TY - JOUR
T1 - Using highly detailed administrative data to predict pneumonia mortality
AU - Rothberg, Michael B.
AU - Pekow, Penelope S.
AU - Priya, Aruna
AU - Zilberberg, Marya D.
AU - Belforti, Raquel
AU - Skiest, Daniel
AU - Lagu, Tara
AU - Higgins, Thomas L.
AU - Lindenauer, Peter K.
PY - 2014/1/31
Y1 - 2014/1/31
N2 - 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. Objectives: To develop and validate a mortality prediction model using administrative data available in the first 2 hospital days. Research Design: 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. Subjects: Patients aged ≥18 years admitted with pneumonia between July 2007 and June 2010 to 347 hospitals in Premier, Inc.'s Perspective database. Measures: 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, noninvasive 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.
AB - 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. Objectives: To develop and validate a mortality prediction model using administrative data available in the first 2 hospital days. Research Design: 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. Subjects: Patients aged ≥18 years admitted with pneumonia between July 2007 and June 2010 to 347 hospitals in Premier, Inc.'s Perspective database. Measures: 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, noninvasive 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.
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U2 - 10.1371/journal.pone.0087382
DO - 10.1371/journal.pone.0087382
M3 - Article
C2 - 24498090
AN - SCOPUS:84900344488
VL - 9
JO - PLoS One
JF - PLoS One
SN - 1932-6203
IS - 1
M1 - e87382
ER -