ABSTRACT
OBJECTIVE(S)/HYPOTHESIS: Virtual therapy (teletherapy) for patients with dysphonia has become ubiquitous in the COVID-19 era. However, barriers to widespread implementation are evident, including unpredictable insurance coverage attributed to limited evidence supporting this approach. In our single-institution cohort, our objective was to show strong evidence for use and effectiveness of teletherapy for patients with dysphonia. STUDY DESIGN: Single institution, retrospective cohort study. MATERIAL AND METHODS: This was an analysis of all patients referred for speech therapy with dysphonia as primary diagnosis from 4/1/2020 to 7/1/2021 and in whom all therapy sessions were delivered in a teletherapy format. We collated and analyzed demographics and clinical characteristics and adherence to the teletherapy program. We assessed changes in perceptual assessments and vocal capabilities (GRBAS, MPT), patient-reported outcomes (V-RQOL), and metrics of session outcomes (complexity of vocal tasks, carry-over of target voice) pre- and post-teletherapy using student's t test and chi-square test. RESULTS: Our cohort included 234 patients (mean [SD] age 52 [20] years) residing a mean (SD) distance of 51.3 (67.1) miles from our institution. The most common referral diagnosis was muscle tension dysphonia (n = 145, 62.0% patients). Patients attended a mean (SD) of 4.2 (3.0) sessions; 68.0% (n = 159) of patients completed four or more sessions and/or were deemed appropriate for discharge from teletherapy program. Statistically significant improvements were seen in complexity and consistency of vocal tasks with consistent gains in carry-over of target voice for isolated tasks and connected speech. CONCLUSIONS: Teletherapy is a versatile and effective approach for treatment of patients with dysphonia of varying age, geography, and diagnoses.
ABSTRACT
BACKGROUND: Long Covid is a widely recognised consequence of COVID-19 infection, but little is known about the burden of symptoms that patients present with in primary care, as these are typically recorded only in free text clinical notes. Our objectives were to compare symptoms in patients with and without a history of COVID-19, and investigate symptoms associated with a Long Covid diagnosis. METHODS: We used primary care electronic health record data from The Health Improvement Network (THIN), a Cegedim database. We included adults registered with participating practices in England, Scotland or Wales. We extracted information about 89 symptoms and 'Long Covid' diagnoses from free text using natural language processing. We calculated hazard ratios (adjusted for age, sex, baseline medical conditions and prior symptoms) for each symptom from 12 weeks after the COVID-19 diagnosis. FINDINGS: We compared 11,015 patients with confirmed COVID-19 and 18,098 unexposed controls. Only 20% of symptom records were coded, with 80% in free text. A wide range of symptoms were associated with COVID-19 at least 12 weeks post-infection, with strongest associations for fatigue (adjusted hazard ratio (aHR) 3.99, 95% confidence interval (CI) 3.59, 4.44), shortness of breath (aHR 3.14, 95% CI 2.88, 3.42), palpitations (aHR 2.75, 95% CI 2.28, 3.32), and phlegm (aHR 2.88, 95% CI 2.30, 3.61). However, a limited subset of symptoms were recorded within 7 days prior to a Long Covid diagnosis in more than 20% of cases: shortness of breath, chest pain, pain, fatigue, cough, and anxiety / depression. INTERPRETATION: Numerous symptoms are reported to primary care at least 12 weeks after COVID-19 infection, but only a subset are commonly associated with a GP diagnosis of Long Covid.
Subject(s)
Anxiety Disorders , Pain , Dyspnea , Chest Pain , Depressive Disorder , COVID-19 , FatigueABSTRACT
Background: Long Covid, characterised by symptoms after Covid-19 infection which persist for longer than 12 weeks, is becoming an important societal and economic problem. As Long Covid was novel in 2020, there has been debate regarding its aetiology and whether it is one, or multiple, syndromes. This study assessed risk factors associated with Long Covid and examined symptom clusters that might indicate sub-types. Methods: 4,040 participants reporting for >4 months in the Covid Symptom Study App were included. Multivariate logistic regression was undertaken to identify risk factors associated with Long Covid. Cluster analysis (K-modes and hierarchical agglomerative clustering) and factor analysis were undertaken to investigate symptom clusters. Results: Long Covid affected 13.6% of participants. Significant risk factors included being female (P < 0.01), pre-existing poor health (P < 0.01), and worse symptoms in the initial illness. A model incorporating sociodemographics, comorbidities, and health status predicted Long Covid with an accuracy (AUROC) of 76%. The three clustering approaches gave rise to different sets of clusters with no consistent pattern across methods. Conclusions: Our model of risk factors may help clinicians predict patients at higher risk of Long Covid, so these patients can rest more, receive treatments, or enter clinical trials; reducing the burden of this long-term and debilitating condition. No consistent subtypes were identified.
Subject(s)
COVID-19ABSTRACT
This report describes the use of serologic tests to detect the presence of SARS-CoV-2 antibodies in two counties in Georgia.