Developing a decision aid for clinical obesity services in the real world : the DACOS nationwide pilot study

Evan Atlantis, Nic Kormas, Milan Piya, Mehdi Sahebol-Amri, Kathryn Williams, Hsin-Chia Carol Huang, Ramy Bishay, Viral Chikani, Teresa Girolamo, Ante Prodan, Paul Fahey

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: The purpose of this study is to develop a decision aid tool using “real-world” data within the Australian health system to predict weight loss after bariatric surgery and non-surgical care. Materials and Methods: We analyzed patient record data (aged 16+years) from initial review between 2015 and 2020 with 6-month (n=219) and 9-/12-month (n=153) follow-ups at eight clinical obesity services. Primary outcome was percentage total weight loss (%TWL) at 6 months and 9/12 months. Predictors were selected by statistical evidence (p<0.20), effect size (±2%), and clinical judgment. Multiple linear regression and bariatric surgery were used to create simple predictive models. Accuracy was measured using percentage of predictions within 5% of the observed value, and sensitivity and specificity for predicting target weight loss of 5% (non-surgical care) and 15% (bariatric surgery). Results: Observed %TWL with bariatric surgery vs. non-surgical care was 19% vs. 5% at 6 months and 22% vs. 5% at 9/12 months. Predictors at 6 months with intercept (non-surgical care) of 6% include bariatric surgery (+11%), BMI>60 (–3%), depression (–2%), anxiety (–2%), and eating disorder (–2%). Accuracy, sensitivity, and specificity were 58%, 69%, and 56%. Predictors at 9/12 months with intercept of 5% include bariatric surgery (+15%), type 2 diabetes (+5%), eating disorder (+4%), fatty liver (+2%), atrial fibrillation (–4%), osteoarthritis (–3%), sleep/mental disorders (–2–3%), and ≥10 alcohol drinks/week (–2%). Accuracy, sensitivity, and specificity were 55%, 86%, and 53%. Conclusion: Clinicians may use DACOS to discuss potential weight loss predictors with patients after surgery or non-surgical care. Graphical Abstract: (Figure presented.)

Original languageEnglish
Pages (from-to)2073-2083
Number of pages11
JournalObesity Surgery
Volume34
Issue number6
Publication statusPublished - Jun 2024

Bibliographical note

Publisher Copyright:
© The Author(s) 2024.

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Open Access This article is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Keywords

  • Obesity
  • Weight loss
  • Management
  • Decision support model

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