Evidence map›Paper›PMID 39187795›Full record

ArticleBMC neurology2024

Machine learning-based predictive model for the development of thrombolysis resistance in patients with acute ischemic stroke.

Xiaorui Wang, Song Luo, Xue Cui, Hongdang Qu, Yujie Zhao, Qirong Liao

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–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

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. [A prediction model for early neurological improvement after intravenous thrombolysis in patients with acute ischemic stroke].Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2026
    Article
  4. Review
  5. Article
  6. Article
  7. Review
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

6 authors.

Xiaorui WangDepartment of Neurology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, 233004, China.
Song LuoDepartment of Neurology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, 233004, China. 542462407@qq.com.
Xue CuiDepartment of Neurology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, 233004, China.
Hongdang QuDepartment of Neurology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, 233004, China.
Yujie ZhaoDepartment of Neurology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, 233004, China.
Qirong LiaoDepartment of Neurology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, 233004, China.

Funding

Anhui Province Jianghuai Famous Doctor Cultivation Project 2022Clinical Medicine Research and Translational Project of Anhui Province 202304295107020076the Bengbu Science and Technology Innovation Guidance Category 20230131the University Natural Science Research Project of Anhui Province 2022AH051480
6 · The paper itself

Abstract

backgroundThe objective of this study was to establish a predictive model utilizing machine learning techniques to anticipate the likelihood of thrombolysis resistance (TR) in acute ischaemic stroke (AIS) patients undergoing recombinant tissue plasminogen activator (rt-PA) intravenous thrombolysis, given that nearly half of such patients exhibit poor clinical outcomes.

methodsRetrospective clinical data were collected from AIS patients who underwent intravenous thrombolysis with rt-PA at the First Affiliated Hospital of Bengbu Medical University. Thrombolysis resistance was defined as ([National Institutes of Health Stroke Scale (NIHSS) at admission - 24-hour NIHSS] × 100％/ NIHSS at admission) ≤ 30%. In this study, we developed five machine learning models: logistic regression (LR), extreme gradient boosting (XGBoost), support vector machine (SVM), the least absolute shrinkage and selection operator (LASSO), and random forest (RF). We assessed the model's performance by using receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA), and presented the results through a nomogram.

resultsThis study included a total of 218 patients with AIS who were treated with intravenous thrombolysis, 88 patients experienced TR. Among the five machine learning models, the LASSO model performed the best. The area under the curve (AUC) on the testing group was 0.765 (sensitivity: 0.767, specificity: 0.694, accuracy: 0.727). The apparent curve in the calibration curve was similar to the ideal curve, and DCA showed a positive net benefit. Key features associated with TR included NIHSS at admission, blood glucose, white blood cell count, neutrophil count, and blood urea nitrogen.

conclusionMachine learning methods with multiple clinical variables can help in early screening of patients at high risk of thrombolysis resistance, particularly in contexts where healthcare resources are limited.

Indexed as

Fibrinolytic AgentsIschemic StrokeMachine LearningThrombolytic TherapyTissue Plasminogen ActivatorAgedAged, 80 and overDrug ResistanceFemaleHumansMaleMiddle AgedRetrospective StudiesFibrinolytic AgentsTissue Plasminogen ActivatorAcute ischaemic strokeIntravenous thrombolysisMachine learningPrediction model

Identifiers

PMID39187795
PMCPMC11346215

What Socratic holds

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LicenceCC BY-NC-ND
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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.