Optimising gout treatment: insights from a nurse-led cohort study.

OBJECTIVES: Currently, gout management, particularly urate-lowering therapy (ULT), is often suboptimal. Nurses successfully manage various diseases including gout. As gout prevalence is rising, and rheumatologists and general practitioners face shortages, a new approach is imperative. This real-life prospective cohort study evaluated the effectiveness of nurse-led care employing a treat-to-target strategy for gout management over a 2-year period. METHODS: All consecutively confirmed gout patients were included. The nurse-led clinic provided a structured treatment plan with consultations, patient leaflets, telephone contacts and laboratory monitoring. After a year of nurse-led care, patients transitioned to continued care in general practice. Follow-up data were complete through registries. The primary outcome was achieving target p-urate levels (<0.36 mmol/L) at 2 years after diagnosis. Secondary outcomes included treatment continuation and achievement of target p-urate levels in specific subgroups. The results were compared with patients diagnosed in the same clinic but followed up in 'usual care'. RESULTS: In the nurse-led group (n=114), 83% achieved target p-urate levels and ULT was continued by 98%. This trend persisted across various patient subgroups. Only 44% of patients in usual care achieved target p-urate and with insufficient doses of allopurinol . Nurse-led care involved an average of two visits and three telephone contacts over 336 days. The 2-year mortality rate was 15%. CONCLUSIONS: Nurse-led gout care, employing a targeted approach, was associated with a very high uptake of and adherence to ULT. The encouraging results were not achieved in usual care although a direct comparison might be influenced by selection bias.

Automatic detection of expressed emotion from Five-Minute Speech Samples: Challenges and opportunities.

Research into clinical applications of speech-based emotion recognition (SER) technologies has been steadily increasing over the past few years. One such potential application is the automatic recognition of expressed emotion (EE) components within family environments. The identification of EE is highly important as they have been linked with a range of adverse life events. Manual coding of these events requires time-consuming specialist training, amplifying the need for automated approaches. Herein we describe an automated machine learning approach for determining the degree of warmth, a key component of EE, from acoustic and text natural language features. Our dataset of 52 recorded interviews is taken from recordings, collected over 20 years ago, from a nationally representative birth cohort of British twin children, and was manually coded for EE by two researchers (inter-rater reliability 0.84-0.90). We demonstrate that the degree of warmth can be predicted with an F1-score of 64.7% despite working with audio recordings of highly variable quality. Our highly promising results suggest that machine learning may be able to assist in the coding of EE in the near future.

Effectiveness of a novel digital patient education programme to support self-management of early rheumatoid arthritis: a randomised controlled trial.

OBJECTIVES: To evaluate the effectiveness of a novel digital patient education (PE) programme in improving self-management in patients newly diagnosed with rheumatoid arthritis (RA). METHODS: This was a parallel, open-label, two arms, randomised controlled trial with superiority design. Patients from five rheumatology clinics were randomised into digital PE (intervention) or face-to-face PE (control). The primary outcome was self-efficacy, measured by average difference in the Rheumatoid Arthritis Self-Efficacy (RASE) score from baseline to month 12. Secondary outcomes were RA knowledge, health literacy, adherence, and quality of life. Healthcare utilisation data and digital PE programme usage were recorded. Self-efficacy, knowledge, and health literacy data were analysed using mixed-effects repeated measures modelling; adherence using logistic regression, and quality of life and healthcare utilization using descriptive statistics with the Wilcoxon rank-sum test. RESULTS: Of the 180 patients randomised (digital PE, n = 89; face-to-face PE, n = 91), 175 had data available for analysis. Median age was 59.0 years, and 61% were women. The average difference in self-efficacy between groups from baseline to month 12 was significant by a -4.34 difference in RASE score, favouring the intervention group (95%CI -8.17 to -0.51; p= 0.026). RA knowledge, health literacy, and quality of life showed minor improvements over time but no difference between groups, except out-patient clinic contacts which were fewer in the intervention group. CONCLUSIONS: The findings suggest that digital PE is effective in improving self-efficacy and therefore self-management in patients with early RA. This intervention has potential to lower healthcare costs by decreasing out-patient clinic contacts. TRIAL REGISTRATION NUMBER: clinicaltrials.gov, NCT04669340.

Predicting the cause of seizures using features extracted from interactions with a virtual agent.

OBJECTIVE: A clinical decision tool for Transient Loss of Consciousness (TLOC) could reduce currently high misdiagnosis rates and waiting times for specialist assessments. Most clinical decision tools based on patient-reported symptom inventories only distinguish between two of the three most common causes of TLOC (epilepsy, functional /dissociative seizures, and syncope) or struggle with the particularly challenging differentiation between epilepsy and FDS. Based on previous research describing differences in spoken accounts of epileptic seizures and FDS seizures, this study explored the feasibility of predicting the cause of TLOC by combining the automated analysis of patient-reported symptoms and spoken TLOC descriptions. METHOD: Participants completed an online web application that consisted of a 34-item medical history and symptom questionnaire (iPEP) and spoken interaction with a virtual agent (VA) that asked eight questions about the most recent experience of TLOC. Support Vector Machines (SVM) were trained using different combinations of features and nested leave-one-out cross validation. The iPEP provided a baseline performance. Inspired by previous qualitative research three spoken language based feature sets were designed to assess: (1) formulation effort, (2) the proportion of words from different semantic categories, and (3) verb, adverb, and adjective usage. RESULTS: 76 participants completed the application (Epilepsy = 24, FDS = 36, syncope = 16). Only 61 participants also completed the VA interaction (Epilepsy = 20, FDS = 29, syncope = 12). The iPEP model accurately predicted 65.8 % of all diagnoses, but the inclusion of the language features increased the accuracy to 85.5 % by improving the differential diagnosis between epilepsy and FDS. CONCLUSION: These findings suggest that an automated analysis of TLOC descriptions collected using an online web application and VA could improve the accuracy of current clinical decisions tools for TLOC and facilitate clinical stratification processes (such as ensuring appropriate referral to cardiological versus neurological investigation and management pathways).

Alcohol use trends in Dallas County during the COVID-19 pandemic.

Alcohol (ethanol) is the most widely detected drug in forensic toxicology casework and an increase in consumption of alcohol was reported during the COVID-19 pandemic. The increase in consumption could be attributed to rising stress levels and social isolation. To determine whether the pandemic had an impact on ethanol positivity and concentrations in cases analyzed by the Dallas County Southwestern Institute of Forensic Sciences, blood ethanol results were evaluated from January 1, 2019, through December 31, 2021. This time frame captured ethanol prevalence and concentrations before, during, and immediately following the pandemic for comparison. The average ethanol concentration in postmortem casework over the three years for each quarter ranged from 0.116 g/100 mL to 0.142 g/100 mL while the average concentration in driving while intoxicated (DWI) was higher, ranging from 0.173 g/100 mL to 0.188 g/100 mL. The ethanol positivity rate for postmortem casework remained relatively the same at approximately 20% during the time frame, while there was a decrease in ethanol positivity rate for DWI casework during the pandemic in April - June (Q2) 2020. However, the positivity rate returned to pre-pandemic levels by the end of 2020. Despite the self-reported surveys of increased alcohol consumption during the pandemic, a corresponding increase in average ethanol concentrations was not observed in Dallas County and the surrounding area.

Speech patterns in responses to questions asked by an intelligent virtual agent can help to distinguish between people with early stage neurodegenerative disorders and healthy controls.

Previous research has provided strong evidence that speech patterns can help to distinguish between people with early stage neurodegenerative disorders (ND) and healthy controls. This study examined speech patterns in responses to questions asked by an intelligent virtual agent (IVA): a talking head on a computer which asks pre-recorded questions. The study investigated whether measures of response length, speech rate and pausing in responses to questions asked by an IVA help to distinguish between healthy control participants and people diagnosed with Mild Cognitive Impairment (MCI) or Alzheimer's disease (AD). The study also considered whether those measures can further help to distinguish between people with MCI, people with AD, and healthy control participants (HC). There were 38 people with ND (31 people with MCI, 7 people with AD) and 26 HC. All interactions took place in English. People with MCI spoke fewer words compared to HC, and people with AD and people with MCI spoke for less time than HC. People with AD spoke at a slower rate than people with MCI and HC. There were significant differences across all three groups for the proportion of time spent pausing and the average pause duration: silent pauses make up the greatest proportion of responses from people with AD, who also have the longest average silent pause duration, followed by people with MCI then HC. Therefore, the study demonstrates the potential of an IVA as a method for collecting data showing patterns which can help to distinguish between diagnostic groups.

The Dysarthric Expressed Emotional Database (DEED): An audio-visual database in British English.

The Dysarthric Expressed Emotional Database (DEED) is a novel, parallel multimodal (audio-visual) database of dysarthric and typical emotional speech in British English which is a first of its kind. It is an induced (elicited) emotional database that includes speech recorded in the six basic emotions: "happiness", "sadness", "anger", "surprise", "fear", and "disgust". A "neutral" state has also been recorded as a baseline condition. The dysarthric speech part includes recordings from 4 speakers: one female speaker with dysarthria due to cerebral palsy and 3 speakers with dysarthria due to Parkinson's disease (2 female and 1 male). The typical speech part includes recordings from 21 typical speakers (9 female and 12 male). This paper describes the collection of the database, covering its design, development, technical information related to the data capture, and description of the data files and presents the validation methodology. The database was validated subjectively (human performance) and objectively (automatic recognition). The achieved results demonstrated that this database will be a valuable resource for understanding emotion communication by people with dysarthria and useful in the research field of dysarthric emotion classification. The database is freely available for research purposes under a Creative Commons licence at: https://sites.google.com/sheffield.ac.uk/deed.

Differentiating between epileptic and functional/dissociative seizures using semantic content analysis of transcripts of routine clinic consultations.

The common causes of Transient Loss of Consciousness (TLOC) are syncope, epilepsy, and functional/dissociative seizures (FDS). Simple, questionnaire-based decision-making tools for non-specialists who may have to deal with TLOC (such as clinicians working in primary or emergency care) reliably differentiate between patients who have experienced syncope and those who have had one or more seizures but are more limited in their ability to differentiate between epileptic seizures and FDS. Previous conversation analysis research has demonstrated that qualitative expert analysis of how people talk to clinicians about their seizures can help distinguish between these two TLOC causes. This paper investigates whether automated language analysis - using semantic categories measured by the Linguistic Inquiry and Word Count (LIWC) toolkit - can contribute to the distinction between epilepsy and FDS. Using patient-only talk manually transcribed from recordings of 58 routine doctor-patient clinic interactions, we compared the word frequencies for 21 semantic categories and explored the predictive performance of these categories using 5 different machine learning algorithms. Machine learning algorithms trained using the chosen semantic categories and leave-one-out cross-validation were able to predict the diagnosis with an accuracy of up to 81%. The results of this proof of principle study suggest that the analysis of semantic variables in seizure descriptions could improve clinical decision tools for patients presenting with TLOC.

Geographical and ecological analyses of multiple myeloma in Denmark: Identification of potential hotspot areas and impact of urbanisation.

BACKGROUND: The aetiology of multiple myeloma (MM) is unknown but various environmental exposures are suspected as risk factors. We present the first paper analysing the geographical distribution of MM in Denmark at the municipal level to investigate variations that could be explained by environmental exposures. METHODS: Patients diagnosed with MM in Denmark during 2005-2020 were identified from nationwide registries and grouped into the 98 Danish municipalities based on residence. The age- and sex-standardised incidence rate (SIR) of each municipality was compared to the national incidence in a funnel plot with 95% control limits. Differences in SIRs of rural, suburban, and urban areas were evaluated with incidence rate ratios. RESULTS: In total, 5243 MM patients were included. Overall, we found a heterogeneous geographical distribution of MM and a potential hotspot in southern Denmark. This hotspot contains three municipalities with SIRs above the 95% control limit assuming considerably higher rate of MM compared to the national incidence rate. A significant higher SIR was found in rural areas compared to urban areas. CONCLUSION: The geographical distribution of MM in Denmark indicates that the risk of developing MM depends on place of residence probably due to environmental factors.

Trends and drivers of pharmaceutical expenditures from systemic anti-cancer therapy.

Expenditures on medicine for systemic anti-cancer therapy (SACT) have seen large increases in recent years. The characterization of patients with high SACT costs is crucial to identify cost-driving factors, but little is known about the distribution of expenditures at the patient-level. We priced 260,834 registrations of SACT for 12,589 patients from 2008 to 2019 by combining them with product-level billings of EUR 142.1 million. Based on this, we defined high-cost patients as the 2.5% most expensive by accumulated SACT expenditures. We found that high-cost patients accounted for 28.8% of the total SACT expenditures and were observed across all major cancer groups except for pancreatic cancer. The risk of becoming a high-cost patient was increased for younger age groups, i.e., 18-44 and 45-64 years, for patients with BMI ≥ 25, and for patients with multiple cancer diagnoses, while no alteration of risk was observed due to comorbidities or sex. Changes in the characteristics of high-cost patients during the study period were found with an increased risk of becoming high-cost in later years for elderly patients and patients with lung cancer and a decreased risk for breast cancer patients.

Inter- and intra-observer variability of computed tomography-based parenchymal- and ductal diameters in chronic pancreatitis: a multi-observer international study.

PURPOSE: The need for incorporation of quantitative imaging biomarkers of pancreatic parenchymal and ductal structures has been highlighted in recent proposals for new scoring systems in chronic pancreatitis (CP). To quantify inter- and intra-observer variability in CT-based measurements of ductal- and gland diameters in CP patients. MATERIALS AND METHODS: Prospectively acquired pancreatic CT examinations from 50 CP patients were reviewed by 12 radiologists and four pancreatologists from 10 institutions. Assessment entailed measuring maximum diameter in the axial plane of four structures: (1) pancreatic head (PDhead), (2) pancreatic body (PDbody), (3) main pancreatic duct in the pancreatic head (MPDhead), and (4) body (MPDbody). Agreement was assessed by the 95% limits of agreement with the mean (LOAM), representing how much a single measurement for a specific subject may plausibly deviate from the mean of all measurements on the specific subject. Bland-Altman limits of agreement (LoA) were generated for intra-observer pairs. RESULTS: The 16 observers completed 6400 caliper placements comprising a first and second measurement session. The widest inter-observer LOAM was seen with PDhead (± 9.1 mm), followed by PDbody (± 5.1 mm), MPDhead (± 3.2 mm), and MPDbody (± 2.6 mm), whereas the mean intra-observer LoA width was ± 7.3, ± 5.1, ± 3.7, and ± 2.4 mm, respectively. CONCLUSION: Substantial intra- and inter-observer variability was observed in pancreatic two-point measurements. This was especially pronounced for parenchymal and duct diameters of the pancreatic head. These findings challenge the implementation of two-point measurements as the foundation for quantitative imaging scoring systems in CP.

Ultra-low-dose non-contrast CT and CT angiography can be used interchangeably for assessing maximal abdominal aortic diameter.

Background: Routine CT scans may increasingly be used to document normal aortic size and to detect incidental abdominal aortic aneurysms. Purpose: To determine whether ultra-low-dose non-contrast CT (ULDNC-CT) can be used instead of the gold standard CT angiography (CTA) for assessment of maximal abdominal aortic diameter. Materials and Methods: This retrospective study included 50 patients who underwent CTA and a normal-dose non-contrast CT for suspected renal artery stenosis. ULDNC-CT datasets were generated from the normal-dose non-contrast CT datasets using a simulation technique. Using the centerline technique, radiology consultants (n = 4) and residents (n = 3) determined maximal abdominal aortic diameter. The limits of agreement with the mean (LOAM) was used to access observer agreement. LOAM represents how much a measurement by a single observer may plausibly deviate from the mean of all observers on the specific subject. Results: Observers completed 1400 measurements encompassing repeated CTA and ULDNC-CT measurements. The mean diameter was 24.0 and 25.0 mm for CTA and ULDNC-CT, respectively, yielding a significant but minor mean difference of 1.0 mm. The 95% LOAM reproducibility was similar for CTA and ULDNC-CT (2.3 vs 2.3 mm). In addition, the 95% LOAM and mean diameters were similar for CTA and ULDNC-CT when observers were grouped as consultants and residents. Conclusions: Ultra-low-dose non-contrast CT exhibited similar accuracy and reproducibility of measurements compared with CTA for assessing maximal abdominal aortic diameter supporting that ULDNC-CT can be used interchangeably with CTA in the lower range of aortic sizes.

The Rising Burden of Inflammatory Bowel Disease in Denmark Over Two Decades: A Nationwide Cohort Study.

BACKGROUND & AIMS: Shifts in epidemiological stages of inflammatory bowel disease (IBD) carry implications toward understanding IBD etiology and managing clinical care. We conducted a temporal analysis of the epidemiology of IBD between 1995 and 2016 in the Danish nationwide cohort. METHODS: We used the Danish registers to obtain data on demographics and IBD-related outpatient and inpatient contacts between 1995 and 2016. IBD diagnosis was defined as having ≥2 registrations related to Crohn's disease (CD) or ulcerative colitis (UC) within a 2-year period. We estimated overall and annual incidence rates and prevalence of CD and UC standardized with respect to age and sex. RESULTS: A total of 47,830 individuals met the criteria for IBD diagnosis, of which 33% were diagnosed with CD and 67% with UC. Between 1995 and 2016, the incidence rate (95% confidence interval) per 100,000 person-years rose from 9.1 (8.3-10.0) to 17.8 (16.8-19.0) for CD, and from 21.0 (19.8-22.3) to 28.4 (27.0-29.8) for UC. The highest increase in CD and UC incidence rates occurred in children and young adults, respectively. The prevalence of IBD doubled from 1995 to 2016; the greatest increase (2.5-fold) was in UC prevalence among individuals aged >40 years. During this period, the median age of the IBD population increased by 6 to 7 years. CONCLUSIONS: In Denmark, the incidence and prevalence of IBD have increased during the last 2 decades. The IBD population is shifting toward an older age. These findings have implications towards understanding environmental shifts as well as preparing health care systems for an aging IBD population.

The Outcome of a New Teledentistry Initiative in Response to the COVID-19 Pandemic: A Cross-sectional Study.

AIM: The purpose of this study was to assess the overall satisfaction of a new learning experience and evaluate the outcome of LLUSD's educational teledentistry initiative through a survey based on Kirkpatrick's multidimensional model of training evaluation. MATERIALS AND METHODS: An IRB application was approved (#5210385) for a cross-sectional study that included Loma Linda University School of Dentistry (LLUSD) dental students of the class of 2022 and 2023. The 9-question survey consisted of three sections. The first section included demographic questions on gender and the graduating class. The second section was related to perceived teaching effectiveness, attitude, behavior, and significance. The third section included an open-ended question. The survey was distributed by three student investigators. Descriptive statistics were calculated, and categorical variables were compared using the Chi-squared test (χ2 test). RESULTS: The perceived teaching effectiveness of the newly implemented educational initiative was high for increasing the ability to communicate with patients and for screening and identifying the need for referrals. A majority of students believed that teledentistry is an important means to improve patients' access to dental care and that the School has been providing a good educational environment in providing teledentistry sessions to patients. There were no significant differences in the frequencies of positive and negative responses to all questions (N = 6) by gender and by class (p >0.05, in all instances). CONCLUSIONS: Teledentistry eVisits allowed the continuation of patient contact and initial assimilation of patient information. There is potential for this educational initiative to be more actively and comprehensively implemented in the future. CLINICAL SIGNIFICANCE: New educational initiatives allow the continuation of patient contact that will ensure that students will graduate as competent oral health care providers despite challenges imposed by the pandemic.

Keeping patient and public partnership at the heart of medical technology development during Covid-19: examples of adaptive practice.

NIHR (National Institute for Health Research) Devices for Dignity MedTech Cooperative (D4D) and NIHR Children and Young People MedTech Cooperative (CYPMedTech) have established track records in keeping patient and public involvement (PPI) at the core of medical technology development, evaluation and implementation. The 2020 global COVID-19 pandemic presented significant challenges to maintaining this crucial focus. In this paper we describe prior successful methodologies and share examples of the adaptations made in order to continue to engage with patients and the public throughout the pandemic and beyond. We reflect on learning gained from these experiences, and new areas of scope and focus relating to broadening the reach of engagement and representation, along with associated resource requirements and impact metrics.

Severe Bartonella henselae bone infection in a kidney transplanted young man.

We present a case of a young kidney transplanted man. He was admitted with lymphadenopathy, fluctuating fever and night sweats 2 months after a cat bite. After admission, he developed severe pain around his right hip. An 18F-fluorodeoxyglucose (FDG)-positron emission tomography/CT revealed intense FDG-uptake in lymph nodes, spleen and bone, suggestive of lymphoma. An extracted lymph node showed confluent granulomas, microabscesses with neutrophils and scattered multinucleated giant cells histologically. The patient had history of latent tuberculosis and proteinase 3 -anti-neutrophil cytoplasmic antibodies associated (PR3-ANCA) vasculitis, making differential diagnostic considerations complicated. Bartonella henselae antibodies was detected in blood and B. henselae DNA in a lymph node. He was started on doxycycline and rifampicin. Due to severe drug interactions with both tacrolimus and increasing morphine doses, rifampicin was changed to azithromycin. He received 12 days of relevant antibiotic treatment and responded well. He was discharged after 16 days with close follow-up and was still in habitual condition 12 months later.

A Rapid Survey of State and Territorial Public Health Partnerships With Faith-Based Organizations to Promote COVID-19 Vaccination.

During the COVID-19 pandemic, media accounts emerged describing faith-based organizations (FBOs) working alongside health departments to support the COVID-19 response. In May 2021, the Department of Health and Human Services, Centers for Disease Control and Prevention, and the Association of State and Territorial Health Officials (ASTHO) sent an electronic survey to the 59 ASTHO member jurisdictions and four major US cities to assess state and territorial engagement with FBOs. Findings suggest that public health officials in many jurisdictions were able to work effectively with FBOs during the COVID-19 pandemic to provide essential education and mitigation tools to diverse communities. (Am J Public Health. 2022;112(3):397-400. https://doi.org/10.2105/AJPH.2021.306620).