Prognostic Models for Predicting Remission of Diabetes Following Bariatric Surgery: A Systematic Review and Meta-analysis

Author:

Singh Pushpa12ORCID,Adderley Nicola J.3ORCID,Hazlehurst Jonathan12,Price Malcolm3,Tahrani Abd A.124,Nirantharakumar Krishnarajah2345ORCID,Bellary Srikanth26ORCID

Affiliation:

1. Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, U.K.

2. Department of Diabetes and Endocrinology, University Hospitals Birmingham NHS Foundation Trust, Birmingham, U.K.

3. Institute of Applied Health Research, University of Birmingham, Birmingham, U.K.

4. Centre for Endocrinology, Diabetes and Metabolism, Birmingham Health Partners, Birmingham, U.K.

5. Midlands Health Data Research, Birmingham, U.K.

6. School of Life and Health Sciences, Aston University, Birmingham, U.K.

Abstract

BACKGROUND Remission of type 2 diabetes following bariatric surgery is well established, but identifying patients who will go into remission is challenging. PURPOSE To perform a systematic review of currently available diabetes remission prediction models, compare their performance, and evaluate their applicability in clinical settings. DATA SOURCES A comprehensive systematic literature search of MEDLINE, MEDLINE In-Process & Other Non-Indexed Citations, Embase, and Cochrane Central Register of Controlled Trials (CENTRAL) was undertaken. The search was restricted to studies published in the last 15 years and in the English language. STUDY SELECTION All studies developing or validating a prediction model for diabetes remission in adults after bariatric surgery were included. DATA EXTRACTION The search identified 4,165 references, of which 38 were included for data extraction. We identified 16 model development and 22 validation studies. DATA SYNTHESIS Of the 16 model development studies, 11 developed scoring systems and 5 proposed logistic regression models. In model development studies, 10 models showed excellent discrimination with area under the receiver operating characteristic curve ≥0.800. Two of these prediction models, ABCD and DiaRem, were widely externally validated in different populations, in a variety of bariatric procedures, and for both short- and long-term diabetes remission. Newer prediction models showed excellent discrimination in test studies, but external validation was limited. LIMITATIONS While the key messages were consistent, a large proportion of the studies were conducted in small cohorts of patients with short duration of follow-up. CONCLUSIONS Among the prediction models identified, the ABCD and DiaRem models were the most widely validated and showed acceptable to excellent discrimination. More studies validating newer models and focusing on long-term diabetes remission are needed.

Publisher

American Diabetes Association

Subject

Advanced and Specialized Nursing,Endocrinology, Diabetes and Metabolism,Internal Medicine

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3