Citations

Insulin resistance (IR) is a major contributor to the pathogenesis of nonalcoholic fatty liver disease (NAFLD). The triglyceride-glucose (TyG) index has recently gained popularity for the assessment of IR and NAFLD due to its ease of acquisition and calculation. Therefore, we conducted this systematic review and meta-analysis to summarize the existing studies in the literature and provide a quantitative assessment of the significance of the TyG index in predicting the incidence of NAFLD. A comprehensive literature search in PubMed, EMBASE, and Web of Science databases from inception until 25 March 2022 was conducted. Published observational studies that evaluated the association between TyG index and NAFLD among the adult population and reported the hazard ratio (HR) or odds ratio (OR) for this association after multivariate analysis were included. The random-effects model was used as the primary statistical analysis model in the estimation of pooled ORs and HRs with the corresponding confidence intervals (CIs). A total of 17 observational studies, including 121,975 participants, were included. For studies analyzing the TyG index as a categorical variable, both pooled OR (6.00, CI 4.12–8.74) and HR (1.70, CI 1.28–2.27) were significant for the association between TyG index and incident NAFLD. For studies analyzing the TyG index as a continuous variable, pooled OR (2.25, CI 1.66–3.04) showed similar results. Consistent results were obtained in subgroup analyses according to the study design, sample size, ethnicity, and diabetic status. In conclusion, our meta-analysis demonstrates that a higher TyG index is associated with higher odds of NAFLD. TyG index may serve as an independent predictive tool to screen patients at high risk of NAFLD in clinical practice, especially in primary care settings. Patients with a high TyG index should be referred for a liver ultrasound and start intense lifestyle modifications. However, further large-scale prospective cohort studies are necessary to validate our findings.

Background: While lack of insurance is known to be associated with worse outcomes after myocardial infarction (MI), it is unknown whether health insurance status is associated with rates of non-elective hospitalizations after MI. Methods: Using data on 3571 U.S. MI patients enrolled between April 2005 to December 2008 in the 24-center, prospective TRIUMPH registry, we examined the association between being insured (reference), underinsured, and uninsured on rates of non-elective readmissions (all-cause) in the 1 st year following MI. Insurance status was obtained from medical records, and underinsurance was defined as those patients with insurance but with financial concerns about accessing care from patient interviews. Multivariable modified Poisson regression models were used to adjust for site, demographics, MI severity, risk factors, socio-economic, and psychological factors. Results: A total of 2269 (63.5%) patients were insured; 650 (18.2%) were underinsured; and 652 (18.3%) were uninsured. Emergency readmissions were highest for uninsured patients (25.0%), followed by the underinsured (21.8%), and lowest in insured patients (19.5%) (P<.001). After multivariable adjustment, uninsured patients had significantly higher rates of emergency readmission (adjusted RR, 1.52 [95% CI: 1.22-1.90] while underinsured patients did not (adjusted RR, 0.99 [95% CI: 0.81-1.21]) (Figure). Conclusions: Uninsured patients had a significantly higher adjusted risk of non-elective readmissions after MI, while underinsured patients did not. Future studies are needed to determine if providing even modest insurance coverage to the uninsured can minimize readmission rates.

Introduction: Development of new intraventricular hemorrhage (IVH) within 24 hours of presentation or expansion of 1mL or more independently predicts poor outcome. Current methods for IVH volume estimation lack sufficient precision to detect this level of change. The best method of quantification with manual/semi-automated segmentation remain clinically impractical. We aimed to train and validate a convolutional neural network (CNN) to autonomously segment and quantify IVH volume in acute intracerebral hemorrhage (ICH) patients. Methods: Imaging data for training and validation was obtained from 3 international multicenter studies. IVH volumes were assessed using CNN, IVH Score (IVHS), Original Graeb Score (oGS), Modified Graeb Score (mGS), and manual segmentation using ITK-SNAP. Agreement between CNN volume and IVHS estimated volume compared to manual segmentation was assessed using intraclass correlation coefficient (ICC) and Bland-Altman charts. Accuracy of CNN segmentation was compared to manual segmentation with Dice similarity coefficient (DSC). Accuracy of hematoma expansion detection was assessed using receiver operating characteristic curves. Results: A total of 172 patients and 311 CT scans were included. Intra-rater reliability was significantly greater for CNN versus IVHS method (ICC 0.99 [95%CI 0.99 - 1.00] vs 0.76 [95%CI 0.66 - 0.83]). Accuracy of CNN segmentation was satisfactory (DSC 0.76 [95%CI 0.75 - 0.78]). Accuracy of hematoma expansion detection was substantially greater in CNN (AUC 0.91 [95%CI 0.85 - 0.97]) versus IVHS, oGS and mGS. Conclusion: Our results demonstrate that a fully automated CNN algorithm is capable of segmenting IVH volumes on multi-center data with higher intra-rater reliability and satisfactory accuracy over the current methods of IVH volume measurement tools. The algorithm has proven to be capable of detecting hematoma expansion with substantially greater accuracy over existing methods of IVH measurement.