Literature DB >> 31884906

Development of a Novel Prognostic Model to Predict 6-Month Swallowing Recovery After Ischemic Stroke.

Woo Hyung Lee1, Min Hyuk Lim1, Han Gil Seo2, Min Yong Seong2, Byung-Mo Oh2, Sungwan Kim1,3.   

Abstract

Background and Purpose- The aim of this study was to explore clinical and radiological prognostic factors for long-term swallowing recovery in patients with poststroke dysphagia and to develop and validate a prognostic model using a machine learning algorithm. Methods- Consecutive patients (N=137) with acute ischemic stroke referred for swallowing examinations were retrospectively reviewed. Dysphagia was monitored in the 6 months poststroke period and then analyzed using the Kaplan-Meier method and Cox regression model for clinical and radiological factors. Bayesian network models were developed using potential prognostic factors to classify patients into those with good (no need for tube feeding or diet modification for 6 months) and poor (tube feeding or diet modification for 6 months) recovery of swallowing function. Results- Twenty-four (17.5%) patients showed persistent dysphagia for the first 6 months with a mean duration of 65.6 days. The time duration of poststroke dysphagia significantly differed by tube feeding status, clinical dysphagia scale, sex, severe white matter hyperintensities, and bilateral lesions at the corona radiata, basal ganglia, or internal capsule (CR/BG/IC). Among these factors, tube feeding status (P<0.001), bilateral lesions at CR/BG/IC (P=0.001), and clinical dysphagia scale (P=0.042) were significant prognostic factors in a multivariate analysis using Cox regression models. The tree-augmented network classifier, based on 10 factors (sex, lesions at CR, BG/IC, and insula, laterality, anterolateral territory of the brain stem, bilateral lesions at CR/BG/IC, severe white matter hyperintensities, clinical dysphagia scale, and tube feeding status), performed better than other benchmarking classifiers developed in this study. Conclusions- Initial dysphagia severity and bilateral lesions at CR/BG/IC are revealed to be significant prognostic factors for 6-month swallowing recovery. The prediction of 6-month swallowing recovery was feasible based on clinical and radiological factors using the Bayesian network model. We emphasize the importance of bilateral subcortical lesions as prognostic factors that can be utilized to develop prediction models for long-term swallowing recovery.

Entities:  

Keywords:  deglutition; machine learning; prognosis; stroke; survival analysis

Mesh:

Year:  2019        PMID: 31884906     DOI: 10.1161/STROKEAHA.119.027439

Source DB:  PubMed          Journal:  Stroke        ISSN: 0039-2499            Impact factor:   7.914


  6 in total

1.  Predicting Failure to Recover Swallowing in Patients with Severe Post-stroke Dysphagia: The DIsPHAGIc Score.

Authors:  Antonio Muscari; Roberta Falcone; Enrico Pirazzoli; Luca Faccioli; Silvia Muscari; Marco Pastore Trossello; Giovanni M Puddu; Loredana Rignanese; Luca Spinardi; Marco Zoli
Journal:  Dysphagia       Date:  2022-06-09       Impact factor: 3.438

2.  Predictive Factors for Oral Intake Recovery After Acute Stroke: Analysis of a Japanese Nationwide Inpatient Database.

Authors:  Yasuhiro Inooka; Hayato Yamana; Yusuke Shinoda; Haruhi Inokuchi; Hiroki Matsui; Kiyohide Fushimi; Hideo Yasunaga; Nobuhiko Haga
Journal:  Dysphagia       Date:  2022-02-26       Impact factor: 3.438

3.  Prediction-Driven Decision Support for Patients With Mild Stroke: A Model Based on Machine Learning Algorithms.

Authors:  Xinping Lin; Shiteng Lin; XiaoLi Cui; Daizun Zou; FuPing Jiang; JunShan Zhou; NiHong Chen; Zhihong Zhao; Juan Zhang; Jianjun Zou
Journal:  Front Neurol       Date:  2021-12-23       Impact factor: 4.003

4.  Characteristics of dysphagia among different lesion sites of stroke: A retrospective study.

Authors:  Jia Qiao; Zhi-Min Wu; Qiu-Ping Ye; Meng Dai; Yong Dai; Zi-Tong He; Zu-Lin Dou
Journal:  Front Neurosci       Date:  2022-08-24       Impact factor: 5.152

5.  Potential Prognostic Impact of Dopamine Receptor D1 (rs4532) Polymorphism in Post-stroke Outcome in the Elderly.

Authors:  Hae-Yeon Park; Youngkook Kim; Hyun Mi Oh; Tae-Woo Kim; Geun-Young Park; Sun Im
Journal:  Front Neurol       Date:  2021-06-30       Impact factor: 4.003

6.  Prediction of Progression to Severe Stroke in Initially Diagnosed Anterior Circulation Ischemic Cerebral Infarction.

Authors:  Lai Wei; Yidi Cao; Kangwei Zhang; Yun Xu; Xiang Zhou; Jinxi Meng; Aijun Shen; Jiong Ni; Jing Yao; Lei Shi; Qi Zhang; Peijun Wang
Journal:  Front Neurol       Date:  2021-06-18       Impact factor: 4.003

  6 in total

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