Objectives: Diabetic Retinopathy screening services aim to reduce the risk of sight loss amongst patients with diabetes. The rising incidence of diabetes in England and the operational need to ensure the accuracy and timeliness of screening lists led to a pilot study of electronic extraction of data from primary care. This study aimed to evaluate the effectiveness of updating the single collated list of patients eligible for diabetic eye screening using extracts from electronic patient records in primary care.
Setting And Methods: The Gloucestershire Diabetic Eye Screening Programme (GDESP) provides screening for 85 General Practices in the county. Of these, 54 using Egton Medical Information Systems (EMIS) practice management system software agreed to participate in this study. The screening list held in 2009 by the Gloucestershire DESP of 14,209 patients known to have diabetes was audited against a list created with automatic extraction from General Practice records of patients marked with the diabetes Read Code C10. Those subsequently screened and referred to the Hospital Eye service were followed up.
Results: The Gloucestershire DESP manual list covering the 54 EMIS practices comprised 14,771 people with diabetes. The audit process identified an additional 709 (4.8%) patients coded C10, including 23 diagnosed more than 5 years ago, and 20 patients under the age of 20 who were diagnosed more than a year ago.
Conclusion: Automatic extraction of data from General Practice identified 709 patients coded as having diabetes not previously known to the Gloucestershire DESP.
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http://dx.doi.org/10.1177/0969141313505747 | DOI Listing |
Biosens Bioelectron
December 2024
Biophotonic Nanosensors Laboratory, Centro de Física Aplicada y Tecnología Avanzada (CFATA), Universidad Nacional Autónoma de México (UNAM), Querétaro, 76230, Mexico. Electronic address:
Smartphone-based colorimetric (bio)sensing is a promising alternative to conventional detection equipment for on-site testing, but it is often limited by sensitivity to lighting conditions. These issues are usually avoided using housings with fixed light sources, increasing the cost and complexity of the on-site test, where simplicity, portability, and affordability are a priority. In this study, we demonstrate that careful optimization of color space can significantly boost the performance of smartphone-based colorimetric sensing, enabling housing-free, illumination-invariant detection.
View Article and Find Full Text PDFMicrobiol Spectr
January 2025
State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Considering that the human microbiota plays a critical role in health and disease, an accurate and high-resolution taxonomic classification is thus essential for meaningful microbiome analysis. In this study, we developed an automatic system, named MultiTax pipeline, for generating taxonomy from full-length 16S rRNA sequences using the Genome Taxonomy Database and other existing reference databases. We first constructed the MultiTax-human database, a high-resolution resource specifically designed for human microbiome research and clinical applications.
View Article and Find Full Text PDFDiagn Interv Radiol
January 2025
Huadong Hospital, Fudan University, Department of Thoracic Surgery, Shanghai, China.
Purpose: Patients with advanced non-small cell lung cancer (NSCLC) have varying responses to immunotherapy, but there are no reliable, accepted biomarkers to accurately predict its therapeutic efficacy. The present study aimed to construct individualized models through automatic machine learning (autoML) to predict the efficacy of immunotherapy in patients with inoperable advanced NSCLC.
Methods: A total of 63 eligible participants were included and randomized into training and validation groups.
Front Antibiot
March 2024
Clinica Malattie Infettive, IRCCS Ospedale Policlinico San Martino, Genoa, Italy.
Antimicrobial resistance in bacteria has been associated with significant morbidity and mortality in hospitalized patients. In the era of big data and of the consequent frequent need for large study populations, manual collection of data for research studies on antimicrobial resistance and antibiotic use has become extremely time-consuming and sometimes impossible to be accomplished by overwhelmed healthcare personnel. In this review, we discuss relevant concepts pertaining to the automated extraction of antibiotic resistance and antibiotic prescription data from laboratory information systems and electronic health records to be used in clinical studies, starting from the currently available literature on the topic.
View Article and Find Full Text PDFRadiat Oncol
January 2025
Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
Background And Purpose: Treatment record contains most of information related to treatment plan delivery in radiation therapy. Reviewing treatment record is an important quality assurance (QA) task for safety and quality of patient treatments. This task is usually performed by senior medical physicists.
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