Bayesian signal detection methods, including the multiitem gamma Poisson shrinker (MGPS), assume a Poisson distribution for the number of reports. However, the database of the adverse event reporting ...system often has a large number of zero‐count cells. A zero‐inflated Poisson (ZIP) distribution can be more appropriate in this situation than a Poisson distribution. Few studies have considered ZIP‐based models for Bayesian signal detection. In addition, most studies on Bayesian signal detection methods include simulation studies conducted assuming a gamma distribution for the prior. Herein, we extend the MGPS method using the ZIP model and apply various prior distributions. We evaluated the extended methods through an extensive simulation using more varied settings for the model and prior than existing methods. We varied the total number of reports, the number of true signals, the relative reporting rate, and the probability of observing a true zero. The results show that as the probability of observing a zero count increased, methods based on the ZIP model outperformed the Poisson model in most cases. We also found that using the mixture log‐normal prior resulted in more conservative detection than other priors when the relative reporting rate is high. Conversely, more signals were found when using the mixture truncated normal distributions. We applied the Bayesian signal detection methods to data from the Korea Adverse Event Reporting System from 2012 to 2016.
Aims and Objectives
To examine the effects of registered nurse staffing levels, work environment and education levels on the residents' quality of life and nurses' job dissatisfaction, burnout and ...turnover intention.
Background
Registered nurse staffing status and work environment are suboptimal in nursing homes worldwide. Nursing home care aims to maximise residents' quality of life. However, evidence on the impact of registered nurse staffing levels, work environment and education levels on the residents' quality of life and nurse outcomes in nursing homes is limited.
Design
This is a cross‐sectional observational study.
Methods
A total of 513 residents and 117 registered nurses from 39 nursing homes in South Korea participated in surveys. The main measures included registered nurses' staffing levels, work environment, education levels, residents' quality of life, registered nurses' job dissatisfaction, burnout and turnover intention. We analysed data using the generalised estimating equations and reported the study using the STROBE checklist.
Results
Overall, the residents' quality‐of‐life score was 13.7 ± 2.6 (out of 17). Residents in nursing homes with a higher number of registered nurses or with work environment evaluated as ‘mixed’ or ‘better’ (compared with ‘poor’) had a higher quality of life. Regarding nurse outcomes, 74.4% of the registered nurses were dissatisfied with their current jobs, 12.0% had burnout and 18.8% had a turnover intention. Registered nurses working in ‘mixed’ or ‘better’ work environment were less likely to have job dissatisfaction. Registered nurses' education levels did not have a statistically significant effect on the resident and nurse outcomes.
Conclusions
Registered nurse staffing levels and work environment should be considered important for improving residents' quality of life and nurses' job satisfaction.
Relevance to Clinical Practice
Regulation and policy reforms are needed to increase the registered nurse staffing levels and to create a good work environment in nursing homes.
Patient or Public Contribution
Nursing home residents and registered nurses participated in the surveys of this study. Registered nurses facilitated resident recruitment by identifying and introducing the study to residents who were eligible for study participation.
Trial Registration
Not applicable.
Abstract
Dialysis adequacy is an important survival indicator in patients with chronic hemodialysis. However, there are inconveniences and disadvantages to measuring dialysis adequacy by blood ...samples. This study used machine learning models to predict dialysis adequacy in chronic hemodialysis patients using repeatedly measured data during hemodialysis. This study included 1333 hemodialysis sessions corresponding to the monthly examination dates of 61 patients. Patient demographics and clinical parameters were continuously measured from the hemodialysis machine; 240 measurements were collected from each hemodialysis session. Machine learning models (random forest and extreme gradient boosting XGBoost) and deep learning models (convolutional neural network and gated recurrent unit) were compared with multivariable linear regression models. The mean absolute percentage error (MAPE), root mean square error (RMSE), and Spearman’s rank correlation coefficient (Corr) for each model using fivefold cross-validation were calculated as performance measurements. The XGBoost model had the best performance among all methods (MAPE = 2.500; RMSE = 2.906; Corr = 0.873). The deep learning models with convolutional neural network (MAPE = 2.835; RMSE = 3.125; Corr = 0.833) and gated recurrent unit (MAPE = 2.974; RMSE = 3.230; Corr = 0.824) had similar performances. The linear regression models had the lowest performance (MAPE = 3.284; RMSE = 3.586; Corr = 0.770) compared with other models. Machine learning methods can accurately infer hemodialysis adequacy using continuously measured data from hemodialysis machines.
Background
Gastrointestinal dysfunction (GI) is the most prevalent non-motor symptom of Parkinson’s disease (PD), and its role in the risk of PD has been studied. In this study, we tried to evaluate ...whether irritable bowel syndrome (IBS) increased the risk of PD development stratified by sex, age, and IBS duration using a large nationwide cohort in Korea.
Methods
Patients aged ≥ 20 years with a primary diagnosis of IBS (ICD-10 codes: G56) more than three times were selected. A randomly matched cohort without IBS was enrolled by exact matching patients for sex, age, socioeconomic status, comorbidities, and year of enrollment to the IBS group with a ratio of 1:3. Cause-specific Cox regression models were used to identify hazards associated with PD development depending on the presence of IBS during the 11-year follow-up period.
Results
In total, 285,064 patients were enrolled in the study: 71,806 in the IBS cohort and 213,258 in the comparison cohort. Cause-specific Cox regression model showed a hazard ratio of 1.436 (95% CI, 1.226–1.682) for PD development in the IBS cohort, which is consistent in both male and female sexes. Subgroup analyses according to age groups showed that IBS increased PD risk only in individuals ≥ 65 years (HR = 1.449, 95% CI, 1.207–1.741).
Conclusions
We found temporal relationship between IBS and PD at aged ≥ 65 years. There might be a possibility that IBS was an early manifestation of PD, and future studies for causal link between the two diseases to elucidate biomechanism are warranted.
Background
Triglyceride and glucose (TyG) index and TyG‐related indices combined with obesity‐related markers are considered important markers of insulin resistance. We aimed to examine the ...association between the TyG index and modified TyG indices with new‐onset hypertension and their predictive ability stratified by sex.
Methods and Results
We analyzed data from 5414 Korean Genome and Epidemiology Study participants aged 40 to 69 years. Multiple Cox proportional hazard regression analyses were conducted to estimate the hazard ratio (HR) and 95% CI for new‐onset hypertension according to sex‐specific tertile groups after confounder adjustments. To evaluate the predictive performance of these indices for new‐onset hypertension, we calculated Harrell's C‐index (95% CI). Over a 9.5‐year follow‐up period, 1014 men and 1012 women developed new‐onset hypertension. Compared with the lowest tertile (T) group, the adjusted HR and 95% CI for new‐onset hypertension in T3 for TyG, TyG‐body mass index, TyG‐waist circumference, and TyG‐waist‐to‐height ratio were 1.16 (0.95–1.40), 1.11 (0.84–1.48), 1.77 (1.38–2.27), and 1.68 (1.33–2.13) in men and 1.37 (1.13–1.66), 1.55 (1.16–2.06), 1.43 (1.15–1.79), and 1.64 (1.30–2.07) in women, respectively. The C‐indices of TyG‐waist‐to‐height ratio for new‐onset hypertension were significantly higher than those of TyG and TyG‐body mass index in both men and women.
Conclusions
TyG and TyG‐body mass index were significantly associated with new‐onset hypertension only in women. TyG‐waist circumference and TyG‐waist‐to‐height ratio were significantly associated with new‐onset hypertension in both men and women. A sex‐specific approach is required when using TyG and modified TyG indices to identify individuals at risk of incident hypertension.
Neural networks suffer performance degradation when the source and target data lie in a different distribution hampering direct deployment of the model to diverse target domains. To this end, domain ...generalization (DG) aims to generalize the model well to an unknown target domain by utilizing multiple source domains. This paper proposes two simple swapping mechanisms, texture and channel swapping (TCX), for DG. Texture swapping augments the source dataset by replacing textures of an image with other textures in source dataset to alleviate the texture bias problem in convolution neural networks (CNNs). Furthermore, channel swapping switches channels of input feature vectors of the classifier along with its labels to encourage the model to utilize more channels when classifying an image. Together, we expect the model to learn less domain-specific features but more generalized class-specific features resulting in better domain generalization performance. We demonstrate the effectiveness of our approach with state-of-the-art results on three domain generalization benchmarks.
De novo donor-specific antibody (dnDSA) is associated with a higher risk of kidney graft failure. However, it is unknown whether preemptive treatment of subclinical dnDSA is beneficial. Here, we ...assessed the efficacy of high-dose intravenous immunoglobulin (IVIG) and rituximab combination therapy for subclinical dnDSA. An open-label randomized controlled clinical trial was conducted at two Korean institutions. Adult (aged ≥ 19 years) kidney transplant patients with subclinical class II dnDSA (mean fluorescence intensity ≥ 1000) were enrolled. Eligible participants were randomly assigned to receive rituximab or rituximab with IVIG at a 1:1 ratio. The primary endpoint was the change in dnDSA titer at 3 and 12 months after treatment. A total of 46 patients (24 for rituximab and 22 for rituximab with IVIG) were included in the analysis. The mean baseline estimated glomerular filtration rate was 66.7 ± 16.3 mL/min/1.73 m
. The titer decline of immune-dominant dnDSA at 12 months in both the preemptive groups was significant. However, there was no difference between the two groups at 12 months. Either kidney allograft function or proteinuria did not differ between the two groups. No antibody-mediated rejection occurred in either group. Preemptive treatment with high-dose IVIG combined with rituximab did not show a better dnDSA reduction compared with rituximab alone.Trial registration: IVIG/Rituximab versus Rituximab in Kidney Transplant With de Novo Donor-specific Antibodies (ClinicalTrials.gov Identifier: NCT04033276, first trial registration (26/07/2019).
•This study investigated secular trends in the of carbohydrate, fat, and protein intake in a sample of South Koreans from 2010 to 2020.•The study population showed a significant decrease in total ...energy intake.•The decrease in the percentage of energy intake from carbohydrates, and the increases from proteins and fats were found during the same period.•The changes in dietary patterns highlight the need for targeted interventions to promote healthier eating habits.
The composition and balance of macronutrient intake play key roles in promoting a longer lifespan. In this study, we aimed to investigate the secular trends in carbohydrate, fat, and protein intakes in South Koreans from 2010 to 2020.
We examined the dietary nutritional intake of South Koreans using data from the Korean National Health and Nutrition Examination Survey. A total of 60,190 adults aged ≥19 y who completed the 24-h dietary recall interviews in a single day on all survey periods were included in this study. The outcomes included changes in macronutrient intake according to subgroups, such as age; sex; and the presence of diabetes, dyslipidemia, stroke, or heart disease, as well as energy intake from macronutrients.
The study population showed a significant decrease in total energy intake from 2010 to 2020, with a corresponding decrease in the percentage of energy intake from carbohydrates (p-values for trend < 0.001). Conversely, the proportions of energy intake from proteins and fats increased during the same period (p < 0.001). Subgroup analyses revealed variations in macronutrient intake trends according to age, sex, obesity status, and underlying diseases. The analysis of trends in energy intake from various fat subtypes, total sugar, and fiber revealed a decrease in the energy intake percentage of total sugar from 2016 to 2020 and an increase in the energy intake percentage of all fat subtypes and fiber from 2013 to 2020.
In the past 10 y, the dietary patterns in Korea have shifted toward the consumption of high-fat and high-protein diets with reduced carbohydrate intake.
We aimed to use deep learning to detect tuberculosis in chest radiographs in annual workers' health examination data and compare the performances of convolutional neural networks (CNNs) based on ...images only (I-CNN) and CNNs including demographic variables (D-CNN). The I-CNN and D-CNN models were trained on 1000 chest X-ray images, both positive and negative, for tuberculosis. Feature extraction was conducted using VGG19, InceptionV3, ResNet50, DenseNet121, and InceptionResNetV2. Age, weight, height, and gender were recorded as demographic variables. The area under the receiver operating characteristic (ROC) curve (AUC) was calculated for model comparison. The AUC values of the D-CNN models were greater than that of I-CNN. The AUC values for VGG19 increased by 0.0144 (0.957 to 0.9714) in the training set, and by 0.0138 (0.9075 to 0.9213) in the test set (both
< 0.05). The D-CNN models show greater sensitivity than I-CNN models (0.815 vs. 0.775, respectively) at the same cut-off point for the same specificity of 0.962. The sensitivity of D-CNN does not attenuate as much as that of I-CNN, even when specificity is increased by cut-off points. Conclusion: Our results indicate that machine learning can facilitate the detection of tuberculosis in chest X-rays, and demographic factors can improve this process.
Background
Although disclosing the predictors of different behavioral and psychological symptoms of dementia (BPSD) is the first step in developing person-centered interventions, current ...understanding is limited, as it considers BPSD as a homogenous construct. This fails to account for their heterogeneity and hinders development of interventions that address the underlying causes of the target BPSD subsyndromes. Moreover, understanding the influence of proximal factors—circadian rhythm–related factors (ie, sleep and activity levels) and physical and psychosocial unmet needs states—on BPSD subsyndromes is limited, due to the challenges of obtaining objective and/or continuous time-varying measures.
Objective
The aim of this study was to explore factors associated with BPSD subsyndromes among community-dwelling older adults with dementia, considering sets of background and proximal factors (ie, actigraphy-measured sleep and physical activity levels and diary-based caregiver-perceived symptom triggers), guided by the need-driven dementia-compromised behavior model.
Methods
A prospective observational study design was employed. Study participants included 145 older adults with dementia living at home. The mean age at baseline was 81.2 (SD 6.01) years and the sample consisted of 86 (59.3%) women. BPSD were measured with a BPSD diary kept by caregivers and were categorized into seven subsyndromes. Independent variables consisted of background characteristics and proximal factors (ie, sleep and physical activity levels measured using actigraphy and caregiver-reported contributing factors assessed using a BPSD diary). Generalized linear mixed models (GLMMs) were used to examine the factors that predicted the occurrence of BPSD subsyndromes. We compared the models based on the Akaike information criterion, the Bayesian information criterion, and likelihood ratio testing.
Results
Compared to the GLMMs with only background factors, the addition of actigraphy and diary-based data improved model fit for every BPSD subsyndrome. The number of hours of nighttime sleep was a predictor of the next day’s sleep and nighttime behaviors (odds ratio OR 0.9, 95% CI 0.8-1.0; P=.005), and the amount of energy expenditure was a predictor for euphoria or elation (OR 0.02, 95% CI 0.0-0.5; P=.02). All subsyndromes, except for euphoria or elation, were significantly associated with hunger or thirst and urination or bowel movements, and all BPSD subsyndromes showed an association with environmental change. Age, marital status, premorbid personality, and taking sedatives were predictors of specific BPSD subsyndromes.
Conclusions
BPSD are clinically heterogeneous, and their occurrence can be predicted by different contributing factors. Our results for various BPSD suggest a critical window for timely intervention and care planning. Findings from this study will help devise symptom-targeted and individualized interventions to prevent and manage BPSD and facilitate personalized dementia care.
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