Predictors of treatment response to liraglutide in type 2 diabetes in a real-world setting.

ABSTRACT:

Aims

There is an unmet need among healthcare providers to identify subgroups of patients with type 2 diabetes who are most likely to respond to treatment.

Methods

Data were taken from electronic medical records of participants of an observational, retrospective study in Italy. We used logistic regression models to assess the odds of achieving glycated haemoglobin (HbA_1c) reduction ≥ 1.0% point after 12-month treatment with liraglutide (primary endpoint), according to various patient-related factors. RECursive Partitioning and AMalgamation (RECPAM) analysis was used to identify distinct homogeneous patient subgroups with different odds of achieving the primary endpoint.

Results

Data from 1325 patients were included, of which 577 (43.5%) achieved HbA_1c</

SUBMITTER: Simioni N 

PROVIDER: S-EPMC5959971 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature