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Surveillance of surgical site infections (SSIs) is important for infection control and is usually performed through retrospective manual chart review. The aim of this study was to develop an algorithm for the surveillance of deep SSIs based on clinical variables to enhance efficiency of surveillance.
Retrospective cohort study (2012–2015).
A Dutch teaching hospital.
We included all consecutive patients who underwent colorectal surgery excluding those with contaminated wounds at the time of surgery. All patients were evaluated for deep SSIs through manual chart review, using the Centers for Disease Control and Prevention (CDC) criteria as the reference standard.
We used logistic regression modeling to identify predictors that contributed to the estimation of diagnostic probability. Bootstrapping was applied to increase generalizability, followed by assessment of statistical performance and clinical implications.
In total, 1,606 patients were included, of whom 129 (8.0%) acquired a deep SSI. The final model included postoperative length of stay, wound class, readmission, reoperation, and 30-day mortality. The model achieved 68.7% specificity and 98.5% sensitivity and an area under the receiver operator characteristic (ROC) curve (AUC) of 0.950 (95% CI, 0.932–0.969). Positive and negative predictive values were 21.5% and 99.8%, respectively. Applying the algorithm resulted in a 63.4% reduction in the number of records requiring full manual review (from 1,606 to 590).
This 5-parameter model identified 98.5% of patients with a deep SSI. The model can be used to develop semiautomatic surveillance of deep SSIs after colorectal surgery, which may further improve efficiency and quality of SSI surveillance.
Extended-spectrum β-lactamase–producing Enterobacteriaceae (ESBL-E) are emerging worldwide. Contact precautions are recommended for known ESBL-E carriers to control the spread of ESBL-E within hospitals.
This study quantified the acquisition of ESBL-E rectal carriage among patients in Dutch hospitals, given the application of contact precautions.
Data were used from 2 cluster-randomized studies on isolation strategies for ESBL-E: (1) the SoM study, performed in 14 Dutch hospitals from 2011 through 2014 and (2) the R-GNOSIS study, for which data were limited to those collected in a Dutch hospital in 2014. Perianal cultures were obtained, either during ward-based prevalence surveys (SoM), or at admission and twice weekly thereafter (R-GNOSIS). In both studies, contact precautions were applied to all known ESBL-E carriers. Estimates for acquisition of ESBL-E were based on the results of admission and discharge cultures from patients hospitalized for more than 2 days (both studies) and a Markov chain Monte Carlo (MCMC) model, applied to all patients hospitalized (R-GNOSIS).
The absolute risk of acquisition of ESBL-E rectal carriage ranged from 2.4% to 2.9% with an ESBL-E acquisition rate of 2.8 to 3.8 acquisitions per 1,000 patient days. In addition, 28% of acquisitions were attributable to patient-dependent transmission, and the per-admission reproduction number was 0.06.
The low ESBL-E acquisition rate in this study demonstrates that it is possible to control the nosocomial transmission of ESBL in a low-endemic, non-ICU setting where Escherichia coli is the most prevalent ESBL-E and standard and contact precautions are applied for known ESBL-E carriers.
Risk factors for rectal carriage of ESBL-E and transmission were investigated in an outbreak of extended-spectrum β-lactamase–producing Enterobacteriaceae (ESBL-E).
Rectal carriage of ESBL-E was determined in a cross-sectional survey by culture of perianal swabs or fecal samples. Both phenotypical and genotypical methods were used to detect the production of ESBL. Nosocomial transmission was defined as the presence of genotypically related strains in ≥2 residents within the NH. Patient characteristics and variables in infection control practices were registered to investigate risk factors for transmission.
A nursing home (NH) in the southern Netherlands.
Of 189 residents, 160 residents (84.7%) were screened for ESBL-E carriage. Of these 160 residents, 33 (20.6%) were ESBL-E positive. ESBL carriage rates varied substantially between wards (range, 0–47%). Four different ESBL-E clusters were observed. A blaCTX-M1-15 positive E. coli ST131 constituted the largest cluster (n=21) and was found in multiple wards (n=7).
Our investigation revealed extensive clonal dissemination of blaCTX-M1-15-positive E. coli ST131 in a nursing home. Unexplained differences in ESBL prevalence were detected among the wards.
As NHs constitute potential sources of multidrug-resistant bacteria, it is important to gain a better understanding of the risks factors and routes of transmission of ESBL-E.
Infect Control Hosp Epidemiol 2014;00(0): 1–7
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