scholarly journals Web-Based Interactive Tool to Identify Facilities at Risk of Receiving Patients with Multidrug-Resistant Organisms

2020 ◽  
Vol 26 (9) ◽  
pp. 2046-2053
Author(s):  
Rany Octaria ◽  
Allison Chan ◽  
Hannah Wolford ◽  
Rose Devasia ◽  
Troy D. Moon ◽  
...  
Keyword(s):  
At Risk ◽  
Multidrug Resistant ◽  
Web Based ◽  
Interactive Tool ◽  
Multidrug Resistant Organisms

A Web Based Interactive Tool to Enhance Schools' Capacity to Aid at-Risk Students

PEDIATRICS ◽  
2016 ◽  
Vol 137 (Supplement 3) ◽  
pp. 110A-110A
Author(s):  
Eliot E. Goldman ◽  
Cyrille Adam ◽  
Rachel J. Goldman
Keyword(s):  
At Risk ◽  
At Risk Students ◽  
Web Based ◽  
Interactive Tool

scholarly journals Challenges of Applying the SHEA/HICPAC Metrics for Multidrug-Resistant Organisms to a Real-World Setting

2011 ◽  
Vol 32 (4) ◽  
pp. 323-332 ◽  
Author(s):  
E. Yoko Furuya ◽  
Elaine Larson ◽  
Timothy Landers ◽  
Haomiao Jia ◽  
Barbara Ross ◽  
...  
Keyword(s):  
At Risk ◽  
Real World ◽  
Multidrug Resistant ◽  
Prior History ◽  
Real World Setting ◽  
Significant Difference ◽  
Multidrug Resistant Organisms ◽  
History Of ◽  
Mrsa Infection ◽  
Not At Risk

Objective.To test in a real-world setting the recommendations for measuring infection with multidrug-resistant organisms (MDRO) from the Society for Healthcare Epidemiology of America (SHEA) and the Centers for Disease Control and Prevention's Healthcare Infection Control Practices Advisory Committee (HICPAC).Methods.Using data from 3 hospital settings within a healthcare network, we applied the SHEA/HICPAC recommendations to measure methicillin-resistant Staphylococcus aureus (MRSA) infection and colonization. Data were obtained from the hospitals' electronic surveillance system and were supplemented by manual medical record review as necessary. Additionally, we tested (1) different definitions for nosocomial incidence, (2) the effect of excluding patients not at risk from the denominator for hospital-onset incidence, and (3) the appropriate time period to use when including or excluding patients with a prior history of MRSA infection or colonization from nosocomial rates. Negative binomial regression models were used to test for differences between rate definitions. A rating scale was created for each metric, assessing the extent to which manual or electronic data elements were required.Results.There was no statistically significant difference between using 72 hours or 3 calendar days as the cutoff to define hospital-onset incidence. Excluding patients not at risk from the denominator when calculating hospital-onset incidence led to statistically significant increases in rates. When excluding patients with a prior history of MRSA infection or colonization from nosocomial incidence rates, rates were similar regardless of whether we looked at 1, 2, or 3 years' worth of prior data.Conclusions.The SHEA/HICPAC MDRO metrics are useful but can be challenging to implement. We include in our description of the data sources and processes required to calculate these metrics information that may simplify the process for institutions.

scholarly journals Noninvasive screening identifies patients at risk for spontaneous bacterial peritonitis caused by multidrug-resistant organisms

2018 ◽  
Vol Volume 11 ◽  
pp. 2047-2061 ◽  
Author(s):  
Philip G Ferstl ◽  
Mona Müller ◽  
Natalie Filmann ◽  
Michael Hogardt ◽  
Volkhard AJ Kempf ◽  
...  
Keyword(s):  
At Risk ◽  
Spontaneous Bacterial Peritonitis ◽  
Multidrug Resistant ◽  
Bacterial Peritonitis ◽  
Multidrug Resistant Organisms ◽  
Patients At Risk

scholarly journals Optimizing Sentinel Surveillance to Target Containment of Emerging Multidrug-Resistant Organisms in Regional Networks

2020 ◽  
Vol 41 (S1) ◽  
pp. s336-s337
Author(s):  
Prabasaj Paul ◽  
Rachel Slayton ◽  
Alexander Kallen ◽  
Maroya Walters ◽  
John Jernigan
Keyword(s):  
At Risk ◽  
Acute Care ◽  
Deterministic Model ◽  
Healthcare Facility ◽  
Multidrug Resistant ◽  
Sentinel Surveillance ◽  
Outbreak Detection ◽  
Care Hospital ◽  
Healthcare Facilities ◽  
Multidrug Resistant Organisms

Background: Successful containment of regional outbreaks of emerging multidrug-resistant organisms (MDROs) relies on early outbreak detection. However, deploying regional containment is resource intensive; understanding the distribution of different types of outbreaks might aid in further classifying types of responses. Objective: We used a stochastic model of disease transmission in a region where healthcare facilities are linked by patient sharing to explore optimal strategies for early outbreak detection. Methods: We simulated the introduction and spread of Candida auris in a region using a lumped-parameter stochastic adaptation of a previously described deterministic model (Clin Infect Dis 2019 Mar 28. doi:10.1093/cid/ciz248). Stochasticity was incorporated to capture early-stage behavior of outbreaks with greater accuracy than was possible with a deterministic model. The model includes the real patient sharing network among healthcare facilities in an exemplary US state, using hospital claims data and the minimum data set from the CMS for 2015. Disease progression rates for C. auris were estimated from surveillance data and the literature. Each simulated outbreak was initiated with an importation to a Dartmouth Atlas of Health Care hospital referral region. To estimate the potential burden, we quantified the “facility-time” period during which infectious patients presented a risk of subsequent transmission within each healthcare facility. Results: Of the 28,000 simulated outbreaks initiated with an importation to the community, 2,534 resulted in patients entering the healthcare facility network. Among those, 2,480 (98%) initiated a short outbreak that died out or quickly attenuated within 2 years without additional intervention. In the simulations, if containment responses were initiated for each of those short outbreaks, facility time at risk decreased by only 3%. If containment responses were initiated for the 54 (2%) outbreaks lasting 2 years or longer, facility time at risk decreased by 79%. Sentinel surveillance through point-prevalence surveys (PPSs) at the 23 skilled-nursing facilities caring for ventilated patients (vSNF) in the network detected 50 (93%) of the 54 longer outbreaks (median, 235 days to detection). Quarterly PPSs at the 23 largest acute-care hospitals (ie, most discharges) detected 48 longer outbreaks (89%), but the time to detection was longer (median, 716 days to detection). Quarterly PPSs also identified 76 short-term outbreaks (in comparison to only 14 via vSNF PPS) that self-terminated without intervention. Conclusions: A vSNF-based sentinel surveillance system likely provides better information for guiding regional intervention for the containment of emerging MDROs than a similarly sized acute-care hospital–based system.Funding: NoneDisclosures: None

Contact precautions for colonisation with multidrug-resistant organisms and haemodialysis patient quality of life and mood: a pilot case-control study

2019 ◽  
pp. 19-25 ◽  
Author(s):  
Rathika Krishnasamy
Keyword(s):  
Quality Of Life ◽  
Qualitative Interviews ◽  
Social Function ◽  
Multidrug Resistant ◽  
Contact Precautions ◽  
Dialysis Vintage ◽  
Multidrug Resistant Organisms ◽  
And Gender ◽  
The Impact

Background: The rate of multidrug-resistant organisms (MDRO) colonisation in dialysis populations has increased over time. This study aimed to assess the effect of contact precautions and isolation on quality of life and mood for haemodialysis (HD) patients colonised with MDRO. Methods: Patients undergoing facility HD completed the Kidney Disease Quality of Life (KDQOL–SFTM), Beck Depression Inventory (BDI) and Personal Wellbeing-Index Adult (PWI-A). Patients colonised with MDRO were case-matched by age and gender with patients not colonised. Results: A total of 16 MDRO-colonised patients were matched with 16 controls. Groups were well matched for demographics and co-morbidities, other than a trend for older dialysis vintage in the MDRO group [7.2 years (interquartile range 4.6–10.0) compared to 3.2 (1.4–7.6) years, p=0.05]. Comparing MDRO-positive with negative patients, physical (30.5±10.7 vs. 34.6±7.3; p=0.2) and mental (46.5±11.2 vs. 48.5±12.5; p = 0.6) composite scores were not different between groups. The MDRO group reported poorer sleep quality (p=0.01) and sleep patterns (p=0.05), and lower social function (p=0.02). BDI scores were similar (MDRO-positive 10(3.5–21.0) vs. MDRO-negative 12(6.5–16.0), p=0.6). PWI-A scores were also similar in both groups; however, MDRO patients reported lower scores for “feeling safe”, p=0.03. Conclusion: While overall scores of quality of life and depression were similar between groups, the MDRO group reported poorer outcomes in sleep and social function. A larger cohort and qualitative interviews may give more detail of the impact of contact precautions and isolation on HD patients. The necessity for contact precautions for different MDRO needs consideration.

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