Brit J Hosp Med
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Aims/Background Gestational diabetes mellitus is a common pregnancy complication that affects approximately 14% of pregnancies worldwide and can lead to adverse maternal and neonatal outcomes. This study aimed to investigate the trajectories of gestational weight gain among gestational diabetes mellitus patients and to inform the development of effective weight management strategies. Methods Demographic and antenatal examination data from 1421 pregnant women diagnosed with gestational diabetes mellitus were retrospectively analysed. ⋯ Patients with gestational diabetes mellitus demonstrated a continuous weight gain throughout pregnancy, while women who were overweight or obese before pregnancy were more likely to follow a low-speed growth trajectory. Women in the rapid growth trajectory group were more inclined to deliver by caesarean section and were more likely to give birth to macrosomic infants. Conclusion Our research underscores the importance of identifying and distinguishing between different gestational weight gain trajectories in pregnant women, thereby identifying high-risk groups, which is crucial for improving the health conditions of both mothers and newborns.
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Adoption of electronic health record systems offers an opportunity to collate massive volumes of complex information about patient care. Healthcare data can inform performance management, enable predictive analytics and enhance strategic decision making. A data-driven approach to improving patient care is vital to address the growing burden of morbidity and mortality associated with major surgery. ⋯ We highlight development of our data-driven vision, technical aspects of processing raw data into metrics relevant to clinical decision making, alongside challenges encountered. Finally, we outline how our data infrastructure supports clinical governance, quality improvement and research. In sharing our experiences, we hope to enable others to embed and access the transformative clinical insights that healthcare data can yield.
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Aims/Background: This investigation sought to establish a possible correlation between thrombin measurement levels and the risk of developing colon adenocarcinoma (COAD). Methods: Thrombin measurement levels were sourced from a study by Pietzner M (2020, PMID: 33328453) and integrated into the IEU database. Data on COAD were obtained from the FinnGen database (2021, C3_COLON_ADENO). ⋯ Results: The IVW analysis indicated a significant inverse association between elevated thrombin levels and the risk of COAD (odds ratio (OR) = 0.76, 95% CI = 0.66-0.88, p = 0.0003). These findings were supported by the weighted median analysis (OR = 0.78, 95% CI = 0.68-0.90, p = 0.0006) and the weighted mode analysis (OR = 0.78, 95% CI = 0.68-0.88, p = 0.0017). Conclusion: This research identified an inverse causal relationship between thrombin measurement levels and the incidence of COAD, suggesting that higher thrombin levels are associated with a reduced risk of developing COAD.
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Aims/Background Reliable health-related quality of life data are critical in developing countries, in order to advocate for government agencies to develop national hemophilia care programmes. This study aims to explore the current status and influencing factors of health-related quality of life among adolescents with hemophilia in Hubei Province, so as to provide empirical data for professionals. Methods A total of 84 children with hemophilia aged 8 to 18, who were registered in Tongji Hemophilia Treatment Center and Hubei Hemophilia Home, were selected using a cluster sampling method. ⋯ The statistically significant influencing factors included residence, annual family income, and disease type. Conclusion This study provides empirical data support for the health management of adolescents with hemophilia, highlighting the importance of improving medical resource access, transfusion convenience, and psychological support in enhancing the quality of life for this group. The results emphasize the need for healthcare systems and policymakers to take specific measures to address these factors to improve the treatment and care conditions for adolescents with hemophilia.
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Aims/Background: The coronavirus disease 2019 (COVID-19) pandemic has highlighted the need for accurate and efficient diagnostic methods. This study aims to improve COVID-19 detection by integrating chest X-ray (CXR) and computerized tomography (CT) images using deep learning techniques, further improving diagnostic accuracy by using a combined imaging approach. Methods: The study used two publicly accessible databases, COVID-19 Questionnaires for Understanding the Exposure (COVID-QU-Ex) and Integrated Clinical and Translational Cancer Foundation (iCTCF), containing CXR and CT images, respectively. ⋯ The EfficientNet-based models, with their superior feature extraction capabilities, show better performance than ResNet models. Grad-CAM Visualizations provide insights into the model's decision-making process, potentially reducing diagnostic errors and accelerating diagnosis processes. This approach can improve patient care and support healthcare systems in managing the pandemic more effectively.