Surveillance Of Healthcare-associated Infections Research Articles

Infection prevention and control programs in Armenian Psychoneurological Hospitals

Aim of the study: to characterize the integration and implementation of the main components of infection prevention and control (IPC), defined by the World Health Organization (WHO), in psychoneurological hospitals in Armenia. Materials and methods. The study was conducted over the period from 2019 to 2022 in all 8 psychoneurological hospitals in Armenia. The research tool was based on using the IPCAF (Infection Prevention and Control Assessment Framework) questionnaire translated into Armenian, consisting of 8 sections. In accordance with the WHO scoring methodology, each possible answer to the question was scored. The maximum total score for each core component could be 100 points, and the final score — 800. Descriptive epidemiology was used during the study. Results. The average final score for all IPCAF components for all neuropsychiatric hospitals in Armenia over the entire study period was increased by 1.46 times (from 345.0±10.7 points in 2019 to 502.5±32.5 in 2022; р 0.01) and in 2022 was characterized as an “intermediate level”. Analysis by individual IPC core components (CC) found that the highest average scores in 2022 were obtained for components CC2 (IPC guidelines) — 81.6 points, CC8 (Built environment, materials and equipment for IPC at the facility level) — 72.8 points, and CC5 (Multimodal strategies) — 70.0 points. The lowest average scores were obtained for the key components: CC4 (Health care-associated infection (HAI) surveillance) — 45.0 points, and for CC3 (IPC education and training) — 56.3 points and CC7 (Workload, staffing and bed occupancy) — 56.9 points. Conclusion. Our study demonstrated for the first time an opportunity for using the IPCAF questionnaire to evaluate IPC programs in neuropsychiatric hospitals and identify gaps in various areas of IPC. Information was received regarding the state of key IPC component implementation in psychoneurological hospitals in Armenia. Analysis by individual core components identified gaps that need to be addressed. It is recommended to implement continuous monitoring of compliance with various aspects of IPC in order to prevent weakening of control and deterioration of epidemiological situation.

Evaluation of the Implementation of WHO Infection Prevention and Control Core Components Among Health Care Facilities

Programs for infection prevention and control comprise a range of action plans, including antiseptic regulations, a healthcare worker training program, and the monitoring of healthcare-associated infections. The purpose of the study is to evaluate the World Health Organization’s implementation of infection prevention and control programs level in Ranya tertiary and healthcare facilities. A cross-sectional study was conducted at Eight healthcare institutions in the Ranya administration in the Kurdistan Region of Iraq from February 2023 to the end of August 2023 using the World Health Organization Infection Prevention and Control Assessment Framework (IPCAF) checklist. The IPCAF consists of eight segments and each part has a score of 100, hence with the greatest score of 800. The respondents in this study were the heads of facilities and employees engaged in IPC-related tasks. The results show that 6 Healthcare staff (75%) had basic IPC levels, and they scored 235, 207.5, 222.5, 330, 362.5, and 347.5 out of 800 points respectively. Further, 2 Healthcare staff (25%) had inadequate IPC levels, and they scored 200 and 180 points. In addition, among the 8 IPC core components, the built environment and materials/equipment at the facility level (C8) had the highest scores, while minimum scores were observed in education, training (C3) and healthcare-associated infection surveillance (C4). In conclusion, the Ranya HCF facilities have certain working IPC aspects without appropriate implementation; further, practically all components need significant improvement.

Comparison of an algorithm, and coding data, with traditional surveillance to identify surgical site infections in Australia: a retrospective multi-centred cohort study

Surveillance of healthcare-associated infections (HAIs) in Australia is disparate, resource intensive, unsustainable, and provides limited information. Traditional HAI surveillance is time intensive and agreement levels between clinicians have been shown to be variable. To compare two methods: a semi-automated algorithm, and coding data, against traditional surgical site infection (SSI) surveillance methods. This retrospective multi-centre cohort study included all patients undergoing a hip (HPRO) or knee (KPRO) prosthesis and coronary artery bypass graft (CABG) surgery during a two-year period at two large metropolitan hospitals. Routine SSI data were obtained via the infection prevention and control (IPC) team, a previously developed algorithm was applied to all patient records, and the ICD-10-AM data were searched for those categorized as having an SSI. Overall, 1447, 1416, and 1026 patients who underwent HPRO, KPRO, and CABG, respectively, were included. The highest sensitivity values were generated by the algorithm: HPRO deep or organ-space (D/O) 0.87 (95% confidence interval: 0.66-0.96), CABG 0.86 (0.64-0.96), and HPRO all SSI 0.77 (0.57-89); the lowest sensitivity was Code CABG D/O 0.03 (0.00-0.21). The highest PPV values were generated by the algorithm: HPRO D/O 0.97 (0.77-0.99), CABG D/O 0.97 (0.76-0.99), and the Code HPRO D/O 0.9 (0.66-0.99). Both the algorithm and coding data resulted in a substantial reduction in the number of medical records required to review. The application of algorithms to enhance SSI surveillance demonstrates high accuracy in identifying patient records that require review by IPC teams to determine the presence of an SSI. Coding data alone should not be used to identify SSIs.

Sepsis of newborns as a problem of epidemiological surveillance of healthcare-associated infections (based on data from health care organizations in Bishkek).

Sepsis of newborns as a problem of epidemiological surveillance of healthcare-associated infections (based on data from health care organizations in Bishkek).

The impact of preoperative biliary drainage on postoperative healthcare-associated infections and clinical outcomes following pancreaticoduodenectomy: a ten-year retrospective analysis

BackgroundPancreaticoduodenectomy (PD) is a complex procedure and easily accompanied by healthcare-associated infections (HAIs). This study aimed to assess the impact of PBD on postoperative infections and clinical outcomes in PD patients.MethodsThe retrospective cohort study were conducted in a tertiary hospital from January 2013 to December 2022. Clinical and epidemiological data were collected from HAIs surveillance system and analyzed.ResultsAmong 2842 patients who underwent PD, 247 (8.7%) were diagnosed with HAIs, with surgical site infection being the most frequent type (n = 177, 71.7%). A total of 369 pathogenic strains were detected, with Klebsiella pneumoniae having the highest proportion, followed by Enterococcu and Escherichia coli. Although no significant association were observed generally between PBD and postoperative HAIs, subgroup analysis revealed that PBD was associated with postoperative HAIs in patients undergoing robotic PD (aRR = 2.174; 95% CI:1.011–4.674; P = 0.047). Prolonging the interval between PBD and PD could reduce postoperative HAIs in patients with cholangiocarcinoma (≥4 week: aRR = 0.292, 95% CI 0.100–0.853; P = 0.024) and robotic PD (≤2 week: aRR = 3.058, 95% CI 1.178–7.940; P = 0.022). PBD was also found to increase transfer of patients to ICU (aRR = 1.351; 95% CI 1.119–1.632; P = 0.002), extended length of stay (P < 0.001) and postoperative length of stay (P = 0.004).ConclusionPBD does not exhibit a significant association with postoperative HAIs or other outcomes. However, the implementation of robotic PD, along with a suitable extension of the interval between PBD and PD, appear to confer advantages concerning patients’ physiological recuperation. These observations suggest potential strategies that may contribute to enhanced patient outcomes.

Assisting the infection preventionist: Use of artificial intelligence for health care–associated infection surveillance

Health care-associated infection (HAI) surveillance is vital for safety in health care settings. It helps identify infection risk factors, enhancing patient safety and quality improvement. However, HAI surveillance is complex, demanding specialized knowledge and resources. This study investigates the use of artificial intelligence (AI), particularly generative large language models, to improve HAI surveillance. We assessed 2 AI agents, OpenAI's chatGPT plus (GPT-4) and a Mixtral 8×7b-based local model, for their ability to identify Central Line-Associated Bloodstream Infection (CLABSI) and Catheter-Associated Urinary Tract Infection (CAUTI) from 6 National Health Care Safety Network training scenarios. The complexity of these scenarios was analyzed, and responses were matched against expert opinions. Both AI models accurately identified CLABSI and CAUTI in all scenarios when given clear prompts. Challenges appeared with ambiguous prompts including Arabic numeral dates, abbreviations, and special characters, causing occasional inaccuracies in repeated tests. The study demonstrates AI's potential in accurately identifying HAIs like CLABSI and CAUTI. Clear, specific prompts are crucial for reliable AI responses, highlighting the need for human oversight in AI-assisted HAI surveillance. AI shows promise in enhancing HAI surveillance, potentially streamlining tasks, and freeing health care staff for patient-focused activities. Effective AI use requires user education and ongoing AI model refinement.

Data quality review of the Brazilian nosocomial infections surveillance system

BackgroundSurveillance of healthcare-associated infections (HAIs) is an essential component of hospital infection prevention and control systems. We aimed to assess the quality of the data compiled by the Brazilian HAI Surveillance System from pediatric (PICUs) and neonatal intensive care units (NICUs), between 2012 and 2021. MethodsData Quality Review, including adherence, completeness, internal consistency, consistency over time, and consistency of population trend, were computed at both national and state levels based on quality metrics from World Health Organization Toolkit. Incidence rates (or incidence density) of ventilator-associated pneumonia (VAP) and central line-associated bloodstream infection (CLABSI) were obtained from the Brazilian National Nosocomial Infections Surveillance (NNIS) system. Data on sepsis-related mortality, spanning the period from 2012 to 2021, were extracted from the Brazilian National Health Service database (DATASUS). Additionally, correlations between sepsis-related mortality and incidence rates of VAP or CLABSI were calculated. ResultsThroughout the majority of the study period, adherence to VAP reporting remained below 75%, exhibiting a positive trend post-2016. Widespread outliers, as well as inconsistencies over time and in population trends, were evident across all 27 states. Only four states maintained consistent adherence levels above 75% for more than 8 years regarding HAI incidence rates. Notably, CLABSI in NICUs boasted the highest reporting adherence among all HAIs, with 148 periods out of 270 (54.8%) exhibiting reporting adherence surpassing 75%. Three states achieved commendable metrics for CLABSI in PICUs, while five states demonstrated favorable results for CLABSI in NICUs. ConclusionsWhile adherence to HAI report is improving among Brazilian states, an important room for improvement in the Brazilian NNIS exists. Additional efforts should be made by the Brazilian government to improve the reliability of HAI data, which could serve as valuable guidance for hospital infection prevention and control policies.

Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review.

Healthcare-associated infections (HAIs) are the most common adverse events in healthcare and constitute a major global public health concern. Surveillance represents the foundation for the effective prevention and control of HAIs, yet conventional surveillance is costly and labor intensive. Artificial intelligence (AI) and machine learning (ML) have the potential to support the development of HAI surveillance algorithms for the understanding of HAI risk factors, the improvement of patient risk stratification as well as the prediction and timely detection and prevention of infections. AI-supported systems have so far been explored for clinical laboratory testing and imaging diagnosis, antimicrobial resistance profiling, antibiotic discovery and prediction-based clinical decision support tools in terms of HAIs. This review aims to provide a comprehensive summary of the current literature on AI applications in the field of HAIs and discuss the future potentials of this emerging technology in infection practice. Following the PRISMA guidelines, this study examined the articles in databases including PubMed and Scopus until November 2023, which were screened based on the inclusion and exclusion criteria, resulting in 162 included articles. By elucidating the advancements in the field, we aim to highlight the potential applications of AI in the field, report related issues and shortcomings and discuss the future directions.

Healthcare-associated infection management in 62 ICUs for patients with congenital heart disease in China: a survey study.

All patients with congenital heart disease (CHD) receive postoperative management in ICUs. Infection prevention and control (IPC) has a significant impact on prognosis. This study provides a preliminary understanding of the fundamental aspects of IPC in ICUs following CHD surgery in China. From September to October 2023, we initiated a survey on healthcare-associated infection (HAI) management in hospitals that perform CHD surgeries independently. The questionnaires were jointly completed by the ICU physicians and IPC personnel. Duplicate or unqualified questionnaires were excluded from the study. The contents of our questionnaires covered hospital and ICU capacity, performance of the infection control department, HAI surveillance, implementation of IPC measures, and antimicrobial stewardship (AMS). Qualified questionnaires were compared according to the volume of annual CHD surgeries performed in different ICUs. Group 1 was defined as volume more than 300 cases and group 2 was defined as volume less than or equal to 300 cases. Sixty-two of the 118 questionnaires were completed, with a response rate of 53%. The CHD surgical volume in 2022 of the 62 hospitals was 36342, accounting for 52% of the annual CHD surgical volume (69672) across the country. The postoperative infection rates obtained from the 15 ICUs varied from 1.3 to 15%, with a median rate of 4.5%. A total of 16 ICUs provided data on drug-resistant bacteria, Klebsiella pneumoniae exhibiting the highest frequency. More than 95% of ICUs have established complete HAI management systems. Information-based HAI surveillance was conducted in 89% of ICUs. Approximately 67% of ICUs stopped prophylactic antibiotics within 48 hours after surgery. In complex cases, carbapenems were administered empirically in 89% of ICUs. Group 1 had an advantage over group 2 in preventing multi-drug-resistant organisms (all instruments should be used alone 100% vs. 86%, P =0.047; cleaning and disinfection of environmental surfaces, 100% vs. 81%, P =0.035; antibiotic consumption control 85% vs. 61%, P =0.044) and in preventing surgical site infections (perioperative blood glucose monitoring, 88% vs. 67%, P =0.048). However, Group 1 did not perform well in preventing catheter-related bloodstream infection (delayed catheter removal due to convenience of laboratory tests, 31% vs. 6%, P =0.021) and catheter-associated urinary tract infection (delayed catheter removal due to muscle relaxant administration, 88% vs. 58%, P =0.022). A relatively complete HAI management system has been established throughout the country in ICUs for CHD patients. Information-based surveillance of HAI needs to be promoted, and actions should be taken to facilitate the implementation of IPC measures and AMS bundles. Training and feedback are critical for implementing IPC measures.

Evaluation of an Infection surveillance program in residential aged care facilities in Victoria, Australia.

Infection surveillance is a key element of infection prevention and control activities in the aged care sector. In 2017, a standardised infection surveillance program was established for public residential aged care services in Victoria, Australia. This program will soon be expanded to a national level for all Australian residential aged care facilities. It has not been evaluated since its inception. The current study aimed to evaluate the Victorian Healthcare Associated Infection Surveillance System (VICNISS) Coordinating Centre Aged Care Infection Indicator Program (ACIIP), to understand its performance and functionality. A mixed methods evaluation was performed using the Updated Guidelines for Evaluating Public Health Surveillance Systems developed by the United States Centers for Disease Control and Prevention as a framework. VICNISS staff who coordinate and manage the ACIIP were invited to participate in interviews. Residential aged care staff who use the program were invited to participate in a survey. Document analysis was also performed. Four VICNISS staff participated in the interviews and 38 aged care staff participated in the survey. The ACIIP is stable and able to be adapted quickly to changing definitions for infections. Users found the system relatively easy to use but have difficulties after the long intervals between data entry year on year. VICNISS staff provide expert guidance which benefits users. Users appreciated the benefit of participating and many use the data for improving local practice. The ACIIP is a usessful state-wide infection surveillance program for aged care. Further development of data validation, IT system capacity and models for education and user support will be required to support future scalability.

The next frontier of healthcare-associated infection (HAI) surveillance metrics: Beyond device-associated infections.

In recent years, it has become increasingly evident that surveillance metrics for invasive device-associated infections (ie, central-line-associated bloodstream infections, ventilator-associated pneumonias, and catheter-associated urinary tract infections) do not capture all harms; they capture only a subset of healthcare-associated infections (HAIs). Although prevention of device-associated infections remains critical, we need to address the full spectrum of potential harms from device use and non-device-associated infections. These include complications associated with additional devices, such as peripheral venous and arterial catheters, non-device-associated infections such as nonventilator hospital-acquired pneumonia, and noninfectious device complications such as trauma, thrombosis, and acute lung injury. As authors of the device-associated infection sections in the SHEA/IDSA/APIC Compendium of Strategies to Prevent Healthcare-Associated Infections in Acute Care Hospitals, we highlight catheter-associated urinary tract infection as an example of the strengths and limitations of the current emphasis on device-associated infection surveillance, suggest performance metrics that present a more comprehensive picture of patient harm, and provide a high-level overview of similar issues with other infection surveillance measures.

Innovative Techniques for Infection Control and Surveillance in Hospital Settings and Long-Term Care Facilities: A Scoping Review.

Healthcare-associated infections (HAIs) pose significant challenges in healthcare systems, with preventable surveillance playing a crucial role. Traditional surveillance, although effective, is resource-intensive. The development of new technologies, such as artificial intelligence (AI), can support traditional surveillance in analysing an increasing amount of health data or meeting patient needs. We conducted a scoping review, following the PRISMA-ScR guideline, searching for studies of new digital technologies applied to the surveillance, control, and prevention of HAIs in hospitals and LTCFs published from 2018 to 4 November 2023. The literature search yielded 1292 articles. After title/abstract screening and full-text screening, 43 articles were included. The mean study duration was 43.7 months. Surgical site infections (SSIs) were the most-investigated HAI and machine learning was the most-applied technology. Three main themes emerged from the thematic analysis: patient empowerment, workload reduction and cost reduction, and improved sensitivity and personalization. Comparative analysis between new technologies and traditional methods showed different population types, with machine learning methods examining larger populations for AI algorithm training. While digital tools show promise in HAI surveillance, especially for SSIs, challenges persist in resource distribution and interdisciplinary integration in healthcare settings, highlighting the need for ongoing development and implementation strategies.

Assessing infection prevention and control programs in residential aged care in Australia: A multi-methods cross-sectional study.

To assess infection prevention and control programs in residential aged care facilities. A cross-sectional survey and structured interviews from 10 residential aged care facilities in Victoria, Australia, were used. Infection prevention and control nurse leads from each facility completed a purpose-built survey based on best practice infection prevention control program core components, including staff training, policies and procedures, governance, and surveillance. Follow-up interviews with residential aged care staff, residents and family visitors were carried out to elaborate and verify survey data. Surveys from all 10 facilities were received and 75 interviews carried out. All facilities had an infection prevention and control lead nurse who had undergone additional training, and 60% of facilities had an infection prevention and control lead position description. All facilities had a committee to oversee their infection prevention and control program, and all had policies and procedures for standard and transmission-based precautions. One facility did not have a policy on healthcare-associated infection surveillance, and two facilities did not have an antimicrobial stewardship policy. All facilities provided staff training in hand hygiene and personal protective equipment use, but not all routinely assessed competency in these. The residential aged care facilities' infection prevention and control programs were generally in a strong position, although there were some areas that require improvement. Further assessment of the quality of infection prevention and control program components, such as content of education and training, and policies and procedures, and ongoing evaluation of programs is recommended. Geriatr Gerontol Int 2024; 24: 358-363.

Assessment of Knowledge and Compliance with Hand Hygiene among Health Personnel to Reduce Methicillin Resistant Staphylococcus aureus Nosocomial Infections: A Prospective Observational Study from a Tertiary Care Hospital in Southern India

Introduction: Following a significant recovery from Coronavirus Disease-2019 (COVID-19) pandemic, the world has recognised that Hand Hygiene (HH) and facial masks are key preventive measures against various cross infections, particularly in hospital settings. Studying the incidence of Methicillin Resistant Staphylococcus aureus (MRSA) as a part of Healthcare Associated Infections (HAI) surveillance is crucial, as MRSA transmission can be effectively prevented through HH. Aim: To assess the knowledge and compliance of HH among health personnel using the World Health Organisation (WHO) HH questionnaire. Materials and Methods: A prospective observational study was conducted at Bhaarath Medical College and Hospital, Chennai, Tamil Nadu, India, over one year, from December 2020 to November 2021. The Hospital Infection Control Committee (HICC) team carried out an HH survey using the WHO HH questionnaire to evaluate knowledge and attitudes among 115 Healthcare Workers (HCWs), including doctors and nursing staff, in a tertiary care hospital in Kanchipuram, Southern India. The study also involved monitoring monthly MRSA incidence density rates in Intensive Care Unit (ICU) settings as part of HAI surveillance, followed by regular HH audits. MRSA infectivity rates were statistically analysed using paired t-tests with Statistical Package for Social Sciences (SPSS) software version 21.0 to determine p-values and address standard deviations. Results: In this study, 26 (65%) of doctors had undergone training, while 57 (76%) of nurses reported recent formal HH training. Only 16 (40%) of doctors and 21 (28%) of nurses used alcohol-based handrub. Doctors exhibited knowledge ranging from 60-77.5% on germ transmission and hand rub-related questions, while nurses demonstrated better knowledge ranging from 72-93.3%. HH adherence was 30% among doctors and 72% among nurses. The MRSA infectivity rate varied over the year, starting at 1.33 per 1000 resident days before HH audits and decreasing to 0.3. Each HH audit led to a significant improvement in MRSA rates, with a p-value &lt;0.0001, indicating statistical significance. Conclusion: Present study evaluated HCWs knowledge and attitudes toward HH, along with the impact of HH audits on reducing MRSA rates, has proven to be beneficial for hospital infection control practices, particularly during the COVID-19 pandemic and its aftermath, when vigilance tends to decrease over time.

A Validation Study of Surveillance Data: Focus on the Korean National Healthcare-associated Infections Surveillance System (KONIS), Intensive Care Unit Module

A Validation Study of Surveillance Data: Focus on the Korean National Healthcare-associated Infections Surveillance System (KONIS), Intensive Care Unit Module

Healthcare‑associated infections in intensive care unit patients with and without COVID-19: a single center prospective surveillance study

BackgroundThe coronavirus disease 2019 (COVID-19) pandemic led to a global increase in healthcare-associated infections (HAI) among intensive care unit (ICU) patients. Whether this increase is directly attributable to COVID-19 or whether the pandemic indirectly (via staff shortages or breaches in infection prevention measures) led to this increase, remains unclear. The objectives of this study were to assess HAI incidence and to identify independent risk factors for HAI in COVID-19 and non-COVID-19 ICU patients.MethodsWe established a monocentric prospective HAI surveillance in the medical ICU of our tertiary care center from September 1st 2021 until August 31st 2022, during circulation of the SARS-CoV-2 delta and omicron variants. We consecutively included patients ≥ 18 years of age with an ICU length of stay of > 2 calendar days. HAI were defined according to the European Centre for Disease Prevention and Control definitions. HAI rate was calculated per 1,000 patient-days or device-days; risk ratios (RR) and corresponding 95% confidence intervals (CI) for COVID-19 versus non-COVID-19 patients were calculated. We used multivariable Cox regression to identify independent risk factors for HAI. As a proxy for institutional COVID-19 burden, weekly COVID-19 density (i.e. percentage of COVID-19 patients among all ICU patients) was included in the model as time-dependent co-variable.ResultsWe included 254 patients, 64 (25.1%) COVID-19 and 190 (74.9%) non-COVID-19 patients; 83 HAI in 72 patients were recorded, thereof 45 ventilator-associated lower respiratory tract infections (VA-LRTI) (54.2%) and 18 blood stream infections (BSI) (21.6%). HAI incidence rate was 49.1/1,000 patient-days in COVID-19 and 22.5/1,000 patient-days in non-COVID-19 patients (RR 2.2, 95%-CI 1.4–3.4). This result was mainly due to different VA-LRTI rates (40.3 vs. 11.7/1,000 ventilator days, p < 0.001), whereas BSI rates were not statistically different (9.4 vs. 5.6/1,000 patient days, p = 0.27). Multivariable analysis identified COVID-19 as main risk factor for HAI development, whereas age, mechanical ventilation and COVID-19 density were not significant.ConclusionsThese data from the fourth and fifth wave of the pandemic show a higher HAI incidence in COVID-19 than in non-COVID-19 ICU patients, mainly due to an increase in pulmonary infections. A diagnosis of COVID-19 was independently associated with HAI development, whereas institutional COVID-19 burden was not.

Comprehensive Evaluation of Healthcare Associated Infection Surveillance System

Background &amp; objectives: The electronic surveillance system has been applied in healthcare associated infection surveillance. We conducted an evaluation of the real-time healthcare associated infection surveillance system (RT-HAISS) to understand the early warning effect. Methods: We evaluated our RT-HAISS on a dataset of 29074 patients at the Wuxi traditional Chinese medicine (TCM) hospital in 2020, encompassing sensitivity, specificity, positive predictive value, negative predictive value, Youden index and false alarm rate.Results: 466 HAIs were confirmed in this hospital in 2020, The RT-HAISS warned the monitors a total of 1715 cases, with 2040 early warning entries and 1736 false alarm entries. The sensitivity and the specificity were 65.24% and 95.74%, respectively. The Youden index was 0.62. In addition, the positive predictive value was 14.90%, and the negative predictive value was 83.86%, with 85.10% of false alarm rate.Conclusion: The RT-HAISS is an important technical mean for healthcare associated infection surveillance, it’s necessary to further explore the improvement of real-time surveillance system.

2470. A Survey of the Collection of Denominator Data in the Korean National Healthcare-associated Infections Surveillance System (KONIS), Intensive Care Unit Module

Abstract Background National surveillance data should be validated to identify data quality issues. While denominator data can have a significant effect on the healthcare-associated infection (HAI) rate, they are relatively overlooked compared to numerator data. The Korean National Healthcare-associated Infections Surveillance System (KONIS) has been conducting data validation, and this study analyzed the current status of, and problems with, collecting denominator data for the first time. Methods The web-based cross-sectional survey was conducted on January 6, 2023 on 183 infection control nurses from 127 hospitals, among a total of 275 participating hospitals in the KONIS ICU module. The questionnaire was delivered using the QR code method and answered online by participants via SurveyMonkey®. The survey examined denominator data collection methods, such as patient days and device days of central lines and urinary catheters. The knowledge of the types of central lines and urinary catheters for KONIS reporting and the responses to case questions about specific situations related to the calculation of device days, were also evaluated. Results The respective proportions of manual vs. electronic methods for counting denominator data were 10.4% vs. 81.4% for patient days, 22.4% vs. 67.8% for central line days, and 23.5% vs. 69.4% for urinary catheter days. About 27.3%, 29.5%, and 31.1% of respondents reported periodically checking the accuracy of patient days, central line days, and urinary catheter days data, respectively. Infection control nurses were responsible for collecting the majority (66.1%) of the denominator data and entering them into the Web-based Report and Analysis Program (88.0%). The survey results indicated that the rate of accurately understanding and responding to the case questions on device days for central lines ranged from 24.6% to 78.7%, while for urinary catheters, it ranged from 27.3% to 92.3%. Conclusion To maintain the reliability of the national HAI surveillance data, it is important to collect accurate denominator data, in addition to numerator data. This study has identified several areas where improvements in denominator data collection methods are needed. Continuing education on the definition of denominator data and accurate data collection methods is crucial. Disclosures All Authors: No reported disclosures

Ventilator-associated pneumonia in Polish intensive care unit dedicated to COVID-19 patients

BackgroundHealthcare-Associated Infections (HAI) are most frequently associated with patients in the Intensive Care Unit (ICU). Coronavirus disease 2019 (COVID-19), caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), led to ICU hospitalization for some patients.MethodsThe study was conducted in 2020 and 2021 at a hospital in southern Poland. The Healthcare-Associated Infections Surveillance Network (HAI-Net) of the European Centre for Disease Prevention and Control (ECDC) was used for HAI diagnosis. The aim of this case-control study was to retrospectively assess the epidemiology of HAIs in ICU patients, distinguishing between COVID-19 and non-COVID-19 cases.ResultsThe study included 416 ICU patients: 125 (30%) with COVID-19 and 291 (70%) without COVID-19, p < 0.05. The mortality rate was 80 (64%) for COVID-19 patients and 45 (16%) for non-COVID-19 patients, p < 0.001. Ventilator-Associated Pneumonia (VAP) occurred in 40 cases, with an incidence rate density of 6.3/1000 patient-days (pds): 14.1/1000 pds for COVID-19 patients vs. 3.6/1000 pds for non-COVID-19 patients. Odds Ratio (OR) was 2.297, p < 0.01. Acinetobacter baumannii was the most often isolated microorganism in VAP, with 25 cases (incidence rate 8.5%): 16 (18.2%) in COVID-19 patients vs. 9 (4.4%) in non-COVID-19 patients. OR was 4.814 (1.084–4.806), p < 0.001.ConclusionsPatients treated in the ICU for COVID-19 faced twice the risk of VAP compared to non-COVID-19 patients. The predominant microorganism in VAP cases was Acinetobacter baumannii.

Healthcare-associated infections in the constituents of Ural and Siberian Federal Districts: results of monitoring their identification and registration in 2022 and in long-term dynamics

Rationale. Monitoring of HAI (health care-associated infections) detection and registration of is one of the key components in the information subsystem of epidemiological surveillance for these infections. Despite a well-established HAI recording system in the Russian Federation as a whole, the constituents of the Ural and Siberian Federal Districts have different approaches to detection of these infections, which is confirmed by the final rating scores of their detection and registration.&#x0D; Objective. We studied HAI detection and registration in the constituents of the Ural and Siberian Federal Districts for ranking and rating their systems of epidemiological HAI surveillance.&#x0D; Materials and methods: The data of federal statistical monitoring forms No. 2 "Information on infectious and parasitic diseases" and No. 23-17 "Information on outbreaks of infectious diseases as well as from additional enquiry materials "Information on medical manipulations and cohorts treated" in 20182022 submitted by the Rospotrebnadzor offices of the respective Russian Federation constituents compared with the long-term and Russias nationwide averages. The quality of HAI registration in the healthcare institutions of the Ural and Siberian Federal Districts was rated using grouping by intensive incidence indicators, and the most significant groups of infections and risk cohorts were selected for analysis.&#x0D; Results: The results of the HAI detection and registration by the most significant groups of infections and risk cohorts among the patients and healthcare workers were analyzed. The data on the group and outbreak HAI morbidity in the healthcare institutions are presented. The quality of HAI detection and registration in the constituents of the Ural and Siberian Federal Districts was rated, and the subjects were ranked with regard to the activity of their epidemiological surveillance system.&#x0D; Conclusion: Active detection and registration of HAI cases over several years is typical for the healthcare institutions of Sverdlovsk and Chelyabinsk Oblasts as well as Yamalo-Nenets Autonomous District. Consistently low rating scores of the studied parameters are typical for the healthcare institutions of the Republics of Tyva, Altai and Krasnoyarsk Krai.