No abstract

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.

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.

Objective: 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.Methods: 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.Results: We included 24 retrospective studies without randomized-controlled trials, involving 10,164 participants (MP ¼ 6934, BP ¼ 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 (b ¼ -0.0135, P ¼ .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).Conclusions: 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.