Dataset Information

Videofluoroscopy dysphagia severity scale is predictive of subsequent remote pneumonia in dysphagia patients.

ABSTRACT: Introduction: Dysphagia-associated pneumonia is a critical health issue especially in the elders and stroke patients which carries a poorer prognosis. Therefore, we aim to identify methods with the potentials to predict subsequent pneumonia in dysphagia patients, which will be of great value in the prevention and early management of pneumonia. Methods: One-hundred dysphagia patients were enrolled and measurements including Dysphagia Severity Scale (DSS), Functional Oral Intake Scale (FOIS), Ohkuma Questionnaire, and Eating Assessment Tool-10 (EAT-10) were assessed by either videofluoroscopy (VF), videoendoscopy (VE), or the study nurse. The patients were categorized into mild or severe groups based on each screening method. All the patients were assessed for pneumonia at 1, 3, 6, and 20 months after the examinations. Results: VF-DSS (p=0.001) is the only measurement being significantly associated with subsequent pneumonia with sensitivity and specificity of 0.857 and 0.486. The Kaplan-Meier curves revealed that significant differences between the mild/severe groups start to emerge 3 months after VF-DSS (p=0.013). Cox regression models used for adjusted hazard ratio of severe VF-DSS in association with subsequent pneumonia of different timepoints after controlling the important covariates showed the following results: 3 months, p=0.026, HR=5.341, 95%CI=1.219-23.405; 6 months, p=0.015, HR=4.557, 95%CI=1.338-15.522; 20 months, p=0.004, HR=4.832, 95%CI=1.670-13.984. Conclusions: Dysphagia severity evaluated by VE-DSS, VE-FOIS, VF-FOIS, Ohkuma Questionnaire, and EAT-10 is not associated with subsequent pneumonia. Only VF-DSS is associated with both short-term and long-term subsequent pneumonia. In patients with dysphagia, VF-DSS is predictive of subsequent pneumonia.

SUBMITTER: Huang P

PROVIDER: S-EPMC9969506 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Videofluoroscopy dysphagia severity scale is predictive of subsequent remote pneumonia in dysphagia patients.

Huang Poyin P Hsu Yi-Chiung YC Li Chien-Hsun CH Hsieh Sun-Wung SW Lee Kuo-Wei KW Wu Kun-Han KH Chen Wen-Ching WC Lin Chung-Wei CW Chen Chun-Hung CH

International journal of medical sciences 20230205 3

<b>Introduction:</b> Dysphagia-associated pneumonia is a critical health issue especially in the elders and stroke patients which carries a poorer prognosis. Therefore, we aim to identify methods with the potentials to predict subsequent pneumonia in dysphagia patients, which will be of great value in the prevention and early management of pneumonia. <b>Methods:</b> One-hundred dysphagia patients were enrolled and measurements including Dysphagia Severity Scale (DSS), Functional Oral Intake Scal ...[more]

PMID: 36860676

Similar Datasets

Project description:Post stroke dysphagia (PSD) is common and associated with poor outcome. The Dysphagia Severity Rating Scale (DSRS), which grades how severe dysphagia is based on fluid and diet modification and supervision requirements for feeding, is used for clinical research but has limited published validation information. Multiple approaches were taken to validate the DSRS, including concurrent- and predictive criterion validity, internal consistency, inter- and intra-rater reliability and sensitivity to change. This was done using data from four studies involving pharyngeal electrical stimulation in acute stroke patients with dysphagia, an individual patient data meta-analysis and unpublished studies (NCT03499574, NCT03700853). In addition, consensual- and content validity and the Minimal Clinically Important Difference (MCID) were assessed using anonymous surveys sent to UK-based Speech and Language Therapists (SLTs). Scores for consensual validity were mostly moderate (62.5-78%) to high or excellent (89-100%) for most scenarios. All but two assessments of content validity were excellent. In concurrent criterion validity assessments, DSRS was most closely associated with measures of radiological aspiration (penetration aspiration scale, Spearman rank rs?=?0.49, p?<?0.001) and swallowing (functional oral intake scale, FOIS, rs?=?-0.96, p?<?0.001); weaker but statistically significant associations were seen with impairment, disability and dependency. A similar pattern of relationships was seen for predictive criterion validity. Internal consistency (Cronbach's alpha) was either "good" or "excellent". Intra and inter-rater reliability were largely "excellent" (intraclass correlation >0.90). DSRS was sensitive to positive change during recovery (medians: 7, 4 and 1 at baseline and 2 and 13 weeks respectively) and in response to an intervention, pharyngeal electrical stimulation, in a published meta-analysis. The MCID was 1.0 and DSRS and FOIS scores may be estimated from each other. The DSRS appears to be a valid tool for grading the severity of swallowing impairment in patients with post stroke dysphagia and is appropriate for use in clinical research and clinical service delivery.

Project description:BackgroundThe machine learning models with dose factors and the deep learning models with dose distribution matrix have been used to building lung toxics models for radiotherapy and achieve promising results. However, few studies have integrated clinical features into deep learning models. This study aimed to explore the role of three-dimension dose distribution and clinical features in predicting radiation pneumonitis (RP) in esophageal cancer patients after radiotherapy and designed a new hybrid deep learning network to predict the incidence of RP.MethodsA total of 105 esophageal cancer patients previously treated with radiotherapy were enrolled in this study. The three-dimension (3D) dose distributions within the lung were extracted from the treatment planning system, converted into 3D matrixes and used as inputs to predict RP with ResNet. In total, 15 clinical factors were normalized and converted into one-dimension (1D) matrixes. A new prediction model (HybridNet) was then built based on a hybrid deep learning network, which combined 3D ResNet18 and 1D convolution layers. Machine learning-based prediction models, which use the traditional dosiomic factors with and without the clinical factors as inputs, were also constructed and their predictive performance compared with that of HybridNet using tenfold cross validation. Accuracy and area under the receiver operator characteristic curve (AUC) were used to evaluate the model effect. DeLong test was used to compare the prediction results of the models.ResultsThe deep learning-based model achieved superior prediction results compared with machine learning-based models. ResNet performed best in the group that only considered dose factors (accuracy, 0.78 ± 0.05; AUC, 0.82 ± 0.25), whereas HybridNet performed best in the group that considered both dose factors and clinical factors (accuracy, 0.85 ± 0.13; AUC, 0.91 ± 0.09). HybridNet had higher accuracy than that of Resnet (p = 0.009).ConclusionBased on prediction results, the proposed HybridNet model could predict RP in esophageal cancer patients after radiotherapy with significantly higher accuracy, suggesting its potential as a useful tool for clinical decision-making. This study demonstrated that the information in dose distribution is worth further exploration, and combining multiple types of features contributes to predict radiotherapy response.

Dataset Information

Videofluoroscopy dysphagia severity scale is predictive of subsequent remote pneumonia in dysphagia patients.

Publications

Videofluoroscopy dysphagia severity scale is predictive of subsequent remote pneumonia in dysphagia patients.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets