Dysphagia following non-traumatic subarachnoid haemorrhage: A prospective pilot study

Prevalence, severity, and predictors of dysphagia among patients with acute stroke in Oman

BACKGROUND

Up to 80% of stroke patients suffer from dysphagia. It is associated with increased morbidity, mortality, and healthcare costs due to aspiration, pneumonia, and malnutrition, which represent a significant burden to stroke survivors, their relatives, and the healthcare system. Early recognition and management of post-stroke dysphagia is key to reducing its complications and enhancing patients' quality of life. Despite the importance of dysphagia following a stroke and its adverse consequences, no study has investigated dysphagia after stroke in Oman. This study was conducted to estimate the prevalence of post-stroke dysphagia and its severity among acute stroke patients, assess the association between the selected factors and dysphagia, and identify dysphagia predictors.

MATERIALS AND METHOD

A cross-sectional descriptive study design was utilized to identify the prevalence and risk factors for post-stroke dysphagia among acute stroke patients (n = 274) admitted to two Omani tertiary hospitals over 6 months. Dysphagia was assessed using the Gugging swallowing screen. Descriptive analysis, correlations, and regression analysis were computed.

RESULTS

The prevalence of dysphagia following stroke was 70.1%. Among those who had dysphagia, 37.5% had severe, 31.25% moderate, and 31.25% mild dysphagia. Aging, obesity, having a medical co-morbidity, hypertension, stroke location, low Glasgow Coma Scale (GCS) score, and the use of thrombectomy or thrombolysis therapy were contributing factors and were found to be correlated with post-stroke dysphagia. This study revealed that old age [odds ratio (OR) = 0.961, 95% confidence interval (CI): 0.933-0.989, P = 0.007)], obesity (OR = 0.387, 95% CI: 0.157-0.952, P = 0.039), and low GCS score (OR = 0.027, 95% CI: 0.009-0.077, p=<0.001) were predictors of dysphagia after stroke.

CONCLUSION

Post-stroke dysphagia is more common among acute stroke patients. There are various risk factors correlated with dysphagia following a stroke. Therefore, dysphagia predictors should be considered when designing dysphagia prevention strategies to reduce its adverse consequences. Recognition of such predictors may help with the early detection and treatment of dysphagic patients and the implementation of preventive approaches.

A predictive model using risk factor categories for hospital-acquired pneumonia in patients with aneurysmal subarachnoid hemorrhage

Objectives

To identify risk factors for hospital-acquired pneumonia (HAP) in patients with aneurysmal subarachnoid hemorrhage (aSAH) and establish a predictive model to aid evaluation.

Methods

The cohorts of 253 aSAH patients were divided into the HAP group (n = 64) and the non-HAP group (n = 189). Univariate and multivariate logistic regression were performed to identify risk factors. A logistic model (Model-Logit) was established based on the independent risk factors. We used risk factor categories to develop a model (Model-Cat). Receiver operating characteristic curves were generated to determine the cutoff values. Areas under the curves (AUCs) were calculated to assess the accuracy of models and single factors. The Delong test was performed to compare the AUCs.

Results

The multivariate logistic analysis showed that the age [p = 0.012, odds ratio (OR) = 1.059, confidence interval (CI) = 1.013-1.107], blood glucose (BG; >7.22 mmol/L; p = 0.011, OR = 2.781, CI = 1.263-6.119), red blood distribution width standard deviation (RDW-SD; p = 0.024, OR = 1.118, CI = 1.015-1.231), and Glasgow coma scale (GCS; p < 0.001, OR = 0.710, CI = 0.633-0.798) were independent risk factors. The Model-Logit was as follows: Logit(P) = -5.467 + 0.057 * Age + 1.023 * BG (>7.22 mmol/L, yes = 1, no = 0) + 0.111 * RDW-SD-0.342 * GCS. The AUCs values of the Model-Logit, GCS, age, BG (>7.22 mmol/L), and RDW-SD were 0.865, 0.819, 0.634, 0.698, and 0.625, respectively. For clinical use, the Model-Cat was established. In the Model-Cat, the AUCs for GCS, age, BG, and RDW-SD were 0.850, 0.760, 0.700, 0.641, and 0.564, respectively. The AUCs of the Model-Logit were insignificantly higher than the Model-Cat (Delong test, p = 0.157). The total points from -3 to 4 and 5 to 14 were classified as low- and high-risk levels, respectively.

Conclusions

Age, BG (> 7.22 mmol/L), GCS, and RDW-SD were independent risk factors for HAP in aSAH patients. The Model-Cat was convenient for practical evaluation. The aSAH patients with total points from 5 to 14 had a high risk for HAP, suggesting the need for more attention during treatment.

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

OBJECTIVE

It remains unclear which factors contribute to dysphagia recovery after an acute stroke. This study aimed to identify factors associated with complete oral intake recovery in patients with post-stroke dysphagia.

METHODS

Data were obtained from the Diagnosis Procedure Combination database, which is a nationwide database of administrative claims and discharge abstracts in Japan. We selected patients admitted within 3 days of stroke onset and conducted multivariable logistic regression analysis. Total oral intake within 30 days of admission was the primary outcome measure.

RESULTS

A total of 151,302 patients were included, and total oral intake was observed in 48% of them within 30 days of admission. Total oral intake was significantly associated with age, sex, stroke subtype, consciousness disturbance, low body mass index, multiple comorbidities, and the modified Rankin Scale before stroke onset. In addition, males had worse outcomes than females (odds ratio, 0.75; 95% confidence interval, 0.73-0.77, p < 0.001), and intracerebral and subarachnoid hemorrhagic stroke subtypes were associated with non-recovery.

CONCLUSION

This study identified several prognostic factors for total oral intake in patients with acute stroke. These results may be useful for predicting a patient's dysphagia prognosis at the time of admission and designing a nutritional management plan for patients with acute stroke.

Risk Score for Predicting Dysphagia in Patients After Neurosurgery: A Prospective Observational Trial

Background: Acquired dysphagia is common in patients with tracheal intubation and neurological disease, leading to increased mortality. This study aimed to ascertain the risk factors and develop a prediction model for acquired dysphagia in patients after neurosurgery.

Methods: A multicenter prospective observational study was performed on 293 patients who underwent neurosurgery. A standardized swallowing assessment was performed bedside within 24 h of extubation, and logistic regression analysis with a best subset selection strategy was performed to select predictors. A nomogram model was then established and verified.

Results: The incidence of acquired dysphagia in our study was 23.2% (68/293). Among the variables, days of neurointensive care unit (NICU) stay [odds ratio (OR), 1.433; 95% confidence interval (CI), 1.141–1.882; P = 0.005], tracheal intubation duration (OR, 1.021; CI, 1.001–1.062; P = 0.175), use of a nasogastric feeding tube (OR, 9.131; CI, 1.364–62.289; P = 0.021), and Acute Physiology and Chronic Health Evaluation (APACHE)-II C score (OR, 1.709; CI, 1.421–2.148; P < 0.001) were selected as risk predictors for dysphagia and included in the nomogram model. The area under the receiver operating characteristic curve was 0.980 (CI, 0.965–0.996) in the training set and 0.971 (0.937–1) in the validation set, with Brier scores of 0.045 and 0.056, respectively.

Conclusion: Patients who stay longer in the NICU, have a longer duration of tracheal intubation, require a nasogastric feeding tube, and have higher APACHE-II C scores after neurosurgery are likely to develop dysphagia. This developed model is a convenient and efficient tool for predicting the development of dysphagia.