Venous thromboembolism (VTE) is a leading cause of preventable in-hospital mortality. Monitoring VTE cases is limited by the challenges of manual chart review and diagnosis code interpretation. Natural language processing (NLP) can automate the process. Rule-based NLP methods are effective but time consuming. Machine learning (ML)-NLP methods present a promising solution. We conducted a systematic review and meta-analysis of studies published before May 2023 that use ML-NLP to identify VTE diagnoses in the electronic health records. Four reviewers screened all manuscripts, excluding studies that only used a rule-based method. A meta-analysis evaluated the pooled performance of each study's best performing model that evaluated for pulmonary embolism (PE) and/or deep vein thrombosis (DVT). Pooled sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) with confidence interval (CI) were calculated by DerSimonian and Laird method using a random-effects model. Study quality was assessed using an adapted TRIPOD tool. Thirteen studies were included in the systematic review and 8 had data available for meta-analysis. Pooled sensitivity was 0.931 (95% CI 0.881-0.962), specificity 0.984 (95% CI 0.967-0.992), PPV 0.910 (95% CI 0.865-0.941) and NPV 0.985 (95% CI 0.977-0.990). All studies met at least 13 of the 21 NLP-modified TRIPOD items, demonstrating fair quality. The highest performing models used vectorization rather than bag-of-words, and deep learning techniques such as convolutional neural networks. There was significant heterogeneity in the studies and only four validated their model on an external dataset. Further standardization of ML studies can help progress this novel technology towards real-world implementation.

As SARS-CoV-2 continues to spread and evolve, detecting emerging variants early is critical for public health interventions. Inferring lineage prevalence by clinical testing is infeasible at scale, especially in areas with limited resources, participation, or testing/sequencing capacity, which can also introduce biases. SARS-CoV-2 RNA concentration in wastewater successfully tracks regional infection dynamics and provides less biased abundance estimates than clinical testing. Tracking virus genomic sequences in wastewater would improve community prevalence estimates and detect emerging variants. However, two factors limit wastewater-based genomic surveillance: low-quality sequence data and inability to estimate relative lineage abundance in mixed samples. Here, we resolve these critical issues to perform a high-resolution, 295-day wastewater and clinical sequencing effort, in the controlled environment of a large university campus and the broader context of the surrounding county. We develop and deploy improved virus concentration protocols and deconvolution software that fully resolve multiple virus strains from wastewater. We detect emerging variants of concern up to 14 days earlier in wastewater samples, and identify multiple instances of virus spread not captured by clinical genomic surveillance. Our study provides a scalable solution for wastewater genomic surveillance that allows early detection of SARS-CoV-2 variants and identification of cryptic transmission.

As SARS-CoV-2 continues to spread and evolve, detecting emerging variants early is critical for public health interventions. Inferring lineage prevalence by clinical testing is infeasible at scale, especially in areas with limited resources, participation, or testing/sequencing capacity, which can also introduce biases(1-3). SARS-CoV-2 RNA concentration in wastewater successfully tracks regional infection dynamics and provides less biased abundance estimates than clinical testing(4,5). Tracking virus genomic sequences in wastewater would improve community prevalence estimates and detect emerging variants. However, two factors limit wastewater-based genomic surveillance: low-quality sequence data and inability to estimate relative lineage abundance in mixed samples. Here, we resolve these critical issues to perform a high-resolution, 295-day wastewater and clinical sequencing effort, in the controlled environment of a large university campus and the broader context of the surrounding county. We develop and deploy improved virus concentration protocols and deconvolution software that fully resolve multiple virus strains from wastewater. We detect emerging variants of concern up to 14 days earlier in wastewater samples, and identify multiple instances of virus spread not captured by clinical genomic surveillance. Our study provides a scalable solution for wastewater genomic surveillance that allows early detection of SARS-CoV-2 variants and identification of cryptic transmission.

BACKGROUND: Youth with Duchenne and Becker muscular dystrophy (DBMD) experience challenges in attaining adult roles, which may impact quality of life. New interventions and treatments may facilitate adult role attainment through improved function. Historical data on adult role attainment is important to assess the impact of new interventions on teens and young adults with DBMD. This study assesses medical knowledge, independence and employment, and relationships among adolescents and young adults with DBMD. METHODS: This study uses data from a 2013 Muscular Dystrophy Surveillance, Tracking, and Research Network (MD STARnet) survey on adult transition. Males with DBMD aged 16-30 years were included. RESULTS: Sixty-five of 258 eligible males participated; we report results on 60 participants with an MD STARnet case definition of DMD or BMD. Individuals with BMD reported higher rates than those with DMD of frequently staying home without supervision (50% BMD; 14% DMD), independently performing daily physical needs (93% BMD; 7% DMD) and being employed full or part time (33% BMD; 4% DMD). Most participants understood medication and physical therapy goals; less than half indicated being often or always responsible for scheduling DMBD-related management and refilling medications. Most had not been in a romantic relationship but reported desiring such relationships. CONCLUSIONS: Our data reinforce the impact of DMD (and to a lesser extent, BMD) on transition to adult roles. These results provide an important historical comparator for teen and adult patients who are trying new interventions and therapies. Such data are important for assessing the quality-of-life impact of new treatments and to inform support and training programs for people with DBMD as they transition to new adult roles and responsibilities.

Previous studies have focused on the comparison of specific laws among multiple countries and regions; for example, laws related to facilitating treatments with orphan drugs or laws seeking to address the multiple needs of patients with rare diseases. The purpose of this scoping review is to examine and compare published reports on national plans, polices and legislation related to all rare diseases in different countries. We also examine strategies or programs that countries may have for these diseases. Articles were obtained from journals and books published between January 1, 2000, through December 15, 2017. Reports from the grey literature (documents issued by government and private organizations) were included if they were available on the internet. The databases used were Google and Google Scholar, PubMed, and the websites of Orphanet and the National Organization for Rare Disorders (NORD). We obtained information on 23 countries. Among these countries, the way in which rare diseases were defined varied from having similar definitions to no definition. Multinational programs supported by common or similar laws are likely to have a greater impact on rare diseases than single country programs.

There is a growing gap between available science and evidence and the ability of service providers to deliver high-quality care in a cost-effective way to the entire population. We believe that the chasm between knowledge and action is due to a lack of concerted effort among all organizations that deliver health care services across the life span of patients. Broad participation is needed and necessitates a far more explicit and concerted public-private partnership focused on large-scale transformation. In this context, the National Heart, Lung, and Blood Institute convened a panel made up of leaders of corporate health care entities, including academic health centers, and government agency representatives to inform contemporary strategic partnerships with health care companies. This article provides insights from the meeting on how to execute a transformative innovation research agenda that will foster improvements in health care service delivery by leveraging the translation of biomedical research evidence in real-world settings.

Typing of Mycoplasma pneumoniae by multiple-locus variable-number tandem repeat analysis (MLVA) is increasingly in use. However, no specific internationally agreed guidance is available. Thirty M. pneumoniae DNA samples including serial dilutions of a type strain were sent to six international laboratories to perform MLVA and results were compared. Good correlation was observed, indicating that this methodology can be robustly performed in multiple sites. However, differences due to interpretation of fragment size, repeat sequence identification and repeat numbering led to inconsistency in the final profiles assigned by laboratories. We propose guidelines for interpreting M. pneumoniae MLVA typing and assigning the number of repeats.