4,619 research outputs found

    A Novel Correction for the Adjusted Box-Pierce Test — New Risk Factors for Emergency Department Return Visits within 72 hours for Children with Respiratory Conditions — General Pediatric Model for Understanding and Predicting Prolonged Length of Stay

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    This thesis represents the results of three research projects that underline the breadth and depth of my interests. Firstly, I devoted some efforts to the well-known Box-Pierce goodness-of-fit tests for time series models which has been an important research topic over the last few decades. All previously proposed tests are focused on changes of the test statistics. Instead, I adopted a different approach that takes the best performing test and modifying the rejection region. Thus, I developed a semiparametric correction of the Adjusted Box-Pierce test that attains the best I error rates for all sample sizes and lags and outperforms all previous global time series goodness-of-fit approaches. Secondly, I aimed to study and identify novel risk factors significantly associated with 72-hour return visits to emergency departments. I queried data consisting of 185,000 ED visits of patients less than 18 years in the United States using the Cerner® Health Facts Database. A nested mixed-effects logistic regression model to provide statistical inference on associated risk factors was built, and a representative set of machine learning algorithms for our predictive modeling task was selected. New respiratory conditions including acute bronchiolitis, pneumonia, and asthma were identified as risk factors for return visits to ED. Thirdly, I ambitioned to design and implement a comprehensive study to identify new clinical and demographic factors associated with prolonged length of stay (3˘e\u3e two weeks) among pediatric patients (aged 18 years and under) in a number of free-standing pediatric and mixed medical facilities. I implemented a mixed effect model to assess the statistical significance and effect sizes of age, race/ethnicity, number of medications, medical family history, presence of infection agents (fungi, bacteria, virus), cancer diagnoses, and other conditions as well as some clinical variables. A stochastic gradient model was also implemented for prediction. From the mixed-effects model, 11 main effect predictors were found to be significantly and statistically associated with an increase in the odds of prolonged length of stay. The area under the operator characteristic curve (AUROC) for the mixed-effects model was 0.887 (0.885, 0.889) and the extreme gradient boosting model attained an AUROC of 0.931 (0.930, 0.933)

    Predicting asthma control deterioration in children

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    Implementation of prognostic machine learning algorithms in paediatric chronic respiratory conditions: a scoping review.

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    Machine learning (ML) holds great potential for predicting clinical outcomes in heterogeneous chronic respiratory diseases (CRD) affecting children, where timely individualised treatments offer opportunities for health optimisation. This paper identifies rate-limiting steps in ML prediction model development that impair clinical translation and discusses regulatory, clinical and ethical considerations for ML implementation. A scoping review of ML prediction models in paediatric CRDs was undertaken using the PRISMA extension scoping review guidelines. From 1209 results, 25 articles published between 2013 and 2021 were evaluated for features of a good clinical prediction model using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines.Most of the studies were in asthma (80%), with few in cystic fibrosis (12%), bronchiolitis (4%) and childhood wheeze (4%). There were inconsistencies in model reporting and studies were limited by a lack of validation, and absence of equations or code for replication. Clinician involvement during ML model development is essential and diversity, equity and inclusion should be assessed at each step of the ML pipeline to ensure algorithms do not promote or amplify health disparities among marginalised groups. As ML prediction studies become more frequent, it is important that models are rigorously developed using published guidelines and take account of regulatory frameworks which depend on model complexity, patient safety, accountability and liability

    Effects of Cumulative Risk on Asthma Outcomes in Urban Children and Adolescents

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    Pediatric asthma disproportionately affects racial/ethnic minority children and children living in low-income, urban areas. Many families living in low-income, urban areas experience a number of stressors that can place children/adolescents at risk for worse asthma outcomes. This study examined the impact of a cumulative risk model of stressors (e.g., ED visits, quick-relief medication use, lung function, asthma control, QOL) in urban children (7-12 years) with persistent asthma. This study further aimed to examine both the original cumulative risk model and an adolescent-specific cumulative risk model as predictors of asthma outcomes in a sample of 60 adolescents (13-17 years). Asthma-related caregiver support was examined as a potential buffer in the association between stress and asthma outcomes. Secondary data analyses were completed on sixty-one caregiver and child dyads (7-12 years old). Data were collected from a separate sample of 60 urban families of adolescents with asthma (13-17 years old). The two cohorts were also combined for analyses. The original cumulative risk model developed for the younger children (7-12 years) was a predictor of child QOL in the younger cohort, and QOL and asthma control in the adolescent cohort. However, this finding in the younger cohort (7-12 years) was not supported in pooled data analyses. The original cumulative risk model predicted QOL, asthma control, and quick-relief medication use in the combined cohort analyses (children 7-17 years). The adolescent-specific cumulative risk model was a significant predictor of adolescent QOL and asthma control. Asthma-related caregiver support was only a significant moderator of the association between cumulative risk and asthma control among adolescents. Child age did not moderate associations between cumulative risk and asthma outcomes in the combined cohort. Overall, findings suggest that the accumulation of stress can have a negative impact on asthma outcomes, especially for urban adolescents with asthma. Further research is needed to determine the most central sources of stress that urban school-aged children with asthma experience and to replicate findings for adolescent with asthma. The buffering role of asthma-related caregiver support in the association between cumulative stress and asthma outcomes needs to be examined further in children and adolescents with asthma

    2023 Medical Student Research Day Abstracts

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    Medical student research day is designed to highlight the breadth of research and scholarly activity that medical students have accomplished during their education at The GW School of Medicine and Health Sciences. All medical students are invited to present research regardless of the area of focus. Abstract submissions represent a broad range of research interests and disciplines, including basic and translational science, clinical research, health policy and public health research, and education-related research

    Parental perception concerning the use of peak flow meters in the child with asthma

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    A descriptive, correlational research design was used to identify parental perceptions concerning the use of peak flow meters (PFM) in their children. The sample consisted of 20 parents who were accessed through outpatient asthma allergy clinics. The questionnaire and semi-structured interview were utilized as tools and analyzed using inferential statistics and content analysis. The revised Health Promotion Model by Dr. Nola Pender provided the theoretical framework; Content analysis provided a rich narrative as it described the perceived barriers and benefits of PFM use, prior related behavior for use or non-use of the PFM, and the nature of education that parents and children receive regarding the PFM. The barriers that decreased the use of the PFM included innovative asthma medication and increased asthma control. The benefits included guiding medication use and valuable for prediction. Statistical significance was found between severity of asthma and many variables, but there was no significance with the frequency of using the PFM
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