Oropharyngeal dysphagia is prevalent in several at-risk populations, including post-stroke patients, patients in intensive care and the elderly. Dysphagia contributes to longer hospital stays and poor outcomes, including pneumonia. Early identification of dysphagia is recommended as part of the evaluation of at-risk patients, but available bedside screening tools perform inconsistently. In this study, we developed algorithms to detect swallowing impairment using a novel accelerometer-based dysphagia detection system (DDS). A sample of 344 individuals was enrolled across seven sites in the United States. Dual-axis accelerometry signals were collected prospectively with simultaneous videofluoroscopy (VFSS) during swallows of liquid barium stimuli in thin, mildly, moderately and extremely thick consistencies. Signal processing classifiers were trained using linear discriminant analysis and 10,000 random training–test data splits. The primary objective was to develop an algorithm to detect impaired swallowing safety with thin liquids with an area under receiver operating characteristic curve (AUC) > 80% compared to the VFSS reference standard. Impaired swallowing safety was identified in 7.2% of the thin liquid boluses collected. At least one unsafe thin liquid bolus was found in 19.7% of participants, but participants did not exhibit impaired safety consistently. The DDS classifier algorithms identified participants with impaired thin liquid swallowing safety with a mean AUC of 81.5%, (sensitivity 90.4%, specificity 60.0%). Thicker consistencies were effective for reducing the frequency of penetration–aspiration. This DDS reached targeted performance goals in detecting impaired swallowing safety with thin liquids. Simultaneous measures by DDS and VFSS, as performed here, will be used for future validation studies. Electronic supplementary material The online version of this article (10.1007/s00455-018-09974-5) contains supplementary material, which is available to authorized users.

IntroductionOropharyngeal dysphagia is frequent in hospitalized post-stroke patients and is associated with increased mortality and comorbidities. The aim of our analysis was to evaluate the impact of dysphagia on Length of Hospital Stay (LOS) and costs. The hospital perspective was used to assess costs.MethodsHospital discharge databases comparing hospital stays for ischemic stroke associated with dysphagia vs stroke without dysphagia in France and Switzerland were analyzed. The French Medical Information System Program (PMSI) database analysis focused on 62’297 stays for stroke in the public sector. 6’037 hospital stays for stroke were analyzed from the Swiss OFS (Office fédéral de la statistique: Statistique des coûts par cas 2012) database. Diagnosis codes and listing of procedures were used to identify dysphagia in stroke patients.ResultsPatients with post-stroke dysphagia accounted for 8.4% of stroke hospital stays in Switzerland, which is consistent with recently reported prevalence of dysphagia at hospital discharge (Arnold et al, 2016). The French database analysis identified 4.2% stays with post-stroke dysphagia. We hypothesize that the difference between the Swiss and French datasets may be explained by the limitations of an analysis based on diagnosis and procedure coding. Patients with post-stroke dysphagia stayed longer at hospitals (LOS of 23.7 vs. 11.8 days in France and LOS of 14.9 vs. 8.9 days in Switzerland) compared with patients without post-stroke dysphagia. Post-stroke dysphagia was associated with about €3’000 and CHF14’000 cost increase in France and Switzerland respectively.DiscussionIn this study post-stroke dysphagia was associated with increased LOS and higher hospital costs. It is difficult to isolate the impact of dysphagia in patients with multiple symptoms and disabilities impacting rehabilitation and recovery. After adjusting for confounding factors by matching stays according to age, sex and stroke complications, post-stroke dysphagia association with increased LOS and higher hospital costs was found to be independent of sensory or motor complications.ConclusionPost-stroke dysphagia is associated with increased length of hospital stay and higher hospital costs.

Clinical trials continue to be the gold standard for evaluating new medical technologies. New advancements in modern computation power have led to increasing interest in Bayesian methods. Despite the multiple benefits of Bayesian approaches, application to clinical trials has been limited. Based on insights from the survey of clinical researchers in drug development conducted by the Drug Information Association Bayesian Scientific Working Group (DIA BSWG), insufficient knowledge of Bayesian approaches was ranked as the most important perceived barrier to implementing Bayesian methods. Results of the same survey indicate that clinical researchers may find the interpretation of results from a Bayesian analysis to be more useful than conventional interpretations. In this article, we illustrate key concepts tied to Bayesian methods, starting with familiar concepts widely used in clinical practice before advancing in complexity, and use practical illustrations from clinical development.

Bayesian strategies for planning and analyzing clinical trials have become a viable choice, especially in rare diseases where drug development faces many challenges and stakeholders are interested in innovations that may help overcome them. Disease natural history and clinical outcomes occurrence and variability are often poorly understood. Standard trial designs are not optimized to obtain adequate safety and efficacy data from small numbers of patients. Bayesian methods are well-suited for adaptive trials, with an accelerated learning curve. Using Bayesian statistics can be advantageous in that design choices and their consequences are considered carefully, continuously monitored, and updated where necessary, which ultimately provides a natural and principled way of seamlessly combining prior clinical information with data, within a solid decision theoretical framework. In this article, we introduce the Bayesian option in the rare disease context to support clinical decision-makers in selecting the best choice for their drug development project. Many researchers in drug development show reluctance to using Bayesian statistics, and the top-two reported barriers are insufficient knowledge of Bayesian approaches and a lack of clarity or guidance from regulators. Here we introduce concepts of borrowing, extrapolation, adaptation, and modeling and illustrate them with examples that have been discussed or developed with regulatory bodies to show how Bayesian strategies can be applied to drug development in rare diseases.

No abstract