HHS action plan to prevent healthcare-associated infections

"The Department of Health and Human Services (HHS) "Action Plan to Prevent Healthcare-Associated Infections" represents a culmination of several months of research, deliberation, and public comment to identify the key actions needed to achieve and sustain progress in protecting patients from the transmission of serious, and in some cases, deadly infections. In response to the increasing threat of HAIs and national and international concern, the Department has composed a Steering Committee of senior-level representatives from the Offices and Operating Divisions of HHS and conducted a number of in-person meetings and conferences with Federal experts. The Department's Action Plan toward the prevention and elimination of HAIs includes goals toward which the healthcare and public health communities have been moving over the past several years." p 1-2Executive summary -- Introduction -- Prevention: metrics and targets -- Prevention: prioritized recommendations -- Research -- Information systems and technology -- Incentives and oversight -- Outreach and messaging -- Coordination, evaluation, and conclusion -- AppendicesAgency for Healthcare Research and Quality, Office of the Assistant Secretary for Public Affairs, Office of the Assistant Secretary for Planning and Evaluation, Centers for Disease Control and Prevention, Centers for Medicare & Medicaid Services, Food and Drug Administration, National Institutes of Health, Office of the National Coordinator for Health Information Technology, Office of Public Health and Science."06222009."Title from title screen (viewed on March 17, 2011)

Emerging infectious diseases

Emerging Infectious Diseases is providing access to these abstracts on behalf of the ICEID 2008 program committee, which performed peer review. Emerging Infectious Diseases has not edited or proofread these materials and is not responsible for inaccuracies or omissions. All information is subject to change.Comments and corrections should be brought to the attention of the authors.Slide Sessions -- Foodborne & waterborne diseases I -- Influenza I -- Surveillance: International -- Zoonotic & animal diseases I -- Methicillin-resistant stapylococcal infections -- Vectorborne diseases -- Foodborne & waterborne diseases II -- Influenza II -- Surveillance: Domestic -- Zoonotic & animal diseases II -- Noscomial infections -- Respiratory diseases -- Health communications -- Blood, organ, & tissue safety -- Tropical diseases -- New rapid diagnostics -- Mobile populations & infectious diseases -- Vaccine-preventable diseases -- Tuberculosis -- Sexually transmitted diseases -- -- Poster Abstracts -- Vaccines & vaccine-preventable diseases -- Antimicrobial resistance -- Climate changes -- Foodborne & waterborne infections -- Health communication -- Infectious causes of chronic diseases -- Influenza -- New or rapid diagnostics -- Nosocomial infections -- Outbreak investigation: Lab & epi response -- Sexually transmitted diseases -- Surveillance: International & new strategies -- Travelers' health & disease importation -- Tropical infections & parasitic diseases -- Vector-borne diseases -- Women, gender, sexual minorities & infectious diseases -- Zoonotic & animal diseases -- Vaccines & vaccine-preventable diseases -- Antimicrobial resistance -- Emerging aspects of HIV -- Foodborne & waterborne infections -- Health communication -- Molecular epidemiology -- Outbreak investigation: Lab & epi response -- Poverty & infectious diseases -- Surveillance: International & new strategies -- Tropical infections & parasitic diseases -- Vector-borne diseases -- Zoonotic & animal diseases -- Vaccines & vaccine-preventable diseases -- Antimicrobial resistance -- Blood, organ, & other tissue safety -- Foodborne & waterborne infections -- Host & microbial genetics -- Influenza -- Molecular epidemiology -- New or rapid diagnostics -- Outbreak investigation: Lab & epi response -- Prevention effectiveness, cost effectiveness, & cost studies -- Surveillance: International & new strategies -- Vector-borne diseases -- Zoonotic & animal diseases -- Vaccines & vaccine-preventable diseases -- Antimicrobial resistance -- Bioterrorism preparedness -- Emerging opportunistic infections -- Foodborne & waterborne infections -- Healthcare worker safety -- Influenza -- Laboratory proficiency testing/quality assurance -- Modeling -- Nosocomial infections -- Outbreak investigation: Lab & epi response -- Vector-borne diseases -- Viral hepatitis -- Zoonotic & animal diseases -- Vaccines & vaccine-preventable diseases -- Antimicrobial resistance -- Emerging opportunistic infections -- Foodborne & waterborne infections -- Influenza -- New or rapid diagnostics -- Nosocomial infections -- Outbreak investigation: Lab & epi response -- Social determinants of infectious disease disparities -- Surveillance: International & new strategies -- Tuberculosis -- Vector-borne diseases -- Zoonotic & animal diseases -- -- Additional Poster Abstracts.Abstracts published in advance of the conference

Surveillance of antimicrobial susceptibility patterns among pathogens isolated in public sector hospitals associated with academic institutions in South Africa

Background: Antimicrobial resistance (AMR) is a global public health challenge since infection with resistant organisms may cause death, can spread across the community, and increase health care costs at individual, community and government level as more expensive antimicrobials will have to be made available for the treatment of infections caused by resistant bacteria. This calls for urgent and consolidated efforts in order to effectively curb this growing crisis, to prevent the world from slipping back to the pre-antibiotic era. The World Health Organization made a call in 2011 advocating for strengthening of surveillance and laboratory capacity as one-way of detecting and monitoring trends and patterns of emerging AMR. Knowledge of AMR guides clinical decisions regarding choice of antimicrobial therapy, during an episode of bacteraemia and forms the basis of key strategies in containing the spread of resistant bacteria. The current study focused on Staphylococcus aureus (SA), Klebsiella pneumoniae (KP), and Pseudomonas aeruginosa (PA), as they are common hospital acquired infections which are prone to developing resistance to multiple antibiotics. Aim: The aim of this project was to assess and utilize the laboratory information system (LIS) at the National Health Laboratory Services (NHLS), as a tool for reporting AMR and monitoring resistance patterns and trends over time of clinical isolates of SA, KP and PA, cultured from the blood of patients admitted to seven tertiary public hospitals in three provinces in South Africa. Methods: A retrospective and prospective analysis was done on isolates of SA, KP, PA from blood specimens collected from patients with bacteraemia and submitted to diagnostic microbiology laboratories of the NHLS at seven tertiary public hospitals in three provinces in South Africa. These hospitals comprised the Charlotte Maxeke Johannesburg Academic Hospital (CMJAH), Chris Hani Baragwanath Hospital (CBH), Helen Joseph Hospital (HJH), Steve Biko Pretoria Academic Hospital (SBPAH), Groote Schuur Hospital (GSH), Tygerberg Hospital (TH) and the Universitas Hospital of the Free State (UH). For retrospective analysis, data submitted during the period July 2005 to December 2009 were used and for prospective analysis, data relating to AMR in SA, KP, PA, collected by the Group for Enteric, Respiratory and Meningeal disease Surveillance in South Africa, (GERMS-SA) from July 2010 to June 2011 were used. AMR in these three pathogens to commonly used antimicrobial drugs was systematically investigated. Multivariate logistic regressions models were used to assess factors associated with AMR. In addition, a systematic review of research done to date on AMR in bacterial pathogens commonly associated with hospital-acquired infections was conducted in order to understand the existing antimicrobial surveillance systems and baseline resistance patterns in South Africa. Results: A total of 9969 isolates were reported from the retrospective dataset. These were 3942 (39.5%) SA, 4466 (44.8%) KP and 1561 (15.7%) PA. From the prospective dataset, a total of 3026 isolates were reported, 1494 (49.4%) SA and 1532 (50.6%) KP isolates respectively. The proportion of invasive bacteraemia was higher in the 30% up to as high as 80% were resistant to methicillin-related drugs among~560 invasive SA isolates over the two year period. Methicillin resistant Staphylococcus aureus (MRSA) rates significantly differed between hospitals (p=<0.001). The proportion of MRSA isolates in relation to methicillin-susceptible strains showed a declining trend from 22.2% in 2005 to 10.5% in 2009 (p=0.042). Emerging resistance was observed for vancomycin: 1 isolate was identified in 2006 and 9 isolates between July 2010-June 2011, and all except 1 were from Gauteng hospitals. The study found increasing rates of carbapenem-resisant KP of 0.4% in 2005 to 4.0% in 2011 for imipenem. The mean rate of extended spectrum beta lactamase (ESBL-KP) producing KP was 74.2%, with the lowest rate of 62.4% in SBPAH and the highest rate of 81.3% in UH, showing a significant geographical variation in rates of resistance (p=0.021). PA showed a tendency for multi-drug resistance with resistance rates of >20% to extended spectrum cephalosporins, fluoroquinolones and aminoglycosides respectively. Emerging resistance in PA isolates was observed to colistin, showing a resistance rate of 1.9% over the 5 years period. In the multivariate model, age <5 years, male gender, and hospital location were factors significantly associated with MRSA, while ESBL-KP was significantly associated with age <5 years and hospital location. Concluding remarks: The study has clearly demonstrated that AMR is relatively common in South Africa among children <5 years. Enhancement of continued surveillance of nosocomial infections through use of routine laboratory data should be reinforced as this will facilitate effective interpretation and mapping of trends and patterns of AMR. Therefore, the LIS as a tool for gathering such data should be strengthened to provide reliable AMR data for improved understanding of the extent of the AMR, and present evidence on which future policies and practices aimed at containing AMR could be based. Key words: Laboratory information system, Trends, Patterns, Antimicrobial resistance, Bacterial pathogens, Nosocomial infections, Surveillance, Bacteraemia, Blood culture

Separator fluid volume requirements in multi-infusion settings

INTRODUCTION. Intravenous (IV) therapy is a widely used method for the administration of medication in hospitals worldwide. ICU and surgical patients in particular often require multiple IV catheters due to incompatibility of certain drugs and the high complexity of medical therapy. This increases discomfort by painful invasive procedures, the risk of infections and costs of medication and disposable considerably. When different drugs are administered through the same lumen, it is common ICU practice to flush with a neutral fluid between the administration of two incompatible drugs in order to optimally use infusion lumens. An important constraint for delivering multiple incompatible drugs is the volume of separator fluid that is sufficient to safely separate them. OBJECTIVES. In this pilot study we investigated whether the choice of separator fluid, solvent, or administration rate affects the separator volume required in a typical ICU infusion setting. METHODS. A standard ICU IV line (2m, 2ml, 1mm internal diameter) was filled with methylene blue (40 mg/l) solution and flushed using an infusion pump with separator fluid. Independent variables were solvent for methylene blue (NaCl 0.9% vs. glucose 5%), separator fluid (NaCl 0.9% vs. glucose 5%), and administration rate (50, 100, or 200 ml/h). Samples were collected using a fraction collector until <2% of the original drug concentration remained and were analyzed using spectrophotometry. RESULTS. We did not find a significant effect of administration rate on separator fluid volume. However, NaCl/G5% (solvent/separator fluid) required significantly less separator fluid than NaCl/NaCl (3.6 ± 0.1 ml vs. 3.9 ± 0.1 ml, p <0.05). Also, G5%/G5% required significantly less separator fluid than NaCl/NaCl (3.6 ± 0.1 ml vs. 3.9 ± 0.1 ml, p <0.05). The significant decrease in required flushing volume might be due to differences in the viscosity of the solutions. However, mean differences were small and were most likely caused by human interactions with the fluid collection setup. The average required flushing volume is 3.7 ml. CONCLUSIONS. The choice of separator fluid, solvent or administration rate had no impact on the required flushing volume in the experiment. Future research should take IV line length, diameter, volume and also drug solution volumes into account in order to provide a full account of variables affecting the required separator fluid volume

ESICM LIVES 2017 : 30th ESICM Annual Congress. September 23-27, 2017.

INTRODUCTION. Unplanned readmission to intensive care is highly undesirable in that it contributes to increased variance in care, disruption, difficulty in resource allocation and may increase length of stay and mortality particularly if subject to delays. Unlike the ICU admission from the ward, readmission prediction has received relatively little attention, perhaps in part because at the point of ICU discharge, full physiological information is systematically available to the clinician and so it is expected that readmission should be largely due to unpredictable factors. However it may be that there are multidimensional trends that are difficult for the clinician to perceive that may nevertheless be predictive of readmission. OBJECTIVES. We investigated whether machine learning (ML) techniques could be used to improve on the simple published SWIFT score [1] for the prediction of unplanned readmission to ICU within 48 hours. METHODS. We extracted systolic BP, pulse pressure, heart and respiration rate, temperature, SpO2, bilirubin, creatinine, INR, lactate, white cell count, platelet count, pH, FiO2, and total Glasgow Coma Score from ICU stays of over 2000 adult patients from our hospital electronic patient record system. We trained our own custom multidimensional / time-sensitive algorithmic ML system to predict failed discharges defined as either readmission or unexpected death within 48 hours of discharge. We used 10-fold cross validation to assess performance. We also assessed the effect of augmenting our system by transfer learning (TL) with 44,000 additional cases from the MIMIC III database. RESULTS. The SWIFT score performed relatively poorly with an AUROC of around 0.6 which our ML system trained on local data was also able to match. However when augmented with an additional dataset by TL, the AUROC for the ML system improved statistically and clinically significantly to over 0.7. CONCLUSIONS. Machine learning is able to improve on predictors based on simple multiple logistic regression. Thus there is likely to be information in the trends and in combinations of variables. A disadvantage with this technique is that ML approaches require large amounts of data for training. However, ML approaches can be improved by TL. Basing prediction models on locally derived data augmented by TL is a potentially novel approach to generating tools that customised to the institution yet can exploit the potential power of ML algorithms. REFERENCES [1] Gajic O, Malinchoc M, Comfere TB, et al. The Stability and Workload Index for Transfer score predicts unplanned intensive care unit patient readmission: initial development and validation. Crit Care Med. 2008;36(3):676–82. Grant Acknowledgement This work was internally funded

ESICM LIVES 2022: part 2.

peer reviewe

Patient Safety and Quality: An Evidence-Based Handbook for Nurses

Compiles peer-reviewed research and literature reviews on issues regarding patient safety and quality of care, ranging from evidence-based practice, patient-centered care, and nurses' working conditions to critical opportunities and tools for improvement