Evidence map›Paper›PMID 40986253›Full record

ArticleAnnals of biomedical engineering2025

StrokeENDPredictor-19: Setting New Prediction Model in Neurological Prognosis in Acute Ischemic Stroke.

Lingli Li, Hongxiao Li, Miaowen Jiang, Jing Fang, Ning Ma, Jianzhuo Yan, Chen Zhou

Abstract readValidation Study
PubMed Publisher
In one paragraph

Article in Annals of biomedical engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

7 authors.

Lingli LiFaculty of Information Science and Technology, Beijing University of Technology, Beijing, 100124, China.
Hongxiao LiHeilongjiang Communications Polytechnic, Harbin, 150028, China.
Miaowen JiangBeijing Institute for Brain Disorders, Capital Medical University, Beijing, 100069, China. jiangmiaowen415@163.com.
Jing FangFaculty of Information Science and Technology, Beijing University of Technology, Beijing, 100124, China.
Ning MaBeijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China.
Jianzhuo YanFaculty of Information Science and Technology, Beijing University of Technology, Beijing, 100124, China. yanjianzhuo@bjut.edu.cn.
Chen ZhouBeijing Institute for Brain Disorders, Capital Medical University, Beijing, 100069, China. chenzhou2013abc@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

background and purposeEarly Neurological Deterioration (END) following intravenous thrombolysis (IVT) highlights potential risks in current management strategies for acute ischemic stroke. Early identification of at-risk patients could enhance treatment efficacy. This study aims to develop an advanced AI predictive model that improves accuracy in forecasting END while ensuring interpretability for clinical application.

methodsThis prospective cohort study included 970 patients with acute ischemic stroke who underwent IVT. Data from 365 patients were used for model development and internal validation, while data from 605 patients were utilized for external validation. Five machine learning models were developed and compared using evaluation metrics such as accuracy and AUC. Feature selection and model optimization were performed using the XGBoost algorithm and SHapley Additive exPlanations (SHAP) method, resulting in the StrokeENDPredictor-19 model.

resultsAmong the five models, XGBoost demonstrated superior performance with an internal validation accuracy of 91% (AUC = 0.96) and external validation accuracy of 90% (AUC = 0.95). Notably, this study established cutoff values for critical clinical features, providing quantifiable reference standards for practical applications.

conclusionThe StrokeENDPredictor-19 model offers neurologists a valuable tool for forecasting the likelihood of END in patients receiving IVT therapy, thereby supporting more precise clinical decision-making.

Indexed as

Ischemic StrokeMachine LearningModels, NeurologicalThrombolytic TherapyAdministration, IntravenousAgedBoosting Machine Learning AlgorithmsClinical Decision-MakingDatasets as TopicDecision Support Systems, ClinicalFemaleFibrinolytic AgentsHumansMaleMiddle AgedPrognosisFibrinolytic AgentsDeep learningEarly neurological deteriorationExplainable artificial intelligenceMachine learningPredictive modelsStroke

Identifiers

PMID40986253

What OpenQuestion 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 OpenQuestion graph.