Background: Metabolic syndrome is a cluster of disorders that significantly influence the development and deterioration of numerous diseases. FibroScan is an ultrasound device that was recently shown to predict metabolic syndrome with moderate accuracy. However, previous research regarding prediction of metabolic syndrome in subjects examined with FibroScan has been mainly based on conventional statistical models. Alternatively, machine learning, whereby a computer algorithm learns from prior experience, has better predictive performance over conventional statistical modeling. Objective: We aimed to evaluate the accuracy of different decision tree machine learning algorithms to predict the state of metabolic syndrome in self-paid health examination subjects who were examined with FibroScan. Methods: Multivariate logistic regression was conducted for every known risk factor of metabolic syndrome. Principal components analysis was used to visualize the distribution of metabolic syndrome patients. We further applied various statistical machine learning techniques to visualize and investigate the pattern and relationship between metabolic syndrome and several risk variables. Results: Obesity, serum glutamic-oxalocetic transaminase, serum glutamic pyruvic transaminase, controlled attenuation parameter score, and glycated hemoglobin emerged as significant risk factors in multivariate logistic regression. The area under the receiver operating characteristic curve values for classification and regression trees and for the random forest were 0.831 and 0.904, respectively. Conclusions: Machine learning technology facilitates the identification of metabolic syndrome in self-paid health examination subjects with high accuracy.

The optimal selection of prosthetic heart valve for dialysis-dependent patients remains controversial. We investigated the comparative effectiveness and safety of mechanical prosthesis (MP) and bioprosthesis (BP) for these patients. After the systematic review, we included studies that involved patients on dialysis undergoing aortic valve replacement or mitral valve replacement (MVR) and reported comparative outcomes of MP and BP. Meta-analysis was performed using random-effects model. We conducted a subgroup analysis based on the valve position and postoperative international normalized ratio (INR), which was extracted from either tables or methods of each study. A meta-regression was used to examine the effects of study-level covariates. We included 24 retrospective studies without randomized-controlled trials, involving 10,164 participants (MP = 6934, BP = 3230). Patients undergoing aortic valve replacement with MP exhibited a better long-term survival effectiveness (hazard ratio, 0.64; 95% confidence interval [CI], 0.47-0.86). Conversely, studies including MVR demonstrated little difference in survival (hazard ratio, 0.90; 95% CI, 0.73-1.12). A meta-regression revealed that age had little effect on long-term survival difference between MP and BP (β = -0.0135, P = .433). MP had a significantly greater bleeding risk than did BP when INR was above 2.5 (incidence rate ratio, 10.58; 95% CI, 2.02-55.41). However, when INR was below 2.5, bleeding events were comparable (incidence rate ratio, 1.73; 95% CI, 0.78-3.82). The structural valve deterioration rate was significantly lower in MP (risk ratio, 0.24; 95% CI, 0.14-0.44). MP is a reasonable choice for dialysis-dependent patients without additional thromboembolic risk requiring aortic valve replacement, for its better long-term survival, durability, and noninferior bleeding risk compared with BP. Conversely, BP might be an appropriate selection for patients with MVR, given its similar survival rate and lower bleeding risk. Although our meta-regression demonstrates little influence of age on long-term survival difference between MP and BP, further studies stratifying patients based on age cut-off are mandatory.

Background: Preventive medicine and primary health care are essential for patients with chronic kidney disease (CKD) because the symptoms of CKD may not appear until the renal function is severely compromised. Early identification of the risk factors of CKD is critical for preventing kidney damage and adverse outcomes. Early recognition of rapid progression to advanced CKD in certain high-risk populations is vital. Methods: This is a retrospective cohort study, the population screened and the site where the study has been performed. Multivariate statistical analysis was used to assess the prediction of CKD as many potential risk factors are involved. The clustering heatmap and random forest provides an interactive visualization for the classification of patients with different CKD stages. Results: uric acid, blood urea nitrogen, waist circumference, serum glutamic oxaloacetic transaminase, and hemoglobin A1c (HbA1c) were significantly associated with CKD. CKD was highly associated with obesity, hyperglycemia, and liver function. Hypertension and HbA1c were in the same cluster with a similar pattern, whereas high-density lipoprotein cholesterol had an opposite pattern, which was also verified using heatmap. Early staged CKD patients who are grouped into the same cluster as advanced staged CKD patients could be at high risk for rapid decline of kidney function and should be closely monitored. Conclusions: The clustering heatmap provided a new predictive model of health care management for patients at high risk of rapid CKD progression. This model could help physicians make an accurate diagnosis of this progressive and complex disease.

Despite the fact that vaccination is an effective primary prevention strategy for the containment of influenza outbreaks, health policymakers have shown great concern over the enormous costs involved in universal immunization, particularly when resources are limited. A two-arm cost-effectiveness analysis (CEA) was conducted that took into account the aspect of herd immunity. The analysis used a study cohort of 100000 residents with a demographic make-up identical to that of the underlying population in Taipei County, Taiwan, during the epidemic influenza season of 2001-2002. The parameters embedded in the dynamic process of infection were estimated through the application of the newly proposed susceptible-infection-complication-recovery (SICR) model to the empirical data, in order to compute the number of deaths and complications averted due to universal vaccination compared to no vaccination. Incremental cost-effectiveness ratios (ICERs) and the cost-effectiveness acceptability curve (CEAC) given maximum amount of willingness-to-pay (WTP) were calculated to delineate the results of the two-arm CEA. The incremental costs involved in the vaccinated group as compared to the unvaccinated group were $1195 to reduce one additional complication and $805 to avert one additional death, allowing for herd immunity. The corresponding figures were higher for the results without considering herd immunity. Given the ceiling ratio of WTP equal to $10000 (approximately two-thirds of GDP), the probability of the vaccination being cost-effective for averting death was 100% and for averting complications was 96.7%. Universal vaccination against seasonal influenza was found to be very cost-effective, particularly when herd immunity is considered. The probability of being cost-effective was almost certain given the maximum amount of WTP within two-thirds of the GDP.

Background: Previous studies and meta-analyses have explored the relationship among testosterone, muscle strength, and physical function, to the best of our knowledge, no meta-analysis has investigated the effects of testosterone replacement therapy (TRT) on subgroup of relatively hypogonadal older men. Objective: The aim of this study was to evaluate the effect of TRT in older men with low testosterone levels. Methods: PubMed, Embase, and Web of Science were systematically searched for articles published between January 1990 and April 2020. We included randomized controlled studies that investigated the effect of TRT and included older men (age >60 years) with relatively low testosterone levels. Studies were extracted following the PRISMA flowchart, and the included randomized controlled trials were evaluated using RoB 2.0. Our main outcome was muscle strength changes after TRT evaluated using a metaregression of confounding factors. Secondary outcomes included changes in physical performance and the risk ratio of adverse events. Random-effects meta-analyses of TRT on muscle strength and physical function were performed. Results: Thirteen studies with 2,043 patients were included. The mean age of subjects in various studies ranged from 65.9 years to 76 years. Transdermal testosterone dosages ranged from 5 to 10 g/day, while intramuscular options were 125 mg/week or 200 mg every 2 weeks. Oral testosterone supplementation was given at 160 mg/day in one study. Pooled meta‐analyses revealed greater muscle strength improvement after TRT compared with placebo (Hedges’ g = 0.21; 95% CI: = 0.15–0.28). Intramuscular administration of TRT had greater efficacy (Hedges’ g = 0.74; 95% CI: = 0.34–1.14) than transdermal and oral TRT (p < 0.001). A metaregression revealed that baseline serum total testosterone was associated with muscle strength improvement (β = −0.004, p = 0.002). The risk ratios of adverse events, including elevated prostate-specific antigen, acute coronary syndrome, and prostate cancer, were not significantly different. Conclusion: TRT improved muscle strength in older, relatively hypogonadal men. The effect was more pronounced in populations with lower baseline testosterone levels.

The association between osteoporosis and periodontal disease (PD) has been revealed by previous studies, but there have been few studies on the association in younger adults. We enrolled a total of 7298 adults aged 40 to 44 who underwent PD screening between 2003 and 2008. Data on quantitative ultrasound for the measurement of bone mineral density (BMD) were collected for the diagnostic criteria of osteopenia and osteoporosis. The Community Periodontal Index (CPI) was measured for defining PD. A multiple logistic regression model was used to assess the effect of low bone mass on the risk of PD. Of 7298 enrollees, 31% had periodontal pockets >3 mm, 36.2% had osteopenia, and 2.1% had osteoporosis. The 39.8% of PD prevalence was high in adults with osteoporosis, followed by 33.3% in osteopenia. A negative association was found between BMD and CPI value (p < 0.0001). Low bone mass was associated with the risk of PD (adjusted OR: 1.13; 95% CI:1.02–1.26) after adjusting the confounding factors, including age, gender, education level, overweight, smoking status, past history of osteoporosis, and diabetes mellitus. An association between BMD and PD among young adults was found. An intervention program for the prevention of PD and osteoporosis could be considered starting in young adults.