To assess differences in SARS-CoV-2 infection rates between patients receiving hemodialysis in outpatient centers (in-center) and those receiving dialysis in their homes (hemodialysis and peritoneal dialysis) from December 29, 2020, through May 9, 2023.

Retrospective cohort study.

Outpatient dialysis facilities in the United States reporting to the Centers for Disease Control and Prevention’s National Healthcare Safety Network.

Maintenance dialysis patients that received hemodialysis treatment at or were affiliated with outpatient dialysis facilities.

SARS-CoV-2 infection rates were assessed by dialysis setting (in-center and home). Weeks were categorized as surge (rate of infection > median) and non-surge (rate of infection ≤ median) and by variant predominance. A negative binomial regression model with generalized estimating equations was constructed to examine differences in rates of infection among patients.

A total of 7,974 dialysis facilities reported 171,338 SARS-CoV-2 infections among patients. In-center hemodialysis patients had higher average rates of SARS-CoV-2 infection at 2.85 infections per 1000 patient-weeks than home patients at 1.69 infections per 1000 patient-weeks. During surge weeks, the differences in rates of infection between in-center and home patients were more pronounced than during non-surge weeks for all variant predominance categories: Delta (relative rate ratio (RRR) = 1.20, CI: 1.09–1.32), B.1 and Other (RRR = 1.11, CI: 1.02–1.22), and Omicron (RRR = 1.07, CI: 1.01–1.12).

Rates of SARS-CoV-2 infection among patients receiving outpatient hemodialysis were persistently higher than rates among patients receiving dialysis treatments at home; these differences were more pronounced during surge weeks.

The purpose of the study is to analyze bloodstream infection (BSI) data reported by outpatient hemodialysis facilities to understand temporal trends, the potential impact of infection prevention practices and the COVID-19 pandemic on BSI rates.

Outpatient hemodialysis facilities report BSI data to the National Healthcare Safety Network. We used interrupted time series with mixed effects negative binomial modeling to estimate the annual change of BSI rates from 2012 to 2021, using March 2020 as the COVID-19 inflection point. The model controlled for seasonal factors, vascular access types, and facility characteristics.

The number of facilities used for analysis increased from 5,581 in 2012 to 7,313 in 2021. Most facilities were freestanding (range: 90%–93%) and belonged to for-profit organizations (range: 85%–88%). The annual adjusted BSI rates decreased by an average of 8.90% (95% CI: −9.10 %, −8.71%) January 2012-February 2020. The annual decrease in BSI rate was not significant during March 2020-December 2021 (P = 0.15). There was a level drop of 32.03% (95%CI: −33.84%, −30.17%) in BSI rates in the period of March 2020-December 2021 compared with the period of January 2012-February 2020.

BSI rates decreased steadily from January 2012 to February 2020 likely due to the identification and adoption of evidence-based prevention practices. BSI rates plateaued at lower levels during March 2020-December 2021. This suggests that infection prevention measures implemented by facilities prior to the emergence of COVID-19 contributed to substantial decreases in BSI rates and may have helped to stabilize BSI rates after March 2020.

This study aims to systematically identify and summarize the key characteristics of Mini-Health Technology Assessment (Mini-HTA) and assess the completeness of its basic reporting information, providing a theoretical foundation for developing future reporting guidelines..

A comprehensive search for Mini-HTAs was performed using CNKI, Wanfang Data, VIP, CBM, PubMed, Embase, Web of Science, HTA database, and major HTA-related websites from inception until February 2024. The completeness of basic information reporting in Mini-HTAs was assessed using the INAHTA checklist. The key characteristics of the included Mini-HTAs were summarized descriptively. Microsoft Excel 2019 was used to analyze and visually present the data.

A total of 21 Mini-HTA reports were included, with the highest number published in 2021 (5 reports, 23.8 percent). China contributed the most reports (18 reports, 85.7 percent). The most common assessment purpose was technology comparison (15 reports, 71.4 percent), with general hospitals being the predominant assessment setting (17 reports, 80.9 percent), and drugs being the most frequently assessed technology type (14 reports, 66.7 percent). The INAHTA checklist evaluation identified notable deficiencies in reporting key methodological aspects, including participant roles, conflict of interest statements, data sources, literature search strategies, and methods for data assessment and analysis.

Mini-HTAs have significantly increased in China since 2020, mainly in technology comparison, drug evaluation, and general hospitals. However, gaps remain in reporting key aspects, such as participant roles, conflict of interest, and data sources. Future efforts should focus on refining reporting guidelines to improve consistency and address these reporting deficiencies in Mini-HTA.

This study employs a longitudinal network approach to investigate the dynamic relationships between COVID-19-related stressors and depressive symptoms among Canadian adults and to explore any sex and age differences in these associations.

The study utilised data from the Canadian Longitudinal Study on Ageing (CLSA), a large, national, long-term study of Canadian adults aged 45 years and older. Depressive symptoms were measured using the Centre for Epidemiologic Studies Depression Scale (CES-D), and COVID-19-related stressors were evaluated using a standardised stress inventory adapted for the pandemic context. The cross-lagged panel network analysis (CLPN) was employed to examine the temporal relationships and dynamic interactions between depressive symptoms and COVID-19-related stressors.

Significant variations in network structures and strengths were identified across demographic groups. Individuals aged between 45 and 65 years and females exhibited stronger connections between COVID-19-related stressors and depressive symptoms. Central symptoms such as “feeling unhappy” were consistent across groups, while “feeling depressed” was more central among males and “increased verbal or physical conflict” among females. Additionally, health-related stressors and family separation emerged as critical bridge symptoms for males and individuals under 65 years, respectively.

Both cross-sectional and longitudinal relationships, and directionality between COVID-19-related stressors and depressive symptoms across sex and age groups were identified. The findings of the study highlight that dedicated mental health intervention and prevention efforts are warranted to ameliorate the negative impact of stressors on depressive symptoms.

A retrospective analysis of paediatric infective endocarditis characterised causative pathogens, antimicrobial susceptibility patterns, and treatment outcomes to guide clinical decision-making.

The data of patients who received infective endocarditis between 2016 and 2023 were retrospectively collected from the medical records database. The clinical characteristics, treatment plans, and pharmaceutical monitoring characteristics were analysed and summarised.

A total of 12 paediatric infective endocarditis cases were identified. Bacterial isolates included 27 Gram-positive and 1 Gram-negative strains. The most common pathogen was Staphylococcus aureus (n = 13), all methicillin-resistant Staphylococcus aureus (MRSA), followed by Abiotrophia defectiva (n = 6), Streptococcus mitis (n = 5), Streptococcus sanguinis (n = 2), Bacillus cereus (n = 1), and Klebsiella oxytoca (n = 1). Antimicrobial therapy primarily involved linezolid, vancomycin, and cephalosporin/enzyme inhibitor combinations. Cardiac glycosides were used in 10 cases, and all patients received phosphocreatine to support myocardial energy metabolism. Therapeutic drug monitoring for vancomycin was performed in 25% of cases, while no therapeutic drug monitoring was conducted for meropenem or linezolid.

All the causative organisms were predominantly Gram-positive cocci, with MRSA accounting for the largest proportion; different streptococci varied considerably in terms of drug resistance. The antimicrobial drugs used were predominantly linezolid and glycopeptides. The rate of blood concentration monitoring was low.

Patients with Guillain–Barré syndrome (GBS) and chronic inflammatory demyelinating polyneuropathy (CIDP) have mental health sequelae that impact their quality of life. The burden of mental health disorders in these patients is poorly established.

To review the literature on the frequency and risk of mental disorders in GBS and CIDP.

A systematic review was conducted to identify primary studies that reported mental health outcomes in patients with GBS and CIDP. Screening, full-text review, data extraction and quality assessment were performed in duplicate, with discrepancies resolved by a third party.

This systematic review included 19 studies. Three studies reported mental health diagnoses using the International Classification of Diseases or Diagnostic and Statistical Manual of Mental Disorders criteria: up to 82%, 67%, 25% and 22% of patients following GBS were diagnosed with anxiety, depression, brief reactive psychosis and post-traumatic stress disorders, respectively. The risk of anxiety disorders following GBS normalized after 3 months, but the risk of depressive disorders remained elevated for 2 years. Although 30%–50% of patients with CIDP described mental health symptoms, no studies reported mental health diagnoses. Active disease and neuropathic pain were associated with more depressive symptoms in patients with CIDP.

Many patients following GBS or with active CIDP experience symptoms that may fulfill the criteria for mental health diagnoses, but the paucity of literature suggests that mental health disorders are underdiagnosed and undertreated in this population. These patients are at higher risk of developing mental health disorders, thereby emphasizing the need for timely mental health care and assessment of their disease-specific risk factors.

Internet addiction (IA) refers to excessive internet use that causes cognitive impairment or distress. Understanding the neurophysiological mechanisms underpinning IA is crucial for enabling an accurate diagnosis and informing treatment and prevention strategies. Despite the recent increase in studies examining the neurophysiological traits of IA, their findings often vary. To enhance the accuracy of identifying key neurophysiological characteristics of IA, this study used the phase lag index (PLI) and weighted PLI (WPLI) methods, which minimize volume conduction effects, to analyze the resting-state electroencephalography (EEG) functional connectivity. We further evaluated the reliability of the identified features for IA classification using various machine learning methods.

Ninety-two participants (42 with IA and 50 healthy controls (HCs)) were included. PLI and WPLI values for each participant were computed, and values exhibiting significant differences between the two groups were selected as features for the subsequent classification task.

Support vector machine (SVM) achieved an 83% accuracy rate using PLI features and an improved 86% accuracy rate using WPLI features. t-test results showed analogous topographical patterns for both the WPLI and PLI. Numerous connections were identified within the delta and gamma frequency bands that exhibited significant differences between the two groups, with the IA group manifesting an elevated level of phase synchronization.

Functional connectivity analysis and machine learning algorithms can jointly distinguish participants with IA from HCs based on EEG data. PLI and WPLI have substantial potential as biomarkers for identifying the neurophysiological traits of IA.

We aimed to validate in-body bioelectrical impedance analysis (BIA) measures with dual-energy X-ray absorptiometry (DXA) as reference and describe the body composition (BC) profiling of Tibetan adults.

This cross-sectional study included 855 participants (391 men and 464 women). Correlation and Bland–Altman analyses were performed for method agreement of in-body BIA and DXA. BC were described by obesity and metabolic status.

In-body BIA and DXA have not been employed to characterise the BC of the Tibetan population living in the Qinghai–Tibet Plateau.

A total of 855 Tibetan adults, including 391 men and 464 women, were enrolled in the study.

Concordance correlation coefficient for total fat mass (FM) and total lean mass (LM) between in-body BIA and DXA were 0·91 and 0·89. The bias of in-body BIA for percentages of total FM and total LM was 0·91 % (2·46 %) and –1·74 % (–2·80 %) compared with DXA, respectively. Absolute limits of agreement were wider for total FM in obese men and women and for total LM in overweight men than their counterparts. Gradience in the distribution of total and regional FM content was observed across different BMI categories and its combinations with waist circumference and metabolic status.

In-body BIA and DXA provided overall good agreement at the group level in Tibetan adults, but the agreement was inferior in participants being overweight or obese.