Plantar pressure measurement in diabetic foot disease: A scoping review.

AIMS/INTRODUCTION: Patients with a healed diabetic foot ulcer (DFU) have a 40% risk of ulcer recurrence within a year. New and effective measures to prevent DFU recurrence are essential. We aimed to highlight emerging trends and future research opportunities in the use of plantar pressure measurement to prevent DFU recurrence. MATERIALS AND METHODS: Our scoping review protocol was drafted using the Preferred Reporting Items for Systematic Reviews and Meta-analysis - Scoping Review protocol. Peer-reviewed, English-language papers were included that addressed both plantar pressure measurement and diabetic foot disease, either as primary studies that have advanced the field or as review papers that provide summaries and/or opinion on the field as a whole, as well as specific papers that provide guidelines for future research and advancement in the field. RESULTS: A total of 24 eligible publications were identified in a literature search using PubMed. A further 36 eligible studies were included after searching the references sections of these publications, leaving a total of 60 publications included in this scoping review. CONCLUSIONS: Plantar pressure measurement can and will play a major role in the prevention of DFU. There is already a strong, albeit limited, evidence base in place to prove its benefit in reducing DFU recurrence. More research is required in larger populations, using remote monitoring in real-world settings, and with improved technology.

Statistical techniques used in analysing simultaneous continuous glucose monitoring and ambulatory electrocardiography in patients with diabetes: A systematic review.

OBJECTIVES: There has been a steady increase in the number of studies of the complex relationship between glucose and electrical cardiac activity which use simultaneous continuous glucose monitors (CGM) and continuous electrocardiogram (ECG). However, data collected on the same individual tend to be similar (yielding correlated or dependent data) and require analyses that take into account that correlation. Many opt for simplified techniques such as calculating one measure from the data collected and analyse one observation per subject. These simplified methods may yield inconsistent and biased results in some instances. In this systematic review, we aim to examine the adequacy of the statistical analyses performed in such studies and make recommendations for future studies. RESEARCH QUESTIONS: What are the objectives of studies collecting simultaneous CGM and ECG data? Do methods used in analysing CGM and continuous ECG data fully optimise the data collected? DESIGN: Systematic review. DATA SOURCES: PubMed and Web of Science. METHODS: A comprehensive search of the PubMed and Web of Science databases to June 2022 was performed. Studies utilising CGM and continuous ECG simultaneously in people with diabetes were included. We extracted information about study objectives, technologies used to collect data and statistical analysis methods used for analysis. Reporting was done following PRISMA guidelines. RESULTS: Out of 118 publications screened, a total of 31 studies met the inclusion criteria. There was a diverse array of study objectives, with only two studies exploring the same exposure-outcome relationship, allowing only qualitative analysis. Only seven studies (23%) incorporated methods which fully utilised the study data using methods that yield the correct power and minimize type I error rate. The rest (77%) used analyses that summarise the data first before analysis and/or totally ignored data dependency. Of those who applied more advanced methods, one study performed both simple and correct analyses and found that ignoring data structure resulted in no association whilst controlling for repeated measures yielded a significant relationship. CONCLUSION: Most studies underutilised statistical methods suitable for analysis of dynamic continuous data, potentially attenuating their statistical power and overall conclusions. We recommend that aggregated data be used only as exploratory analysis, while primary analysis should use methods applied to the raw data such as mixed models or functional data analyses. These methods are widely available in many free, open source software applications.

Dissociating COVID-19 from other respiratory infections based on acoustic, motor coordination, and phonemic patterns.

In the face of the global pandemic caused by the disease COVID-19, researchers have increasingly turned to simple measures to detect and monitor the presence of the disease in individuals at home. We sought to determine if measures of neuromotor coordination, derived from acoustic time series, as well as phoneme-based and standard acoustic features extracted from recordings of simple speech tasks could aid in detecting the presence of COVID-19. We further hypothesized that these features would aid in characterizing the effect of COVID-19 on speech production systems. A protocol, consisting of a variety of speech tasks, was administered to 12 individuals with COVID-19 and 15 individuals with other viral infections at University Hospital Galway. From these recordings, we extracted a set of acoustic time series representative of speech production subsystems, as well as their univariate statistics. The time series were further utilized to derive correlation-based features, a proxy for speech production motor coordination. We additionally extracted phoneme-based features. These features were used to create machine learning models to distinguish between the COVID-19 positive and other viral infection groups, with respiratory- and laryngeal-based features resulting in the highest performance. Coordination-based features derived from harmonic-to-noise ratio time series from read speech discriminated between the two groups with an area under the ROC curve (AUC) of 0.94. A longitudinal case study of two subjects, one from each group, revealed differences in laryngeal based acoustic features, consistent with observed physiological differences between the two groups. The results from this analysis highlight the promise of using nonintrusive sensing through simple speech recordings for early warning and tracking of COVID-19.

Bites by the noble false widow spider Steatoda nobilis can induce Latrodectus-like symptoms and vector-borne bacterial infections with implications for public health: a case series.

CONTEXT: In recent years, the Noble false widow spider Steatoda nobilis (Thorell, 1875) has expanded its range globally and may represent a potential threat to native ecosystems and public health. Increasing numbers in synanthropic habitats have led to more human encounters and envenomations. Steatoda nobilis bites were previously classed as medically significant with similarities to bites from true black widows of the genus Latrodectus but deemed milder in onset, with symptoms generally ranging from mild to moderate. CASE DETAILS: In this manuscript we present 16 new cases of S. nobilis envenomations bringing the total number of confirmed cases reported in the literature to 24. We report new symptoms and provide discussion on the contributing factors to pathology following bites by S. nobilis. DISCUSSION: We report a range of pathologies including necrosis, Latrodectus-like envenomation symptoms that include debilitating pain, tremors, fatigue, nausea, hypotension, and vectored bacterial infections including cellulitis and dermatitis. Symptoms ranged from mild to severe, requiring hospitalisation in some cases.

Artificial Intelligence for Detecting COVID-19 With the Aid of Human Cough, Breathing and Speech Signals: Scoping Review.

Goal: Official tests for COVID-19 are time consuming, costly, can produce high false negatives, use up vital chemicals and may violate social distancing laws. Therefore, a fast and reliable additional solution using recordings of cough, breathing and speech data for preliminary screening may help alleviate these issues. Objective: This scoping review explores how Artificial Intelligence (AI) technology aims to detect COVID-19 disease by using cough, breathing and speech recordings, as reported in the literature. Here, we describe and summarize attributes of the identified AI techniques and datasets used for their implementation. Methods: A scoping review was conducted following the guidelines of PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). Electronic databases (Google Scholar, Science Direct, and IEEE Xplore) were searched between 1st April 2020 and 15th August 2021. Terms were selected based on the target intervention (i.e., AI), the target disease (i.e., COVID-19) and acoustic correlates of the disease (i.e., speech, breathing and cough). A narrative approach was used to summarize the extracted data. Results: 24 studies and 8 Apps out of the 86 retrieved studies met the inclusion criteria. Half of the publications and Apps were from the USA. The most prominent AI architecture used was a convolutional neural network, followed by a recurrent neural network. AI models were mainly trained, tested and run-on websites and personal computers, rather than on phone apps. More than half of the included studies reported area-under-the-curve performance of greater than 0.90 on symptomatic and negative datasets while one study achieved 100% sensitivity in predicting asymptomatic COVID-19 from cough-, breathing- or speech-based acoustic features. Conclusions: The included studies show that AI has the potential to help detect COVID-19 using cough, breathing and speech samples. The proposed methods (with some time and appropriate clinical testing) could prove to be an effective method in detecting various diseases related to respiratory and neurophysiological changes in the human body.

An analysis of patient self-reported COVID-19 symptoms during the first wave of the pandemic in Ireland.

BACKGROUND: Since the outbreak of COVID-19 in December 2019, there have been more than 115 million cases worldwide (1). Symptoms of COVID-19 vary widely and the spectrum of clinical presentation has yet to be fully characterised (2). Many countries have detailed their early experience with COVID-19, with a focus on the clinical characteristics of the disease. However, to our knowledge, there has been no such study detailing symptoms in the Irish population. AIM: Our aim is to describe COVID-19 symptoms in the Irish population at the beginning of the COVID-19 pandemic and compare symptoms between those reporting positive and negative test results. METHOD: A Web page MyCovidSymptoms.ie was created by researchers at the National University of Ireland, Galway, in April 2020 to investigate COVID-19 symptoms in Ireland. The Web page invited participants to self-report RT-PCR test outcome data (positive, negative, untested), temperature and a range of symptoms (cough, shortness of breath, fatigue, loss of taste, loss of smell). RESULTS: One hundred and twenty-three Irish participants who had a RT-PCR test for COVID-19 logged their symptoms. Eighty-four patients reported that they tested positive for COVID-19, and 39 patients reported a negative COVID-19 test. In our cohort of respondents with a positive COVID-19 test, 49/84 (58%) respondents reported a cough. Of the 39 respondents with a negative COVID-19 test, 17 (44%) reported having a cough. The distribution of temperature was similar in both those with and without COVID-19. Levels of self-reported fatigue were high in both groups with 65/84 (77%) of COVID-19-positive patients reporting fatigue and 30/39 (77%) of those who were COVID-19-negative reporting fatigue. New symptoms emerging at the time of data collection included loss of taste and smell. We demonstrated a higher proportion of loss of smell (p = 0.02) and taste (p = 0.01) in those reporting a positive result, compared to those reporting a negative result. CONCLUSION: These data represents an early picture of the clinical characteristics of COVID-19 in an Irish population. It also highlights the potential use of self-reported data globally as a powerful tool in helping with the pandemic.

Ultrasound Image Classification of Thyroid Nodules Using Machine Learning Techniques.

Background and Objectives: Thyroid nodules are lumps of solid or liquid-filled tumors that form inside the thyroid gland, which can be malignant or benign. Our aim was to test whether the described features of the Thyroid Imaging Reporting and Data System (TI-RADS) could improve radiologists' decision making when integrated into a computer system. In this study, we developed a computer-aided diagnosis system integrated into multiple-instance learning (MIL) that would focus on benign-malignant classification. Data were available from the Universidad Nacional de Colombia. Materials and Methods: There were 99 cases (33 Benign and 66 malignant). In this study, the median filter and image binarization were used for image pre-processing and segmentation. The grey level co-occurrence matrix (GLCM) was used to extract seven ultrasound image features. These data were divided into 87% training and 13% validation sets. We compared the support vector machine (SVM) and artificial neural network (ANN) classification algorithms based on their accuracy score, sensitivity, and specificity. The outcome measure was whether the thyroid nodule was benign or malignant. We also developed a graphic user interface (GUI) to display the image features that would help radiologists with decision making. Results: ANN and SVM achieved an accuracy of 75% and 96% respectively. SVM outperformed all the other models on all performance metrics, achieving higher accuracy, sensitivity, and specificity score. Conclusions: Our study suggests promising results from MIL in thyroid cancer detection. Further testing with external data is required before our classification model can be employed in practice.

Knowledge, Attitudes, Beliefs, and Treatment Burden Related to the Use of Levothyroxine in Hypothyroid Pregnant Women in the United States.

Background: The use of prescribed medications during pregnancy is a challenge and an underestimated source of treatment burden. Levothyroxine (LT4) for the treatment of overt and subclinical hypothyroidism is extensively prescribed during pregnancy. To this end, we aimed to explore the patients' perceived benefits and risks, knowledge, beliefs, attitudes, and related burden of LT4 therapy during pregnancy. Methods: In this cross-sectional study, we surveyed pregnant women who were treated with LT4 during pregnancy from January 1, 2019, to December 31, 2019, in a tertiary academic medical center of the United States. The anonymous online survey included questions to gather demographic data and multiple-choice questions regarding the benefits and risks, knowledge, beliefs, attitudes, and burden related to LT4 use during pregnancy. Results: Sixty-four pregnant women (mean age 31.5 years) completed the study survey (response rate: 96%): 62% were diagnosed with hypothyroidism more than 12 months before pregnancy, 16% less than or about 12 months before pregnancy, and 22% during pregnancy. We found that one-third of pregnant women using LT4 had a feeling of uneasiness/anxiety due to their hypothyroidism diagnosis. About half of the respondents (45%) reported that they did not receive an explanation by their clinician regarding the maternal/fetal risks of uncontrolled hypothyroidism or the benefits of adequate control. Finally, two in three patients expressed various concerns of LT4-related treatment burden. Conclusions: Our findings support the need for increased effective communication and tailored counseling to address fears, anxiety, and uncertainties about the benefits and risks of LT4 use in pregnancy. For patients with clear benefits from LT4 treatment in pregnancy, it could help to overcome their concerns, promote adherence, and decrease adverse maternal/fetal outcomes. For patients with no clear benefits established, clinicians need to be aware of LT4-related treatment burden in pregnancy and implement patient-centered approaches in their clinical practices.

Long-Term Changes in Weight in Patients With Severe and Complicated Obesity After Completion of a Milk-Based Meal Replacement Programme.

Introduction: Even with very significant short term weight loss with intensive dietary restriction, subsequent weight regain remains a challenge for most patients. We sought to assess long-term weight change in patients with obesity following completion of a 24-week milk-based meal replacement programme. Methods: We conducted a retrospective cohort study of bariatric patients who completed our milk-based meal replacement programme. This programme started with an 8-week weight loss phase, followed by weight stabilization (8 weeks) and weight maintenance (8 weeks) phases, after which patients were followed up in the bariatric outpatient clinics. A paired sample t-test was used to compare mean differences in weight at the start and the end of the programme and at follow-up. Linear regression was used to identify predictors of weight regain. Results: In total, 78 patients had long term follow-up data at a mean of 34.4 ± 19.8 months after the start of the milk diet and were included in this analysis. Mean body mass index at baseline was 50.5 ± 7.6 kg m-2, 41 (52.6%) were female and the mean age was 51.6 ± 12.0 (range 18.0-71.5) years. Weight decreased from144 ± 26 kg at the start of the milk diet to 121.2 ± 24 kg at completion (P < 0.001), with a non-significant trend upwards in the 1st and 2nd years of follow-up to 129.0 ± 27.7 (P = 0.07 compared to nadir) and 123.4 ± 29.0kg (P = 0.17), respectively. Although regains in the 3rd and 4th follow-up years were substantial to 131.0 ± 22.3 (P < 0.001), and 139.8 ± 35.4 kg (P < 0.001), there was still a moderate net weight loss of 4.7 [9.5, 0.21] and 7.0 [13.9, 0.26] kg (both P = 0.04) between the start and the 3rd and 4th follow-up years, respectively. The amount of weight regain was inversely associated with weight loss at completion of the programme, age, and directly associated with the duration of follow up in months (ß = 1.2 [0.46, 1.9] P = 0.002). Conclusion: In patients with severe obesity who completed a milk-based meal replacement programme and lost a large amount of weight, over 4 years of follow-up there was very substantial weight regain. Greater initial weight loss and older age were associated with less subsequent weight regain.

Biallelic CYP24A1 variants presenting during pregnancy: clinical and biochemical phenotypes.

INTRODUCTION: Inactivating mutations in CYP24A1, encoding vitamin D-24-hydroxylase, can lead to an accumulation of active vitamin D metabolites and consequent hypercalcaemia. Patient (infantile and adult) presentation is varied and includes mild-severe hypercalcaemia, hypercalciuria, nephrocalcinosis and nephrolithiasis. This study aimed to characterize the clinical and biochemical phenotypes of a family with two CYP24A1 missense variants. METHODS: The proband and seven family members underwent detailed clinical and biochemical evaluation. Laboratory measurements included serum calcium, intact parathyroid hormone (iPTH), vitamin D metabolites and urine calcium and creatinine. RESULTS: The proband presented during the second trimester of a planned pregnancy with flu-like symptoms. Laboratory tests showed elevated adjusted calcium of 3.27 (upper reference limit (URL: 2.30) mmol/L), suppressed iPTH (<6 ng/L), elevated 25(OH)D (264 (URL: 55) nmol/L) and elevated 1,25(OH)D (293 (URL: <280) pmol/L). Ionized calcium was 1.55 (URL: 1.28) mmol/L. Sanger sequencing revealed two heterozygous missense variants in the CYP24A1: p.(Arg439Cys), R439C and p.(Trp275Arg), W275R. The proband's brother and sister had the same genotype. The brother had intermittent hypercalcaemia and hypervitaminosis D. Only the sister had a history of nephrolithiasis. The proband's daughter and two nephews were heterozygous for the R439C variant. The proband and her brother frequently had elevated 25(OH)D:24,25(OH)2D ratios (>50) during follow-up. CONCLUSIONS: W275R is a new pathogenic CYP24A1 mutation in compound heterozygotic form with R439C in this family.

Practice Variation in the Care of Subclinical Hypothyroidism During Pregnancy: A National Survey of Physicians in the United States.

Evidence regarding the effects of subclinical hypothyroidism (SCH) on adverse pregnancy outcomes and the ability of levothyroxine (LT4) treatment to prevent them is unclear. Available recommendations for the management of SCH during pregnancy are inconsistent. We conducted a nationwide survey among physicians assessing their knowledge of and current practices in the care of SCH in pregnancy and compared these with the most recent American Thyroid Association (ATA) recommendations. In this cross-sectional study, an online survey was sent to active US members of the Endocrine Society. This survey included questions about current practices and clinical scenarios aimed at assessing diagnostic evaluation, initiation of therapy, and follow-up in pregnant women with SCH. In total, 162 physicians completed the survey. ATA guidelines were reviewed by 76%, of whom 53% indicated that these guidelines actually changed their practice. Universal screening was the preferred screening approach (54%), followed by targeted screening (30%). For SCH diagnosis, most respondents (52%) endorsed a TSH level >2.5 mIU/L as a cutoff, whereas 5% endorsed a population-based cutoff as recommended by the ATA. The decision to initiate treatment varied depending on the specific clinical scenario; however, when LT4 was initiated, respondents expected a small/very small reduction in maternofetal complications. In conclusion, despite recently updated guidelines, there is still wide variation in clinical practices regarding the care of women with SCH in pregnancy. Highly reliable randomized trials are required to evaluate the effectiveness of the most uncertain treatment practices on the care of pregnant women with SCH.

Variation in Treatment Practices for Subclinical Hypothyroidism in Pregnancy: US National Assessment.

CONTEXT: Although thyroid hormone replacement may improve outcomes in pregnant women with subclinical hypothyroidism (SCH), the extent to which they receive treatment is unknown. OBJECTIVE: To describe levothyroxine (LT4) treatment practices for pregnant women with SCH. DESIGN: Retrospective cohort study. SETTING: Large US administrative claims database. PARTICIPANTS: Pregnant women with SCH defined by untreated TSH 2.5 to 10 mIU/L. MAIN OUTCOME MEASURE: Initiation of LT4 as a function of treating clinician specialty (endocrinology, obstetrics/gynecology, primary care, or other), baseline TSH, patient clinical and demographic factors, and US region. RESULTS: We identified 7990 pregnant women with SCH; only 1214 (15.2%) received LT4. Treatment was more likely in patients with higher TSH, obesity, recurrent pregnancy loss, thyroid disease, and cared for by endocrinologists. Proportion of treated women increased over time; LT4 treatment was twice as likely in 2014 as in 2010. Women in Northeast and West US were more likely to receive LT4 compared with other regions. Asian women were more likely, whereas Hispanic women were less likely, to receive LT4 compared with white women. Endocrinologists started LT4 at lower TSH thresholds than other specialties, and treated women who were more likely to have had recurrent pregnancy loss and thyroid disease than women treated by other clinicians. CONCLUSIONS: We found large variation in the prescription of LT4 to pregnant women with SCH, although most treatment-eligible women remained untreated. Therapy initiation is associated with geographic, clinician, and patient characteristics. This evidence can inform quality improvement efforts to optimize care for pregnant women with SCH.

Subclinical Hypothyroidism in Women Planning Conception and During Pregnancy: Who Should Be Treated and How?

Subclinical hypothyroidism (SCH), a mild form of hypothyroidism defined as elevated TSH with normal free thyroxine levels, is a common diagnosis among women of reproductive age. In some, but not all, studies, it has been associated with infertility, an increased risk of adverse pregnancy and neonatal outcomes, and possibly with an increased risk of neurocognitive deficits in offspring. Despite well-established recommendations on treatment of overt hypothyroid pregnant women, a consensus has not yet been reached on whether to treat women with SCH. This review focuses on examining the evidence informing the clinical strategy for using levothyroxine (LT4) in women with SCH during pregnancy and those who are planning conception. A crucial first step is to accurately diagnose SCH using the appropriate population-based reference range. For pregnant women, if this is unavailable, the recommended TSH upper normal limit cutoff is 4.0 mIU/L. There is evidence supporting a decreased risk for pregnancy loss and preterm delivery for pregnant women with TSH > 4.0 mIU/L receiving LT4 therapy. LT4 treatment has been associated with better reproductive outcomes in women with SCH undergoing artificial reproductive techniques, but not in those who are attempting natural conception. Thyroid function tests need to be repeated throughout pregnancy to monitor LT4 therapy. In addition to potential harms, LT4 contributes to treatment burden. During a consultation, clinicians and patients should engage in a careful consideration of the current evidence in the context of the patients' values and preferences to determine whether LT4 therapy initiation is the best next step.

Thyroid hormone treatment among pregnant women with subclinical hypothyroidism: US national assessment.

OBJECTIVE:  To estimate the effectiveness and safety of thyroid hormone treatment among pregnant women with subclinical hypothyroidism. DESIGN:  Retrospective cohort study. SETTING:  Large US administrative database between 1 January 2010 and 31 December 2014. PARTICIPANTS:  5405 pregnant women with subclinical hypothyroidism, defined as untreated thyroid stimulating hormone (TSH) concentration 2.5-10 mIU/L. EXPOSURE:  Thyroid hormone therapy. MAIN OUTCOME MEASURE:  Pregnancy loss and other pre-specified maternal and fetal pregnancy related adverse outcomes. RESULTS:  Among 5405 pregnant women with subclinical hypothyroidism, 843 with a mean pre-treatment TSH concentration of 4.8 (SD 1.7) mIU/L were treated with thyroid hormone and 4562 with a mean baseline TSH concentration of 3.3 (SD 0.9) mIU/L were not treated (P<0.01). Pregnancy loss was significantly less common among treated women (n=89; 10.6%) than among untreated women (n=614; 13.5%) (P<0.01). Compared with the untreated group, treated women had lower adjusted odds of pregnancy loss (odds ratio 0.62, 95% confidence interval 0.48 to 0.82) but higher odds of preterm delivery (1.60, 1.14 to 2.24), gestational diabetes (1.37, 1.05 to 1.79), and pre-eclampsia (1.61, 1.10 to 2.37); other pregnancy related adverse outcomes were similar between the two groups. The adjusted odds of pregnancy loss were lower in treated women than in untreated women if their pre-treatment TSH concentration was 4.1-10 mIU/L (odds ratio 0.45, 0.30 to 0.65) but not if it was 2.5-4.0 mIU/L (0.91, 0.65 to 1.23) (P<0.01). CONCLUSION:  Thyroid hormone treatment was associated with decreased risk of pregnancy loss among women with subclinical hypothyroidism, especially those with pre-treatment TSH concentrations of 4.1-10 mIU/L. However, the increased risk of other pregnancy related adverse outcomes calls for additional studies evaluating the safety of thyroid hormone treatment in this patient population.