Using highly detailed administrative data to predict pneumonia mortality.
BACKGROUND
Mortality prediction models generally require clinical data or are derived from information coded at discharge, limiting adjustment for presenting severity of illness in observational studies using administrative data.


OBJECTIVE
To develop and validate a mortality prediction model using administrative data available in the first 2 hospital days.


METHODS
After dividing the dataset into derivation and validation sets, we created a hierarchical generalized linear mortality model that included patient demographics, comorbidities, medications, therapies, and diagnostic tests administered in the first 2 hospital days. We then applied the model to the validation set.


METHODS
Patients aged ≥ 18 years admitted with pneumonia between July 2007 and June 2010 to 347 hospitals in Premier, Inc.'s Perspective database.


METHODS
In hospital mortality.


RESULTS
The derivation cohort included 200,870 patients and the validation cohort had 50,037. Mortality was 7.2%. In the multivariable model, 3 demographic factors, 25 comorbidities, 41 medications, 7 diagnostic tests, and 9 treatments were associated with mortality. Factors that were most strongly associated with mortality included receipt of vasopressors, non-invasive ventilation, and bicarbonate. The model had a c-statistic of 0.85 in both cohorts. In the validation cohort, deciles of predicted risk ranged from 0.3% to 34.3% with observed risk over the same deciles from 0.1% to 33.7%.


CONCLUSIONS
A mortality model based on detailed administrative data available in the first 2 hospital days had good discrimination and calibration. The model compares favorably to clinically based prediction models and may be useful in observational studies when clinical data are not available.
Department of Medicine, Medicine Institute, Cleveland Clinic, Cleveland, Ohio, United States of America.
Michael B
MB
Rothberg
Center for Quality of Care Research, Baystate Medical Center, Springfield, Massachusetts, United States of America ; Department of Medicine, Tufts University School of Medicine, Boston, Massachusetts, United States of America.
Penelope S
PS
Pekow
Center for Quality of Care Research, Baystate Medical Center, Springfield, Massachusetts, United States of America.
Aruna
A
Priya
University of Massachusetts Amherst, Amherst, Massachusetts, United States of America ; EviMed Research Group, LLC, Goshen, Massachusetts, United States of America.
Marya D
MD
Zilberberg
Division of General Medicine, Baystate Medical Center, Springfield, Massachusetts, United States of America.
Raquel
R
Belforti
Division of Infectious Diseases, Baystate Medical Center, Springfield, Massachusetts, United States of America.
Daniel
D
Skiest
Division of General Medicine, Baystate Medical Center, Springfield, Massachusetts, United States of America ; Center for Quality of Care Research, Baystate Medical Center, Springfield, Massachusetts, United States of America ; Department of Medicine, Tufts University School of Medicine, Boston, Massachusetts, United States of America.
Tara
T
Lagu
Division of Pulmonary and Critical Care, Baystate Medical Center, Springfield, Massachusetts, United States of America.
Thomas L
TL
Higgins
Division of General Medicine, Baystate Medical Center, Springfield, Massachusetts, United States of America ; Center for Quality of Care Research, Baystate Medical Center, Springfield, Massachusetts, United States of America ; Department of Medicine, Tufts University School of Medicine, Boston, Massachusetts, United States of America.
Peter K
PK
Lindenauer
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D005260:Female; D006751:Hospital Information Systems; D017052:Hospital Mortality; D006801:Humans; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D011014:Pneumonia / Q000401:mortality* / Q000628:therapy; D012189:Retrospective Studies; D012307:Risk Factors