Developing Information Model of Central Line-Associated Bloodstream Infection (CLABSI) Prevention

2021 ◽  
Author(s):  
Young-Shin Park ◽  
Lisiane Pruinelli
Keyword(s):  
Hierarchical Structures ◽  
Clinical Decision ◽  
Information Model ◽  
Central Line ◽  
Data Sets ◽  
Data Value ◽  
Prevention Guidelines ◽  
Advanced Analytics ◽  
Value Sets ◽  
Healthcare Associated

CLABSIs are one of the most lethal and costly types of healthcare associated infections (HAIs). Regulatory organizations have mandated hospitals to submit monthly surveillance reports. However, there is an inaccuracy of presenting this report because of the lack of data standardization. This descriptive qualitative study aimed to develop a CLABSI prevention Information Model (IM) so the CLABSI prevention guidelines can be incorporated into structured nursing documentations. The flowsheet metadata stored in the Clinical Decision Repository was analyzed using an advanced analytics tool. The CLABSI prevention flowsheet data were mapped to 25 concepts, 45 data attributes and over 200 data value sets after organizing hierarchical structures. Seven domains of CLABSI prevention were identified in a CLABSI prevention IM. It would provide tangible benefits to create a practice reminder of the high risk for CLABSIs based on the nursing flowsheet data sets and multidisciplinary Electronic Health Record (EHR).

scholarly journals Pathogens Associated With Repeat Versus Single Central-Line–Associated Bloodstream Infections, Acute-Care Hospitals, NHSN

2020 ◽  
Vol 41 (S1) ◽  
pp. s343-s344
Author(s):  
Margaret A. Dudeck ◽  
Katherine Allen-Bridson ◽  
Jonathan R. Edwards
Keyword(s):  
Acute Care ◽  
National Level ◽  
Bloodstream Infections ◽  
Acute Care Hospitals ◽  
Central Line ◽  
Patient Specific ◽  
Healthcare Associated Infections ◽  
Healthcare Associated ◽  
Repeat Infection ◽  
Single Versus

Background: The NHSN is the nation’s largest surveillance system for healthcare-associated infections. Since 2011, acute-care hospitals (ACHs) have been required to report intensive care unit (ICU) central-line–associated bloodstream infections (CLABSIs) to the NHSN pursuant to CMS requirements. In 2015, this requirement included general medical, surgical, and medical-surgical wards. Also in 2015, the NHSN implemented a repeat infection timeframe (RIT) that required repeat CLABSIs, in the same patient and admission, to be excluded if onset was within 14 days. This analysis is the first at the national level to describe repeat CLABSIs. Methods: Index CLABSIs reported in ACH ICUs and select wards during 2015–2108 were included, in addition to repeat CLABSIs occurring at any location during the same period. CLABSIs were stratified into 2 groups: single and repeat CLABSIs. The repeat CLABSI group included the index CLABSI and subsequent CLABSI(s) reported for the same patient. Up to 5 CLABSIs were included for a single patient. Pathogen analyses were limited to the first pathogen reported for each CLABSI, which is considered to be the most important cause of the event. Likelihood ratio χ2 tests were used to determine differences in proportions. Results: Of the 70,214 CLABSIs reported, 5,983 (8.5%) were repeat CLABSIs. Of 3,264 nonindex CLABSIs, 425 (13%) were identified in non-ICU or non-select ward locations. Staphylococcus aureus was the most common pathogen in both the single and repeat CLABSI groups (14.2% and 12%, respectively) (Fig. 1). Compared to all other pathogens, CLABSIs reported with Candida spp were less likely in a repeat CLABSI event than in a single CLABSI event (P < .0001). Insertion-related organisms were more likely to be associated with single CLABSIs than repeat CLABSIs (P < .0001) (Fig. 2). Alternatively, Enterococcus spp or Klebsiella pneumoniae and K. oxytoca were more likely to be associated with repeat CLABSIs than single CLABSIs (P < .0001). Conclusions: This analysis highlights differences in the aggregate pathogen distributions comparing single versus repeat CLABSIs. Assessing the pathogens associated with repeat CLABSIs may offer another way to assess the success of CLABSI prevention efforts (eg, clean insertion practices). Pathogens such as Enterococcus spp and Klebsiella spp demonstrate a greater association with repeat CLABSIs. Thus, instituting prevention efforts focused on these organisms may warrant greater attention and could impact the likelihood of repeat CLABSIs. Additional analysis of patient-specific pathogens identified in the repeat CLABSI group may yield further clarification.Funding: NoneDisclosures: None

Burden of healthcare-associated infections among hospitalized children within community hospitals participating in an infection control network

2021 ◽  
pp. 1-3
Author(s):  
Ibukunoluwa C. Akinboyo ◽  
Rebecca R. Young ◽  
Michael J. Smith ◽  
Sarah S. Lewis ◽  
Becky A. Smith ◽  
...  
Keyword(s):  
Infection Control ◽  
Bloodstream Infections ◽  
Central Line ◽  
Control Network ◽  
Hospitalized Children ◽  
Healthcare Associated Infections ◽  
Community Hospitals ◽  
Prospective Surveillance ◽  
Healthcare Associated ◽  
Pediatric Healthcare

Abstract We describe the frequency of pediatric healthcare-associated infections (HAIs) identified through prospective surveillance in community hospitals participating in an infection control network. Over a 6-year period, 84 HAIs were identified. Of these 51 (61%) were pediatric central-line–associated bloodstream infections, and they often occurred in children <1 year of age.

The Past, Present, and Future of Healthcare-Associated Infection Prevention in Pediatrics: Catheter-Associated Bloodstream Infections

2010 ◽  
Vol 31 (S1) ◽  
pp. S27-S31 ◽  
Author(s):  
Kristina A. Bryant ◽  
Danielle M. Zerr ◽  
W. Charles Huskins ◽  
Aaron M. Milstone
Keyword(s):  
Infection Prevention ◽  
Bloodstream Infections ◽  
Central Line ◽  
Morbidity And Mortality ◽  
Prevention Strategies ◽  
Healthcare Associated Infection ◽  
Research Topics ◽  
The Past ◽  
Healthcare Associated ◽  
Current Practices

Central line–associated bloodstream infections cause morbidity and mortality in children. We explore the evidence for prevention of central line–associated bloodstream infections in children, assess current practices, and propose research topics to improve prevention strategies.

scholarly journals Desiderata for computable representations of electronic health records-driven phenotype algorithms

2015 ◽  
Vol 22 (6) ◽  
pp. 1220-1230 ◽  
Author(s):  
Huan Mo ◽  
William K Thompson ◽  
Luke V Rasmussen ◽  
Jennifer A Pacheco ◽  
Guoqian Jiang ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Language Processing ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Relational Algebra ◽  
Common Data Model ◽  
Health Records ◽  
Electronic Health ◽  
Value Sets ◽  
Text Searching

Abstract Background Electronic health records (EHRs) are increasingly used for clinical and translational research through the creation of phenotype algorithms. Currently, phenotype algorithms are most commonly represented as noncomputable descriptive documents and knowledge artifacts that detail the protocols for querying diagnoses, symptoms, procedures, medications, and/or text-driven medical concepts, and are primarily meant for human comprehension. We present desiderata for developing a computable phenotype representation model (PheRM). Methods A team of clinicians and informaticians reviewed common features for multisite phenotype algorithms published in PheKB.org and existing phenotype representation platforms. We also evaluated well-known diagnostic criteria and clinical decision-making guidelines to encompass a broader category of algorithms. Results We propose 10 desired characteristics for a flexible, computable PheRM: (1) structure clinical data into queryable forms; (2) recommend use of a common data model, but also support customization for the variability and availability of EHR data among sites; (3) support both human-readable and computable representations of phenotype algorithms; (4) implement set operations and relational algebra for modeling phenotype algorithms; (5) represent phenotype criteria with structured rules; (6) support defining temporal relations between events; (7) use standardized terminologies and ontologies, and facilitate reuse of value sets; (8) define representations for text searching and natural language processing; (9) provide interfaces for external software algorithms; and (10) maintain backward compatibility. Conclusion A computable PheRM is needed for true phenotype portability and reliability across different EHR products and healthcare systems. These desiderata are a guide to inform the establishment and evolution of EHR phenotype algorithm authoring platforms and languages.

scholarly journals Impact of Healthcare-Associated Infections Connected to Medical Devices—An Update

2021 ◽  
Vol 9 (11) ◽  
pp. 2332
Author(s):  
Nitin Chandra Teja Dadi ◽  
Barbora Radochová ◽  
Jarmila Vargová ◽  
Helena Bujdáková
Keyword(s):  
Medical Devices ◽  
Surgical Site Infections ◽  
Central Line ◽  
Ventilator Associated Pneumonia ◽  
Healthcare Associated Infections ◽  
Care Bundles ◽  
Planktonic Form ◽  
Novel Approaches ◽  
Healthcare Associated ◽  
The Impact

Healthcare-associated infections (HAIs) are caused by nosocomial pathogens. HAIs have an immense impact not only on developing countries but also on highly developed parts of world. They are predominantly device-associated infections that are caused by the planktonic form of microorganisms as well as those organized in biofilms. This review elucidates the impact of HAIs, focusing on device-associated infections such as central line-associated bloodstream infection including catheter infection, catheter-associated urinary tract infection, ventilator-associated pneumonia, and surgical site infections. The most relevant microorganisms are mentioned in terms of their frequency of infection on medical devices. Standard care bundles, conventional therapy, and novel approaches against device-associated infections are briefly mentioned as well. This review concisely summarizes relevant and up-to-date information on HAIs and HAI-associated microorganisms and also provides a description of several useful approaches for tackling HAIs.

scholarly journals Data linkage and statistical modelling to provide stratified risk assessment for HAI

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Kim Kavanagh ◽  
Jiafeng Pan ◽  
Chris Robertson ◽  
Marion Bennie ◽  
Charis Marwick ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Decision Making ◽  
Gastric Acid ◽  
Clinical Decision Making ◽  
Care Home ◽  
Clinical Decision ◽  
Elevated Risk ◽  
Defined Daily Dose ◽  
Healthcare Associated Infection ◽  
Healthcare Associated

ABSTRACT ObjectivesThe use of “real-time” data to support individual patient management and outcome assessment requires the development of risk assessment models. This could be delivered through a learning health system by the building robust statistical analysis tools onto the existing linked data held by NHS Scotland’s Infection Intelligence Platform (IIP) and developed within the Scottish Healthcare Associated Infection Prevention Institute (SHAIPI). This project will create prediction models for the risk of acquiring a healthcare associated infection (HAI), and particular outcomes, at the point of GP consultation/ hospital admission which could aid clinical decision making. ApproachWe demonstrate the capability using the HAI Clostridium difficile (CDI) from 2010-2013. Using linked national individual level data on community prescribing, hospitalisations, infections and death records we extracted all cases of CDI and by comparing to matched population-based controls, examined the impact of prior hospital admissions, care home residence, comorbidities, exposure to gastric acid suppressive drugs and antibiotic exposure, defined as both cumulative (total defined daily dose (DDD)) and temporal antimicrobial exposure in the previous 6 months, to the risk of CDI acquisition. Antimicrobial exposure was considered for all drugs and the higher risk broad spectrum antibiotics (4Cs). Associations are assessed using conditional logistic regression. Using cross-validation we assess the ability of the model to accurately predict CDI infection. Risk scores for acquisition of CDI are estimated by combining these predictions with age and gender population incidence. ResultsIn the period 2010-2013 there were 1446 cases of CDI with matched 7964 controls. A significant dose-response relationship for exposure to any antimicrobial (1-7 DDDs OR=2.3 rising to OR=4.4 for 29+ DDDs) and, with elevated risk, to the 4C group (1-7 DDDs OR=3.8 rising to OR=17.9 for 29+ DDDs). Exposure elevates CDI risk most in the month after prescription but for 4C antimicrobials the elevated risk remains 6 months later (4C OR=12.4 within 1 month, OR=2.6 4-6 months later). The risk of CDI was also increased with more co-morbidities, previous hospitalisations, care home residency, increased number of prescriptions, and gastric acid suppression. ConclusionDespite limitations to current application in practice,(paucity of patient level in-hospital prescribing data and constraints of the timeliness of the data), when fully developed this system will enable risk classification to identify patients most at risk of HAI and adverse outcomes to aid clinical decision making.

scholarly journals Current Pattern and Clinico-Bacteriological Profile of Healthcare Associated Infections (HAI) in an ICU Setting: An Observational Study

2021 ◽  
Author(s):  
Mradul Kumar Daga ◽  
Govind Mawari ◽  
Saman Wasi ◽  
Naresh Kumar ◽  
Udbhav Sharma ◽  
...  
Keyword(s):  
Age Groups ◽  
Healthcare Facility ◽  
Central Line ◽  
Multidrug Resistant ◽  
Gram Negative Bacteria ◽  
Healthcare Associated Infections ◽  
Acinetobacter Species ◽  
Gram Negative ◽  
Causative Agents ◽  
Healthcare Associated

Abstract Objective To understand the pattern and types of healthcare associated infections (HAI) at our healthcare facility, and to determine the common causative agents and their antibiotic susceptibility profile. Methods One hundred consecutive patients diagnosed with HAI were enrolled and monitored; the causative organisms isolated on culture were recorded and their sensitivity profile was generated. Results Of the 100 patients diagnosed with HAI (mean age ± SD being 42 ± 17 years), there were a total of 110 hospital acquired infections with 10 patients having two infections each. Out of 100 patients with HAI, 69 patients had ventilator associated pneumonia (VAP), 21 patients had catheter associated urinary tract infection (CAUTI) patients, and 20 patients had central line associated bloodstream infection (CLABSI). There were 10 patients with both VAP and CAUTI. All of the HAIs were device associated. A total of 76 pathogens were isolated on culture. No organism was isolated in 40 HAI. Majority (94.7%) of the organisms isolated from HAIs were gram-negative bacteria and all were multidrug resistant. Seventy-seven of the enrolled patients expired while 23 were discharged from the hospital Conclusions Our study demonstrated that HAIs occur in patients of all age groups; younger patients are not spared. Majority of the HAIs were caused by multidrug resistant gram-negative bacteria and were associated with high patient mortality. Acinetobacter species was the most common organism associated with HAI.

scholarly journals Device associated and surgical site infections, quality indicators in a tertiary care hospital: A 5 year study

2021 ◽  
Vol 9 (3) ◽  
pp. 132-146
Author(s):  
Bilolikar AK ◽  
Banerjee J ◽  
Thomas KM
Keyword(s):  
Quality Indicators ◽  
Tertiary Care ◽  
Surgical Site Infections ◽  
Central Line ◽  
Bench Mark ◽  
Care Hospital ◽  
Statistical Tool ◽  
Hospital Infection Control ◽  
Control And Prevention ◽  
Healthcare Associated

Purpose: In the present study, an attempt is made to understand the pattern of HAIs (Healthcare Associated Infections) [device associated infections such as Catheter Associated Urinary Tract Infection (CAUTI), Ventilator Associated Event (VAE), Central Line-Associated Bloodstream Infection (CLABSI) & Surgical Site Infection (SSI) by analyzing statistical tool of quality indicators] and to establish a bench mark for HAIs in a single hospital for a period of 5 years. Methods: The Microbiologist & ICN’s conduct rounds in ICU’s & wards and collect data for active surveillance. The details of culture positive samples are collected by Microbiologist from the laboratory for passive surveillance. The surveillance forms (active & passive) capture details of individual patients. The data collection forms are prepared and updated as per Centers for Disease Control and Prevention (CDC), National Healthcare Safety Network (NHSN) guidelines. The data is analyzed and presented in the meeting of Hospital Infection Control Committee meeting & discussed with clinicians. Results: The cumulative (5 years) CAUTI rate is 0.45, VAE is 2.42, CLABSI is 1.35 & SSI is 0.21. HAI rates were highest for VAE (2.42/1000 ventilator days), the next was CLABSI (1.35/1000 central line days), followed by CAUTI (0.45/1000 urinary catheter days). SSI rate was 0.21/ 100 surgeries. Conclusions: Before the study was started, the benchmark were 2 for CAUTI, 5.5 for VAE, 3 for CLABSI and 2 for SSI. We could able to reduce the baseline benchmark and established our new benchmark as 1 for CAUTI, 3 for VAE, 2 for CLABSI and 1 for SSI that can be used in developing HAI prevention policies by the institution.

Infection preventionist staffing levels and rates of 10 types of healthcare-associated infections: A 9-year ambidirectional observation

2022 ◽  
pp. 1-6
Author(s):  
Robert J. Clifford ◽  
Donna Newhart ◽  
Maryrose R. Laguio-Vila ◽  
Jennifer L. Gutowski ◽  
Melissa Z. Bronstein ◽  
...  
Keyword(s):  
Control Charts ◽  
Bloodstream Infections ◽  
Central Line ◽  
Healthcare Associated Infections ◽  
Full Time ◽  
Staffing Levels ◽  
Clostridioides Difficile ◽  
Carbapenem Resistant ◽  
Healthcare Associated ◽  
Carbapenem Resistant Enterobacteriaceae

Abstract Objective: To quantitatively evaluate relationships between infection preventionists (IPs) staffing levels, nursing hours, and rates of 10 types of healthcare-associated infections (HAIs). Design and setting: An ambidirectional observation in a 528-bed teaching hospital. Patients: All inpatients from July 1, 2012, to February 1, 2021. Methods: Standardized US National Health Safety Network (NHSN) definitions were used for HAIs. Staffing levels were measured in full-time equivalents (FTE) for IPs and total monthly hours worked for nurses. A time-trend analysis using control charts, t tests, Poisson tests, and regression analysis was performed using Minitab and R computing programs on rates and standardized infection ratios (SIRs) of 10 types of HAIs. An additional analysis was performed on 3 stratifications: critically low (2–3 FTE), below recommended IP levels (4–6 FTE), and at recommended IP levels (7–8 FTE). Results: The observation covered 1.6 million patient days of surveillance. IP staffing levels fluctuated from ≤2 IP FTE (critically low) to 7–8 IP FTE (recommended levels). Periods of highest catheter-associated urinary tract infection SIRs, hospital-onset Clostridioides difficile and carbapenem-resistant Enterobacteriaceae infection rates, along with 4 of 5 types of surgical site SIRs coincided with the periods of lowest IP staffing levels and the absence of certified IPs and a healthcare epidemiologist. Central-line–associated bloodstream infections increased amid lower nursing levels despite the increased presence of an IP and a hospital epidemiologist. Conclusions: Of 10 HAIs, 8 had highest incidences during periods of lowest IP staffing and experience. Some HAI rates varied inversely with levels of IP staffing and experience and others appeared to be more influenced by nursing levels or other confounders.

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