Infection Control and Hospital Epidemiology最新文献

This single center retrospective observational study of serial plasma metagenomic next-generation sequencing testing shows that >95% of serial testing was without meaningful clinical impact. Only 5/173 cases were adjudicated as having significant clinical impact.

Objective: To develop an approach for creating facility-specific urinary antibiograms accounting for the low number of isolates recovered in nursing homes (NHs).

Design: Retrospective analysis of urine culture data collected in NHs in five states.

Setting: Data on 5097 urine culture isolates collected across 59 study NHs from January 1, 2020 to December 31, 2021. Four consulting microbiology laboratories served the study homes.

Methods: We compared a Clinical and Laboratory Standards Institute (CLSI) standard antibiogram model to four weighted-incidence syndromic antibiogram (WISCA) models utilizing alternate formatting rules. Ability to produce a facility-specific antibiogram with at least 30 isolates and the impact on susceptibility predictions were compared.

Results: Only one facility could generate a CLSI standard antibiogram for the three most commonly recovered Gram-negative isolates over a one-year period. Ability to generate an antibiogram increased with each of the four WISCA models trialed (36%, 54%, 85%, 85%) with the most successful models combining all Gram-negative isolates over a two-year period. Shortening the definition of duplicate isolates from 12 to 3 months did not improve performance. Using all Gram-negative isolates, rather than the three most recovered pathogens, resulted in meaningful changes in the predicted activity of ampicillin-sulbactam, cefazolin, ceftriaxone, and trimethoprim-sulfamethoxazole in several study NHs.

Conclusions: These results suggest that WISCAs using 2-years of urinary culture data including all gram-negative isolates and excluding duplicate isolates within twelve months maximizes the number of NHs able to create a valid antibiogram.

Objective: To assess differences in SARS-CoV-2 infection rates between patients receiving hemodialysis in outpatient centers (in-center) and those receiving dialysis in their homes (hemodialysis and peritoneal dialysis) from December 29, 2020, through May 9, 2023.

Design: Retrospective cohort study.

Setting: Outpatient dialysis facilities in the United States reporting to the Centers for Disease Control and Prevention's National Healthcare Safety Network.

Patients: Maintenance dialysis patients that received hemodialysis treatment at or were affiliated with outpatient dialysis facilities.

Methods: SARS-CoV-2 infection rates were assessed by dialysis setting (in-center and home). Weeks were categorized as surge (rate of infection > median) and non-surge (rate of infection ≤ median) and by variant predominance. A negative binomial regression model with generalized estimating equations was constructed to examine differences in rates of infection among patients.

Results: A total of 7,974 dialysis facilities reported 171,338 SARS-CoV-2 infections among patients. In-center hemodialysis patients had higher average rates of SARS-CoV-2 infection at 2.85 infections per 1000 patient-weeks than home patients at 1.69 infections per 1000 patient-weeks. During surge weeks, the differences in rates of infection between in-center and home patients were more pronounced than during non-surge weeks for all variant predominance categories: Delta (relative rate ratio (RRR) = 1.20, CI: 1.09-1.32), B.1 and Other (RRR = 1.11, CI: 1.02-1.22), and Omicron (RRR = 1.07, CI: 1.01-1.12).

Conclusion: Rates of SARS-CoV-2 infection among patients receiving outpatient hemodialysis were persistently higher than rates among patients receiving dialysis treatments at home; these differences were more pronounced during surge weeks.

Background: A semi-automated surveillance system for surgical site infections (SSIs), SPICMI (Surveillance and Prevention Program for Infectious Risk in Surgery and Interventional Medicine), has been implemented in French hospitals, leveraging data from electronic health records (EHRs).

Objective: To evaluate the performance of the SPICMI algorithm in detecting SSIs in orthopedic and digestive surgery.

Setting: Surveillance data were collected annually from the EHRs. The algorithm identified suspected SSIs based on two criteria: (1) surgical revision during the index stay or readmission, (2) positive microbiological samples from the wound. Suspected SSIs identified were subsequently validated by surgeons.

Methods: A stochastic modeling approach was used to estimate probability intervals for performance indicators. Various detection scenarios were constructed based on SPICMI criteria. Logistic regression analysis was performed using surveillance data. Data unavailable in the database were estimated through a literature review and expert opinions.

Results: The probability of surgical revision following an SSI varied significantly between surgical specialties, ranging from 92% in orthopedic surgery to 45.2% in gynecology. In orthopedic and digestive surgery, the SPICMI algorithm demonstrated good reliability for detecting SSIs in minimizing false-negative and false-positive cases (Youden index: 0.96 and 0.79, respectively). Sensitivity (Se) was lower in digestive surgery (0.7-0.9) compared to orthopedic surgery (0.9-1), while specificity (Sp) remained high (0.9-1) in both specialties.

Conclusion: The SPICMI algorithm shows potential to support efficient use of time and resources in SSIs surveillance management. Further evaluation is needed with a broader panel of surgery procedures.

Objective: Estimate bacterial pathogen contamination of healthcare workers' (HCW) long-sleeved attire.

Design: Prospective observational study.

Setting: Tertiary care hospital.

Participants: HCWs wearing long-sleeved attire providing direct inpatient care.

Intervention: Sampling of both sleeves of HCWs wearing long-sleeved attire was performed using a swab and cultured for aerobic bacterial growth classified as potential pathogens or presumptive skin commensals. Potential predictors of sleeve contamination, including participant survey responses related to attire and infection prevention practices, were analyzed using univariate analyses. Whole genome sequencing compared isolates to a genomic surveillance database of patient clinical isolates.

Results: Among 280 samples, 81.1% (n = 227) demonstrated any bacterial growth and 20.7% (n = 58) grew ≥1 potential pathogen. Speciated organisms included alpha-hemolytic streptococci (n = 28), Bacillus sp. (n = 20), and Pantoea/Mixta sp. (n = 8), gram-negative bacilli (n = 6), and Staphylococcus aureus (n = 2). Univariate analysis demonstrated that sleeves sampled on non-intensive care units (P = .038) were significantly associated with any bacterial growth, and attire type (P = .002) and sleeve material (P = .004) were associated with growth of ≥1 potential pathogen. Fleece attire and material were more likely to be contaminated than other attire and material types. Sequenced isolates from sleeve samples were not genetically related to any patient isolates.

Conclusions: HCW long sleeve contamination occurs frequently, including with potential pathogens. Changing trends in attire type may have an impact on bacterial transmissibility. While this study could not infer transmission events associated with clinically diagnosed patient infections, the potential benefit of a "bare below the elbows" attire policy warrants further investigation.

Objective: To identify host and clinical risk factors contributing to the development of Clostridioides difficile infection (CDI) among colonized patients.

Design: Retrospective, matched case-control study.

Setting: Duke University Health System, including 3 hospitals and affiliated outpatient clinics.

Participants: Adult patients who underwent ≥2 two-step C. difficile tests (nucleic acid amplification test (NAAT) followed by toxin enzyme immunoassay) between 03/15/2020-12/31/2023. Cases were patients with C. difficile colonization (NAAT+/toxin-) who converted to CDI (NAAT+/toxin+) within 90 days; controls were colonized patients who remained toxin-negative. Cases were matched to controls by date of index testing (±1 year).

Methods: Data collection encompassed a 90-day "pre-exposure" period preceding index testing and a ≤ 90-day "exposure" period between index and repeat testing. Antibiotic use was stratified by risk for each period. Multivariate conditional logistic regression with forward selection was used to identify predictors of progression.

Results: Among 2,212 colonized patients, 71 cases and 133 matched controls were identified. Several host and clinical characteristics were independently associated with progression to CDI in our multivariate model. Notably, high-risk antibiotic use across the pre-exposure and exposure periods was associated with greater odds of progression to CDI compared to other patterns of antibiotic use (adjusted odds ratio 2.70; P = .03).

Conclusions: Sustained exposure to high-risk antibiotics was a strong predictor of the progression from C. difficile colonization to infection, underscoring the need for further research on longitudinal stewardship strategies for CDI prevention, particularly among patients previously identified as colonized.

Objective: The purpose of the study is to analyze bloodstream infection (BSI) data reported by outpatient hemodialysis facilities to understand temporal trends, the potential impact of infection prevention practices and the COVID-19 pandemic on BSI rates.

Methods: Outpatient hemodialysis facilities report BSI data to the National Healthcare Safety Network. We used interrupted time series with mixed effects negative binomial modeling to estimate the annual change of BSI rates from 2012 to 2021, using March 2020 as the COVID-19 inflection point. The model controlled for seasonal factors, vascular access types, and facility characteristics.

Results: The number of facilities used for analysis increased from 5,581 in 2012 to 7,313 in 2021. Most facilities were freestanding (range: 90%-93%) and belonged to for-profit organizations (range: 85%-88%). The annual adjusted BSI rates decreased by an average of 8.90% (95% CI: -9.10 %, -8.71%) January 2012-February 2020. The annual decrease in BSI rate was not significant during March 2020-December 2021 (P = 0.15). There was a level drop of 32.03% (95%CI: -33.84%, -30.17%) in BSI rates in the period of March 2020-December 2021 compared with the period of January 2012-February 2020.

Conclusions: BSI rates decreased steadily from January 2012 to February 2020 likely due to the identification and adoption of evidence-based prevention practices. BSI rates plateaued at lower levels during March 2020-December 2021. This suggests that infection prevention measures implemented by facilities prior to the emergence of COVID-19 contributed to substantial decreases in BSI rates and may have helped to stabilize BSI rates after March 2020.