ArticleFrontiers in pharmacology2026
Evaluation and AI-powered prediction of the efficacy of alirocumab use among patients with cardiovascular disease.
Article in Frontiers in pharmacology, 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
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Elevated low-density lipoprotein cholesterol (LDL-C) is a major risk factor for atherosclerotic cardiovascular disease (ASCVD). Despite statins and ezetimibe, many high-risk patients fail to reach lipid targets. Alirocumab, a PCSK9 inhibitor, is an effective alternative, but real-world data are limited. Objectives: This study evaluated the real-world effectiveness of Alirocumab in improving lipid profiles among patients with established cardiovascular disease and assessed the impact of patient characteristics, comorbidities, and concurrent medications. Additionally, artificial intelligence (AI) models were explored to predict treatment outcomes. Methods: A retrospective observational study was conducted at the Royal Commission Health Services Program in Aljubail, Saudi-Arabia. Data from 206 adults receiving Alirocumab (75 mg every 2 weeks) from July 2021 to July 2023 were analyzed. Lipid profiles were assessed at baseline, 12 weeks, and 24 weeks. Logistic regression identified factors associated with lipid target achievement. Machine learning models including Logistic-Regression, K-Neighbors, XGBoost, Support Vector Machine (SVM), Gaussian Naive Bayes, Decision Tree, and Random Forest were used to predict outcomes, and an AI-based SVM tool was developed as an exploratory research prototype. Results: Alirocumab significantly reduced LDL-C, total cholesterol, and triglycerides while increasing HDL-C at 12 and 24 weeks (p < 0.001). Ezetimibe and metformin improved lipid outcomes, whereas aspirin and anticoagulants were associated with lower target achievement. AI model accuracies ranged from 0.50 to 0.63, with the SVM classifier performing best (accuracy 0.625, F1 score 0.61). Conclusion: Alirocumab is effective in lowering lipids in real-world cardiovascular patients. Certain medications may enhance outcomes. AI models provide moderate predictive ability, supporting potential personalized treatment strategies.
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.