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2019 HSR&D/QUERI National Conference Abstract

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4114 — Development of Standard Algorithms for Weight Outcomes using VHA Administrative Data

Lead/Presenter: Wyndy Wiitala,  COIN - Ann Arbor
All Authors: Wiitala WL (VA Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, MI), Evans R (VA Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, MI), Annis A (VA Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, MI) Burns JA (VA Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, MI) Freitag MB (VA Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, MI) Raffa SD (VHA National Center for Health Promotion and Disease Prevention) Goldstein MG (VHA National Center for Health Promotion and Disease Prevention) Damschroder LJ (VA Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, MI)

Objectives:
Tracking measures of body weight in VHA data systems provides critical health information and is necessary for weight management program evaluations. However, there is conflicting documentation on constructing weight measures, presenting challenges for research and evaluation. We sought to describe and compare methods for extracting and cleaning weight data to develop guidelines for standardized approaches that promote reproducibility.

Methods:
We conducted a systematic review of studies that used VHA electronic health record (EHR) weight data, published from 2008 - 2018, and documented the algorithms for constructing patient weight. We applied these algorithms to four cohorts of Veterans in 2008 and 2016 who had primary care visits or were enrolled in the MOVE! Weight Management Program for Veterans. Resulting weight measures were compared at the patient and site levels.

Results:
We identified 496 studies and included 62 that utilized weight as outcome variables; 48% included a replicable algorithm. Algorithms varied from cut-offs of implausible weights to complex models using measures within patient over time. We found differences in the number of weight values after applying the algorithms (86% to 99% of raw data) and decreased variance (SD = 68 to 54), but little difference in average weights across methods (216 to 220 lbs). The percent of patients with at least 5% weight loss over one year ranged from 18% to 24%. In preliminary site-level analysis, we ranked sites by the percent of patients with at least 5% weight loss. The median rank difference for sites across methods was 16; differences ranged from 1 to 85 across the 129 sites.

Implications:
Patient weight is an important outcome. Determining the best method to assess weight using EHR data can be computationally demanding. Our preliminary results suggest that for many studies, applying simple cut-offs that require fewer computing resources and are cognitively easier to understand may be sufficient. We present guidelines for situations where more complex approaches may be warranted.

Impacts:
EHR systems provide clinically rich data that can be used for research and evaluation to improve patient care and outcomes. Our work informed the development of guidelines to facilitate standardization across projects and to promote reproducibility and replication of findings.