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The identification of risk factors for future prediabetes in young men remains largely unexamined. This study enrolled 6247 young ethnic Chinese men with normal fasting plasma glucose at the baseline (FPGbase), and used machine learning (Mach-L) methods to predict prediabetes after 5.8 years. The study seeks to achieve the following: 1. Evaluate whether Mach-L outperformed traditional multiple linear regression (MLR). 2. Identify the most important risk factors. The baseline data included demographic, biochemistry, and lifestyle information. Two models were built, where Model 1 included all variables and Model 2 excluded FPGbase, since it had the most profound effect on prediction. Random forest, stochastic gradient boosting, eXtreme gradient boosting, and elastic net were used, and the model performance was compared using different error metrics. All the Mach-L errors were smaller than those for MLR, thus Mach-L provided the most accurate results. In descending order of importance, the key factors for Model 1 were FPGbase, body fat (BF), creatinine (Cr), thyroid stimulating hormone (TSH), WBC, and age, while those for Model 2 were BF, white blood cell, age, TSH, TG, and LDL-C. We concluded that FPGbase was the most important factor to predict future prediabetes. However, after removing FPGbase, WBC, TSH, BF, HDL-C, and age were the key factors after 5.8 years. [ABSTRACT FROM AUTHOR]

The challenges of respiratory infections persist as a global health crisis, placing substantial stress on healthcare infrastructures and necessitating ongoing investigation into efficacious treatment modalities. The persistent challenge of respiratory infections, including COVID-19, underscores the critical need for enhanced diagnostic methodologies to support early treatment interventions. This study introduces an innovative two-stage data analytics framework that leverages deep learning algorithms through a strategic combinatorial fusion technique, aimed at refining the accuracy of early-stage diagnosis of such infections. Utilizing a comprehensive dataset compiled from publicly available lung X-ray images, the research employs advanced pre-trained deep learning models to navigate the complexities of disease classification, addressing inherent data imbalances through methodical validation processes. The core contribution of this work lies in its novel application of combinatorial fusion, integrating select models to significantly elevate diagnostic precision. This approach not only showcases the adaptability and strength of deep learning in navigating the intricacies of medical imaging but also marks a significant step forward in the utilization of artificial intelligence to improve outcomes in healthcare diagnostics. The study's findings illuminate the path toward leveraging technological advancements in enhancing diagnostic accuracies, ultimately contributing to the timely and effective treatment of respiratory diseases. [ABSTRACT FROM AUTHOR]

The diagnostic performance of the bispectral index (BIS) to early predict neurological outcomes in patients achieving return of spontaneous circulation (ROSC) after cardiac arrest (CA) remained unclear. We searched PubMed, EMBASE, Scopus and CENTRAL for relevant studies through October 2019. Methodologic quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Meta-analysis was performed using a linear mixed-effects model to the log-transformed data with a logistic distribution assumption. Bivariate meta-regression was performed to explore heterogeneity. In total, 13 studies with 999 CA adult patients were included. At the optimal threshold of 32, BIS obtained within 72 h of ROSC elicits a pooled sensitivity of 84.9% (95% confidence interval (CI), 71.1% to 92.7%), a pooled specificity of 85.9% (95% CI, 71.2% to 93.8%) and an area under the curve of 0.92. Moreover, a BIS cutoff < 12 yielded a pooled specificity of 95.0% (95% CI, 77.8% to 99.0%). In bivariate meta-regression, the timing of neurological outcome assessment, the adoption of targeted temperature management, and the administration of sedative agents or neuromuscular blocking agents (NMBA) were not identified as the potential source of heterogeneity. BIS retains good diagnostic performance during targeted temperature management (TTM) and in the presence of administrated sedative agents and NMBA. In conclusion, BIS can predict poor neurological outcomes early in patients with ROSC after CA with good diagnostic performance and should be incorporated into the neuroprognostication strategy algorithm. [ABSTRACT FROM AUTHOR]

Background: Early trauma team activation (TTA) may improve clinical outcomes through early diagnosis and timely intervention by a dedicated multidisciplinary team. Controversy seems to exist about the effect of establishing trauma team systems in traumatic injury populations. Our aim was to identify factors that may be associated with clinical outcomes in trauma injury and to investigate the effect of trauma team activation. Method: This retrospective descriptive study included all traumatic patients from the Taipei Tzu Chi Hospital Trauma Database. All prehospital vital signs, management, injury type, injury mechanisms, hospitalization history, and clinical outcomes were analyzed, and multivariable logistic regression was used to investigate the association between trauma team activation and clinical outcomes. Subgroups of TTA in minor injury and non-TTA in major injury were also analyzed. Result: In this study, a total of 11,946 patients were included, of which 10,831 (90.7%) patients were minor injury (ISS < 16), and 1115 (9.3%) patients were major injury (ISS ≥ 16). In the minor injury population, TTA had a higher intensive care unit (ICU) admission rate, operation rate, re-operation rate, and prolonged total length of stay (LOS). In the major injury population, TTA had a higher mortality rate, prolonged total LOS, and prolonged ICU LOS. After adjusting for mechanism of injury and injury severity, there was no association between in-hospital mortality and TTA, compared with the non-TTA group. However, the TTA group had a higher risk of ICU admission, prolonged ICU LOS, and prolonged total LOS. The subgroup analysis showed trauma team activation had a higher risk of mortality in the 60- to 80-year-old population, major injury (ISS ≥ 16), consciousness clear population, and non-head injury group. Conclusions: We found there was no significant association between in-hospital mortality and TTA. However, in the TTA group, there was a higher risk of ICU admission, prolonged total, LOS, and prolonged ICU LOS. In the subgroup analysis, TTA had a higher risk of mortality in the 60- to 80-year-old population, major injury (ISS ≥ 16), consciousness clear population, and non-head injury group. Our results reflect TTA-criteria-selected patients with greater ISS and a high risk of mortality. [ABSTRACT FROM AUTHOR]

Traditional otoscopy has some limitations, including poor visualization and inadequate time for evaluation in suboptimal environments. Smartphone-enabled otoscopy may improve examination quality and serve as a potential diagnostic tool for middle ear diseases using a telemedicine approach. The main objectives are to compare the correctness of smartphone-enabled otoscopy and traditional otoscopy and to evaluate the diagnostic confidence of the examiner via meta-analysis. From inception through 20 January 2022, the Cochrane Library, PubMed, EMBASE, Web of Science, and Scopus databases were searched. Studies comparing smartphone-enabled otoscopy with traditional otoscopy regarding the outcome of interest were eligible. The relative risk (RR) for the rate of correctness in diagnosing ear conditions and the standardized mean difference (SMD) in diagnostic confidence were extracted. Sensitivity analysis and trial sequential analyses (TSAs) were conducted to further examine the pooled results. Study quality was evaluated by using the revised Cochrane risk of bias tool 2. Consequently, a total of 1840 examinees were divided into the smartphone-enabled otoscopy group and the traditional otoscopy group. Overall, the pooled result showed that smartphone-enabled otoscopy was associated with higher correctness than traditional otoscopy (RR, 1.26; 95% CI, 1.06 to 1.51; p = 0.01; I2 = 70.0%). Consistently significant associations were also observed in the analysis after excluding the simulation study (RR, 1.10; 95% CI, 1.00 to 1.21; p = 0.04; I2 = 0%) and normal ear conditions (RR, 1.18; 95% CI, 1.01 to 1.40; p = 0.04; I2 = 65.0%). For the confidence of examiners using both otoscopy methods, the pooled result was nonsignificant between the smartphone-enabled otoscopy and traditional otoscopy groups (SMD, 0.08; 95% CI, -0.24 to 0.40; p = 0.61; I2 = 16.3%). In conclusion, smartphone-enabled otoscopy was associated with a higher rate of correctness in the detection of middle ear diseases, and in patients with otologic complaints, the use of smartphone-enabled otoscopy may be considered. More large-scale studies should be performed to consolidate the results. [ABSTRACT FROM AUTHOR]