20 research outputs found
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Sequential regression and simulation: a method for estimating causal effects from heterogeneous clinical trials without a common control group
BackgroundThe advent of clinical trial data sharing platforms has created opportunities for making new discoveries and answering important questions using already collected data. However, existing methods for meta-analyzing these data require the presence of shared control groups across studies, significantly limiting the number of questions that can be confidently addressed. We sought to develop a method for meta-analyzing potentially heterogeneous clinical trials even in the absence of a common control group.MethodsThis work was conducted within the context of a broader effort to study comparative efficacy in Crohn's disease. Following a search of clnicaltrials.gov we obtained access to the individual participant data from nine trials of FDA-approved treatments in Crohn's Disease (N = 3392). We developed a method involving sequences of regression and simulation to separately model the placebo- and drug-attributable effects, and to simulate head-to-head trials against an appropriately normalized background. We validated this method by comparing the outcome of a simulated trial comparing the efficacies of adalimumab and ustekinumab against the recently published results of SEAVUE, an actual head-to-head trial of these drugs. This study was pre-registered on PROSPERO (#157,827) prior to the completion of SEAVUE.ResultsUsing our method of sequential regression and simulation, we compared the week eight outcomes of two virtual cohorts subject to the same patient selection criteria as SEAVUE and treated with adalimumab or ustekinumab. Our primary analysis replicated the corresponding published results from SEAVUE (p = 0.9). This finding proved stable under multiple sensitivity analyses.ConclusionsThis new method may help reduce the bias of individual participant data meta-analyses, expand the scope of what can be learned from these already-collected data, and reduce the costs of obtaining high-quality evidence to guide patient care
Concordance of ompA types in children re-infected with ocular Chlamydia trachomatis following mass azithromycin treatment for trachoma.
BackgroundThe chlamydial major outer membrane protein, encoded by the ompA gene, is a primary target for chlamydial vaccine research. However, human studies of ompA-specific immunity are limited, and prior studies have been limited in differentiating re-infection from persistent infection. The purpose of this study was to assess whether children living in trachoma-endemic communities with re-infections of ocular chlamydia were more likely to be infected with a different or similar genovar.Methodology and findingsThe study included 21 communities from a trachoma-hyperendemic area of Ethiopia that had been treated with a mass azithromycin distribution for trachoma. Conjunctival swabbing was offered to all children younger than 5 years of age at baseline (i.e., pre-treatment), and then at follow-up visits 2 and 6 months later. Swabs were subjected to polymerase chain reaction (PCR) to detect C. trachomatis. A random sample of 359 PCR-positive swabs, stratified by study visit and study community, was chosen for ompA sequencing. In addition, ompA sequencing was performed on all swabs of 24 children who experienced chlamydial re-infection (i.e., positive chlamydial test before treatment, negative test 2 months following mass distribution of azithromycin, and again a positive test 6 months post-treatment). ompA sequencing was successful for 351 of 359 swabs of the random sample and 44 of 48 swabs of the re-infection sample. In the random sample, ompA types clustered within households more than would be expected by chance. Among the 21 re-infected children with complete ompA data, 14 had the same ompA type before and after treatment.ConclusionThe high frequency of ompA concordance suggests incomplete genovar-specific protective immunity and the need for multiple antigens as vaccine targets
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CovidCounties is an interactive real time tracker of the COVID19 pandemic at the level of US counties.
Management of the COVID-19 pandemic has proven to be a significant challenge to policy makers. This is in large part due to uneven reporting and the absence of open-access visualization tools to present local trends and infer healthcare needs. Here we report the development of CovidCounties.org, an interactive web application that depicts daily disease trends at the level of US counties using time series plots and maps. This application is accompanied by a manually curated dataset that catalogs all major public policy actions made at the state-level, as well as technical validation of the primary data. Finally, the underlying code for the site is also provided as open source, enabling others to validate and learn from this work
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CovidCounties is an interactive real time tracker of the COVID19 pandemic at the level of US counties.
Management of the COVID-19 pandemic has proven to be a significant challenge to policy makers. This is in large part due to uneven reporting and the absence of open-access visualization tools to present local trends and infer healthcare needs. Here we report the development of CovidCounties.org, an interactive web application that depicts daily disease trends at the level of US counties using time series plots and maps. This application is accompanied by a manually curated dataset that catalogs all major public policy actions made at the state-level, as well as technical validation of the primary data. Finally, the underlying code for the site is also provided as open source, enabling others to validate and learn from this work
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Smart Healthcare
Internet-of-Things and machine learning promise a new era for healthcare. The emergence of transformative technologies, such as Implantable and Wearable Medical Devices (IWMDs), has enabled collection and analysis of physiological signals from anyone anywhere anytime. Machine learning allows us to unearth patterns in these signals and make healthcare predictions in both daily and clinical situations. This broadens the reach of healthcare from conventional clinical contexts to pervasive everyday scenarios, from passive data collection to active decision-making. Despite the existence of a rich literature on IWMD-based and clinical healthcare systems, the fundamental challenges associated with design and implementation of smart healthcare systems have not been well-addressed. The main objectives of this article are to define a standard framework for smart healthcare aimed at both daily and clinical settings, investigate state-of-the-art smart healthcare systems and their constituent components, discuss various considerations and challenges that should be taken into account while designing smart healthcare systems, explain how existing studies have tackled these design challenges, and finally suggest some avenues for future research based on a set of open issues and challenges
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When the Neighboring Village is Not Treated: Role of Geographic Proximity to Communities Not Receiving Mass Antibiotics for Trachoma.
BackgroundMass administration of azithromycin is an established strategy for decreasing the prevalence of trachoma in endemic areas. However, nearby untreated communities could serve as a reservoir that may increase the chances of chlamydia reinfection in treated communities.MethodsAs part of a cluster-randomized trial in Ethiopia, 60 communities were randomized to receive mass azithromycin distributions and 12 communities were randomized to no treatments until after the first year. Ocular chlamydia was assessed from a random sample of children per community at baseline and month 12. Distances between treated and untreated communities were assessed from global positioning system coordinates collected for the study.ResultsThe pretreatment prevalence of ocular chlamydia among 0 to 9 year olds was 43% (95% confidence interval [CI], 39%-47%), which decreased to 11% (95% CI, 9%-14%) at the 12-month visit. The posttreatment prevalence of chlamydia was significantly higher in communities that were closer to an untreated community after adjusting for baseline prevalence and the number of mass treatments during the year (odds ratio, 1.12 [95% CI, 1.03-1.22] for each 1 km closer to an untreated community).ConclusionsMass azithromycin distributions to wide, contiguous geographic areas may reduce the likelihood of continued ocular chlamydia infection in the setting of mass antibiotic treatments
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Creation of an ustekinumab external control arm for Crohn's disease using electronic health records data: A pilot study.
BackgroundRandomized trials are the gold-standard for clinical evidence generation, but they can sometimes be limited by infeasibility and unclear generalizability to real-world practice. External control arm (ECA) studies may help address this evidence gaps by constructing retrospective cohorts that closely emulate prospective ones. Experience in constructing these outside the context of rare diseases or cancer is limited. We piloted an approach for developing an ECA in Crohn's disease using electronic health records (EHR) data.MethodsWe queried EHR databases and manually screened records at the University of California, San Francisco to identify patients meeting the eligibility criteria of TRIDENT, a recently completed interventional trial involving an ustekinumab reference arm. We defined timepoints to balance missing data and bias. We compared imputation models by their impacts on cohort membership and outcomes. We assessed the accuracy of algorithmic data curation against manual review. Lastly, we assessed disease activity following treatment with ustekinumab.ResultsScreening identified 183 patients. 30% of the cohort had missing baseline data. Nonetheless, cohort membership and outcomes were robust to the method of imputation. Algorithms for ascertaining non-symptom-based elements of disease activity using structured data were accurate against manual review. The cohort consisted of 56 patients, exceeding planned enrollment in TRIDENT. 34% of the cohort was in steroid-free remission at week 24.ConclusionWe piloted an approach for creating an ECA in Crohn's disease from EHR data by using a combination of informatics and manual methods. However, our study reveals significant missing data when standard-of-care clinical data are repurposed. More work will be needed to improve the alignment of trial design with typical patterns of clinical practice, and thereby enable a future of more robust ECAs in chronic diseases like Crohn's disease