A systematic review on sonoelastography for rotator-cuff post-repair assessment.

Surgical repair of rotator cuff tears is performed routinely; however, the risks of re-tears and the associated consequences are significant. Sonoelastography, an imaging modality that evaluates the mechanical properties of tissues, can examine the dynamic transitions in rotator cuff stiffness following retear and investigate the relationship between these changes and the occurrences of retears. This systematic review aimed to summarize the role of perioperative sonoelastography in repaired rotator cuffs. A comprehensive search of the PubMed, Embase, and Cochrane databases was conducted, covering studies published until June 19, 2023. The Newcastle-Ottawa scale was used for quality assessment. The key information extracted from each study included the injury/surgery type, follow-up duration, sonoelastography mode, and main sonoelastographic findings. Eleven eligible studies comprising 355 patients were included. All studies focused on supraspinatus muscles and tendons with previous arthroscopic repairs. During the postoperative 1st - 6th months, muscle stiffness increased in the supraspinatus and decreased in the ipsilateral deltoid. Failure to recover supraspinatus muscle elasticity might be indicative of potential tendon re-tear; however, it is imperative to first establish correlations with other imaging modalities. Conflicting findings have been observed regarding stiffening or softening of the supraspinatus tendon after surgical repair. The preoperative stiffness of the supraspinatus tendon did not correlate with postoperative tendon integrity or function.

Factors influencing willingness to participate in ophthalmic clinical trials and strategies for effective recruitment.

AIM: To explore the factors influencing individuals' willingness to participate in ophthalmic clinical trials. METHODS: A questionnaire survey was conducted from January to April 2021 among patients and their family members at Zhongshan Ophthalmic Center, Sun Yat-sen University, in Guangzhou, China. The survey gathered data on respondents' willingness, demographic and socioeconomic profiles, as well as their reasons and concerns regarding engagement in clinical trials. RESULTS: Of the 1078 residents surveyed (mean age 31.2±13.1y; 65.8% females) in Guangzhou, 749 (69.5%) expressed a willingness to participate in future ophthalmic clinical trials. Specific characteristics associated with greater willingness included a younger age, lower annual income, higher education, prior participation experience, previous ophthalmic treatment, and a better understanding of clinical trials. With the exception of age, these characteristics were significantly linked to a higher willingness. The primary barrier to participation, expressed by 64.8% of those willing and 54.4% of those unwilling, was "Uncertain efficacy". In terms of motivations, the willing group ranked "Better therapeutic benefits" (35.0%), "Professional monitoring" (34.3%), and "Trust in healthcare professionals" (33.1%) as their top three reasons, whereas the unwilling participants indicated "Full comprehension of the protocol" (46.2%) as the key facilitator. CONCLUSION: This study reveals a substantial willingness to participate in ophthalmic clinical trials and demonstrates the predictive role of demographic and socioeconomic factors. Variations in motivators and concerns between willing and unwilling participants highlight the significance of tailored recruitment strategies. Importantly, the need for and trust in healthcare professionals stand out as powerful motivations, underscoring the importance of enhancing physician-patient relationships, adopting patient-centered communication approaches, and addressing individualized needs to improve accrual rates.

Auto-CA: Automated Cobb Angle Measurement Based on Vertebrae Detection for Assessment of Spinal Curvature Deformity.

An accurate identification and localization of vertebrae in X-ray images can assist doctors in measuring Cobb angles for treating patients with adolescent idiopathic scoliosis. It is useful for clinical decision support systems for diagnosis, surgery planning, and spinal health analysis. Currently, publicly available annotated datasets on spinal vertebrae are small, making deep-learning-based detection methods that are highly data-dependent less accurate. In this article, we propose an algorithm based on convolutional neural networks that can be trained to detect vertebrae from a small set of images. This method can display critical information on a patient's spine, display vertebrae and their labels on the thoracic and lumbar, calculate the Cobb angle, and evaluate the severity of spinal deformities. The proposed achieved an average accuracy of 0.958 and 0.962 for classifying spinal deformities (i.e., C-shaped, S-shaped type 1, and S-shaped type 2) and severity of Cobb angle (i.e., normal, mild, moderate, and severe), respectively. The Cobb angle measurement had a median difference of less than 5° from the ground-truth with SMAPE of 5.27% and an error on landmark detection of 19.73. In addition, Lenke classification is used to analyze spinal deformities as types A, B, and C, which have an average accuracy of 0.924. Physicians can use the proposed system in clinical practice by providing X-ray images via the user interface.

Assessment of the carpal tunnel and associated neural structures with superb microvascular imaging: a scoping review.

INTRODUCTION: Superb microvascular imaging (SMI) is an advanced ultrasound technique that portrays microcirculation. Its clinical applications have been studied in various diseases, including carpal tunnel syndrome (CTS) i.e. the most common entrapment neuropathy. This scoping review explores the role of SMI in diagnosing CTS or the assessment of relevant neural structures. METHODS: We conducted a comprehensive search of electronic databases (PubMed, Embase and Web of Science) up to 26 September 2023. Two independent authors conducted the literature search, quality assessment, and data extraction. RESULTS: This review includes seven studies comprising 385 wrists. SMI consistently revealed increased intraneural vascularity in the median nerves of patients with CTS compared to healthy individuals. While SMI demonstrated higher sensitivity than traditional Doppler methods for detecting CTS, its specificity was somewhat lower. Combining SMI with B-mode ultrasound appears to enhance the diagnostic accuracy for CTS. However, the relationship between SMI findings and CTS severity remains unclear. CONCLUSIONS: This review highlighted the ability of SMI to provide detailed vascular structures in both healthy wrists and those with CTS. Additional research is crucial to determine the typical SMI findings of the carpal tunnel and within that context, tailor more precise diagnostic/therapeutic applications for the CTS population.

Comparison of health resource usage and costs associated with first-generation and second-generation long-acting injectable antipsychotics for patients with schizophrenia: A nationwide population-based cohort study.

The aim of this study was to examine psychiatric resource utilization, medical costs and clinical outcomes for patients with schizophrenia who received either first-generation or second-generation long-acting injectable (LAI) antipsychotics. A retrospective cohort study was conducted using data from Taiwan's National Health Insurance Research Database (NHIRD). Patients who began either first-generation or second-generation LAI treatment between 2015 and 2017 were enrolled and followed for three years. The data were evaluated using survival analysis and Cox proportional hazards regression models. Our findings demonstrated that both first- and second-generation LAI therapies led to notable reductions in the frequency of psychiatric hospitalizations and the duration of hospital stays when compared to the initial measurements. Additionally, the second-generation LAI group exhibited significantly lower rates of psychiatric emergencies and hospitalizations, as well as shorter hospital stays, compared to the first-generation LAI group. However, it is worth noting that the second-generation LAI group incurred higher pharmacy fees despite these favorable outcomes. The utilization of both first- and second-generation LAIs can enhance medication adherence and decrease the risk of acute exacerbation in patients with schizophrenia. These findings hold significant implications for schizophrenia management and the efficient allocation of healthcare resources.

Cost-Effectiveness Analysis of Personalized Hypertension Prevention.

BACKGROUND: While a population-wide strategy involving lifestyle changes and a high-risk strategy involving pharmacological interventions have been described, the recently proposed personalized medicine approach combining both strategies for the prevention of hypertension has increasingly gained attention. However, a cost-effectiveness analysis has been hardly addressed. This study was set out to build a Markov analytical decision model with a variety of prevention strategies in order to conduct an economic analysis for tailored preventative methods. METHODS: The Markov decision model was used to perform an economic analysis of four preventative strategies: usual care, a population-based universal approach, a population-based high-risk approach, and a personalized strategy. In all decisions, the cohort in each prevention method was tracked throughout time to clarify the four-state model-based natural history of hypertension. Utilizing the Monte Carlo simulation, a probabilistic cost-effectiveness analysis was carried out. The incremental cost-effectiveness ratio was calculated to estimate the additional cost to save an additional life year. RESULTS: The incremental cost-effectiveness ratios (ICER) for the personalized preventive strategy versus those for standard care were -USD 3317 per QALY gained, whereas they were, respectively, USD 120,781 and USD 53,223 per Quality-Adjusted Life Year (QALY) gained for the population-wide universal approach and the population-based high-risk approach. When the ceiling ratio of willingness to pay was USD 300,000, the probability of being cost-effective reached 74% for the universal approach and was almost certain for the personalized preventive strategy. The equivalent analysis for the personalized strategy against a general plan showed that the former was still cost-effective. CONCLUSIONS: To support a health economic decision model for the financial evaluation of hypertension preventative measures, a personalized four-state natural history of hypertension model was created. The personalized preventive treatment appeared more cost-effective than population-based conventional care. These findings are extremely valuable for making hypertension-based health decisions based on precise preventive medication.

How to promote the balanced development of urban and rural China? Evidences from reallocating idle rural residential land of Zhejiang province, China.

As more attention is given to green and sustainable industries, an analysis of the industrial impacts on all aspects of life, including inclusive affluence, is gradually developing. Idle rural residential land is a valuable resource and an important factor in promoting sustainable development. Balanced urban and rural development contributes to inclusive prosperity, so understanding the relationship between industry and the balanced development of urban and rural can significantly impact social development. In China, achieving the balanced development requires narrowing the urban-rural income gap. This paper analyzed the impact of reallocating idle rural residential land on promoting the balanced development. The study found that industry development has a positive impact on the balanced development, with a regression coefficient of 1.478. Regions with higher industry indices in counties had better outcomes regarding the balanced development. When the development of rural industry derived from idle residential land was in good condition, the effect increased by 3.326 percentage. The results showed heterogeneity, with the regression coefficient of industry development on the balanced development in county-level cities being 0.498 larger than in urban areas. In summary, the reallocation of idle residential land can promote sustainable development, increase residents' income, and improve overall regional economic development. The results are applicable to the comprehensive reallocation of rural land resources.

Chinese Intelligence Prescription System improves prescription accuracy while decreasing labor and drug costs.

BACKGROUND AND AIM: The traditional method of taking Chinese Medicine involves creating a decoction by cooking medicinal Chinese herbs. However, this method has become less popular, being replaced by the more convenient method of consuming concentrated Chinese herbal extracts, which creates challenges related to the complexity of stacking multiple formulas. METHODS: We developed the Chinese Intelligence Prescription System (CIPS) to simplify the prescription process. In this study, we used data from our institutions pharmacy to calculate the number of reductions, average dispensing time, and resulting cost savings. RESULTS: The mean number of prescriptions was reduced from 8.19 ± 3.65 to 7.37 ± 3.34 ([Formula: see text]). The reduction in the number of prescriptions directly resulted in decreased dispensing time, reducing it from 1.79 ± 0.25 to 1.63 ± 0.66 min ([Formula: see text]). The reduced dispensing time totaled 3.75 h per month per pharmacist, equivalent to an annual labor cost savings of $15,488 NTD per pharmacist. In addition, drug loss was reduced during the prescription process, with a mean savings of $4,517 NTD per year. The combined savings adds up to a not insignificant $20,005 NTD per year per pharmacist. When taking all TCM clinics/hospitals in Taiwan into account, the total annual savings would be $77 million NTD. CONCLUSION: CIPS assists clinicians and pharmacists to formulate precise prescriptions in a clinical setting to simplify the dispensing process while reducing medical resource waste and labor costs.

New Surveillance Metrics for Alerting Community-Acquired Outbreaks of Emerging SARS-CoV-2 Variants Using Imported Case Data: Bayesian Markov Chain Monte Carlo Approach.

BACKGROUND: Global transmission from imported cases to domestic cluster infections is often the origin of local community-acquired outbreaks when facing emerging SARS-CoV-2 variants. OBJECTIVE: We aimed to develop new surveillance metrics for alerting emerging community-acquired outbreaks arising from new strains by monitoring the risk of small domestic cluster infections originating from few imported cases of emerging variants. METHODS: We used Taiwanese COVID-19 weekly data on imported cases, domestic cluster infections, and community-acquired outbreaks. The study period included the D614G strain in February 2020, the Alpha and Delta variants of concern (VOCs) in 2021, and the Omicron BA.1 and BA.2 VOCs in April 2022. The number of cases arising from domestic cluster infection caused by imported cases (Dci/Imc) per week was used as the SARS-CoV-2 strain-dependent surveillance metric for alerting local community-acquired outbreaks. Its upper 95% credible interval was used as the alert threshold for guiding the rapid preparedness of containment measures, including nonpharmaceutical interventions (NPIs), testing, and vaccination. The 2 metrics were estimated by using the Bayesian Monte Carlo Markov Chain method underpinning the directed acyclic graphic diagram constructed by the extra-Poisson (random-effect) regression model. The proposed model was also used to assess the most likely week lag of imported cases prior to the current week of domestic cluster infections. RESULTS: A 1-week lag of imported cases prior to the current week of domestic cluster infections was considered optimal. Both metrics of Dci/Imc and the alert threshold varied with SARS-CoV-2 variants and available containment measures. The estimates were 9.54% and 12.59%, respectively, for D614G and increased to 14.14% and 25.10%, respectively, for the Alpha VOC when only NPIs and testing were available. The corresponding figures were 10.01% and 13.32% for the Delta VOC, but reduced to 4.29% and 5.19% for the Omicron VOC when NPIs, testing, and vaccination were available. The rapid preparedness of containment measures guided by the estimated metrics accounted for the lack of community-acquired outbreaks during the D614G period, the early Alpha VOC period, the Delta VOC period, and the Omicron VOC period between BA.1 and BA.2. In contrast, community-acquired outbreaks of the Alpha VOC in mid-May 2021, Omicron BA.1 VOC in January 2022, and Omicron BA.2 VOC from April 2022 onwards, were indicative of the failure to prepare containment measures guided by the alert threshold. CONCLUSIONS: We developed new surveillance metrics for estimating the risk of domestic cluster infections with increasing imported cases and its alert threshold for community-acquired infections varying with emerging SARS-CoV-2 strains and the availability of containment measures. The use of new surveillance metrics is important in the rapid preparedness of containment measures for averting large-scale community-acquired outbreaks arising from emerging imported SARS-CoV-2 variants.

A Hidden Markov Ensemble Algorithm Design for Time Series Analysis.

With the exponential growth of data, solving classification or regression tasks by mining time series data has become a research hotspot. Commonly used methods include machine learning, artificial neural networks, and so on. However, these methods only extract the continuous or discrete features of sequences, which have the drawbacks of low information utilization, poor robustness, and computational complexity. To solve these problems, this paper innovatively uses Wasserstein distance instead of Kullback-Leibler divergence and uses it to construct an autoencoder to learn discrete features of time series. Then, a hidden Markov model is used to learn the continuous features of the sequence. Finally, stacking is used to ensemble the two models to obtain the final model. This paper experimentally verifies that the ensemble model has lower computational complexity and is close to state-of-the-art classification accuracy.

Risk Reduction Assessment of Vibrio parahaemolyticus on Shrimp by a Chinese Eating Habit.

In China, a traditional perspective recommended that consuming seafood should be mixed or matched with vinegar, because people thought this traditional Chinese eating habit could reduce the risk of pathogenic microorganism infection, such as Vibrio parahaemolyticus induced diarrhea. However, this empirical viewpoint has not yet been evaluated scientifically. This study conducted a simplified quantitative microbiological risk assessment (QMRA) model, which was employed to estimate the risk reduction of V. parahaemolyticus on ready-to-eat (RTE) shrimp by consuming with vinegars (white vinegar, aromatic vinegar, or mature vinegar). Results showed the reduction of V. parahaemolyticus density on RTE shrimp after consuming with white vinegar, aromatic vinegar and mature vinegar was respectively 0.9953 log CFU/g (90% confidence interval 0.23 to 1.76), 0.7018 log CFU/g (90% confidence interval 0.3430 to 1.060) and 0.6538 log CFU/g (90% confidence interval 0.346 to 0.9620). The infection risk of V. parahaemolyticus per meal in this QMRA model was quantified by a mean of 0.1250 with the standard deviation of 0.2437. After consuming with white vinegar, aromatic vinegar, and mature vinegar, the mean infection risk of V. parahaemolyticus on shrimp decreased to 0.0478, 0.0652, and 0.0686. The QMRA scenarios indicated significant reductions in infection risk when eating RTE shrimp by the Chinese eating habit (consuming with vinegar). This good eating habit should be recommended to promote the spread of around the world.

Estimating global burden of COVID-19 with disability-adjusted life years and value of statistical life metrics.

BACKGROUND: Global burden of COVID-19 has not been well studied, disability-adjusted life years (DALYs) and value of statistical life (VSL) metrics were therefore proposed to quantify its impacts on health and economic loss globally. METHODS: The life expectancy, cases, and death numbers of COVID-19 until 30th April 2021 were retrieved from open data to derive the epidemiological profiles and DALYs (including years of life lost (YLL) and years loss due to disability (YLD)) by four periods. The VSL estimates were estimated by using hedonic wage method (HWM) and contingent valuation method (CVM). The estimate of willingness to pay using CVM was based on the meta-regression mixed model. Machine learning method was used for classification. RESULTS: Globally, DALYs (in thousands) due to COVID-19 was tallied as 31,930 from Period I to IV. YLL dominated over YLD. The estimates of VSL were US$591 billion and US$5135 billion based on HWM and CVM, respectively. The estimate of VSL increased from US$579 billion in Period I to US$2160 billion in Period IV using CVM. The higher the human development index (HDI), the higher the value of DALYs and VSL. However, there exits the disparity even at the same level of HDI. Machine learning analysis categorized eight patterns of global burden of COVID-19 with a large variation from US$0.001 billion to US$691.4 billion. CONCLUSION: Global burden of COVID-19 pandemic resulted in substantial health and value of life loss particularly in developed economies. Classifications of such health and economic loss is informative to early preparation of adequate resource to reduce impacts.

Associations between Daily Work Hassles and Energy-Balance Behaviors in Female African American Workers: An Ecological Momentary Assessment Study.

Objective: Despite their high rate of labor force participation, African American women earn less and are overrepresented in service jobs that tend to have fewer benefits, longer work hours, and less flexibility. The aim of our study was to examine associations between work-related daily hassles and energy balance behaviors among female African American workers. Design: A secondary analysis of a 7-day intensive longitudinal study using ecological momentary assessment (EMA). Setting: Metropolitan area of Chicago, Illinois, United States; July 2012 through January 2013. Participants: A convenience sample of 70 female African American workers. Methods: EMA was used to collect information over seven days on work hassles and energy balance behaviors: empty calorie food intake; moderate-to-vigorous physical activity (MVPA); sedentary behavior; sleep duration; and sleep disturbance. Within-person associations between daily work hassles and each of these daily energy balance behaviors were analyzed using person fixed-effects regression. Results: A total of 334 person-day observations from 70 female African American workers were included in the final analysis. Reporting at least one daily work hassle was associated with same-day higher empty calorie food intake (OR: 2.2, 95% CI: 1.0, 4.6) and more daily minutes of sedentary behavior (b: 35.8, 95% CI; .2, 71.3). However, no significant associations were found between prior-day work hassles and either food intake or sedentary behavior. Daily work hassles were not related to MVPA, sleep duration, or sleep disturbance. Conclusions: Our study showed that daily work hassles were associated with female African American workers' empty calorie food intake and sedentary behaviors. Strategies to eliminate daily work hassles may help to improve their energy balance behaviors.

Development of a two-step nucleic acid amplification test for accurate diagnosis of the Mycobacterium tuberculosis complex.

The Mycobacterium tuberculosis complex (MTBC) remains one of the top 10 leading causes of death globally. The early diagnosis of MTBC can reduce mortality and mitigate disease transmission. However, current nucleic acid amplification diagnostic test methods are generally time-consuming and show suboptimal diagnostic performance, especially in extrapulmonary MTBC samples or acid-fast stain (AFS)-negative cases. Thus, development of an accurate assay for the diagnosis of MTBC is necessary, particularly under the above mentioned conditions. In this study, a single-tube nested real-time PCR assay (N-RTP) was developed and compared with a newly in-house-developed high-sensitivity real-time PCR assay (HS-RTP) using 134 clinical specimens (including 73 pulmonary and 61 extrapulmonary specimens). The amplification efficiency of HS-RTP and N-RTP was 99.8% and 100.7%, respectively. The sensitivity and specificity of HS-RTP and N-RTP for the diagnosis of MTBC in these specimens were 97.5% (77/79) versus 94.9% (75/79) and 80.0% (44/55) versus 89.1% (49/55), respectively. The sensitivity and specificity of HS-RTP and N-RTP for the diagnosis of MTBC in pulmonary specimens were 96.3% (52/54) versus 96.3% (52/54) and 73.7.0% (14/19) versus 89.5% (17/19), respectively; in extrapulmonary specimens, the sensitivity and specificity of HS-RTP and N-RTP were 100% (25/25) versus 92% (23/25) and 83.3% (30/36) versus 88.9% (32/36), respectively. Among the AFS-negative cases, the sensitivity and specificity of HS-RTP and N-RTP were 97.0% (32/33) versus 90.9% (30/33) and 88.0% (44/50) versus 92.0% (46/50), respectively. Overall, the sensitivity of HS-RTP was higher than that of N-RTP, and the performance was not compromised in extrapulmonary specimens and under AFS-negative conditions. In contrast, the specificity of the N-RTP assay was higher than that of the HS-RTP assay in all types of specimens. In conclusion, the HS-RTP assay would be useful for screening patients suspected of exhibiting an MTBC infection due to its higher sensitivity, while the N-RTP assay could be used for confirmation because of its higher specificity. Our results provide a two-step method (screen to confirm) that simultaneously achieves high sensitivity and specificity in the diagnosis of MTBC.

Likelihood function for estimating parameters in multistate disease process with Laplace-transformation-based transition probabilities.

Multistate statistical models are often used to characterize the complex multi-compartment progression of the disease such as cancer. However, the derivation of multistate transition kernels is often involved with the intractable convolution that requires intensive computation. Moreover, the estimation of parameters pertaining to transition kernel requires the individualized time-stamped history data while the traditional likelihood function forms are constructed. In this paper, we came up with a novel likelihood function derived from Laplace transformation-based transition probabilities in conjunction with Expectation-Maximization algorithm to estimate parameters. The proposed method was applied to two large population-based screening data with only aggregated count data without relying on individual time-stamped history data.

Dynamics of detailed components of metabolic syndrome associated with the risk of cardiovascular disease and death.

Few studies quantify a cascade of dynamic transitions on the detailed components of metabolic syndrome (MetS) and subsequent progressions to cardiovascular disease (CVD) and its death. A total of 47,495 subjects repeatedly attending a community-based integrated screening program in Taiwan were recruited. The refined MetS-related classification (RMRC) in relation to five criteria of MetS was defined as free of metabolic disorder (FMD, none of any criteria), mild metabolic disorder (MMD, 1-2 criteria) and MetS. A multistate Markov model was used for modelling such a multistate process. The estimated progression rate from FMD to MMD was 44.82% (95% CI 42.95-46.70%) whereas the regression rate was estimated as 29.11% (95% CI 27.77-30.45%). The progression rate from MMD to MetS was estimated as 6.15% (95% CI 5.89-6.42%). The estimated annual incidence rates of CVD increased with the severity of RMRC, being 1.62% (95% CI 1.46-1.79%) for FMD, 4.74% (95% CI 4.52-4.96%) for MMD, to 20.22% (95% CI 19.52-20.92%) for MetS. The estimated hazard rate of CVD death was 6.1 (95% CI 4.6-7.7) per thousand. Elucidating the dynamics of MetS-related transition and quantifying the incidence and prognosis of CVD provide a new insight into the design and the evaluation of intervention programs for CVD.

Association of subjective global assessment of nutritional status with gut microbiota in hemodialysis patients: a case-control study.

BACKGROUND: Protein-energy wasting (PEW) is prevalent and associated with adverse outcomes in patients with chronic kidney disease (CKD). However, the pathogenesis of PEW in CKD patients has not been fully identified. The gut microbiota has been implicated in the regulation of host metabolism and energy balance. Therefore, we aimed to explore the association between nutritional status and the composition of the gut microbiota in hemodialysis patients. METHODS: Gut microbial diversity and taxonomy were examined in 88 hemodialysis patients with PEW (n = 22) and normal nutritional status (n = 66) who were matched 1:3 for age and sex. Nutritional status was assessed by using the 7-point subjective global assessment (SGA) score (1-3 = severe PEW; 4-5 = moderate PEW and 6-7 = normal nutrition). The gut microbiota was assessed by 16S ribosomal RNA gene sequencing. RESULTS: Patients with normal nutritional status had a significantly higher body mass index and physical activity and serum albumin levels, but significantly lower levels of inflammatory cytokines than patients with PEW. The most striking finding was that the α-diversity of the gut microbiota was significantly lower in patients with PEW. In a multivariate analysis, the SGA score was independently and positively associated with α-diversity (P = 0.049). Patients with or without PEW were different with respect to the principal coordinate analysis of ß-diversity. Notably, the relative abundance of Faecalibacterium prausnitzii, a butyrate-producing bacteria, was markedly reduced in patients with PEW. CONCLUSION: In hemodialysis patients, PEW assessed with the SGA was associated with gut dysbiosis.

Large-scale assessment of consistency in sleep stage scoring rules among multiple sleep centers using an interpretable machine learning algorithm.

STUDY OBJECTIVES: Polysomnography is the gold standard in identifying sleep stages; however, there are discrepancies in how technicians use the standards. Because organizing meetings to evaluate this discrepancy and/or reach a consensus among multiple sleep centers is time-consuming, we developed an artificial intelligence system to efficiently evaluate the reliability and consistency of sleep scoring and hence the sleep center quality. METHODS: An interpretable machine learning algorithm was used to evaluate the interrater reliability (IRR) of sleep stage annotation among sleep centers. The artificial intelligence system was trained to learn raters from 1 hospital and was applied to patients from the same or other hospitals. The results were compared with the experts' annotation to determine IRR. Intracenter and intercenter assessments were conducted on 679 patients without sleep apnea from 6 sleep centers in Taiwan. Centers with potential quality issues were identified by the estimated IRR. RESULTS: In the intracenter assessment, the median accuracy ranged from 80.3%-83.3%, with the exception of 1 hospital, which had an accuracy of 72.3%. In the intercenter assessment, the median accuracy ranged from 75.7%-83.3% when the 1 hospital was excluded from testing and training. The performance of the proposed method was higher for the N2, awake, and REM sleep stages than for the N1 and N3 stages. The significant IRR discrepancy of the 1 hospital suggested a quality issue. This quality issue was confirmed by the physicians in charge of the 1 hospital. CONCLUSIONS: The proposed artificial intelligence system proved effective in assessing IRR and hence the sleep center quality.

Validation and Disease Risk Assessment of Previously Reported Genome-Wide Genetic Variants Associated With Brugada Syndrome: SADS-TW BrS Registry.

BACKGROUND: Brugada syndrome (BrS) is an oligogenic arrhythmic disease with increased risk of sudden cardiac arrest. Several BrS or ECG traits-related single-nucleotide polymorphisms (SNPs) were identified through previous genome-wide association studies in white patients. We aimed to validate these SNPs in BrS patients in the Taiwanese population, assessing the cumulative effect of risk alleles and the BrS-polygenic risk score in predicting cardiac events. METHODS: We genotyped 190 unrelated BrS patients using the TWB Array, and Taiwan Biobank was used as controls. SNPs not included in the array were imputed by IMPUTE2. Cox proportional hazards model was used to evaluate the associations between each particular SNP, the collective BrS-polygenic risk score, and clinical outcomes. RESULTS: Of the 88 previously reported SNPs, 22 were validated in Taiwanese BrS patients (P<0.05). Of the 22 SNPs, 2 (rs10428132 and rs9388451) were linked with susceptibility to BrS, 10 were SNPs previously reaching genome-wide significance, and 10 were SNPs associated with ECG traits. For the 3 most commonly reported SNPs, disease risk increased consistently with the number of risk alleles (odds ratio, 3.54; Ptrend=1.38×10-9 for 5 risk alleles versus 1). Similar patterns were observed in both SCN5A mutation+ (odds ratio, 3.66; Ptrend=0.049) and SCN5A mutation- (odds ratio, 3.75; Ptrend=8.54×10-9) subgroups. Furthermore, BrS patients without SCN5A mutations had more risk alleles than BrS patients with SCN5A mutations regardless of the range of polygenic risk scores. Three SNPs (rs4687718, rs7784776, and rs2968863) showed significant associations with the composite outcome (sudden cardiac arrest plus syncope, hazard ratio, 2.13, 1.48, and 0.41; P=0.02, 0.006, and 0.008, respectively). CONCLUSIONS: Our findings suggested that some SNPs associated with BrS or ECG traits exist across multiple populations. The cumulative risk of the BrS-related SNPs is similar to that in white BrS patients, but it appears to correlate with the absence of SCN5A mutations.