6 research outputs found

    Opportunities, barriers, and recommendations in down syndrome research

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    Recent advances in medical care have increased life expectancy and improved the quality of life for people with Down syndrome (DS). These advances are the result of both pre-clinical and clinical research but much about DS is still poorly understood. In 2020, the NIH announced their plan to update their DS research plan and requested input from the scientific and advocacy community. The National Down Syndrome Society (NDSS) and the LuMind IDSC Foundation worked together with scientific and medical experts to develop recommendations for the NIH research plan. NDSS and LuMind IDSC assembled over 50 experts across multiple disciplines and organized them in eleven working groups focused on specific issues for people with DS. This review article summarizes the research gaps and recommendations that have the potential to improve the health and quality of life for people with DS within the next decade. This review highlights many of the scientific gaps that exist in DS research. Based on these gaps, a multidisciplinary group of DS experts has made recommendations to advance DS research. This paper may also aid policymakers and the DS community to build a comprehensive national DS research strategy

    Estimating Weight in Children With Down Syndrome

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    Objective . Significant attention has been paid to weight estimation in settings where scales are impractical or unavailable; however, no studies have evaluated the performance of published weight estimation methods in children with Down syndrome. This study was designed to evaluate the predictive performance of various methods in this population with well-established differences in height and weight for age. Methods . This was a prospective study of children aged 0 to 18 years with Down syndrome. Anthropometric measurements including height, weight, humeral length, and mid-upper arm circumference were collected and applied to 4 distinct weight estimation strategies based on age (APLS), length (Broselow), habitus (Cattermole), and length plus habitus (Mercy). Predictive performance was evaluated by examining residual error (RE), percentage error (PE), root mean square error (RMSE), limits of agreement, and intraclass correlation coefficients. Results . A total of 318 children distributed across age, gender, and body mass index percentile were enrolled. APLS and Mercy showed the smallest degree of bias (PE = 7.8 ± 24.5% and −3.9 ± 12.4%, respectively). Broselow suffered the most extreme underestimation (−63%), whereas the APLS suffered the greatest degree of overestimation (107%). Mercy demonstrated the highest intraclass correlation coefficient (0.987 vs 0.867-0.885) and predicted weight within 20% of actual in the largest proportion of participants (88% vs 40% to 76%). All methods were less robust in children with Down syndrome than reported for unaffected children. Conclusions . Mercy offered the best option for weight estimation in children with Down syndrome. Additional anthropometric data collected in this special population would allow investigators to refine existing weight estimation strategies specifically for these children
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