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.

中文翻译：

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新型预测模型的开发，以预测缺血性卒中后六个月的吞咽恢复。

背景与目的-这项研究的目的是探讨卒中后吞咽困难患者长期吞咽恢复的临床和放射学预后因素，并使用机器学习算法开发和验证预后模型。方法-回顾性分析了被吞咽检查的连续性急性缺血性中风患者（N = 137）。在卒中后的6个月中监测吞咽困难，然后使用Kaplan-Meier方法和Cox回归模型对临床和放射学因素进行分析。使用潜在的预后因素开发贝叶斯网络模型，将吞咽功能恢复良好的患者（无需进行管饲或饮食调整为6个月）和差的患者（进行管饲或饮食调整为6个月）。结果-24（17。5％）的患者在头6个月出现持续性吞咽困难，平均持续时间为65.6天。脑卒中后吞咽困难的持续时间因管饲状态，临床吞咽困难程度，性别，严重的白质亢进以及电晕放射线，基底神经节或内囊（CR / BG / IC）的双侧病变而明显不同。在这些因素中，使用Cox回归模型进行多变量分析时，管饲状态（P <0.001），CR / BG / IC处的双侧病变（P = 0.001）和临床吞咽困难量表（P = 0.042）是重要的预后因素。树增强的网络分类器，基于10个因素（性别，CR，BG / IC和脑岛的病变，偏侧性，脑干前外侧区域，CR / BG / IC的双侧病变，严重的白质高信号，吞咽困难）规模和进料状态），表现比本研究中开发的其他基准分类器更好。结论：最初的吞咽困难严重程度和CR / BG / IC处的双侧病变被认为是6个月吞咽恢复的重要预后因素。使用贝叶斯网络模型，基于临床和放射学因素，对6个月吞咽恢复的预测是可行的。我们强调双侧皮质下病变作为预后因素的重要性，可以将其用于开发长期吞咽恢复的预测模型。使用贝叶斯网络模型，基于临床和放射学因素，对6个月吞咽恢复的预测是可行的。我们强调双侧皮质下病变作为预后因素的重要性，可以将其用于开发长期吞咽恢复的预测模型。使用贝叶斯网络模型，基于临床和放射学因素，对6个月吞咽恢复的预测是可行的。我们强调双侧皮质下病变作为预后因素的重要性，可以将其用于开发长期吞咽恢复的预测模型。