Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 11 de 11
Filtrar
Más filtros











Base de datos
Intervalo de año de publicación
1.
IEEE Access ; 12: 100772-100791, 2024.
Artículo en Inglés | MEDLINE | ID: mdl-39286062

RESUMEN

Antimicrobial resistance (AMR) emerges when disease-causing microorganisms develop the ability to withstand the effects of antimicrobial therapy. This phenomenon is often fueled by the human-to-human transmission of pathogens and the overuse of antibiotics. Over the past 50 years, increased computational power has facilitated the application of Bayesian inference algorithms. In this comprehensive review, the basic theory of Markov Chain Monte Carlo (MCMC) and Sequential Monte Carlo (SMC) methods are explained. These inference algorithms are instrumental in calibrating complex statistical models to the vast amounts of AMR-related data. Popular statistical models include hierarchical and mixture models as well as discrete and stochastic epidemiological compartmental and agent based models. Studies encompassed multi-drug resistance, economic implications of vaccines, and modeling AMR in vitro as well as within specific populations. We describe how combining these topics in a coherent framework can result in an effective antimicrobial stewardship. We also outline recent advancements in the methodology of Bayesian inference algorithms and provide insights into their prospective applicability for modeling AMR in the future.

4.
J Antimicrob Chemother ; 79(9): 2317-2326, 2024 Sep 03.
Artículo en Inglés | MEDLINE | ID: mdl-39051678

RESUMEN

BACKGROUND: Estimates of the prevalence of antimicrobial resistance (AMR) underpin effective antimicrobial stewardship, infection prevention and control, and optimal deployment of antimicrobial agents. Typically, the prevalence of AMR is determined from real-world antimicrobial susceptibility data that are time delimited, sparse, and often biased, potentially resulting in harmful and wasteful decision-making. Frequentist methods are resource intensive because they rely on large datasets. OBJECTIVES: To determine whether a Bayesian approach could present a more reliable and more resource-efficient way to estimate population prevalence of AMR than traditional frequentist methods. METHODS: Retrospectively collected, open-source, real-world pseudonymized healthcare data were used to develop a Bayesian approach for estimating the prevalence of AMR by combination with prior AMR information from a contextualized review of literature. Iterative random sampling and cross-validation were used to assess the predictive accuracy and potential resource efficiency of the Bayesian approach compared with a standard frequentist approach. RESULTS: Bayesian estimation of AMR prevalence made fewer extreme estimation errors than a frequentist estimation approach [n = 74 (6.4%) versus n = 136 (11.8%)] and required fewer observed antimicrobial susceptibility results per pathogen on average [mean = 28.8 (SD = 22.1) versus mean = 34.4 (SD = 30.1)] to avoid any extreme estimation errors in 50 iterations of the cross-validation. The Bayesian approach was maximally effective and efficient for drug-pathogen combinations where the actual prevalence of resistance was not close to 0% or 100%. CONCLUSIONS: Bayesian estimation of the prevalence of AMR could provide a simple, resource-efficient approach to better inform population infection management where uncertainty about AMR prevalence is high.


Asunto(s)
Teorema de Bayes , Farmacorresistencia Bacteriana , Humanos , Prevalencia , Estudios Retrospectivos , Antibacterianos/farmacología , Modelos Teóricos , Pruebas de Sensibilidad Microbiana , Programas de Optimización del Uso de los Antimicrobianos
5.
Microbiol Spectr ; 12(5): e0420923, 2024 May 02.
Artículo en Inglés | MEDLINE | ID: mdl-38517194

RESUMEN

Effective policy to address the global threat of antimicrobial resistance requires robust antimicrobial susceptibility data. Traditional methods for measuring minimum inhibitory concentration (MIC) are resource intensive, subject to human error, and require considerable infrastructure. AIgarMIC streamlines and standardizes MIC measurement and is especially valuable for large-scale surveillance activities. MICs were measured using agar dilution for n = 10 antibiotics against clinical Enterobacterales isolates (n = 1,086) obtained from a large tertiary hospital microbiology laboratory. Escherichia coli (n = 827, 76%) was the most common organism. Photographs of agar plates were divided into smaller images covering one inoculation site. A labeled data set of colony images was created and used to train a convolutional neural network to classify images based on whether a bacterial colony was present (first-step model). If growth was present, a second-step model determined whether colony morphology suggested antimicrobial growth inhibition. The ability of the AI to determine MIC was then compared with standard visual determination. The first-step model classified bacterial growth as present/absent with 94.3% accuracy. The second-step model classified colonies as "inhibited" or "good growth" with 88.6% accuracy. For the determination of MIC, the rate of essential agreement was 98.9% (644/651), with a bias of -7.8%, compared with manual annotation. AIgarMIC uses artificial intelligence to automate endpoint assessments for agar dilution and potentially increases throughput without bespoke equipment. AIgarMIC reduces laboratory barriers to generating high-quality MIC data that can be used for large-scale surveillance programs. IMPORTANCE: This research uses modern artificial intelligence and machine-learning approaches to standardize and automate the interpretation of agar dilution minimum inhibitory concentration testing. Artificial intelligence is currently of significant topical interest to researchers and clinicians. In our manuscript, we demonstrate a use-case in the microbiology laboratory and present validation data for the model's performance against manual interpretation.


Asunto(s)
Agar , Antibacterianos , Aprendizaje Automático , Pruebas de Sensibilidad Microbiana , Pruebas de Sensibilidad Microbiana/métodos , Antibacterianos/farmacología , Humanos , Agar/química , Escherichia coli/efectos de los fármacos , Escherichia coli/crecimiento & desarrollo , Enterobacteriaceae/efectos de los fármacos , Enterobacteriaceae/crecimiento & desarrollo , Redes Neurales de la Computación
6.
Clin Microbiol Rev ; 37(2): e0013923, 2024 Jun 13.
Artículo en Inglés | MEDLINE | ID: mdl-38436564

RESUMEN

SUMMARYThe World Health Organisation's 2022 AWaRe Book provides guidance for the use of 39 antibiotics to treat 35 infections in primary healthcare and hospital facilities. We review the evidence underpinning suggested dosing regimens. Few (n = 18) population pharmacokinetic studies exist for key oral AWaRe antibiotics, largely conducted in homogenous and unrepresentative populations hindering robust estimates of drug exposures. Databases of minimum inhibitory concentration distributions are limited, especially for community pathogen-antibiotic combinations. Minimum inhibitory concentration data sources are not routinely reported and lack regional diversity and community representation. Of studies defining a pharmacodynamic target for ß-lactams (n = 80), 42 (52.5%) differed from traditionally accepted 30%-50% time above minimum inhibitory concentration targets. Heterogeneity in model systems and pharmacodynamic endpoints is common, and models generally use intravenous ß-lactams. One-size-fits-all pharmacodynamic targets are used for regimen planning despite complexity in drug-pathogen-disease combinations. We present solutions to enable the development of global evidence-based antibiotic dosing guidance that provides adequate treatment in the context of the increasing prevalence of antimicrobial resistance and, moreover, minimizes the emergence of resistance.


Asunto(s)
Antibacterianos , Organización Mundial de la Salud , Antibacterianos/farmacocinética , Antibacterianos/administración & dosificación , Antibacterianos/farmacología , Humanos , Pruebas de Sensibilidad Microbiana , Farmacorresistencia Bacteriana , Infecciones Bacterianas/tratamiento farmacológico , Infecciones Bacterianas/microbiología , Medicamentos Esenciales/administración & dosificación , Medicamentos Esenciales/farmacocinética , Salud Global
7.
Lancet Infect Dis ; 24(1): e47-e58, 2024 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-37660712

RESUMEN

Health-care systems, food supply chains, and society in general are threatened by the inexorable rise of antimicrobial resistance. This threat is driven by many factors, one of which is inappropriate antimicrobial treatment. The ability of policy makers and leaders in health care, public health, regulatory agencies, and research and development to deliver frameworks for appropriate, sustainable antimicrobial treatment is hampered by a scarcity of tangible outcome-based measures of the damage it causes. In this Personal View, a mathematically grounded, outcome-based measure of antimicrobial treatment appropriateness, called imprecision, is proposed. We outline a framework for policy makers and health-care leaders to use this metric to deliver more effective antimicrobial stewardship interventions to future patient pathways. This will be achieved using learning antimicrobial systems built on public and practitioner engagement; solid implementation science; advances in artificial intelligence; and changes to regulation, research, and development. The outcomes of this framework would be more ecologically and organisationally sustainable patterns of antimicrobial development, regulation, and prescribing. We discuss practical, ethical, and regulatory considerations involved in the delivery of novel antimicrobial drug development, and policy and patient pathways built on artificial intelligence-augmented measures of antimicrobial treatment imprecision.


Asunto(s)
Antiinfecciosos , Inteligencia Artificial , Humanos , Antiinfecciosos/uso terapéutico , Salud Pública , Instituciones de Salud , Políticas
8.
Lancet Digit Health ; 6(1): e79-e86, 2024 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-38123255

RESUMEN

The proliferation of various forms of artificial intelligence (AI) brings many opportunities to improve health care. AI models can harness complex evolving data, inform and augment human actions, and learn from health outcomes such as morbidity and mortality. The global public health challenge of antimicrobial resistance (AMR) needs large-scale optimisation of antimicrobial use and wider infection care, which could be enabled by carefully constructed AI models. As AI models become increasingly useful and robust, health-care systems remain challenging places for their deployment. An implementation gap exists between the promise of AI models and their use in patient and population care. Here, we outline an adaptive implementation and maintenance framework for AI models to improve antimicrobial use and infection care as a learning system. The roles of AMR problem identification, law and regulation, organisational support, data processing, and AI development, assessment, maintenance, and scalability in the implementation of AMR-targeted AI models are considered.


Asunto(s)
Antibacterianos , Antiinfecciosos , Humanos , Antibacterianos/farmacología , Antibacterianos/uso terapéutico , Inteligencia Artificial , Farmacorresistencia Bacteriana , Instituciones de Salud
11.
Case Rep Psychiatry ; 2013: 360348, 2013.
Artículo en Inglés | MEDLINE | ID: mdl-23956911

RESUMEN

Adults with mental illness are at a higher risk of aspiration pneumonia than the general population. We describe the case of a patient with bipolar affective disorder and two separate episodes of aspiration pneumonia associated with acute mania. We propose that he had multiple predisposing factors, including hyperverbosity, sedative medications, polydipsia (psychogenic and secondary to a comorbidity of diabetes insipidus), and neuroleptic side effects.

SELECCIÓN DE REFERENCIAS
DETALLE DE LA BÚSQUEDA