Evidence map›Paper›PMID 42638696›Full record

ArticleFrontiers in neurology2026

LASSO-based nomogram and machine learning models for predicting 30-day nasogastric tube dependence after acute ischemic stroke.

Qianqian Shang, Yu Lei, Hongguang Chen, Zhongli Liu, Minyue Sun, Shuang Wang, Shiyu Wen, Yingying Deng, Min Luo

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Article in Frontiers in neurology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Qianqian Shang *Department of Neurology, Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Mianyang, Sichuan, China.
Yu Lei *Department of Neurology, Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Mianyang, Sichuan, China.
Hongguang ChenDepartment of Neurology, Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Mianyang, Sichuan, China.
Zhongli LiuDepartment of Neurology, Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Mianyang, Sichuan, China.
Minyue SunDepartment of Neurology, Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Mianyang, Sichuan, China.
Shuang WangDepartment of Neurology, Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Mianyang, Sichuan, China.
Shiyu WenDepartment of Neurology, Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Mianyang, Sichuan, China.
Yingying DengDepartment of Neurology, Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Mianyang, Sichuan, China.
Min LuoDepartment of Neurology, Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Mianyang, Sichuan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Nasogastric tube (NGT) feeding is a common method for providing enteral nutrition to patients with acute ischemic stroke (AIS) and dysphagia. However, prolonged NGT dependence contributes to adverse clinical consequences. Early identification of patients who may develop persistent NGT dependence remains challenging. Methods: A total of 852 AIS patients requiring NGT were included and allocated to the training ( Results: Four key independent predictors were retained: Diabetes Mellitus (DM), age, admission National Institutes of Health Stroke Scale (NIHSS) score, and exclusive NGT feeding. The model demonstrated strong discrimination (AUC = 0.924 in training, 0.920 in internal validation, and 0.935 in external validation), good calibration, and favorable clinical utility in DCA. Among the machine learning models, GBM demonstrated the highest accuracy, with an AUC of 0.918. The model confirmed age and NIHSS score as the most influential predictors, followed by exclusive NGT feeding and DM. Conclusion: The developed nomogram provides an effective approach for predicting 30-day NGT dependence in AIS patients, enabling timely risk stratification and individualized clinical management.

Indexed as

Enteral NutritionIntubation, GastrointestinalIschemic StrokeMachine LearningNomogramsAgedBoosting Machine Learning AlgorithmsClassification AlgorithmsFemaleHumansMaleMiddle AgedPrediction AlgorithmsPredictive Learning Modelsacute ischemic strokeLASSO regressionmachine learningnasogastric tubenomogram

Identifiers

PMID42638696
PMCPMC13500336

What Socratic holds

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Registered trials

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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.