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.

K.N. and S.B. are joint senior authors and contributed equally to this manuscript.

This article contains supplementary material online at https://doi.org/10.2337/figshare.15173232.

Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. More information is available at https://www.diabetesjournals.org/content/license.
You do not currently have access to this content.