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1.
PLoS One ; 13(11): e0206860, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-30403746

RESUMO

BACKGROUND: Reporting of strategic healthcare-associated infections (HCAIs) to Public Health England is mandatory for all acute hospital trusts in England, via a web-based HCAI Data Capture System (HCAI-DCS). AIM: Investigate the feasibility of automating the current, manual, HCAI reporting using linked electronic health records (linked-EHR), and assess its level of accuracy. METHODS: All data previously submitted through the HCAI-DCS by the Oxford University Hospitals infection control (IC) team for methicillin-resistant and methicillin-susceptible Staphylococcus aureus (MRSA, MSSA), Clostridium difficile, and Escherichia coli, through March 2017 were downloaded and compared to outputs created from linked-EHR, with detailed comparisons between 2013-2017. FINDINGS: Total MRSA, MSSA, E. coli and C. difficile cases entered by the IC team vs linked-EHR were 428 vs 432, 795 vs 816, 2454 vs 2450 and 3365 vs 3393 respectively. From 2013-2017, most discrepancies (32/37 (86%)) were likely due to IC recording errors. Patient and specimen identifiers were completed for >98% of cases by both methods, with very high agreement (>97%). Fields relating to the patient at the time the specimen was taken were complete to a similarly high level (>99% IC, >97% linked-EHR), and agreement was fairly good (>80%) except for the main and treatment specialties (57% and 54% respectively) and the patient category (55%). Optional, organism-specific data-fields were less complete, by both methods. Where comparisons were possible, agreement was reasonably high (mostly 70-90%). CONCLUSION: Basic factual information, such as demographic data, is almost-certainly better automated, and many other data fields can potentially be populated successfully from linked-EHR. Manual data collection is time-consuming and inefficient; automated electronic data collection would leave healthcare professionals free to focus on clinical rather than administrative work.


Assuntos
Infecção Hospitalar/epidemiologia , Registros Eletrônicos de Saúde/estatística & dados numéricos , Monitoramento Epidemiológico , Controle de Infecções/métodos , Informática em Saúde Pública/métodos , Conjuntos de Dados como Assunto , Notificação de Doenças/métodos , Notificação de Doenças/estatística & dados numéricos , Inglaterra/epidemiologia , Implementação de Plano de Saúde/organização & administração , Implementação de Plano de Saúde/estatística & dados numéricos , Hospitais Universitários/estatística & dados numéricos , Humanos , Controle de Infecções/organização & administração , Programas Obrigatórios/organização & administração , Programas Obrigatórios/estatística & dados numéricos , Avaliação de Programas e Projetos de Saúde , Administração em Saúde Pública , Informática em Saúde Pública/estatística & dados numéricos , Fatores de Tempo
2.
N Engl J Med ; 379(14): 1322-1331, 2018 10 04.
Artigo em Inglês | MEDLINE | ID: mdl-30281988

RESUMO

BACKGROUND: Candida auris is an emerging and multidrug-resistant pathogen. Here we report the epidemiology of a hospital outbreak of C. auris colonization and infection. METHODS: After identification of a cluster of C. auris infections in the neurosciences intensive care unit (ICU) of the Oxford University Hospitals, United Kingdom, we instituted an intensive patient and environmental screening program and package of interventions. Multivariable logistic regression was used to identify predictors of C. auris colonization and infection. Isolates from patients and from the environment were analyzed by whole-genome sequencing. RESULTS: A total of 70 patients were identified as being colonized or infected with C. auris between February 2, 2015, and August 31, 2017; of these patients, 66 (94%) had been admitted to the neurosciences ICU before diagnosis. Invasive C. auris infections developed in 7 patients. When length of stay in the neurosciences ICU and patient vital signs and laboratory results were controlled for, the predictors of C. auris colonization or infection included the use of reusable skin-surface axillary temperature probes (multivariable odds ratio, 6.80; 95% confidence interval [CI], 2.96 to 15.63; P<0.001) and systemic fluconazole exposure (multivariable odds ratio, 10.34; 95% CI, 1.64 to 65.18; P=0.01). C. auris was rarely detected in the general environment. However, it was detected in isolates from reusable equipment, including multiple axillary skin-surface temperature probes. Despite a bundle of infection-control interventions, the incidence of new cases was reduced only after removal of the temperature probes. All outbreak sequences formed a single genetic cluster within the C. auris South African clade. The sequenced isolates from reusable equipment were genetically related to isolates from the patients. CONCLUSIONS: The transmission of C. auris in this hospital outbreak was found to be linked to reusable axillary temperature probes, indicating that this emerging pathogen can persist in the environment and be transmitted in health care settings. (Funded by the National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at Oxford University and others.).


Assuntos
Candida , Candidíase/epidemiologia , Infecção Hospitalar/epidemiologia , Surtos de Doenças , Contaminação de Equipamentos , Reutilização de Equipamento , Controle de Infecções/métodos , Unidades de Terapia Intensiva , Termômetros/microbiologia , Adulto , Candida/genética , Candida/isolamento & purificação , Candidíase/mortalidade , Candidíase/transmissão , Estudos de Casos e Controles , Infecção Hospitalar/mortalidade , Infecção Hospitalar/transmissão , Feminino , Departamentos Hospitalares , Humanos , Incidência , Masculino , Testes de Sensibilidade Microbiana , Pessoa de Meia-Idade , Análise Multivariada , Neurologia , Filogenia , Fatores de Risco , Reino Unido/epidemiologia
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