Skip to main content
×
Home
    • Aa
    • Aa

Classification algorithms to improve the accuracy of identifying patients hospitalized with community-acquired pneumonia using administrative data

  • O. YU (a1), J. C. NELSON (a1) (a2), L. BOUNDS (a3) and L. A. JACKSON (a3) (a4)
Abstract
SUMMARY

In epidemiological studies of community-acquired pneumonia (CAP) that utilize administrative data, cases are typically defined by the presence of a pneumonia hospital discharge diagnosis code. However, not all such hospitalizations represent true CAP cases. We identified 3991 hospitalizations during 1997–2005 in a managed care organization, and validated them as CAP or not by reviewing medical records. To improve the accuracy of CAP identification, classification algorithms that incorporated additional administrative information associated with the hospitalization were developed using the classification and regression tree analysis. We found that a pneumonia code designated as the primary discharge diagnosis and duration of hospital stay improved the classification of CAP hospitalizations. Compared to the commonly used method that is based on the presence of a primary discharge diagnosis code of pneumonia alone, these algorithms had higher sensitivity (81–98%) and positive predictive values (82–84%) with only modest decreases in specificity (48–82%) and negative predictive values (75–90%).

Copyright
Corresponding author
*Author for correspondence: Ms. O. Yu, Group Health Research Institute, 1730 Minor Ave, Suite 1600, Seattle, WA 98101, USA (Email: yu.o@ghc.org)
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Epidemiology & Infection
  • ISSN: 0950-2688
  • EISSN: 1469-4409
  • URL: /core/journals/epidemiology-and-infection
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
×

Keywords:

Metrics

Altmetric attention score

Full text views

Total number of HTML views: 3
Total number of PDF views: 29 *
Loading metrics...

Abstract views

Total abstract views: 96 *
Loading metrics...

* Views captured on Cambridge Core between September 2016 - 24th March 2017. This data will be updated every 24 hours.