Surveillance for Hospital-Acquired Infections on Surgical Wards in a Dutch University Hospital

Kamp-Hopmans, T. E. M.; Blok, Hetty E. M.; Troelstra, Annet; Gigengack-Baars, A.; Weersink, A. J. L.; Vandenbroucke-Grauls, Christina M. J. E.; Verhoef, J.; Mascini, Ellen M.

doi:10.1086/502258

Cited by 12 publications

(7 citation statements)

References 13 publications

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“…The host is susceptible to increased risk of infection due to immunosuppressive effect of steroids involving release of cytokines and other anti-inflammatory mediators. In our study, we found that systemic steroidal therapy plays an important role in contributing HAIs, and was compatible with other studies [30], [37].…”

Section: Discussionsupporting

confidence: 93%

“…Another German study conveyed by Kamp-Hopmans et al found that the risk factors contributing HAI in surgical wards were: RR of enteral tube feeding over 48 hours was 6.6 (95% CI: 3.2–7.9) followed by ventilation used over 24 hours of 5.0 (95% CI: 3.2–7.9) and used of steroids of 3.4 (95% CI: 2.0–6.0) for respiratory infection; urinary catheter used for UTI was 3.9 (95% CI: 1.7–9.0) [30].…”

Section: Discussionmentioning

confidence: 99%

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Predicting Hospital-Acquired Infections by Scoring System with Simple Parameters

Yeh

et al. 2011

PLoS ONE

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BackgroundHospital-acquired infections (HAI) are associated with increased attributable morbidity, mortality, prolonged hospitalization, and economic costs. A simple, reliable prediction model for HAI has great clinical relevance. The objective of this study is to develop a scoring system to predict HAI that was derived from Logistic Regression (LR) and validated by Artificial Neural Networks (ANN) simultaneously.Methodology/Principal FindingsA total of 476 patients from all the 806 HAI inpatients were included for the study between 2004 and 2005. A sample of 1,376 non-HAI inpatients was randomly drawn from all the admitted patients in the same period of time as the control group. External validation of 2,500 patients was abstracted from another academic teaching center. Sixteen variables were extracted from the Electronic Health Records (EHR) and fed into ANN and LR models. With stepwise selection, the following seven variables were identified by LR models as statistically significant: Foley catheterization, central venous catheterization, arterial line, nasogastric tube, hemodialysis, stress ulcer prophylaxes and systemic glucocorticosteroids. Both ANN and LR models displayed excellent discrimination (area under the receiver operating characteristic curve [AUC]: 0.964 versus 0.969, p = 0.507) to identify infection in internal validation. During external validation, high AUC was obtained from both models (AUC: 0.850 versus 0.870, p = 0.447). The scoring system also performed extremely well in the internal (AUC: 0.965) and external (AUC: 0.871) validations.ConclusionsWe developed a scoring system to predict HAI with simple parameters validated with ANN and LR models. Armed with this scoring system, infectious disease specialists can more efficiently identify patients at high risk for HAI during hospitalization. Further, using parameters either by observation of medical devices used or data obtained from EHR also provided good prediction outcome that can be utilized in different clinical settings.

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Section: Discussionsupporting

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Predicting Hospital-Acquired Infections by Scoring System with Simple Parameters

Yeh

et al. 2011

PLoS ONE

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“…For example, a United States study estimated that S. aureus bacteraemia in this population was associated with a mortality rate of approximately 20%, with a mean additional health-care cost of approximately US$24,000. [83] Risk for infection varies according to the device type, with uncuffed catheters associated with the highest risk and native arteriovenous fistulae with the lowest risk. [84] Australian data support these findings, with rates of device-associated BSI of 0.4 per 1000 patient days for native fistulae, compared to 20.2 per 1000 patient days for non-tunnelled catheters.…”

Section: Patients Requiring Haemodialysismentioning

confidence: 99%

Characterising health care‐associated bloodstream infections in public hospitals in Queensland, 2008–2012

Runnegar

Marquess

et al. 2016

Medical Journal of Australia

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Objective: To describe the epidemiology and rates of all health care‐associated bloodstream infections (HA‐BSIs) and of specific HA‐BSI subsets in public hospitals in Queensland. Design and setting: Standardised HA‐BSI surveillance data were collected in 23 Queensland public hospitals, 2008–2012. Main outcome measures: HA‐BSIs were prospectively classified in terms of place of acquisition (inpatient, non‐inpatient); focus of infection (intravascular catheter‐associated, organ site focus, neutropenic sepsis, or unknown focus); and causative organisms. Inpatient HA‐BSI rates (per 10 000 patient‐days) were calculated. Results: There were 8092 HA‐BSIs and 9418 causative organisms reported. Inpatient HA‐BSIs accounted for 79% of all cases. The focus of infection in 2792 cases (35%) was an organ site, intravascular catheters in 2755 (34%; including 2240 central line catheters), neutropenic sepsis in 1063 (13%), and unknown in 1482 (18%). Five per cent (117 of 2240) of central line‐associated BSIs (CLABSIs) were attributable to intensive care units (ICUs). Eight groups of organisms provided 79% of causative agents: coagulase‐negative staphylococci (18%), Staphylococcus aureus (15%), Escherichia coli (11%), Pseudomonas species (9%), Klebsiella pneumoniae/oxytoca (8%), Enterococcus species (7%), Enterobacter species (6%), and Candida species (5%). The overall inpatient HA‐BSI rate was 6.0 per 10 000 patient‐days. The rates for important BSI subsets included: intravascular catheter‐associated BSIs, 1.9 per 10 000 patient‐days; S. aureus BSIs, 1.0 per 10 000 patient‐days; and methicillin‐resistant S. aureus BSIs, 0.3 per 10 000 patient‐days. Conclusions: The rate of HA‐BSIs in Queensland public hospitals is lower than reported by similar studies elsewhere. About one‐third of HA‐BSIs are attributable to intravascular catheters, predominantly central venous lines, but the vast majority of CLABSIs are contracted outside ICUs. Different sources of HA‐BSIs require different prevention strategies.

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“…UMC Utrecht is a tertiary dardized prospective surveillance for hospital-acquired infec-care hospital with 1,042 beds for adults and children; aption was performed on 4 surgical wards from 1993 through proximately 27,000 admissions per year; 6 intensive care units 1997. 7 Although this survey provided detailed information (ICUs), including 2 pediatric ICUs; and all medical specialties about hospital-acquired infection in surgical patients, we…”

Section: Infect Control Hosp Epidemiol 2007; 28:459-465mentioning

confidence: 99%

Prevalence of Hospital-Acquired Infections During Successive Surveillance Surveys Conducted at a University Hospital in The Netherlands

Hopmans

Blok²,

Troelstra³

et al. 2007

Infect. Control Hosp. Epidemiol.

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Surveillance for Hospital-Acquired Infections on Surgical Wards in a Dutch University Hospital

Cited by 12 publications

References 13 publications

Predicting Hospital-Acquired Infections by Scoring System with Simple Parameters

Predicting Hospital-Acquired Infections by Scoring System with Simple Parameters

Characterising health care‐associated bloodstream infections in public hospitals in Queensland, 2008–2012

Prevalence of Hospital-Acquired Infections During Successive Surveillance Surveys Conducted at a University Hospital in The Netherlands

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