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Clinical Trial
. 2014 Jan 31;9(1):e87382.
doi: 10.1371/journal.pone.0087382. eCollection 2014.

Using highly detailed administrative data to predict pneumonia mortality

Michael B Rothberg  1 , Penelope S Pekow  2 , Aruna Priya  3 , Marya D Zilberberg  4 , Raquel Belforti  5 , Daniel Skiest  6 , Tara Lagu  7 , Thomas L Higgins  8 , Peter K Lindenauer  7
Affiliations
Clinical Trial

Using highly detailed administrative data to predict pneumonia mortality

Michael B Rothberg et al. PLoS One. .

Abstract

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, 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.

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Conflict of interest statement

Competing Interests: The authors have the following interests: Dr. Marya D. Zilberberg is employed by EviMed Research Group, LLC. Dr. Zilberberg has received research funding and served as a consultant for Pfizer, Astellas, Cubist, Forest, and Johnson and Johnson. There are no patents, products in development or marketed products to declare. This does not alter the authors’ adherence to all the PLOS ONE policies on sharing data and materials, as detailed online in the guide for authors.

Figures

Figure 1
Figure 1. Comparison of Model Components’ Discrimination in the Derivation Cohort.
Factors not significant at p<.05 and interaction terms are not included. All medications, tests and therapies are within the first 2 hospital days. Legend includes area under the ROC curve and 95% confidence intervals.
Figure 2
Figure 2. Model Calibration by Deciles of Predicted Risk in the Development and Validation Cohorts.
See this image and copyright information in PMC

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