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Surveillance of central-line–associated bloodstream infections requires the labor-intensive counting of central-line days (CLDs). This workload could be reduced by sampling. Our objective was to evaluate the accuracy of various sampling strategies in the estimation of CLDs in intensive care units (ICUs) and to establish a set of rules to identify optimal sampling strategies depending on ICU characteristics.
Analyses of existing data collected according to the European protocol for patient-based surveillance of ICU-acquired infections in Belgium between 2004 and 2012.
SETTING AND PARTICIPANTS
CLD data were reported by 56 ICUs in 39 hospitals during 364 trimesters.
We compared estimated CLD data obtained from weekly and monthly sampling schemes with the observed exhaustive CLD data over the trimester by assessing the CLD percentage error (ie, observed CLDs – estimated CLDs/observed CLDs). We identified predictors of improved accuracy using linear mixed models.
When sampling once per week or 3 times per month, 80% of ICU trimesters had a CLD percentage error within 10%. When sampling twice per week, this was >90% of ICU trimesters. Sampling on Tuesdays provided the best estimations. In the linear mixed model, the observed CLD count was the best predictor for a smaller percentage error. The following sampling strategies provided an estimate within 10% of the actual CLD for 97% of the ICU trimesters with 90% confidence: 3 times per month in an ICU with >650 CLDs per trimester or each Tuesday in an ICU with >480 CLDs per trimester.
Sampling of CLDs provides an acceptable alternative to daily collection of CLD data.
More than 10% of patients admitted to intensive care units (ICUs) experience a severe, healthcare-associated infection, such as ventilator-associated pneumonia (VAP) or bloodstream infection (BSI). What could be a public health target for prevention is hotly debated, because properly adjusting for intrinsic risk factors in the patient population is difficult. We aimed to estimate the proportion of ICU-acquired VAP and BSI cases that are amenable to prevention in routine conditions.
We analyzed routine data collected prospectively according to the European standard protocol for patient-based surveillance of healthcare-acquired infections in ICUs. We computed the number of infections to be expected if, after adjustment for case mix, the infection incidence in ICUs with higher infection rates could be reduced to that of the top-tenth-percentile-ranked ICU. Computations came from model-based simulation of individual patient profiles over time in the ICU. The preventable proportion was computed as the number of observed cases minus the number of expected cases divided by the number of observed cases.
Data for 78,222 patients admitted for more than 2 days to 525 ICUs in 6 European countries from 2005 to 2008 were available for analysis. We calculated that 52% of VAP and 69% of BSI was preventable.
Our pragmatic, if highly conservative, estimates quantify the potential for prevention of VAP and BSI in routine conditions, assuming that variation in infection incidence between ICUs can be eliminated with improved quality of care, apart from variation attributable to differential case mix.
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