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

Pushpa Singh; Nicola Adderley; Jonathan Hazlehurst; Malcolm  Price; Abd A. Tahrani; Krishnarajah Nirantharakumar; Srikanth Bellary

doi:10.2337/dc21-0166

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

Pushpa Singh
, Nicola Adderley
, Jonathan Hazlehurst
, Malcolm Price
, Abd A. Tahrani
, Krishnarajah Nirantharakumar^*
, Srikanth Bellary

^*Corresponding author for this work

Birmingham Heartlands Hospital
University College Birmingham
Heart of England NHS Foundation Trust
Birmingham Heath Partners
Department of Diabetes and Endocrinology
Birmingham Health Partners
Birmingham Heartlands Hospital
University Hospitals Birmingham NHS Foundation Trust
Christie NHS Foundation Trust
Health Data Research UK
University of Birmingham

Research output: Contribution to journal › Review article › peer-review

32 Citations (Scopus)

80 Downloads (Pure)

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.

Original language	English
Pages (from-to)	2626-2641
Number of pages	16
Journal	Diabetes Care
Volume	44
Issue number	11
Early online date	18 Oct 2021
DOIs	https://doi.org/10.2337/dc21-0166
Publication status	Published - Nov 2021

Bibliographical note

This is an author-created, uncopyedited electronic version of an article accepted for publication in Diabetes. The American Diabetes Association (ADA), publisher of Diabetes, is not responsible for any errors or omissions in this version of the manuscript or any version derived from it by third parties. The definitive publisher-authenticated version will be available in a future issue of Diabetes in print and online at http://diabetes.diabetesjournals.org.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Adult
Bariatric Surgery
Diabetes Mellitus, Type 2/surgery
Humans
Prognosis

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10.2337/dc21-0166

Systematic_Review_Diabetes_remission
This is an author-created, uncopyedited electronic version of an article accepted for publication in Diabetes Care. The American Diabetes Association (ADA), publisher of Diabetes Care, is not responsible for any errors or omissions in this version of the manuscript or any version derived from it by third parties. The definitive publisher-authenticated version will be available in a future issue of Diabetes Care in print and online at http://care.diabetesjournals.org.
Accepted author manuscript, 986 KB

https://research.birmingham.ac.uk/portal/en/publications/prognostic-models-for-predicting-remission-of-diabetes-following-bariatric-surgery(de3a7f00-f798-4517-9419-fbad5faf2961).html

Cite this

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

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.",

keywords = "Adult, Bariatric Surgery, Diabetes Mellitus, Type 2/surgery, Humans, Prognosis",

author = "Pushpa Singh and Nicola Adderley and Jonathan Hazlehurst and Malcolm Price and Tahrani, \{Abd A.\} and Krishnarajah Nirantharakumar and Srikanth Bellary",

note = "This is an author-created, uncopyedited electronic version of an article accepted for publication in Diabetes. The American Diabetes Association (ADA), publisher of Diabetes, is not responsible for any errors or omissions in this version of the manuscript or any version derived from it by third parties. The definitive publisher-authenticated version will be available in a future issue of Diabetes in print and online at http://diabetes.diabetesjournals.org.",

year = "2021",

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journal = "Diabetes Care",

issn = "0149-5992",

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T2 - A Systematic Review and Meta-analysis

AU - Singh, Pushpa

AU - Adderley, Nicola

AU - Hazlehurst, Jonathan

AU - Price, Malcolm

AU - Tahrani, Abd A.

AU - Nirantharakumar, Krishnarajah

AU - Bellary, Srikanth

N1 - This is an author-created, uncopyedited electronic version of an article accepted for publication in Diabetes. The American Diabetes Association (ADA), publisher of Diabetes, is not responsible for any errors or omissions in this version of the manuscript or any version derived from it by third parties. The definitive publisher-authenticated version will be available in a future issue of Diabetes in print and online at http://diabetes.diabetesjournals.org.

PY - 2021/11

Y1 - 2021/11

N2 - 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.

AB - 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.

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KW - Bariatric Surgery

KW - Diabetes Mellitus, Type 2/surgery

KW - Humans

KW - Prognosis

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DO - 10.2337/dc21-0166

M3 - Review article

C2 - 34670787

SN - 0149-5992

VL - 44

SP - 2626

EP - 2641

JO - Diabetes Care

JF - Diabetes Care

IS - 11

ER -

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

Abstract

Bibliographical note

UN SDGs

Keywords

Access to Document

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