Assessment of a selective surveillance method for detecting nosocomial infections in patients in the intensive care department

Systematic review of electronic surveillance of infectious diseases with emphasis on antimicrobial resistance surveillance in resource-limited settings

BACKGROUND

Electronic surveillance of infectious diseases involves rapidly collecting, collating, and analyzing vast amounts of data from interrelated multiple databases. Although many developed countries have invested in electronic surveillance for infectious diseases, the system still presents a challenge for resource-limited health care settings.

METHODS

We conducted a systematic review by performing a comprehensive literature search on MEDLINE (January 2000-December 2015) to identify studies relevant to electronic surveillance of infectious diseases. Study characteristics and results were extracted and systematically reviewed by 3 infectious disease physicians.

RESULTS

A total of 110 studies were included. Most surveillance systems were developed and implemented in high-income countries; less than one-quarter were conducted in low-or middle-income countries. Information technologies can be used to facilitate the process of obtaining laboratory, clinical, and pharmacologic data for the surveillance of infectious diseases, including antimicrobial resistance (AMR) infections. These novel systems require greater resources; however, we found that using electronic surveillance systems could result in shorter times to detect targeted infectious diseases and improvement of data collection.

CONCLUSIONS

This study highlights a lack of resources in areas where an effective, rapid surveillance system is most needed. The availability of information technology for the electronic surveillance of infectious diseases, including AMR infections, will facilitate the prevention and containment of such emerging infectious diseases.

Molecular identification of Acinetobacter baumannii isolated from intensive care units and their antimicrobial resistance patterns

Background:

Acinetobacter baumannii is one of the most important pathogens in hospital-acquired infections especially in intensive care units (ICUs). This opportunistic pathogen can be easily isolated from water, soil, and hospital facilities. A. baumannii as a nosocomial opportunistic pathogen is resistant to a wide range of antibiotics and responsible for multiple infections, including bacteremia, pneumonia, meningitis, urinary tract infections, and surgical wounds. The aim of this study was to determine frequency and resistance patterns of A. baumannii isolated in ICUs of Isfahan Hospitals.

Materials and Methods:

During 1 year period (2012-2013), 350 specimens were collected from ICUs of Isfahan hospitals. The isolates were characterized as A. baumannii by conventional phenotypic, biochemical tests and confirmed by PCR for OXA-51-like gene. Susceptibility of isolates was determined by standard disk diffusion method according to CLSI.

Results:

From total of 350 specimens, 43 isolates were A. baumannii. The antimicrobial patterns of isolates showed that 53.5% of isolates were resistant to amikacin, 83.7% to tetracyclin, 86% to ceftazidime, 90.7% to Trimethoprim sulfametoxazol, 93% to imipenem, cefepime, meropenem, ampicillin–sulbactam. All isolates were resistant to ciprofloxacin.

Conclusion:

This study showed a high resistance of A. baumannii to a wide range of antimicrobial agent. It is necessary to adopt appropriate strategies to control the spread of the bacteria in care unit centers and wards.

Simultaneous placement of multiple central lines increases central line-associated bloodstream infection rates

BACKGROUND

Surveillance for central line (CL)-associated bloodstream infections (CLABSIs) is generally advocated. However, the standard definition of this surveillance does not take into account the number of CLs in place and thus the possibility of increased infection risk with multiple CLs in place simultaneously. In this study, we tested the hypothesis that simultaneous placement of more than 1 CL is associated with an increased CLABSI rate.

METHODS

The number of CLs, CL-days, and CLABSIs and CLABSI rates with regard to the number of CLs in place simultaneously was documented in 2 intensive care units between 2001 and 2011. Standard CLABSI rates, as well as the rates for 1 CL and multiple CLs in place, were calculated.

RESULTS

The average CLABSI rate was significantly lower in patients with 1 CL in place compared with those with more than 1 CL in place (3.69 per 1,000 CL-days vs 13.09/1,000 CL-days; incidence rate ratio [IRR], 3.63; 95% confidence interval [CI], 2.61-5.05). Importantly, all differences from the standard rate (5.94/1,000 CL-days) were significant (1 CL vs standard: IRR, 0.61; 95% CI, 0.51-0.74; more than 1 CL vs standard: IRR, 2.23; 95% CI, 1.87-2.65; both P < .0001).

CONCLUSIONS

Our data show that the number of CLs in place had a strong influence on CLABSI rates. Thus, we advocate stratifying patients by the number of CLs in place to take this increased risk of infection into account during surveillance.

[Comparative study of three selective methods for nosocomial pneumonia surveillance in non-critical adult patients]

BACKGROUND AND OBJECTIVE

Among nosocomial infections, pneumonia is one of the most important ones due to the mortality, morbidity and the increase of costs that it causes. The implementation of a cost-effective surveillance system for these infections is a challenge for hospitals. The objective of this work was to evaluate three selective methods of surveillance for nosocomial pneumonias in non-critical adult patients.

PATIENTS AND METHOD

A prospective surveillance of nosocomial pneumonias was carried out during 4 months in an university hospital of the Valencian Community, by means of three methods based on reports of chest x-ray, consumption of several antibiotics and health-care workers' liaison. Subsequently, possible cases were confirmed through revision of patient's charts, comparing each method with the reference one.

RESULTS

Of 541 possible cases of pneumonia, 27 were confirmed. The cumulative incidence was 3.67 cases/1,000 admissions and the incidence density was 5.9 cases/10,000 patient-days. The method with the highest sensitivity was that dealing with the consumption of antibiotics (85.2%), followed by x-rays (70.4%). The method based on the personnel's liaison had 99.4% specificity, while that of x-rays was 75.5%. Negative predictive values were above 95% for the three methods.

CONCLUSIONS

A surveillance system for nosocomial pneumonia based on reports of chest x-rays may be very efficient, providing the necessary information to program and to evaluate the prevention and control activities for these infections in the hospital.

Computer-assisted surveillance for detecting clonal outbreaks of nosocomial infection

Whole-house surveillance for healthcare-associated infection is no longer the recommended practice because of the large personnel time investment required. We developed a computer-based tracking system using microbiologic data as an aid in detecting potential outbreaks of healthcare-associated infections on a hospital-wide basis. Monthly total isolates of 25 clinically significant hospital pathogens were tallied from 1991 to 1998 to form a database for future comparison. Two different algorithm tools (based on increases of organism numbers over baseline) were applied to determine alert thresholds for suspected outbreaks using this information. The first algorithm (2SD) defined an alert as two standard deviations above the mean monthly number of isolates. The second (MI) defined an alert as either a 100% increase from the baseline organism number over 2 months or a >/=50% increase (compared to baseline) during a three-consecutive-month period. These two methods were compared to standard infection control professional surveillance (ICP) for the detection of clonal outbreaks over 12 months. Overall, a total of seven clonal outbreaks were detected during the 1-year study. Using standard methods, ICP investigated nine suspected outbreaks, four of which were associated with clonal microbes. The 2SD method signaled a suspected outbreak 15 times, of which three were clonal and ICP had detected one. The MI method signaled a suspected outbreak 30 times; four of these were clonal, and ICP had detected one. The sensitivity and specificity values for ICP, 2SD, and MI for detecting clonal outbreaks were 57, 43, and 57% and 17, 83, and 67%, respectively. Statistical methods applied to clinical microbiology laboratory information system data efficiently supplement infection control efforts for outbreak detection.