Cardiovasc Prev Pharmacother.  2022 Apr;4(2):87-93. 10.36011/cpp.2022.4.e12.

Development of a predictive model for the side effects of liraglutide

• Min J
• Shinn J
• Kim HS

• KMID: 2536925
• DOI: http://doi.org/10.36011/cpp.2022.4.e12

Abstract

Background
Liraglutide, a drug used for the management of obesity, has many known side effects. In this study, we developed a predictive model for the occurrence of liraglutide-related side effects using data from electronic medical records (EMRs).
Methods
This study included 237 patients from Seoul St. Mary's Hospital and Eunpyeong St. Mary's Hospital who were prescribed liraglutide. An endocrinologist obtained medical data through an EMR chart review. Model performance was evaluated using the mean of the area under the receiver operating characteristic curve (AUROC) with a 95% confidence interval (CI).
Results
A predictive model was developed for patients who were prescribed liraglutide. However, 37.1% to 75.5% of many variables were missing, and the AUROC of the developed predictive model was 0.630 (95% CI, 0.551–0.708). Patients who had previously taken antiobesity medication had significantly fewer side effects than those without previous antiobesity medication use (20.7% vs. 41.4%, P<0.003). The risk of side effect occurrence was significantly higher in patients with diabetes than in patients without diabetes by 2.389 times (odds ratio, 2.389; 95% CI, 1.115–5.174).
Conclusions
This study did not successfully develop a predictive model for liraglutide-related side effects, primarily due to issues related to missing data. When prescribing antiobesity drugs, detailed records and basic blood tests are expected to be essential. Further large-scale studies on liraglutide-related side effects are needed after obtaining high-quality data.

Keyword

Liraglutide; Obesity; Side effect; Electronic medical records

Figure

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  Fig. 1. Area under the receiver operating characteristic curve (AUROC) of the side effect prediction model. CI, confidence interval.

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  Fig. 2. Contribution of features to the model predictions.

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