Evidence map›Paper›PMID 40658305›Full record

ArticleCardiovascular toxicology2025

Construction of a Machine Learning-Based Clopidogrel Resistance Risk Prediction Model.

Ruo-Ying Wang, Shui-Di Yan, Jian-Qi Zeng, Tong Mu, Ya Yan, Yuan-Yi Zhao, Lin Xie, Li-Li Liu

Abstract read
PubMed Publisher
In one paragraph

Article in Cardiovascular toxicology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Ruo-Ying Wang *Center of Clinical Laboratory, School of Medicine, Zhongshan Hospital of Xiamen University, Xiamen University, Xiamen, China.
Shui-Di Yan *Center of Clinical Laboratory, School of Medicine, Zhongshan Hospital of Xiamen University, Xiamen University, Xiamen, China.
Jian-Qi Zeng *Department of Neurology, School of Medicine, Zhongshan Hospital of Xiamen University, Xiamen University, Xiamen, China.
Tong Mu *Center of Clinical Laboratory, School of Medicine, Zhongshan Hospital of Xiamen University, Xiamen University, Xiamen, China.
Ya YanCenter of Clinical Laboratory, School of Medicine, Zhongshan Hospital of Xiamen University, Xiamen University, Xiamen, China.
Yuan-Yi ZhaoCenter of Clinical Laboratory, School of Medicine, Zhongshan Hospital of Xiamen University, Xiamen University, Xiamen, China.
Lin XieCenter of Clinical Laboratory, School of Medicine, Zhongshan Hospital of Xiamen University, Xiamen University, Xiamen, China.
Li-Li LiuCenter of Clinical Laboratory, School of Medicine, Zhongshan Hospital of Xiamen University, Xiamen University, Xiamen, China. liulili@xmu.edu.cn.

Funding

Fujian provincial health technology project 2024CXB017Fujian Provincial Natural Science Foundation of China 2022J02055National Natural Science Foundation of China 82271387Xiamen Key Medical and Health Project 3502Z20234004Xiamen Municipal Science and Technology Bureau Technology Development Project 2024350204000098
6 · The paper itself

Abstract

Clopidogrel is extensively utilized for the prevention and treatment of cardiovascular, cerebrovascular, and other arterial circulation disorders attributed to platelet hyperaggregation. Nevertheless, its antiplatelet efficacy displays substantial individual variability and unpredictability. Our aim was to develop a machine learning model based on clinical data, incorporating various laboratory indicators, to predict the risk of clopidogrel resistance in clinical patients. This study included 1592 cardiovascular disease patients treated with clopidogrel. Potential predictive variables included age, sex, hematological, coagulation, biochemical parameters, and CYP2C19 genetic polymorphisms. Lasso regression and multivariable logistic regression were used for variable selection. Modeling was performed using Logistic Regression, LGBM Classifier, Random Forest Classifier, and SVC machine learning models, followed by model comparison, to ultimately construct the clopidogrel resistance risk prediction model. The clopidogrel resistance rate increased year by year from 2020 to 2022, but decreased slightly in 2023. There was a significant difference in clopidogrel resistance rate among different years (χ

Indexed as

Cardiovascular DiseasesClopidogrelDecision Support TechniquesDrug ResistanceMachine LearningPlatelet AggregationPlatelet Aggregation InhibitorsAgedClinical Decision-MakingCytochrome P-450 CYP2C19FemaleHumansMaleMiddle AgedPharmacogenomic VariantsPhenotypeClopidogrelCYP2C19 protein, humanCytochrome P-450 CYP2C19Platelet Aggregation InhibitorsClopidogrelDrug resistanceMachine learningRisk assessmentThrombosis

Identifiers

PMID40658305

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.