index (CSI) was developed by a research team at the Johns Hopkins University. This article describes an initial assessment of some aspects of the validity and reliability of the CSI on a sample of 2,378 patients within 27 high-volume DRGs from five teaching hospitals. The 27 DRGs predicted 27% of the variation in LOS, while DRGs adjusted for Admission CSI scores predicted 38% and DRGs adjusted for Maximum CSI scores throughout the hospital stay predicted 54% of this variation. Thus, the Maximum CSI score increased the predictability of DRGs by 100%. We explored the impact of including a 7-day cutoff criterion along with the Maximum CSI score similar to a criterion used in an alternative severity of illness measure. The DRG/Maximum CSI score's predictive power increased to 63% when the 7-day cutoff was added to the CSI definition. The Admission CSI score was used to predict in-hospital mortality and correlated R = 0.603 with mortality. The reliability of Admission and Maximum CSI data collection was high, with agreement of 95% and kappa statistics of 0.88 and 0.90, respectively.
Horn
S D
SD
Department of Health Policy and Management, School of Hygiene and Public Health, Johns Hopkins University, Baltimore, MD.
Sharkey
P D
PD
Buckle
J M
JM
Backofen
J E
JE
Averill
R F
RF
Horn
R A
RA
D003953:Diagnosis-Related Groups / Q000706:statistics & numerical data; D006302:Health Services Research / Q000379:methods; D006784:Hospitals, Teaching / Q000706:statistics & numerical data*; D006801:Humans; D007902:Length of Stay / Q000706:statistics & numerical data*; D016015:Logistic Models; D009026:Mortality; D011336:Probability; D015203:Reproducibility of Results; D012720:Severity of Illness Index; D012984:Software; D014481:United States