Estimation of extra hospital stay attributable to nosocomial infections

Schulgen, Gabi; Kropec, Andrea; Kappstein, Ines; Daschner, Franz; Schumacher, Martin

doi:10.1016/s0895-4356(99)00182-1

Cited by 81 publications

(79 citation statements)

References 39 publications

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“…Our estimate further reinforces the view that such approaches usually overestimate the impact of an infection in terms of length of stay (Schulgen and Schumacher (1996) report an overestimated value of 12:5), one reason being that an ad-hoc approach cannot adequately discriminate between a long length of stay that eventually leads to an infection and a long stay that is the consequence of an infection.…”

Section: Example: Hospital-acquired Pneumonia and Associated Change Isupporting

confidence: 84%

A Random Time Interval Approach for Analysing the Impact of a Possible Intermediate Event on a Terminal Event

Beyersmann¹

2007

Biometrical J

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We consider the impact of a possible intermediate event on a terminal event in an illness-death model with states 'initial', 'intermediate' and 'terminal'. One aim is to unambiguously describe the occurrence of the intermediate event in terms of the observable data, the problem being that the intermediate event may not occur. We propose to consider a random time interval, whose length is the time spent in the intermediate state. We derive an estimator of the joint distribution of the left and right limit of the random time interval from the Aalen-Johansen estimator of the matrix of transition probabilities and study its asymptotic properties. We apply our approach to hospital infection data. Estimating the distribution of the random time interval will usually be only a first step of an analysis. We illustrate this by analysing change in length of hospital stay following an infection and derive the large sample properties of the respective estimator.

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Section: Example: Hospital-acquired Pneumonia and Associated Change Isupporting

confidence: 84%

A Random Time Interval Approach for Analysing the Impact of a Possible Intermediate Event on a Terminal Event

Beyersmann¹

2007

Biometrical J

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“…In Australia, surveillance for these infections is performed in a number of states and reported using a denominator of per 1000 central venous catheter days. [79,80] …”

Section: Intensive Care Unit Patientsmentioning

confidence: 99%

“…[78] A detailed cost analysis of the stringent approach to MRSA taken in the Netherlands was recently published. [79] The authors analysed the detailed costs of the 'search and destroy' MRSA policy at Utrecht University Hospital over a 10-year period. Hypothetical costs were at least twice as much in the absence of such a policy.…”

Section: Impact Of Methicillin-resistant Staphylococcus Aureus On Thementioning

confidence: 99%

“…Six best-practice interventions, including the ventilator care bundle and central line bundle, were introduced. [79] An estimated 122,000 lives were saved over 18 months at 3100 participating hospitals. The program has since been expanded into the 5 Million Lives Campaign, which includes the ventilator and central line bundles, and aims to protect patients from five million incidents of medical harm between December 2006 and December 2008.…”

Section: Prevention Of Health Care Associated Infections In Intensivementioning

confidence: 99%

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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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“…Moreover, treatment costs are higher in this group of patients 6,7 . Up to 50% of all deaths from nosocomial infections are related to nosocomial pneumonia 8 . The overall mortality from HAP is 20-60% (ref.…”

Section: Introductionmentioning

confidence: 99%

Management of mechanical ventilation in patients with hospital-acquired pneumonia: A retrospective, observational study

Uvizl¹,

Herkel²,

Langová³

et al. 2018

BIOMED PAP

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Estimation of extra hospital stay attributable to nosocomial infections

Cited by 81 publications

References 39 publications

A Random Time Interval Approach for Analysing the Impact of a Possible Intermediate Event on a Terminal Event

A Random Time Interval Approach for Analysing the Impact of a Possible Intermediate Event on a Terminal Event

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

Management of mechanical ventilation in patients with hospital-acquired pneumonia: A retrospective, observational study

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