Minimum clinically important difference of the Social Functioning in Dementia Scale (SF-DEM): cross-sectional study and Delphi survey.

OBJECTIVES: Good social functioning is important for people living with dementia and their families. The Social Functioning in Dementia Scale (SF-DEM) is a valid and reliable instrument measuring social functioning in dementia. However the minimum clinically important difference (MCID) has not yet been derived for SF-DEM. This study aims to define the MCID for the SF-DEM. DESIGN: We used triangulation, incorporating data from a cross-sectional study to calculate the MCID using distribution-based and anchor-based methods, and a Delphi survey. SETTING AND PARTICIPANTS: The cross-sectional survey comprised 299 family carers of people with dementia. Twenty dementia experts (researchers, clinicians, family carers) rated whether changes on clinical vignettes represented a meaningful change in the Delphi survey. PRIMARY OUTCOME MEASURES: We calculated the distribution-based MCID as 0.5 of an SD for each of the three SF-DEM domains (1-spending time with others, 2-communicating with others, 3-sensitivity to others). We used the carers' rating of social functioning to calculate the anchor-based MCID. For the Delphi survey, we defined consensus as ≥75% agreement. Where there was lack of consensus, experts were asked to complete a further survey round. RESULTS: We found that 0.5 SD of SF-DEM was 1.9 points, 2.2 and 1.4 points in domains 1, 2 and 3, respectively. Using the anchoring analysis, the MCIDs were 1.7 points, 1.7 points, and 0.9 points in domains 1, 2 and 3, respectively. The Delphi method required two rounds. In the second round, a consensus was reached that a 2-point change was considered significant in all three domains, but no consensus was reached on a 1-point change. CONCLUSIONS: By triangulating all three methods, the SF-DEM's MCIDs were 1.9, 2.0 and 1.4 points for domains 1, 2 and 3, respectively. For individuals, these values should be rounded to a 2-point change for each domain.

Monitoring COVID-19 on Social Media: Development of an End-to-End Natural Language Processing Pipeline Using a Novel Triage and Diagnosis Approach.

BACKGROUND: The COVID-19 pandemic has created a pressing need for integrating information from disparate sources in order to assist decision makers. Social media is important in this respect; however, to make sense of the textual information it provides and be able to automate the processing of large amounts of data, natural language processing methods are needed. Social media posts are often noisy, yet they may provide valuable insights regarding the severity and prevalence of the disease in the population. Here, we adopt a triage and diagnosis approach to analyzing social media posts using machine learning techniques for the purpose of disease detection and surveillance. We thus obtain useful prevalence and incidence statistics to identify disease symptoms and their severities, motivated by public health concerns. OBJECTIVE: This study aims to develop an end-to-end natural language processing pipeline for triage and diagnosis of COVID-19 from patient-authored social media posts in order to provide researchers and public health practitioners with additional information on the symptoms, severity, and prevalence of the disease rather than to provide an actionable decision at the individual level. METHODS: The text processing pipeline first extracted COVID-19 symptoms and related concepts, such as severity, duration, negations, and body parts, from patients' posts using conditional random fields. An unsupervised rule-based algorithm was then applied to establish relations between concepts in the next step of the pipeline. The extracted concepts and relations were subsequently used to construct 2 different vector representations of each post. These vectors were separately applied to build support vector machine learning models to triage patients into 3 categories and diagnose them for COVID-19. RESULTS: We reported macro- and microaveraged F1 scores in the range of 71%-96% and 61%-87%, respectively, for the triage and diagnosis of COVID-19 when the models were trained on human-labeled data. Our experimental results indicated that similar performance can be achieved when the models are trained using predicted labels from concept extraction and rule-based classifiers, thus yielding end-to-end machine learning. In addition, we highlighted important features uncovered by our diagnostic machine learning models and compared them with the most frequent symptoms revealed in another COVID-19 data set. In particular, we found that the most important features are not always the most frequent ones. CONCLUSIONS: Our preliminary results show that it is possible to automatically triage and diagnose patients for COVID-19 from social media natural language narratives, using a machine learning pipeline in order to provide information on the severity and prevalence of the disease for use within health surveillance systems.

Fatal large-vessel cerebrovascular infarct presenting with severe coronavirus disease 2019 in a 39-year-old patient: a case report.

BACKGROUND: Emerging reports are describing stroke in young, otherwise healthy patients with coronavirus disease 2019, consistent with the theory that some of the most serious complications of coronavirus disease 2019 are due to a systemic coagulopathy. However, the relevance of both the severity of coronavirus disease 2019 illness and established vascular risk factors in these younger patients is unknown, as reports are inconsistent. CASE PRESENTATION: Here we describe a 39-year-old white male, who died after presenting simultaneously with a malignant large-vessel cerebrovascular infarct and a critical coronavirus disease 2019 respiratory illness. Doppler ultrasound revealed evidence of carotid plaque thrombosis. Blood tests revealed evidence of undiagnosed diabetes mellitus; however, the patient was otherwise healthy, fit, and active. CONCLUSIONS: This unique case highlights a possible interaction between established risk factors and large-vessel thrombosis in young patients with coronavirus disease 2019, and informs future research into the benefits of anticoagulation in these patients.