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Background: Surveillance of healthcare-associated infection (HAI) is the basis of infection prevention programs. However, manual review of medical records is a labor-intensive and time-consuming process. We evaluated the diagnostic performance of automated surveillance of HAI using electronic screening algorithms. Methods: Between April and June 2022, we conducted surveillance of HAI manually and automatically using electronic screening algorithm on 75 units (general medical and surgical wards and ICUs) in a 2,700-bed, tertiary-care hospital in South Korea. Algorithms for surveillance of HAI were developed accordance with NHSN surveillance definitions (Fig. 1). Catheter-associated urinary tract infections (CAUTIs) were automatically detected when eligible pathogen and fever (>38°C) were matched within infection window period. Other specific types of infection were automatically classified based on laboratory results that met NHSN criteria. After the algorithm showed possible cases that met laboratory-confirmed bloodstream infection (LCBI) criteria, we excluded secondary BSIs using the automatic surveillance algorithm. We analyzed sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for the automated surveillance system compared to manual surveillance. Results: An algorithm for detecting CAUTI showed 98.7% sensitivity (78 of 79), 100.0% specificity (2,443 of 2,443), 100.0% PPV (78 of 78), and 100.0% NPV (2,443 of 2,444). For CLABSI, the algorithm had 97.3% sensitivity (214 of 220), 98.3% specificity (5,759 of 5,861), 67.7% PPV (214 of 316), and 99.9% NPV (5,759 of 5,765). In total, 102 cases of possible CLABSI were identified by the algorithm, and 76 (74.5%) were eventually diagnosed as secondary BSIs. Also, by chart review, 20 BSIs (19.6%) were present on arrival in ER (ER-POA). In 4 cases (3.9%), an original pathogen reoccurred in a repeated infection timeframe (RIT), and 2 cases (2%) were mucosal barrier injury-LCBI (MBI-LCBI). When we additionally performed manual surveillance for intra-abdominal infection secondary BSI, ER-POA, and assigning pathogen to original BSI in RIT, PPV increased to 87.7% (214 of 244). Conclusions: Algorithm for automated surveillance of CAUTI had good performance; however, automated surveillance of CLABSI was suboptimal. More elaborate screening algorithm for diagnosis CLABSI is needed, and further studies are needed to determine whether an automated surveillance system can reduce workload for surveillance of HAI.
We quantitatively assessed the fit failure rate of N95 respirators according to the number of donning/doffing and hours worn.
A tertiary-care referral center in South Korea.
In total, 10 infection control practitioners participated in the fit test.
The first experiment comprised 4 consecutive 1-hour donnings and fit tests between each donning. The second experiment comprised 2 consecutive 3-hour donnings and fit tests between each donning. The final experiment comprised fit tests after an 1-hour donning or a 2-hour donning.
For 1-hour donnings, 60%, 70%, and 90% of the participants had fit failures after 2, 3, and 4 consecutive donnings, respectively. For 3-hour donnings, 50% had fit failure after the first donning and 70% had failures after 2 consecutive donnings. All participants passed the fit test after refitting whenever fit failure occurred. The final experiment showed that 50% had fit failure after a single use of 1 hour, and 30% had fit failure after a single use of 2 hours.
High fit-failure rates were recorded after repeated donning and extended use of N95 respirators. Caution is needed for reuse (≥1 time) and extended use (≥1 hour) of N95 respirators in high-risk settings such as those involving aerosol-generating procedures. Although adequate refitting may recover the fit factor, the use of clean gloves and strict hand hygiene afterward should be ensured when touching the outer surfaces of N95 respirators for refitting.
This chapter reports on one aspect of the argument-based validation research conducted to evaluate the interpretation and use of scores from the Oral English Certification Test (OECT). The test of English speaking ability for prospective international teaching assistants (ITAs) was updated by introducing a new a web-based rating system, called Rater-Platform (R-PLAT). R-PLAT was intended to improve the efficiency of the rating process, but research was needed to investigate its effects on all aspects of the interpretation/use argument (Kane, 2013). The study investigated the warrant underlying the evaluation inference: the observed performance on the OECT recorded via R-PLAT provides observed scores and observed performance descriptors reflective of targeted speaking ability. The assumption in need of support was that the quality of rating conditions created by R-PLAT was sufficient for gathering accurate scores. Backing was found through analysis of raters’ perceptions towards and their use of R-PLAT collected through questionnaires and interviews. This chapter concludes with the validity argument showing how evidence collected from this study supported the assumptions underlying the evaluation inference. It suggests future research needed to build the complete validity argument for the OECT with R-PLAT, and potential use of a web-based rating system for other speaking tests.
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