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2.
Clin Infect Dis ; 69(2): 290-294, 2019 07 02.
Artigo em Inglês | MEDLINE | ID: mdl-30321301

RESUMO

BACKGROUND: Preliminary studies that analyzed surrogate markers have suggested that operating room (OR) door openings may be a risk factor for surgical site infection (SSI). We therefore aimed to estimate the effect of OR door openings on SSI risk in patients undergoing cardiac surgery. METHODS: This prospective, observational study involved consecutive patients undergoing cardiac surgery in 2 prespecified ORs equipped with automatic door-counting devices from June 2016 to October 2017. Occurrence of an SSI within 30 days after cardiac surgery was our primary outcome measure. Respective outcome data were obtained from a national SSI surveillance cohort. We analyzed the relationship between mean OR door opening frequencies and SSI risk by use of uni- and multivariable Cox regression models. RESULTS: A total of 301 594 OR door openings were recorded during the study period, with 87 676 eligible door openings being logged between incision and skin closure. There were 688 patients included in the study, of whom 24 (3.5%) developed an SSI within 30 days after surgery. In uni- and multivariable analysis, an increased mean door opening frequency during cardiac surgery was associated with higher risk for consecutive SSI (adjusted hazard ratio per 5-unit increment, 1.49; 95% confidence interval, 1.11-2.00; P = .008). The observed effect was driven by internal OR door openings toward the clean instrument preparation room. CONCLUSIONS: Frequent door openings during cardiac surgery were independently associated with an increased risk for SSI. This finding warrants further study to establish a potentially causal relationship between OR door openings and the occurrence of SSI.


Assuntos
Microbiologia do Ar , Salas Cirúrgicas , Infecção da Ferida Cirúrgica/epidemiologia , Cirurgia Torácica , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Feminino , Humanos , Incidência , Masculino , Pessoa de Meia-Idade , Estudos Prospectivos , Fatores de Risco , Adulto Jovem
3.
Infect Control Hosp Epidemiol ; 39(12): 1457-1462, 2018 12.
Artigo em Inglês | MEDLINE | ID: mdl-30394238

RESUMO

To exploit the full potential of big routine data in healthcare and to efficiently communicate and collaborate with information technology specialists and data analysts, healthcare epidemiologists should have some knowledge of large-scale analysis techniques, particularly about machine learning. This review focuses on the broad area of machine learning and its first applications in the emerging field of digital healthcare epidemiology.


Assuntos
Big Data , Estudos Epidemiológicos , Aprendizado de Máquina , Pesquisa Biomédica/métodos , Registros Eletrônicos de Saúde , Humanos
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