ArticleActa diabetologica2026
Super learner model for predicting carotid plaque regression in type 2 diabetes on PCSK9 inhibitors.
Article in Acta diabetologica, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
objectivesEvidence regarding the effects of proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors on carotid plaque regression in patients with type 2 diabetes mellitus (T2DM) and subclinical atherosclerosis remains limited; therefore, this study aimed to develop and validate a machine learning-based model for predicting carotid plaque regression in this population.
methodsThis retrospective study included a development cohort of 204 patients with T2DM and subclinical atherosclerosis receiving combined statin and evolocumab therapy, with external validation performed in an independent cohort. The primary outcome was the change in mean carotid plaque thickness. Thirteen predictors were selected using the Boruta algorithm to construct a Super Learner model, which was validated through repeated five-fold cross-validation. Model interpretability was assessed using SHapley Additive exPlanations analysis.
resultsAfter 24 weeks of treatment, carotid plaque regression was observed in 52.9% of patients. The model demonstrated strong discriminatory performance in the internal validation set (area under the curve [AUC] = 0.958; sensitivity = 0.881; specificity = 0.908) and moderate performance in the external validation cohort (AUC = 0.755; sensitivity = 0.875; specificity = 0.560). Low-density lipoprotein cholesterol, total cholesterol, high-sensitivity C-reactive protein, high-density lipoprotein cholesterol, and alanine aminotransferase were identified as the most influential predictors.
conclusionsThis study demonstrates the feasibility of applying machine learning algorithms to predict responses to evolocumab in patients with T2DM and subclinical atherosclerosis. Machine learning may support individualized risk stratification by identifying individuals most likely to benefit from therapy, thereby supporting personalized strategies for secondary cardiovascular disease prevention.
Indexed as
Identifiers
42329281What Socratic 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 Socratic graph.