JSM 2011 Online Program

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Abstract Details

Activity Number: 407
Type: Contributed
Date/Time: Tuesday, August 2, 2011 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #300801
Title: Challenges of Summarizing Physiological Data from Anesthesia Information Management Systems
Author(s): Michael Bronsert*+ and Karl Hammermeister and William Henderson and Michael Mangione and Jennifer Nguyen and John Sum-Ping and Deyne Bentt and David Kazdan and Terri Monk
Companies: University of Colorado at Denver and University of Colorado at Denver and University of Colorado at Denver and VA Pittsburgh Healthcare System/University of Pittsburgh School of Medicine and Veterans Affairs Medical Center and VA North Texas Health Care System/UT Southwestern Medical Center and Washington DC VA Medical Center and Cleveland Department of Veterans Affairs Medical Center and Durham VA Medical Center
Address: Colorado Health Outcomes, Aurora, CO, 80045,
Keywords: Data Reduction ; Data Validaion ; Summary Measures
Abstract:

Anesthesia information management systems (AIMS) are used to collect perioperative physiologic data. We propose to test the hypothesis that perturbations in perioperative physiologic variables are associated with both short and long term adverse surgical outcomes. We obtained AIMS and surgical data for 19,468 patients having 26,830 surgical operations. The challenges addresed are how or whether to combine disparate field names for some physiologic concepts, removal of outliers or implausible values, and the design of summary measures for data reduction. We made decisions on combining variables on available samples sizes, clinical judgment, and descriptive statistics and distribution of values. Decisions were made about outliers by clinical judgment and large drops in distributional frequencies. Algorithms were developed to eliminate artifactual spikes that are common with invasive monitoring of arterial pressure. We explored data reduction to one or two values per physiologic concept per procedure through the use of area under or over the time-amplitude curve. Examples of our methods will be presented.


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