ArticleThe Journal of clinical endocrinology and metabolism2023
Predictors of Metformin Failure: Repurposing Electronic Health Record Data to Identify High-Risk Patients.
Article in The Journal of clinical endocrinology and metabolism, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Early identification of suboptimal responders to metformin in type 2 diabetes using long-term real-world HbA1c trajectories.medRxiv : the preprint server for health sciences · 2026Article
- MENTSH: A novel mitochondrial microprotein linked to a SNP associated with type 2 diabetes.Theranostics · 2026Article
- Article
- Predicting responsiveness to GLP-1 pathway drugs using real-world data.BMC endocrine disorders · 2024Article
- Exome Sequence Data of Eight SLC Transporters Reveal ThatPharmaceuticals (Basel, Switzerland) · 2024Article
- Greater persistence and adherence to basal insulin therapy is associated with lower healthcare utilization and medical costs in patients with type 2 diabetes: a retrospective database analysis.BMJ open diabetes research & care · 2024Article
- Predictors of Metformin Failure: Repurposing Electronic Health Record Data to Identify High-Risk Patients.The Journal of clinical endocrinology and metabolism · 2023Article
- Obesity, diabetes mellitus, and cardiometabolic risk: An Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) 2023.Obesity pillars · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
23 authors.
Funding
Abstract
contextMetformin is the first-line drug for treating diabetes but has a high failure rate.
objectiveTo identify demographic and clinical factors available in the electronic health record (EHR) that predict metformin failure.
methodsA cohort of patients with at least 1 abnormal diabetes screening test that initiated metformin was identified at 3 sites (Arizona, Mississippi, and Minnesota). We identified 22 047 metformin initiators (48% female, mean age of 57 ± 14 years) including 2141 African Americans, 440 Asians, 962 Other/Multiracial, 1539 Hispanics, and 16 764 non-Hispanic White people. We defined metformin failure as either the lack of a target glycated hemoglobin (HbA1c) (<7%) within 18 months of index or the start of dual therapy. We used tree-based extreme gradient boosting (XGBoost) models to assess overall risk prediction performance and relative contribution of individual factors when using EHR data for risk of metformin failure.
resultsIn this large diverse population, we observed a high rate of metformin failure (43%). The XGBoost model that included baseline HbA1c, age, sex, and race/ethnicity corresponded to high discrimination performance (C-index of 0.731; 95% CI 0.722, 0.740) for risk of metformin failure. Baseline HbA1c corresponded to the largest feature performance with higher levels associated with metformin failure. The addition of other clinical factors improved model performance (0.745; 95% CI 0.737, 0.754, P < .0001).
conclusionBaseline HbA1c was the strongest predictor of metformin failure and additional factors substantially improved performance suggesting that routinely available clinical data could be used to identify patients at high risk of metformin failure who might benefit from closer monitoring and earlier treatment intensification.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.