Predictors of nasogastric tube removal in patients with stroke and dysphagia

Dysphagia is present in 25-50% of patients with stroke. Therefore, studying the probability of nasogastric tube removal in such patients before discharge from the rehabilitation ward is crucial. In this study, we developed a model to predict the outcome of dysphagia in patients with stroke. A retrospective study was performed from May 2015 to December 2018. We reviewed the medical charts of all patients with a diagnosis of stroke receiving nasogastric tube feeding. Patients were divided into weaned and nonweaned groups to compare baseline characteristics and functional status. The weaned and nonweaned groups comprised 55 and 65 patients, respectively. In the final logistic regression analysis model, the Barthel index at admission, lip closing status, ability to answer simple questions and functional independence before stroke were used to develop a predictive model (Logit = 0.8942 x functional independence before this stroke + 1.1279 x ability to answer simple question + 0.5345 x lip-close status + 0.0546 x Barthel index at admission - 2.2805). The optimal cutoff point based on Youden's index was more than -0.8403 with a sensitivity and specificity of 85.45 and 73.85%, respectively. The positive predicted value was 73.44%. In patients with stroke and dysphagia, a high Barthel index, intact lip closing status, ability to answer simple questions and better functional status before stroke appeared to affect nasogastric tube removal before discharge from the rehabilitation ward. Based on the final regression model, the proposed equation will help physicians and speech pathologists in planning patient care.

Automated summary

This study developed a predictive model to determine the likelihood of nasogastric tube removal in stroke patients with dysphagia before discharge. Factors such as Barthel index, lip closing status, ability to answer simple questions, and functional independence before stroke were found to influence tube removal. The proposed equation from the logistic regression analysis may aid in patient care planning for physicians and speech pathologists.