Designing Surveillance of Healthcare-Associated Infections in the Era of Automation and Reporting Mandates

Federated systems for automated infection surveillance: a perspective

Automation of surveillance of infectious diseases-where algorithms are applied to routine care data to replace manual decisions-likely reduces workload and improves quality of surveillance. However, various barriers limit large-scale implementation of automated surveillance (AS). Current implementation strategies for AS in surveillance networks include central implementation (i.e. collecting all data centrally, and central algorithm application for case ascertainment) or local implementation (i.e. local algorithm application and sharing surveillance results with the network coordinating center). In this perspective, we explore whether current challenges can be solved by federated AS. In federated AS, scripts for analyses are developed centrally and applied locally. We focus on the potential of federated AS in the context of healthcare associated infections (AS-HAI) and of severe acute respiratory illness (AS-SARI). AS-HAI and AS-SARI have common and specific requirements, but both would benefit from decreased local surveillance burden, alignment of AS and increased central and local oversight, and improved access to data while preserving privacy. Federated AS combines some benefits of a centrally implemented system, such as standardization and alignment of an easily scalable methodology, with some of the benefits of a locally implemented system including (near) real-time access to data and flexibility in algorithms, meeting different information needs and improving sustainability, and allowance of a broader range of clinically relevant case-definitions. From a global perspective, it can promote the development of automated surveillance where it is not currently possible and foster international collaboration.The necessary transformation of source data likely will place a significant burden on healthcare facilities. However, this may be outweighed by the potential benefits: improved comparability of surveillance results, flexibility and reuse of data for multiple purposes. Governance and stakeholder agreement to address accuracy, accountability, transparency, digital literacy, and data protection, warrants clear attention to create acceptance of the methodology. In conclusion, federated automated surveillance seems a potential solution for current barriers of large-scale implementation of AS-HAI and AS-SARI. Prerequisites for successful implementation include validation of results and evaluation requirements of network participants to govern understanding and acceptance of the methodology.

Performance of ICD-10-AM codes for quality improvement monitoring of hospital-acquired pneumonia in a haematology-oncology casemix in Victoria, Australia

BACKGROUND

The Australian hospital-acquired complication (HAC) policy was introduced to facilitate negative funding adjustments in Australian hospitals using ICD-10-AM codes.

OBJECTIVE

The aim of this study was to determine the positive predictive value (PPV) of the ICD-10-AM codes in the HAC framework to detect hospital-acquired pneumonia in patients with cancer and to describe any change in PPV before and after implementation of an electronic medical record (EMR) at our centre.

METHOD

A retrospective case review of all coded pneumonia episodes at the Peter MacCallum Cancer Centre in Melbourne, Australia spanning two time periods (01 July 2015 to 30 June 2017 [pre-EMR period] and 01 September 2020 to 28 February 2021 [EMR period]) was performed to determine the proportion of events satisfying standardised surveillance definitions.

RESULTS

HAC-coded pneumonia occurred in 3.66% (n = 151) of 41,260 separations during the study period. Of the 151 coded pneumonia separations, 27 satisfied consensus surveillance criteria, corresponding to an overall PPV of 0.18 (95% CI: 0.12, 0.25). The PPV was approximately three times higher following EMR implementation (0.34 [95% CI: 0.19, 0.53] versus 0.13 [95% CI: 0.08, 0.21]; p = .013).

CONCLUSION

The current HAC definition is a poor-to-moderate classifier for hospital-acquired pneumonia in patients with cancer and, therefore, may not accurately reflect hospital-level quality improvement. Implementation of an EMR did enhance case detection, and future refinements to administratively coded data in support of robust monitoring frameworks should focus on EMR systems.

IMPLICATIONS

Although ICD-10-AM data are readily available in Australian healthcare settings, these data are not sufficient for monitoring and reporting of hospital-acquired pneumonia in haematology-oncology patients.

Surveillance of catheter-associated bloodstream infections: development and validation of a fully automated algorithm

BACKGROUND

Most surveillance systems for catheter-related bloodstream infections (CRBSI) and central line-associated bloodstream infections (CLABSI) are based on manual chart review. Our objective was to validate a fully automated algorithm for CRBSI and CLABSI surveillance in intensive care units (ICU).

METHODS

We developed a fully automated algorithm to detect CRBSI, CLABSI and ICU-onset bloodstream infections (ICU-BSI) in patients admitted to the ICU of a tertiary care hospital in Switzerland. The parameters included in the algorithm were based on a recently performed systematic review. Structured data on demographics, administrative data, central vascular catheter and microbiological results (blood cultures and other clinical cultures) obtained from the hospital's data warehouse were processed by the algorithm. Validation for CRBSI was performed by comparing results with prospective manual BSI surveillance data over a 6-year period. CLABSI were retrospectively assessed over a 2-year period.

RESULTS

From January 2016 to December 2021, 854 positive blood cultures were identified in 346 ICU patients. The median age was 61.7 years [IQR 50-70]; 205 (24%) positive samples were collected from female patients. The algorithm detected 5 CRBSI, 109 CLABSI and 280 ICU-BSI. The overall CRBSI and CLABSI incidence rates determined by automated surveillance for the period 2016 to 2021 were 0.18/1000 catheter-days (95% CI 0.06-0.41) and 3.86/1000 catheter days (95% CI: 3.17-4.65). The sensitivity, specificity, positive predictive and negative predictive values of the algorithm for CRBSI, were 83% (95% CI 43.7-96.9), 100% (95% CI 99.5-100), 100% (95% CI 56.5-100), and 99.9% (95% CI 99.2-100), respectively. One CRBSI was misclassified as an ICU-BSI by the algorithm because the same bacterium was identified in the blood culture and in a lower respiratory tract specimen. Manual review of CLABSI from January 2020 to December 2021 (n = 51) did not identify any errors in the algorithm.

CONCLUSIONS

A fully automated algorithm for CRBSI and CLABSI detection in critically-ill patients using only structured data provided valid results. The next step will be to assess the feasibility and external validity of implementing it in several hospitals with different electronic health record systems.

Development of machine learning models for the surveillance of colon surgical site infections

BACKGROUND

Conventional surgical site infection (SSI) surveillance is labour-intensive. We aimed to develop machine learning (ML) models for the surveillance of SSIs for colon surgery and to assess whether the ML could improve surveillance process efficiency.

METHODS

This study included cases who underwent colon surgery at a tertiary center between 2013 and 2014. Logistic regression and four ML algorithms including random forest (RF), gradient boosting (GB), and neural networks (NNs) with or without recursive feature elimination (RFE) were first trained on the entire cohort, and then re-trained on cases selected based on a previous rule-based algorithm. We assessed model performance based on the area under the curve (AUC), sensitivity, and positive predictive value (PPV). The estimated proportion of reduction in workload for chart review based on the ML models was evaluated and compared with the conventional method.

RESULTS

At a sensitivity of 95%, the NN with RFE using 29 variables had the best performance with an AUC of 0.963 and PPV of 21.1%. When combining both the rule-based algorithm and ML algorithms, the NN with RFE using 19 variables had a higher PPV (28.9%) than with the ML algorithm alone, which could decrease the number of cases requiring chart review by 83.9% compared with the conventional method.

CONCLUSION

We demonstrated that ML can improve the efficiency of SSI surveillance for colon surgery by decreasing the burden of chart review while providing high sensitivity. In particular, the hybrid approach of ML with a rule-based algorithm showed the best performance in terms of PPV.

Establishing semi-automated infection surveillance in obstetrics and gynaecology

BACKGROUND

Surveillance is an acknowledged method to decrease nosocomial infections, such as surgical site infections (SSIs). Electronic healthcare records create the opportunity for automated surveillance. While approaches for different types of surgeries and indicators already exist, there are very few for obstetrics and gynaecology.

AIM

To analyse the sensitivity and workload reduction of semi-automated surveillance in obstetrics and gynaecology.

METHODS

In this retrospective, single-centre study at a 1438-bed tertiary care hospital in Germany, semi-automated SSI surveillance using the indicators 'antibiotic prescription', 'microbiological data' and 'administrative data' (diagnosis codes, readmission, post-hospitalization care) was compared with manual analysis and categorization of all patient files. Breast surgeries (BSs) conducted in 2018 and caesarean sections (CSs) that met the inclusion criteria between May 2013 and December 2019 were included. Indicators were analysed for sensitivity, number of analysed procedures needed to identify one case, and potential workload reduction in detecting SSIs in comparison with the control group.

FINDINGS

The reference standard showed nine SSIs in 416 BSs (2.2%). Sensitivities for the indicators 'antibiotic prescription', 'diagnosis code', 'microbiological sample taken', and the combination 'diagnosis code or microbiological sample' were 100%, 88.9%, 66.7% and 100%, respectively. The reference standard showed 54 SSIs in 3438 CSs (1.6%). Sensitivities for the indicators 'collection of microbiological samples', 'diagnosis codes', 'readmission/post-hospitalization care', and the combination of all indicators were 38.9%, 27.8%, 85.2% and 94.4%, respectively.

CONCLUSIONS

Semi-automated surveillance systems may reduce workload by maintaining high sensitivity depending on the type of surgery, local circumstances and thorough digitalization.

The accuracy of fully-automated algorithms for the surveillance of central venous catheter-related bloodstream infection in hospitalised patients

BACKGROUND

Continuous surveillance for healthcare-associated infections such as central venous catheter-related bloodstream infections (CVC-BSI) is crucial for prevention. However, traditional surveillance methods are resource-intensive and prone to bias. This study aimed to develop and validate fully-automated surveillance algorithms for CVC-BSI.

METHODS

Two algorithms were developed using electronic health record data from 1000 admissions with a positive blood culture (BCx) at Karolinska University Hospital from 2017: (1) Combining microbiological findings in BCx and CVC cultures with BSI symptoms; (2) Only using microbiological findings. These algorithms were validated in 5170 potential CVC-BSI-episodes from all admissions in 2018-2019, and results extrapolated to all potential CVC-BSI-episodes within this period (n = 181,354). The reference standard was manual record review according to ECDC's definition of microbiologically confirmed CVC-BSI (CRI3-CVC).

RESULTS

In the potential CVC-BSI-episodes, 51 fulfilled ECDC's definition and the algorithms identified 47 and 49 episodes as CVC-BSI, respectively. Both algorithms performed well in assessing CVC-BSI. Overall, algorithm 2 performed slightly better with in the total period a sensitivity of 0.880 (95%-CI 0.783-0.959), specificity of 1.000 (95%-CI 0.999-1.000), PPV of 0.918 (95%-CI 0.833-0.981) and NPV of 1.000 (95%-CI 0.999-1.000). Incidence according to the reference and algorithm 2 was 0.33 and 0.31 per 1000 in-patient hospital-days, respectively.

CONCLUSIONS

Both fully-automated surveillance algorithms for CVC-BSI performed well and could effectively replace manual surveillance. The simpler algorithm, using only microbiology data, is suitable when BCx testing adheres to recommendations, otherwise the algorithm using symptom data might be required. Further validation in other settings is necessary to assess the algorithms' generalisability.

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.

The epidemiology of multidrug-resistant organisms in persons diagnosed with cancer in Norway, 2008-2018: expanding surveillance using existing laboratory and register data

Surveillance has revealed an increase of multidrug-resistant organisms (MDROs), even in low-prevalent settings such as Norway. MDROs pose a particular threat to at-risk populations, including persons with cancer. It is necessary to include such populations in future infection surveillance. By combining existing data sources, we aimed to describe the epidemiology of MDROs in persons diagnosed with cancer in Norway from 2008 to 2018. A cohort was established using data from the Cancer Registry of Norway, which was then linked to notifications of methicillin-resistant Staphylococcus aureus (MRSA), vancomycin- and/or linezolid-resistant enterococci (V/LRE), and carbapenemase-producing Gram-negative bacilli (CP-GNB) from the Norwegian Surveillance System for Communicable Diseases, and laboratory data on third-generation cephalosporin-resistant Enterobacterales (3GCR-E) from Oslo University Hospital (OUH). We described the incidence of MDROs and resistance proportion in Enterobacterales from 6 months prior to the person's first cancer diagnosis and up to 3 years after. The cohort included 322,005 persons, of which 0.3% (878) were diagnosed with notifiable MDROs. Peak incidence rates per 100,000 person-years were 60.9 for MRSA, 97.2 for V/LRE, and 6.8 for CP-GNB. The proportion of 3GCR-E in Enterobacterales in blood or urine cultures at OUH was 6% (746/12,534). Despite overall low MDRO incidence, there was an unfavourable trend in the incidence and resistance proportion of Gram-negative bacteria. To address this, there is a need for effective infection control and surveillance. Our study demonstrated the feasibility of expanding the surveillance of MDROs and at-risk populations through the linkage of existing laboratory and register data.

Effects of environmental conditions on healthcare worker wellbeing and quality of care: A qualitative study in Niger

Environmental conditions (water, sanitation, hygiene, waste management, cleaning, energy, building design) are important for a safe and functional healthcare environment. Yet their full range of impacts are not well understood. In this study, we assessed the impact of environmental conditions on healthcare workers' wellbeing and quality of care, using qualitative interviews with 81 healthcare workers at 26 small healthcare facilities in rural Niger. We asked participants to report successes and challenges with environmental conditions and their impacts on wellbeing (physical, social, mental, and economic) and quality of care. We found that all environmental conditions contributed to healthcare workers' wellbeing and quality of care. The norm in facilities of our sample was poor environmental conditions, and thus participants primarily reported detrimental effects. We identified previously documented effects on physical health and safety from pathogen exposure, but also several novel effects on healthcare workers' mental and economic wellbeing and on efficiency, timeliness, and patient centeredness of care. Key wellbeing impacts included pathogen exposure for healthcare workers, stress from unsafe and chaotic working environments, staff dissatisfaction and retention challenges, out-of-pocket spending to avoid stockouts, and uncompensated labor. Key quality of care impacts included pathogen exposure for patients, healthcare worker time dedicated to non-medical tasks like water fetching (i.e., reduced efficiency), breakdowns and spoilage of equipment and supplies, and patient satisfaction with cleanliness and privacy. Inefficiency due to time lost and damaged supplies and equipment likely have substantial economic value and warrant greater consideration in research and policy making. Impacts on staff retention and care efficiency also have implications for health systems. We recommend that future research and decision making for policy and practice incorporate more holistic impact measures beyond just healthcare acquired infections and reconsider the substantial contribution that environmental conditions make to the safety of healthcare facilities and strength of health systems.

National Healthcare-Associated Infections Report 2022 - Saudi Arabia

BACKGROUND

Surveillance data are very essential for the effective use of available resources, the prioritization of infection control practices, and setting goals for intervention. The aim was to present the current rates of healthcare-associated infections (HAIs) and device utilization ratios (DUR) among the Saudi Ministry of health (MOH) hospitals.

METHODS

MOH analyzed the surveillance data collected from 106 MOH hospitals enrolled in the health electronic surveillance network (HESN) between January 2022 and December 2022. The surveillance methodology was similar to the methods of the US National Healthcare Safety Network (NHSN) and the Gulf Cooperation Council (GCC) center for infection control.

RESULTS

More than one million device-days of surveillance were analyzed. The rate of central line associated bloodstream infection (CLABSI) was 2.57 per 1000 central lines days. The rate of catheter-associated urinary tract infection (CAUTI) was 1.08 per 1000 urinary catheter days. The rate of ventilator-associated events (VAE) was 4.21 per 1000 ventilator days. The average rate of pediatric/neonatal ventilator-associated pneumonia (VAP) was 1.53 per 1000 ventilator days. The average DURs were 0.33 for central line, 0.61 for urinary catheter, 0.44 for ventilator in adult patients, and 0.26 in ventilator in pediatric/neonatal patients. In 238632 months of surveillance, the rate of dialysis events (DE) was 0.97 per 100 patient-months. In 86324 surgeries monitored, the rate of surgical site infection (SSI) was 0.87 per 100 surgeries surveyed.

CONCLUSIONS

The current report can serve as a national benchmark for MOH hospitals and a regional benchmark for similar hospitals in the region.

Development of a knowledge-based healthcare-associated infections surveillance system in China

BACKGROUND

In the modern era of antibiotics, healthcare-associated infections (HAIs) have emerged as a prominent and concerning health threat worldwide. Implementing an electronic surveillance system for healthcare-associated infections offers the potential to not only alleviate the manual workload of clinical physicians in surveillance and reporting but also enhance patient safety and the overall quality of medical care. Despite the widespread adoption of healthcare-associated infections surveillance systems in numerous hospitals across China, several challenges persist. These encompass incomplete coverage of all infection types in the surveillance, lack of clarity in the alerting results provided by the system, and discrepancies in sensitivity and specificity that fall short of practical expectations.

METHODS

We design and develop a knowledge-based healthcare-associated infections surveillance system (KBHAIS) with the primary goal of supporting clinicians in their surveillance of HAIs. The system operates by automatically extracting infection factors from both structured and unstructured electronic health data. Each patient visit is represented as a tuple list, which is then processed by the rule engine within KBHAIS. As a result, the system generates comprehensive warning results, encompassing infection site, infection diagnoses, infection time, and infection probability. These knowledge rules utilized by the rule engine are derived from infection-related clinical guidelines and the collective expertise of domain experts.

RESULTS

We develop and evaluate our KBHAIS on a dataset of 106,769 samples collected from 84,839 patients at Gansu Provincial Hospital in China. The experimental results reveal that the system achieves a sensitivity rate surpassing 0.83, offering compelling evidence of its effectiveness and reliability.

CONCLUSIONS

Our healthcare-associated infections surveillance system demonstrates its effectiveness in promptly alerting patients to healthcare-associated infections. Consequently, our system holds the potential to considerably diminish the occurrence of delayed and missed reporting of such infections, thereby bolstering patient safety and elevating the overall quality of healthcare delivery.

Factors Influencing Clinicians' Use of Hospital Information Systems for Infection Prevention and Control: Cross-Sectional Study Based on the Extended DeLone and McLean Model

BACKGROUND

Healthcare-associated infections have become a serious public health problem. Various types of information systems have begun to be applied in hospital infection prevention and control (IPC) practice. Clinicians are the key users of these systems, but few studies have assessed the use of infection prevention and control information systems (IPCISs) from their perspective.

OBJECTIVE

This study aimed to (1) apply the extended DeLone and McLean Information Systems Success model (D&M model) that incorporates IPC culture to examine how technical factors like information quality, system quality, and service quality, as well as organizational culture factors affect clinicians' use intention, satisfaction, and perceived net benefits, and (2) identify which factors are the most important for clinicians' use intention.

METHODS

A total of 12,317 clinicians from secondary and tertiary hospitals were surveyed online. Data were analyzed using partial least squares-structural equation modeling and the importance-performance matrix analysis.

RESULTS

Among the technical factors, system quality (β=.089-.252; P<.001), information quality (β=.294-.102; P<.001), and service quality (β=.126-.411; P<.001) were significantly related to user satisfaction (R2=0.833), use intention (R2=0.821), and perceived net benefits (communication benefits [R2=0.676], decision-making benefits [R2=0.624], and organizational benefits [R2=0.656]). IPC culture had an effect on use intention (β=.059; P<.001), and it also indirectly affected perceived net benefits (β=.461-.474; P<.001). In the importance-performance matrix analysis, the attributes of service quality (providing user training) and information quality (readability) were present in the fourth quadrant, indicating their high importance and low performance.

CONCLUSIONS

This study provides valuable insights into IPCIS usage among clinicians from the perspectives of technology and organization culture factors. It found that technical factors (system quality, information quality, and service quality) and hospital IPC culture have an impact on the successful use of IPCISs after evaluating the application of IPCISs based on the extended D&M model. Furthermore, service quality and information quality showed higher importance and lower performance for use intention. These findings provide empirical evidence and specific practical directions for further improving the construction of IPCISs.

Clusters of healthcare-associated SARS-CoV-2 infections in Norwegian hospitals detected by a fully automatic register-based surveillance system

BACKGROUND

Notifications to the Norwegian Institute of Public Health of outbreaks in Norwegian healthcare institutions are mandatory by law, but underreporting is suspected due to failure to identify clusters, or because of human or system-based factors. This study aimed to establish and describe a fully automatic, register-based surveillance system to identify clusters of healthcare-associated infections (HAIs) of SARS-CoV-2 in hospitals and compare these with outbreaks notified through the mandated outbreak system Vesuv.

METHODS

We used linked data from the emergency preparedness register Beredt C19, based on the Norwegian Patient Registry and the Norwegian Surveillance System for Communicable Diseases. We tested two different algorithms for HAI clusters, described their size and compared them to outbreaks notified through Vesuv.

RESULTS

5033 patients were registered with an indeterminate, probable, or definite HAI. Depending on the algorithm, our system detected 44 or 36 of the 56 officially notified outbreaks. Both algorithms detected more clusters then officially reported (301 and 206, respectively).

CONCLUSIONS

It was possible to use existing data sources to establish a fully automatic surveillance system identifying clusters of SARS-CoV-2 s. Automatic surveillance can improve preparedness through earlier identification of clusters of HAIs, and by lowering the workloads of infection control specialists in hospitals.

Infection prevention and control in Indonesian hospitals: identification of strengths, gaps, and challenges

BACKGROUND

Infection prevention and control (IPC) in hospitals is key to safe patient care. There is currently no data regarding the implementation of IPC in hospitals in Indonesia. The aim of this study was to assess the existing IPC level in a nationwide survey, using the World Health Organization (WHO) IPC assessment framework tool (IPCAF), and to identify strengths, gaps, and challenges.

METHODS

A cross-sectional study was conducted from July to November 2021. Of all general hospitals in Indonesia, 20% (N = 475) were selected using stratified random sampling based on class (A, B, C and D; class D with a maximum of 50 beds and class A with ≥ 250 beds) and region. The IPCAF was translated into Indonesian and tested in four hospitals. Questions were added regarding challenges in the implementation of IPC. Quantitative IPCAF scores are reported as median (minimum-maximum). IPC levels were calculated according to WHO tools.

RESULTS

In total, 355 hospitals (74.7%) participated in this study. The overall median IPCAF score was 620.0 (535.0-687.5). The level of IPC was mostly assessed as advanced (56.9% of hospitals), followed by intermediate (35.8%), basic (7.0%) and inadequate (0.3%). In the eastern region of the country, the majority of hospitals scored intermediate level. Of the eight core components, the one with the highest score was IPC guidelines. Almost all hospitals had guidelines on the most important topics, including hand hygiene. Core components with the lowest score were surveillance of healthcare-associated infections (HAIs), education and training, and multimodal strategies. Although > 90% of hospitals indicated that surveillance of HAIs was performed, 57.2% reported no availability of adequate microbiology laboratory capacity to support HAIs surveillance. The most frequently reported challenges in the implementation of IPC were communication with the management of the hospitals, followed by the unavailability of antimicrobial susceptibility testing results and insufficient staffing of full-time IPC nurses.

CONCLUSION

The IPC level in the majority of Indonesian hospitals was assessed as advanced, but there was no even distribution over the country. The IPCAF in combination with interviews identified several priority areas for interventions to improve IPC in Indonesian hospitals.

Qualidade das práticas de profissionais dos programas de controle de infecção no Brasil: estudo transversal

RESUMO Objetivo Analisar a qualidade das práticas de profissionais dos programas de controle de infecção em relação aos componentes de estrutura, processo e resultado. Método Trata-se de um estudo de abordagem quantitativa, do tipo descritivo e transversal realizado em 114 serviços de controle de infecção hospitalar das cinco regiões oficiais do Brasil. Coletaram-se os dados por meio de um instrumento estruturado, cujas propriedades psicométricas foram validadas previamente. O tratamento dos dados foi realizado pela análise de componentes principais e o teste não paramétrico Kruskal-Wallis. Resultados O melhor índice de qualidade dos programas de controle de infecção foi atribuído à região Sul, aos hospitais que continham 300 leitos ou mais, aos que utilizavam o critério National Healthcare Safety Network para vigilância das infecções e aos locais que realizavam busca ativa prospectiva como método de vigilância. Conclusão e implicações para a prática O índice de qualidade dos programas de controle de infecção está relacionado à localização, ao tamanho do hospital e ao método adotado para vigilância de infecções. A criação de um índice de qualidade, até então inédito em estudos nacionais, chama atenção para o desempenho precário dos serviços de saúde.

The quality of professional practices in infection control programs in Brazil: a cross-sectional study

ABSTRACT Objective To analyze the quality of professional practices in infection control programs regarding structure, process, and outcome. Method This is a quantitative, descriptive, and cross-sectional study carried out in 114 hospital infection control services in the five official regions of Brazil. The data were collected using a structured instrument whose psychometric properties were previously validated. Data treatment was performed by principal component analysis and non-parametric Kruskal-Wallis test. Results The best quality index of infection control programs was attributed to the South region, to hospitals that had 300 beds or more, to those that used the National Healthcare Safety Network criterion for infection surveillance and to places that carried out an active prospective search as their surveillance method. Conclusion and implications for practice: The quality of infection control programs is related to hospital location, size, and infection surveillance method. The creation of a quality index, hitherto unheard of in Brazilian studies, draws attention to the precarious performance of health services.

Automated surveillance systems for healthcare-associated infections: results from a European survey and experiences from real-life utilization

BACKGROUND

As most automated surveillance (AS) methods to detect healthcare-associated infections (HAIs) have been developed and implemented in research settings, information about the feasibility of large-scale implementation is scarce.

AIM

We aimed to describe key aspects of the design of AS systems and implementation in European institutions and hospitals.

METHODS

An online survey was distributed via email in February/March 2019 among 1) PRAISE (Providing a Roadmap for Automated Infection Surveillance in Europe) network members; 2) corresponding authors of peer-reviewed European publications on existing AS systems; and 3) the mailing list of national infection prevention and control focal points of the European Centre for Disease Prevention and Control. Three AS systems from the survey were selected, based on quintessential features, for in-depth review focusing on implementation in practice.

FINDINGS

Through the survey and the review of three selected AS systems, notable differences regarding the methods, algorithms, data sources and targeted HAIs were identified. The majority of AS systems used a classification algorithm for semi-automated surveillance and targeted HAIs were mostly surgical site infections, urinary tract infections, sepsis or other bloodstream infections. AS systems yielded a reduction of workload for hospital staff. Principal barriers of implementation were strict data security regulations as well as creating and maintaining an information technology infrastructure.

CONCLUSION

AS in Europe is characterized by heterogeneity in methods and surveillance targets. To allow for comparisons and encourage homogenization, future publications on AS systems should provide detailed information on source data, methods and the state of implementation.

A computerized indicator for surgical site infection (SSI) assessment after total hip or total knee replacement: The French ISO-ORTHO indicator

OBJECTIVE

The French National Authority for Health (HAS), with a multidisciplinary working group, developed an indicator 'ISO-ORTHO' to assess surgical site infections (SSIs) after total hip arthroplasty or total knee arthroplasty (THA/TKA) based on the hospital discharge database. We present the ISO-ORTHO indicator designed for SSI automated detection and its relevance for quality improvement and hospital benchmarks.

METHODS

The algorithm is based on a combination of International Statistical Classification of Diseases, Tenth Revision (ICD-10) and procedure codes of the hospital stay. The target population was selected among adult patients who had a THA or TKA between January 1, 2017, and September 30, 2017. Patients at very high risk of SSI and/or with SSI not related to hospital care were excluded. We searched databases for SSIs up to 3 months after THA/TKA. The standardized infection ratio (SIR) of observed versus expected SSIs was calculated (logistic regression) and displayed as funnel plot with 2 and 3 standard deviations (SD) after adjustment for 13 factors known to increase SSI risk.

RESULTS

In total, 790 hospitals and 139,926 THA/TKA stays were assessed; 1,253 SSI were detected in the 473 included hospitals (incidence, 0.9%: 1.0% for THA, 0.80% for TKA). The SSI rate was significantly higher in males (1.2%), in patients with previous osteo-articular infection (4.4%), and those with cancer (2.3%), obesity, or diabetes. Most hospitals (89.9%) were within 2 SD; however, 12 hospitals were classified as outliers at more than +3 SD (1.6% of facilities), and 59 hospitals (7.9%) were outliers between +2 SD and +3 SD.

CONCLUSION

ISO-ORTHO is a relevant indicator for automated surveillance; it can provide hospitals a metric for SSI assessment that may contribute to improving patient outcomes.

Health Care-Acquired Infections in Low- and Middle-Income Countries and the Role of Infection Prevention and Control

Health care-associated infections (HAIs) account for many morbidity and mortality worldwide, with disproportionate adverse effects in low- and middle-income countries (LMIC). Many factors contribute to the impact in LMIC, including lack of infrastructure, inconsistent surveillance, deficiency in trained personnel and infection control programs, and poverty-related factors. Therefore, optimal approaches must be tailored for LMIC and balance effectiveness and cost in the control of HAIs.

Information technology aspects of large-scale implementation of automated surveillance of healthcare-associated infections

INTRODUCTION

Healthcare-associated infections (HAI) are a major public health concern. Monitoring of HAI rates, with feedback, is a core component of infection prevention and control programmes. Digitalization of healthcare data has created novel opportunities for automating the HAI surveillance process to varying degrees. However, methods are not standardized and vary widely between different healthcare facilities. Most current automated surveillance (AS) systems have been confined to local settings, and practical guidance on how to implement large-scale AS is needed.

METHODS

This document was written by a task force formed in March 2019 within the PRAISE network (Providing a Roadmap for Automated Infection Surveillance in Europe), gathering experts in HAI surveillance from ten European countries.

RESULTS

The document provides an overview of the key e-health aspects of implementing an AS system of HAI in a clinical environment to support both the infection prevention and control team and information technology (IT) departments. The focus is on understanding the basic principles of storage and structure of healthcare data, as well as the general organization of IT infrastructure in surveillance networks and participating healthcare facilities. The fundamentals of data standardization, interoperability and algorithms in relation to HAI surveillance are covered. Finally, technical aspects and practical examples of accessing, storing and sharing healthcare data within a HAI surveillance network, as well as maintenance and quality control of such a system, are discussed.

CONCLUSIONS

With the guidance given in this document, along with the PRAISE roadmap and governance documents, readers will find comprehensive support to implement large-scale AS in a surveillance network.

Governance aspects of large-scale implementation of automated surveillance of healthcare-associated infections

OBJECTIVES

Surveillance of healthcare-associated infections (HAI) is increasingly automated by applying algorithms to routine-care data stored in electronic health records. Hitherto, initiatives have mainly been confined to single healthcare facilities and research settings, leading to heterogeneity in design. The PRAISE network - Providing a Roadmap for Automated Infection Surveillance in Europe - designed a roadmap to provide guidance on how to move automated surveillance (AS) from the research setting to large-scale implementation. Supplementary to this roadmap, we here discuss the governance aspects of automated HAI surveillance within networks, aiming to support both the coordinating centres and participating healthcare facilities as they set up governance structures and to enhance involvement of legal specialists.

METHODS

This article is based on PRAISE network discussions during two workshops. A taskforce was installed that further elaborated governance aspects for AS networks by reviewing documents and websites, consulting experts and organizing teleconferences. Finally, the article has been reviewed by an independent panel of international experts.

RESULTS

Strict governance is indispensable in surveillance networks, especially when manual decisions are replaced by algorithms and electronically stored routine-care data are reused for the purpose of surveillance. For endorsement of AS networks, governance aspects specifically related to AS networks need to be addressed. Key considerations include enabling participation and inclusion, trust in the collection, use and quality of data (including data protection), accountability and transparency.

CONCLUSIONS

This article on governance aspects can be used by coordinating centres and healthcare facilities participating in an AS network as a starting point to set up governance structures. Involvement of main stakeholders and legal specialists early in the development of an AS network is important for endorsement, inclusivity and compliance with the laws and regulations that apply.

PRAISE: providing a roadmap for automated infection surveillance in Europe

INTRODUCTION

Healthcare-associated infections (HAI) are among the most common adverse events of medical care. Surveillance of HAI is a key component of successful infection prevention programmes. Conventional surveillance - manual chart review - is resource intensive and limited by concerns regarding interrater reliability. This has led to the development and use of automated surveillance (AS). Many AS systems are the product of in-house development efforts and heterogeneous in their design and methods. With this roadmap, the PRAISE network aims to provide guidance on how to move AS from the research setting to large-scale implementation, and how to ensure the delivery of surveillance data that are uniform and useful for improvement of quality of care.

METHODS

The PRAISE network brings together 30 experts from ten European countries. This roadmap is based on the outcome of two workshops, teleconference meetings and review by an independent panel of international experts.

RESULTS

This roadmap focuses on the surveillance of HAI within networks of healthcare facilities for the purpose of comparison, prevention and quality improvement initiatives. The roadmap does the following: discusses the selection of surveillance targets, different organizational and methodologic approaches and their advantages, disadvantages and risks; defines key performance requirements of AS systems and suggestions for their design; provides guidance on successful implementation and maintenance; and discusses areas of future research and training requirements for the infection prevention and related disciplines. The roadmap is supported by accompanying documents regarding the governance and information technology aspects of implementing AS.

CONCLUSIONS

Large-scale implementation of AS requires guidance and coordination within and across surveillance networks. Transitions to large-scale AS entail redevelopment of surveillance methods and their interpretation, intensive dialogue with stakeholders and the investment of considerable resources. This roadmap can be used to guide future steps towards implementation, including designing solutions for AS and practical guidance checklists.

Registration of catheter-related complications in adverse events reporting systems: a major underestimation of the real complication practice

Reporting and learning from preventable adverse events is crucial to improve patient safety. Although physicians should file and analyse adverse events by law in The Netherlands, it is unknown if these reporting systems are sufficiently used in clinical practice. This study is a substudy of the multicenter RICAT trial, a successful quality improvement project to reduce inappropriate use of intravenous and urinary catheters in medical wards in seven hospitals, in which we screened 5696 patients and documented 803 catheter-related complications. We also checked the adverse events reporting systems of these patients and found that only 13 (1.6%) of 803 catheter-related complications were registered. Of the infectious complications only five (10.9%) of 46 catheter-associated bloodstream infections and urinary tract infections were registered. We conclude that the reported complications were a major underestimation of the real complication practice in medical wards in The Netherlands.

The RICAT trial is registered at Netherlands Trial Register, trial NL5438.

Automated surveillance in French ICUs: is it feasible? Results from a survey in French ICUs participating in a surveillance network

A survey was undertaken to evaluate the level of computerization in intensive care units (ICUs) within a French network dedicated to the surveillance of healthcare-associated infections, antimicrobial use (AMU) and antimicrobial resistance (AMR) in ICUs (REA-REZO). Ninety-eight ICUs responded, and patient records were computerized in 57%, antimicrobial prescriptions were computerized in 59% and AMR epidemiology was computerized in 72%. AMU and AMR feedback was provided to the ICU itself for 77% and 65% of ICUs, respectively, and feedback was provided to the national surveillance for 79% and 65% of ICUs, respectively. This study suggests that the level of computerization in ICUs requires further improvement.

Improving surgical site infection prevention in Asia-Pacific through appropriate surveillance programs: Challenges and recommendation

BACKGROUND

Surgical site infections (SSIs) represent a substantial clinical and economic burden on patients and the healthcare system. The prevention of SSIs entails surveillance activities which lead to effective mitigation strategies, which are lacking across Asia Pacific (APAC). This manuscript aims to document gaps and challenges across APAC that affect the undertaking of a successful SSI surveillance activities and to provide recommendations on overcoming such challenges.

METHODS

A targeted literature review with relevance to APAC identified a series of salient points pertaining to SSI prevention guidelines, implementation, surveillance and outcomes, which was discussed in July 2019 at the APAC Surgical Site Infection Prevention Symposium. An expert panel, comprising eight multidisciplinary experts from APAC and the USA, subsequently amalgamated the key discussion points from the Symposium and their clinical experiences in developing this article.

RESULTS

The barriers to implementing a successful and effective APAC SSI surveillance program were identified as: (a) lack of standardized definitions, reporting methodology and accountability, (b) lack of fiscal resources, (c) reporting variability and under-reporting, and (d) lack of safety culture. Implementing an effective surveillance program in APAC will require countries to develop a well-designed and robust surveillance plan and ensure adequate training for staffs involved.

CONCLUSION

To improve SSI prevention in the region, it is imperative to encourage implementation of national programs with standardized methodologies and accountabilities. An ongoing APAC information exchange, including data and methodologies, will enable continuous learning within the APAC region.

Expert commentary on the challenges and opportunities for surgical site infection prevention through implementation of evidence-based guidelines in the Asia-Pacific Region

INTRODUCTION

Surgical site infections (SSIs) are a significant source of morbidity and mortality in the Asia-Pacific region (APAC), adversely impacting patient quality of life, fiscal productivity and placing a major economic burden on the country's healthcare system. This commentary reports the findings of a two-day meeting that was held in Singapore on July 30-31, 2019, where a series of consensus recommendations were developed by an expert panel composed of infection control, surgical and quality experts from APAC nations in an effort to develop an evidence-based pathway to improving surgical patient outcomes in APAC.

METHODS

The expert panel conducted a literature review targeting four sentinel areas within the APAC region: national and societal guidelines, implementation strategies, postoperative surveillance and clinical outcomes. The panel formulated a series of key questions regarding APAC-specific challenges and opportunities for SSI prevention.

RESULTS

The expert panel identified several challenges for mitigating SSIs in APAC; (a) constraints on human resources, (b) lack of adequate policies and procedures, (c) lack of a strong safety culture, (d) limitation in funding resources, (e) environmental and geographic challenges, (f) cultural diversity, (g) poor patient awareness and (h) limitation in self-responsibility. Corrective strategies for guideline implementation in APAC were proposed that included: (a) institutional ownership of infection prevention strategies, (b) perform baseline assessments, (c) review evidence-based practices within the local context, (d) develop a plan for guideline implementation, (e) assess outcome and stakeholder feedback, and (f) ensure long-term sustainability.

CONCLUSIONS

Reducing the risk of SSIs in APAC region will require: (a) ongoing consultation and collaboration among stakeholders with a high level of clinical staff engagement and (b) a strong institutional and national commitment to alleviate the burden of SSIs by embracing a safety culture and accountability.

Development of a fully automated surgical site infection detection algorithm for use in cardiac and orthopedic surgery research

Objective:

To develop a fully automated algorithm using data from the Veterans’ Affairs (VA) electrical medical record (EMR) to identify deep-incisional surgical site infections (SSIs) after cardiac surgeries and total joint arthroplasties (TJAs) to be used for research studies.

Design:

Retrospective cohort study.

Setting:

This study was conducted in 11 VA hospitals.

Participants:

Patients who underwent coronary artery bypass grafting or valve replacement between January 1, 2010, and March 31, 2018 (cardiac cohort) and patients who underwent total hip arthroplasty or total knee arthroplasty between January 1, 2007, and March 31, 2018 (TJA cohort).

Methods:

Relevant clinical information and administrative code data were extracted from the EMR. The outcomes of interest were mediastinitis, endocarditis, or deep-incisional or organ-space SSI within 30 days after surgery. Multiple logistic regression analysis with a repeated regular bootstrap procedure was used to select variables and to assign points in the models. Sensitivities, specificities, positive predictive values (PPVs) and negative predictive values were calculated with comparison to outcomes collected by the Veterans’ Affairs Surgical Quality Improvement Program (VASQIP).

Results:

Overall, 49 (0.5%) of the 13,341 cardiac surgeries were classified as mediastinitis or endocarditis, and 83 (0.6%) of the 12,992 TJAs were classified as deep-incisional or organ-space SSIs. With at least 60% sensitivity, the PPVs of the SSI detection algorithms after cardiac surgeries and TJAs were 52.5% and 62.0%, respectively.

Conclusions:

Considering the low prevalence rate of SSIs, our algorithms were successful in identifying a majority of patients with a true SSI while simultaneously reducing false-positive cases. As a next step, validation of these algorithms in different hospital systems with EMR will be needed.

Sustainable neonatal CLABSI surveillance: consensus towards new criteria in the Netherlands

BACKGROUND

Central line-associated bloodstream infections (CLABSI) are a main focus of infection prevention and control initiatives in neonatal care. Standardised surveillance of neonatal CLABSI enables intra- and interfacility comparisons which can contribute to quality improvement. To date, there is no national registration system for CLABSI in neonatal care in the Netherlands and several criteria are used for local monitoring of CLABSI incidence rates. To achieve standardised CLABSI surveillance we conducted a consensus procedure with regard to nationwide neonatal CLABSI surveillance criteria (SC).

METHODS

A modified Delphi consensus procedure for the development of nationwide neonatal CLABSI SC was performed between January 2016 and January 2017 in the Netherlands. An expert panel was formed by members of the Working Group on Neonatal Infectious Diseases of the Section of Neonatology of the Dutch Paediatric Society. The consensus procedure consisted of three expert panel rounds.

RESULTS

The expert panel achieved consensus on Dutch neonatal CLABSI SC. Neonatal CLABSI is defined as a bloodstream infection occurring more than 72 h after birth, associated with an indwelling central venous or arterial line and laboratory confirmed by one or more blood cultures. In addition, the blood culture finding should not be related to an infection at another site and one of the following criteria can be applied: 1. a bacterial or fungal pathogen is identified from one or more blood cultures; 2. the patient has clinical symptoms of sepsis and 2A) a common commensal is identified in two separate blood cultures or 2B) a common commensal is identified by one blood culture and C-reactive protein level is above 10 mg/L in the first 36 h following blood culture collection.

CONCLUSIONS

The newly developed Dutch neonatal CLABSI SC are concise, specified to the neonatal population and comply with a single blood culture policy in actual neonatal clinical practice. International agreement upon neonatal CLABSI SC is needed to identify best practices for infection prevention and control.

The accuracy of fully automated algorithms for surveillance of healthcare-associated urinary tract infections in hospitalized patients

BACKGROUND

Surveillance for healthcare-associated infections such as healthcare-associated urinary tract infections (HA-UTI) is important for directing resources and evaluating interventions. However, traditional surveillance methods are resource-intensive and subject to bias.

AIM

To develop and validate a fully automated surveillance algorithm for HA-UTI using electronic health record (EHR) data.

METHODS

Five algorithms were developed using EHR data from 2979 admissions at Karolinska University Hospital from 2010 to 2011: (1) positive urine culture (UCx); (2) positive UCx + UTI codes (International Statistical Classification of Diseases and Related Health Problems, 10^th revision); (3) positive UCx + UTI-specific antibiotics; (4) positive UCx + fever and/or UTI symptoms; (5) algorithm 4 with negation for fever without UTI symptoms. Natural language processing (NLP) was used for processing free-text medical notes. The algorithms were validated in 1258 potential UTI episodes from January to March 2012 and results extrapolated to all UTI episodes within this period (N = 16,712). The reference standard for HA-UTIs was manual record review according to the European Centre for Disease Prevention and Control (and US Centers for Disease Control and Prevention) definitions by trained healthcare personnel.

FINDINGS

Of the 1258 UTI episodes, 163 fulfilled the ECDC HA-UTI definition and the algorithms classified 391, 150, 189, 194, and 153 UTI episodes, respectively, as HA-UTI. Algorithms 1, 2, and 3 had insufficient performances. Algorithm 4 achieved better performance and algorithm 5 performed best for surveillance purposes with sensitivity 0.667 (95% confidence interval: 0.594-0.733), specificity 0.997 (0.996-0.998), positive predictive value 0.719 (0.624-0.807) and negative predictive value 0.997 (0.996-0.997).

CONCLUSION

A fully automated surveillance algorithm based on NLP to find UTI symptoms in free-text had acceptable performance to detect HA-UTI compared to manual record review. Algorithms based on administrative and microbiology data only were not sufficient.

A framework to develop semiautomated surveillance of surgical site infections: An international multicenter study

OBJECTIVE

Automated surveillance of healthcare-associated infections reduces workload and improves standardization, but it has not yet been adopted widely. In this study, we assessed the performance and feasibility of an easy implementable framework to develop algorithms for semiautomated surveillance of deep incisional and organ-space surgical site infections (SSIs) after orthopedic, cardiac, and colon surgeries.

DESIGN

Retrospective cohort study in multiple countries.

METHODS

European hospitals were recruited and selected based on the availability of manual SSI surveillance data from 2012 onward (reference standard) and on the ability to extract relevant data from electronic health records. A questionnaire on local manual surveillance and clinical practices was administered to participating hospitals, and the information collected was used to pre-emptively design semiautomated surveillance algorithms standardized for multiple hospitals and for center-specific application. Algorithm sensitivity, positive predictive value, and reduction of manual charts requiring review were calculated. Reasons for misclassification were explored using discrepancy analyses.

RESULTS

The study included 3 hospitals, in the Netherlands, France, and Spain. Classification algorithms were developed to indicate procedures with a high probability of SSI. Components concerned microbiology, prolonged length of stay or readmission, and reinterventions. Antibiotics and radiology ordering were optional. In total, 4,770 orthopedic procedures, 5,047 cardiac procedures, and 3,906 colon procedures were analyzed. Across hospitals, standardized algorithm sensitivity ranged between 82% and 100% for orthopedic surgery, between 67% and 100% for cardiac surgery, and between 84% and 100% for colon surgery, with 72%-98% workload reduction. Center-specific algorithms had lower sensitivity.

CONCLUSIONS

Using this framework, algorithms for semiautomated surveillance of SSI can be successfully developed. The high performance of standardized algorithms holds promise for large-scale standardization.

Effectiveness of implementing link nurses and audits and feedback to improve nurses' compliance with standard precautions: A cluster randomized controlled trial

BACKGROUND

To prevent health care-associated infections, health organizations recommend that health care workers stringently observe standard precautions (SPs). Nevertheless, compliance with SPs is still suboptimal, emphasizing the need for improvement interventions.

METHODS

A cluster randomized controlled trial with a pretest-post-test design was conducted with 121 clinical nurses who worked in different wards of a university hospital. The intervention group (n = 61) had 3 infection control link nurses nominated and attended systematic audits and feedback. The control group (n = 60) received only the standard multimodal approach used in the hospital. Pre- and post-test assessment of SPs compliance was performed via the World Health Organization observational hand hygiene form and Compliance with Standard Precaution Scale Italian version.

RESULTS

At the post-test, nurses in the intervention group reported significantly increased compliance with hand hygiene, whereas no significant improvement was found in the control group. Nurses in both groups reported significantly increased Compliance with Standard Precaution Scale Italian version scores; however, a higher increase and practical significance was observed in the intervention group. Participants who improved their scores were also compared between groups, showing a significantly greater increase of individual scores in intervention group compared to the control group.

CONCLUSIONS

The findings of this study provide significant practical implications for hospitals seeking to improve compliance with SPs among nurses, showing the effectiveness of using infection control link nurses combined with systematic audits and feedback.

Validation of an algorithm for semiautomated surveillance to detect deep surgical site infections after primary total hip or knee arthroplasty-A multicenter study

OBJECTIVE

Surveillance of healthcare-associated infections is often performed by manual chart review. Semiautomated surveillance may substantially reduce workload and subjective data interpretation. We assessed the validity of a previously published algorithm for semiautomated surveillance of deep surgical site infections (SSIs) after total hip arthroplasty (THA) or total knee arthroplasty (TKA) in Dutch hospitals. In addition, we explored the ability of a hospital to automatically select the patients under surveillance.

DESIGN

Multicenter retrospective cohort study.

METHODS

Hospitals identified patients who underwent THA or TKA either by procedure codes or by conventional surveillance. For these patients, routine care data regarding microbiology results, antibiotics, (re)admissions, and surgeries within 120 days following THA or TKA were extracted from electronic health records. Patient selection was compared with conventional surveillance and patients were retrospectively classified as low or high probability of having developed deep SSI by the algorithm. Sensitivity, positive predictive value (PPV), and workload reduction were calculated and compared to conventional surveillance.

RESULTS

Of 9,554 extracted THA and TKA surgeries, 1,175 (12.3%) were revisions, and 8,378 primary surgeries remained for algorithm validation (95 deep SSIs, 1.1%). Sensitivity ranged from 93.6% to 100% and PPV ranged from 55.8% to 72.2%. Workload was reduced by ≥98%. Also, 2 SSIs (2.1%) missed by the algorithm were explained by flaws in data selection.

CONCLUSIONS

This algorithm reliably detects patients with a high probability of having developed deep SSI after THA or TKA in Dutch hospitals. Our results provide essential information for successful implementation of semiautomated surveillance for deep SSIs after THA or TKA.

Validation of a semi-automated surveillance system for surgical site infections: Improving exhaustiveness, representativeness, and efficiency

OBJECTIVES

To assess whether electronic records data could improve the efficiency, exhaustiveness, and representativeness of SSI surveillance by selecting a group of high-risk patients for manual review.

METHODS

Colorectal surgeries (2016-2018) and cholecystectomies (2017-2018) were selected. Post-surgical antibiotic use, positive culture, C-reactive protein (CRP) values, body temperature, leukocyte count, surgical re-intervention, admission to the emergency room, and hospital readmission were retrieved. For representativeness, procedures registered in HAI-Net were compared with non-included procedures, and the validity of each variable (or combination) was tested considering the presence of SSI as the gold standard. The proportion of procedures flagged for manual review by each criterion was estimated.

RESULTS

Little more than 50% of procedures were included in HAI-Net (SSI risk: 10.6% for colorectal and 2.9% for cholecystectomies). Non-included procedures showed higher proportions of infection markers. Antibiotic use and CRP >100 mg/dl presented the highest sensitivity for both surgical groups, while antibiotic use achieved the highest positive predictive value in both groups (22% and 21%, respectively) and flagged fewer colorectal procedures (47.7%).

CONCLUSIONS

Current SSI surveillance has major limitations. Thus, the reported incidence seems unreliable and underestimated. Antibiotic use appears to be the best criterion to select a sub-sample of procedures for manual review, improving the exhaustiveness and efficiency of the system.

Validation of automated sepsis surveillance based on the Sepsis-3 clinical criteria against physician record review in a general hospital population: observational study using electronic health records data

BACKGROUND

Surveillance of sepsis incidence is important for directing resources and evaluating quality-of-care interventions. The aim was to develop and validate a fully-automated Sepsis-3 based surveillance system in non-intensive care wards using electronic health record (EHR) data, and demonstrate utility by determining the burden of hospital-onset sepsis and variations between wards.

METHODS

A rule-based algorithm was developed using EHR data from a cohort of all adult patients admitted at an academic centre between July 2012 and December 2013. Time in intensive care units was censored. To validate algorithm performance, a stratified random sample of 1000 hospital admissions (674 with and 326 without suspected infection) was classified according to the Sepsis-3 clinical criteria (suspected infection defined as having any culture taken and at least two doses of antimicrobials administered, and an increase in Sequential Organ Failure Assessment (SOFA) score by >2 points) and the likelihood of infection by physician medical record review.

RESULTS

In total 82 653 hospital admissions were included. The Sepsis-3 clinical criteria determined by physician review were met in 343 of 1000 episodes. Among them, 313 (91%) had possible, probable or definite infection. Based on this reference, the algorithm achieved sensitivity 0.887 (95% CI: 0.799 to 0.964), specificity 0.985 (95% CI: 0.978 to 0.991), positive predictive value 0.881 (95% CI: 0.833 to 0.926) and negative predictive value 0.986 (95% CI: 0.973 to 0.996). When applied to the total cohort taking into account the sampling proportions of those with and without suspected infection, the algorithm identified 8599 (10.4%) sepsis episodes. The burden of hospital-onset sepsis (>48 hour after admission) and related in-hospital mortality varied between wards.

CONCLUSIONS

A fully-automated Sepsis-3 based surveillance algorithm using EHR data performed well compared with physician medical record review in non-intensive care wards, and exposed variations in hospital-onset sepsis incidence between wards.

Healthcare Associated Infections: An Interoperable Infrastructure for Multidrug Resistant Organism Surveillance

Prevention and surveillance of healthcare associated infections caused by multidrug resistant organisms (MDROs) has been given increasing attention in recent years and is nowadays a major priority for health care systems. The creation of automated regional, national and international surveillance networks plays a key role in this respect. A surveillance system has been designed for the Abruzzo region in Italy, focusing on the monitoring of the MDROs prevalence in patients, on the appropriateness of antibiotic prescription in hospitalized patients and on foreseeable interactions with other networks at national and international level. The system has been designed according to the Service Oriented Architecture (SOA) principles, and Healthcare Service Specification (HSSP) standards and Clinical Document Architecture Release 2 (CDAR2) have been adopted. A description is given with special reference to implementation state, specific design and implementation choices and next foreseeable steps. The first release will be delivered at the Complex Operating Unit of Infectious Diseases of the Local Health Authority of Pescara (Italy).

Healthcare-Associated Infections: Not Only a Clinical Burden, But a Forensic Point of View

BACKGROUND

Healthcare-associated infections (HCAIs) occur when patients receiving treatment in a health care setting develop an infection. They represent a major public health problem, requiring the integration of clinical medicine, pathology, epidemiology, laboratory sciences, and, finally, forensic medicine.

METHODS

The determination of cause of death is fundamental not only in the cases of presumed malpractice to ascertain the causal link with any negligent behavior both of health facilities and of individual professionals, but also for epidemiological purposes since it may help to know the global burden of HCAIs, that remains undetermined because of the difficulty of gathering reliable diagnostic data. A complete methodological approach, integrating clinical data by means of autopsy and histological and laboratory findings aiming to identify and demonstrate the host response to infectious insult, is mandatory in HCAIs related deaths.

RESULTS

Important tasks for forensic specialists in hospitals and health services centers are the promotion of transparency and open communication by health-care workers on the risk of HCAIs, thus facilitating patients' engagement and the implementation of educational interventions for professionals aimed to improve their knowledge and adherence to prevention and control measures.

CONCLUSION

HCAIs are a major problem for patient safety in every health-care facility and system around the world and their control and prevention represent a challenging priority for healthcare institution and workers committed to making healthcare safer. Clinicians are at the forefront in the war against HCAIs, however, also forensic pathologists have a remarkable role.

How do surgeons feel about the 'Getting it Right First Time' national audit? Results from a qualitative assessment

The implementation of the national 'Getting It Right First Time' was assessed by interviewing six surgeons involved at various levels in surgical site infection (SSI) audit. The positive impacts were to create new professional collaboration, improve stakeholder engagement, and increase the profile of SSIs. One particular knowledge gap highlighted was that some participants had been unaware until that point of the criteria for diagnosing an SSI. The quality of data collected was felt to be poor due to methodological flaws. The audit was described as highly time-consuming and unsustainable if leaning on junior surgeons, without protected time and designated responsibility.

Evaluation of manual and electronic healthcare-associated infections surveillance: a multi-center study with 21 tertiary general hospitals in China

Background

Healthcare-associated infections (HAIs) are still a major health threats worldwide. Traditional surveillance methods involving manual surveillance by infection control practitioners (ICPs) for data collection processes are laborious, inefficient, and generate data of variable quality. In this study, we sought to evaluate the impact of surveillance and interaction platform system (SIPS) for HAIs surveillance compared to manual survey in tertiary general hospitals.

Methods

A large multi-center study including 21 tertiary general hospitals and 63 wards were performed to evaluate the impact of electronic SIPS for HAIs.

Results

We collected 4,098 consecutive patients and found that the hospitals installed with SIPS significantly increased work efficiency of ICPs achieving satisfactory diagnostic performance of HAIs with 0.73 for sensitivity, 0.81 for specificity and 0.81 area under the curve (AUC). However, there were significant heterogeneity own to regions, time of SIPS installation, departments and sample size.

Conclusions

SIPS significantly improved ICPs efficiency and HAIs monitoring effectiveness, but there were shortcomings such as untimely maintenance and high cost.

The Minimum Data Set and Quality Indicators for National Healthcare-Associated Infection Surveillance in Mainland China: Towards Precision Management

The magnitude and scope of the healthcare-associated infections (HCAIs) burden are underestimated worldwide, and have raised public concerns for their adverse effect on patient safety. In China, HCAIs still present an unneglected challenge and economic burden in recent decades. With the purpose of reducing the HCAI prevalence and enhancing precision management, China's National Nosocomial Infection Management and Quality Control Center (NNIMQCC) had developed a Minimum Data Set (MDS) and corresponding Quality Indicators (QIs) for establishing national HCAI surveillance system, the data elements of which were repeatedly discussed, investigated, and confirmed by consensus of the expert team. The total number of data elements in MDS and QIs were 70 and 64, and they were both classified into seven categorical items. The NNIMQCC also had started two pilot projects to inspect the applicability, feasibility, and reliability of MDS. After years of hard work, more than 400 health facilities in 14 provinces have realized the importance of HCAI surveillance and contributed to developing an ability of exporting automatically standardized data to meet the requirement of MDS and participate in the regional surveillance system. Generally, the emergence of MDS and QIs in China indicates the beginning of the national HCAI surveillance based on information technology and computerized process data. The establishment of MDS aimed to use electronic health process data to ensure the data accuracy and comparability and to provide instructive and ongoing QIs to estimate and monitor the burden of HCAIs, and to evaluate the effects of interventions and direct health policy decision-making.

Healthcare-associated infections in intensive care units in Taiwan, South Korea, and Japan: recent trends based on national surveillance reports

Background

Sustainable systematic interventions are important for infection prevention and control (IPC). Data from surveillance of healthcare-associated infections (HAI) provides feedback for implementation of IPC programs. To address the paucity of such data in Asia, we searched for national HAI surveillance and IPC programs in this region.

Methods

Data were analysed from open access national surveillance reports of three Asian countries: Taiwan, South Korea and Japan from 2008 to 2015. National IPC programs were identified.

Results

There were differences among the countries in surveillance protocols, hospital coverage rates, and national IPC policies and programs. Nevertheless, there was a 53.0% reduction in overall HAI over the 8-year period. This consisted of a decrease from 9.34 to 5.03 infections per 1000 patient-days in Taiwan, from 7.56 to 2.76 in Korea, and from 4.41 to 2.74 in Japan (Poisson regression, all p < 0.05). Across the three countries, Escherichia coli and Candida albicans were the major pathogens for urinary tract infection. Staphylococcus aureus, Acinetobacter baumannii and Enterococcus faecium were common bloodstream pathogens. For pneumonia, S. aureus, A. baumannii, Pseudomonas aeruginosa, and Klebsiella pneumoniae were the predominant pathogens, with considerable country differences. There was a 64.6% decrease in the number of isolates of methicillin-resistant S. aureus, 38.4% decrease in carbapenem-resistant P. aeruginosa and 49.2% decrease in carbapenem-resistant A. baumannii (CRAB) in Taiwan (all p < 0.05), and similarly in Korea with the exception of CRAB (30.5 and 50.4% reduction, respectively, both p < 0.05).

Conclusion

We found a significant decrease in HAI across the three countries in association with sequential multifaceted interventions such as hand hygiene, care bundles, and antimicrobial stewardships. Further regional collaboration could be forged to develop joint strategies to prevent HAI.