27 research outputs found

    The value of diastolic function parameters in the prediction of left atrial appendage thrombus in patients with nonvalvular atrial fibrillation

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    BACKGROUND: Left ventricular diastolic impairment and consequently elevated filling pressure may contribute to stasis leading to left atrial appendage thrombus (LAAT) in nonvalvular atrial fibrillation (AF). We investigated whether transthoracic echocardiographic parameters can predict LAAT independent of traditional clinical predictors. METHODS: We conducted a retrospective cohort study of 297 consecutive nonvalvular AF patients who underwent transthoracic echocardiogram followed by a transesophageal echocardiogram within one year. Multivariate logistic regression analysis models were used to determine factors independently associated with LAAT. RESULTS: Nineteen subjects (6.4%) were demonstrated to have LAAT by transesophageal echocardiography. These patients had higher mean CHADS(2) scores [2.6 ± 1.2 vs. 1.9 ± 1.3, P = 0.009], higher E:e’ ratios [16.6 ± 6.1 vs. 12.0 ± 5.4, P = 0.001], and lower mean e’ velocities [6.5 ± 2.1 cm/sec vs. 9.1 ± 3.2 cm/sec, P = 0.001]. Both E:e’ and e’ velocity were associated with LAAT formation independent of the CHADS(2) score, warfarin therapy, left ventricular ejection fraction (LVEF), and left atrial volume index (LAVI) [E:e’ odds-ratio = 1.14 (95% confidence interval = 1.03 – 1.3), P = 0.009; e’ velocity odds-ratio = 0.68 (95% confidence interval = 0.5 – 0.9), P = 0.007]. Similarly, diastolic function parameters were independently associated with spontaneous echo contrast. CONCLUSION: The diastolic function indices E:e’ and e’ velocity are independently associated with LAAT in nonvalvular AF patients and may help identify patients at risk for LAAT

    Developing Interactive Learning for Continuous Glucose Monitoring

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    This research project investigates the development of aninteractive visual learning tool for patients utilizingcontinuous glucose monitoring. A beta product of thisinteractive learning tool was developed to run real-lifescenarios that visualize how meal choices and medicationaffect glucose trajectories in both healthy patients andpatients with type 2 diabetes. This learning tool isintended to be used by the clinician to engage thepatient in an educational discussion about continuousglucose monitoring. The tool may enhance a patient’sunderstanding of graphic glucose trajectories so that theymay discover how their body reacts to different lifestylechoices and learn to make better real-time decisions withtheir own CGM data. The effectiveness of this betaproduct was evaluated by 17 health professionals

    The Association between Self-Reported Energy Intake and Intra-Abdominal Adipose Tissue in Perimenopausal Women

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    We have previously shown that physical activity predicts intra-abdominal adipose tissue (IAT), but it is unknown whether energy intake predicts IAT independently of physical activity in a community-based, naturalistic environment. The association of energy intake with IAT was explored cross-sectionally in women, recruited between 2002 and 2005 for a study of fat patterning in midlife. IAT at L4-L5 vertebral interspace was assessed by computed tomography, energy intake by the Block Food Frequency Questionnaire, and physical activity by the Kaiser Physical Activity Survey. Linear regression models were used for the principal analyses. Among the 257 women, 48% were African American and 52% were Caucasian. Women were 52±3 years old, and 49% were postmenopausal. Every 500 kcal increase in energy intake was associated with a 6% higher IAT (P=0.02), independent of physical activity (P=0.02), after adjustment for ethnicity, menopausal status, age, smoking, income, and DXA-assessed percent body fat. Energy intake had a significant interaction with ethnicity (P=0.02), but not with physical activity. Models using the IAT to subcutaneous abdominal adipose tissue ratio as an outcome had similar associations. In conclusion, self-reported EI was associated with preferential IAT accumulation in midlife women, independent of physical activity. This association was significantly stronger in Caucasian than African American women. Future longitudinal studies are needed to explore lifestyle predictors of IAT accumulation during the menopausal transition

    Identifying the necessary capacities for the adaptation of a diabetes phenotyping algorithm in countries of differing economic development status

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    Background In 2019, the World Health Organization recognised diabetes as a clinically and pathophysiologically heterogeneous set of related diseases. Little is currently known about the diabetes phenotypes in the population of low- and middle-income countries (LMICs), yet identifying their different risks and aetiology has great potential to guide the development of more effective, tailored prevention and treatment. Objectives This study reviewed the scope of diabetes datasets, health information ecosystems, and human resource capacity in four countries to assess whether a diabetes phenotyping algorithm (developed under a companion study) could be successfully applied. Methods The capacity assessment was undertaken with four countries: Trinidad, Malaysia, Kenya, and Rwanda. Diabetes programme staff completed a checklist of available diabetes data variables and then participated in semi-structured interviews about Health Information System (HIS) ecosystem conditions, diabetes programme context, and human resource needs. Descriptive analysis was undertaken. Results Only Malaysia collected the full set of the required diabetes data for the diabetes algorithm, although all countries did collect the required diabetes complication data. An HIS ecosystem existed in all settings, with variations in data hosting and sharing. All countries had access to HIS or ICT support, and epidemiologists or biostatisticians to support dataset preparation and algorithm application. Conclusions Malaysia was found to be most ready to apply the phenotyping algorithm. A fundamental impediment in the other settings was the absence of several core diabetes data variables. Additionally, if countries digitise diabetes data collection and centralise diabetes data hosting, this will simplify dataset preparation for algorithm application. These issues reflect common LMIC health systems’ weaknesses in relation to diabetes care, and specifically highlight the importance of investment in improving diabetes data, which can guide population-tailored prevention and management approaches
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