Cross-Cultural Adaptation and Validation of the SWAL-QoL Questionnaire in Greek.

The purpose of this study was to translate and adapt the 44-item SWAL-QoL into Greek and examine its internal consistency, test-retest reliability, external construct validity, and discriminant validity in order to provide a validated dysphagia-specific QoL instrument in the Greek language. The instrument was translated into Greek using the back translation to ensure linguistic validity and was culturally adapted resulting in the SWAL-QoL-GR. Two groups of participants were included: a patient group of 86 adults (48 males; age range: 18-87 years) diagnosed with oropharyngeal dysphagia, and an age-matched healthy control group (39 adults; 19 males; age range: 18-84 years). The Greek 30-item version of the WHOQOL-BREF was used for assessment of construct validity. Overall, the questionnaire achieved good to excellent psychometric values. Internal consistency of all 10 subscales and the physical symptoms scale of the SWAL-QoL-GR assessed by Cronbach's α was good to excellent (0.811 < α < 0.940). Test-retest validity was found to be good to excellent as well. In addition, moderate to strong correlations were found between seven of the ten subscales of the SWAL-QoL-GR with limited items of the WHOQΟL-BREF (0.401 < ρ < 0.65), supporting good construct validity of the SWAL-QoL-GR. The SWAL-QoL-GR also correctly differentiated between patients with dysphagia and age-matched healthy controls (p < 0.001) on all 11 scales, further indicating excellent discriminant validity. Finally, no significant differences were found between the two sexes. This cultural adaptation and validation allows the use of this tool in Greece, further enhancing our clinical and scientific efforts to increase the evidence-based practice resources for dysphagia rehabilitation in Greece.

Postextubation Dysphagia in Critical Patients: A First Report From the Largest Step-Down Intensive Care Unit in Greece.

PURPOSE: This study provided preliminary data on the occurrence and impact of postextubation dysphagia in the largest Greek step-down intensive care unit (ICU) over 2 years. METHOD: A retrospective observational cohort study of patients referred for swallowing assessment postextubation was conducted from November, 2011, to August, 2013. RESULTS: Of the 357 patients admitted to the unit during this period, 87, aged 55.8 ± 18.1 years (61 male, 26 female), were referred and evaluated. Of these, 2.3% were found to have no dysphagia, and 21.8% and 75.9% were diagnosed with mild and moderate/severe dysphagia, respectively. Across severity levels, 23% of patients in the unit were found to have dysphagia. Patients with moderate/severe dysphagia were more likely to have had prolonged intubation (> 48 hr; p = .02) and exhibit signs of aspiration (p = .002) than those with no or mild dysphagia. Prolonged intubation was associated with increased likelihood of moderate/severe dysphagia by a factor of 12 (p = .042, odds ratio = 12.355) compared to short intubation. Moderate/severe dysphagia was correlated with pneumonia (p = .02), feeding tube placement (p = .004) and in-hospital mortality (p = .034). CONCLUSION: In this sample, moderate/severe dysphagia was correlated with prolonged intubation, and was found to increase the risk for pneumonia and in-hospital mortality. Our results suggest the importance of early dysphagia management in critical patients in Greece and globally.

Fuzzy Cognitive Map scenario-based medical decision support systems for education.

Soft Computing (SC) techniques are based on exploiting human knowledge and experience and they are extremely useful to model any complex decision making procedure. Thus, they have a key role in the development of Medical Decision Support Systems (MDSS). The soft computing methodology of Fuzzy Cognitive Maps has successfully been used to represent human reasoning and to infer conclusions and decisions in a human-like way and thus, FCM-MDSSs have been developed. Such systems are able to assist in critical decision-making, support diagnosis procedures and consult medical professionals. Here a new methodology is introduced to expand the utilization of FCM-MDSS for learning and educational purposes using a scenario-based learning (SBL) approach. This is particularly important in medical education since it allows future medical professionals to safely explore extensive "what-if" scenarios in case studies and prepare for dealing with critical adverse events.

An international pilot study of asynchronous teleconsultation for oropharyngeal dysphagia.

We investigated whether an expert's consultation provided via telemedicine could improve the quality of care for patients with dysphagia. A trained clinician completed videofluoroscopic swallowing studies (VFSS) of 17 consecutive patients in a Greek hospital. The videofluoroscopic images were then stored on a website for independent review by an expert Speech and Language Pathologist in the US. An extra Rater evaluated 20% of all data for additional reliability testing. Eight diagnostic indicators of swallowing impairment and an overall subjective severity index were recorded for each study. Clinicians were also asked to choose from ten common treatment options for patients with dysphagia. There was good inter-rater agreement for most of the diagnostic indicators examined (ranging from 78% to 90%; kappa = 0.52-0.71) between all three Raters. Agreement on overall severity ratings was exact for more than half of the patients and within one-point on the 4-point scale for all other patients except one. However, the quality of care would have been substandard for more than half of the patients if teleconsultation had not been employed. In settings where a swallowing expert is not available and real-time telemedicine is not feasible, the use of asynchronous teleconsultation can produce better quality of care for patients with dysphagia.

Fuzzy cognitive maps for medical decision support - a paradigm from obstetrics.

Medical Decision Support Systems can provide assistance in crucial clinical judgments, particularly for inexperienced medical professionals. Fuzzy Cognitive Maps (FCMs) is a soft computing technique for modeling complex systems following an approach similar to human reasoning and decision-making. FCMs successfully represent knowledge and human experience, introducing concepts to represent the essential elements and the cause and effect relationships among the concepts to model the behavior of any system. Medical Decision Systems are complex systems that can be decomposed to subsystems and elements, where many factors have to be taken into consideration that may be complementary, contradictory, and competitive; these factors influence each other and determine the overall clinical decision with varying degrees. Here a Medical Decision Support System based on an appropriate FCM architecture is proposed and developed, as well as a corresponding paradigm from obstetrics is described.

Diagnosis support using Fuzzy Cognitive Maps combined with Genetic Algorithms.

A new hybrid modeling methodology to support medical diagnosis decisions is developed here. It extends previous work on Competitive Fuzzy Cognitive Maps for Medical Diagnosis Support Systems by complementing them with Genetic Algorithms Methods for concept interaction. The synergy of these methodologies is accomplished by a new proposed algorithm that leads to more dependable Advanced Medical Diagnosis Support Systems that are suitable to handle situations where the decisions are not clearly distinct. The technique developed here is applied successfully to model and test a differential diagnosis problem from the speech pathology area for the diagnosis of language impairments.

Speech sound classification and detection of articulation disorders with support vector machines and wavelets.

This paper proposes a novel integrated methodology to extract features and classify speech sounds with intent to detect the possible existence of a speech articulation disorder in a speaker. Articulation, in effect, is the specific and characteristic way that an individual produces the speech sounds. A methodology to process the speech signal, extract features and finally classify the signal and detect articulation problems in a speaker is presented. The use of support vector machines (SVMs), for the classification of speech sounds and detection of articulation disorders is introduced. The proposed method is implemented on a data set where different sets of features and different schemes of SVMs are tested leading to satisfactory performance.

A fuzzy cognitive map approach to differential diagnosis of specific language impairment.

This paper presents a computer-based model for differential diagnosis of specific language impairment (SLI), a language disorder that, in many cases, cannot be easily diagnosed. This difficulty necessitates the development of a methodology to assist the speech therapist in the diagnostic process. The methodology tool is based on fuzzy cognitive maps and constitutes a qualitative and quantitative computer model comprised of the experience and knowledge of specialists. The development of the model was based on knowledge from the literature and then it was successfully tested on four clinical cases. The results obtained point to its final integration in the future and to its valid contribution as a differential diagnosis model of SLI.