Title

Using health administrative databases to identify mechanical ventilation in transported pediatric critically ill patients

Authors

Janice A. Tijssen, Western UniversityFollow
L. Richard, Western University
S. Z. Shariff, Western University
T. To, University of Toronto
C. Parshuram, University of Toronto

Document Type

Article

Publication Date

8-20-2020

Journal

Canadian Journal of Respiratory, Critical Care, and Sleep Medicine

URL with Digital Object Identifier

https://doi.org/10.1080/24745332.2020.1802627

Abstract

RATIONALE: Accurate identification of children who received mechanical ventilation (MV) is important for operational decisions related to resource allocation and to provide valid population-level evaluations of critically ill children.

OBJECTIVES: The object of this study was to validate health administrative database codes for the identification of MV in children.

METHODS: Algorithms composed of hospital-based Canadian Classification of Health Interventions (CCI) and physician billing codes were validated against a reference dataset composed of critically ill pediatric patients transported to the pediatric critical care unit at the Hospital for Sick Children in Toronto, Canada between 2004 and 2012.

MEASUREMENTS: Descriptive statistics, sensitivity, specificity and positive and negative predictive values (PPV and NPV, respectively) were obtained.

MAIN RESULTS: Of 611 patients, 75% received MV, and of these, 94% received invasive MV only. For all types of MV (invasive and noninvasive), CCI and billing codes had a sensitivity of 85.6% and 87.8% and a specificity of 67.8% and 41.4%, respectively. The combination of CCI and billing codes yielded a sensitivity of 98% and a specificity of 37.5% for MV. Invasive MV CCI codes had a sensitivity of 86.3% and specificity of 71.5%. When only noninvasive MV CCI codes were tested, the sensitivity was 40.2% and the specificity was 97.1%. The PPV was highest using the CCI codes alone for any MV (89%) and for invasive MV (88%).

CONCLUSIONS: The combination of CCI and billing codes yielded an excellent sensitivity for MV; however, there is a risk of overestimating MV as specificity was low for all algorithms.

Notes

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