Lean back and wait for the alarm? Testing an automated alarm system for nosocomial outbreaks to provide support for infection control professionals

Comparing automated surveillance systems for detection of pathogen-related clusters in healthcare settings

BACKGROUND

Detection of pathogen-related clusters within a hospital is key to early intervention to prevent onward transmission. Various automated surveillance methods for outbreak detection have been implemented in hospital settings. However, direct comparison is difficult due to heterogenicity of data sources and methodologies. In the hospital setting, we assess the performance of three different methods for identifying microbiological clusters when applied to various pathogens with distinct occurrence patterns.

METHODS

In this retrospective cohort study we use WHONET-SaTScan, CLAR (CLuster AleRt system) and our currently used percentile-based system (P75) for the means of cluster detection. The three methods are applied to the same data curated from 1st January 2014 to 31st December 2021 from a tertiary care hospital. We show the results for the following case studies: the introduction of a new pathogen with subsequent endemicity, an endemic species, rising levels of an endemic organism, and a sporadically occurring species.

RESULTS

All three cluster detection methods showed congruence only in endemic organisms. However, there was a paucity of alerts from WHONET-SaTScan (n = 9) compared to CLAR (n = 319) and the P75 system (n = 472). WHONET-SaTScan did not pick up smaller variations in baseline numbers of endemic organisms as well as sporadic organisms as compared to CLAR and the P75 system. CLAR and the P75 system revealed congruence in alerts for both endemic and sporadic organisms.

CONCLUSIONS

Use of statistically based automated cluster alert systems (such as CLAR and WHONET-Satscan) are comparable to rule-based alert systems only for endemic pathogens. For sporadic pathogens WHONET-SaTScan returned fewer alerts compared to rule-based alert systems. Further work is required regarding clinical relevance, timelines of cluster alerts and implementation.

Risk factors for transmission of carbapenem-resistant Acinetobacter baumannii in outbreak situations: results of a case-control study

BACKGROUND

An increase in patients with multidrug-resistant organisms and associated outbreaks during the COVID-19 pandemic have been reported in various settings, including low-endemic settings. Here, we report three distinct carbapenem-resistant Acinetobacter baumannii (CRAB) outbreaks in five intensive care units of a university hospital in Berlin, Germany during the COVID-19 pandemic.

METHODS

A case-control study was conducted with the objective of identifying risk factors for CRAB acquisition in outbreak situations. Data utilized for the case-control study came from the investigation of three separate CRAB outbreaks during the COVID-19 pandemic (August 2020- March 2021). Cases were defined as outbreak patients with hospital-acquired CRAB. Controls did not have any CRAB positive microbiological findings and were hospitalized at the same ward and for a similar duration as the respective case. Control patients were matched retrospectively in a 2:1 ratio. Parameters routinely collected in the context of outbreak management and data obtained retrospectively specifically for the case-control study were included in the analysis. To analyze risk factors for CRAB acquisition, univariable and multivariable analyses to calculate odds ratios (OR) and 95% confidence intervals (CI) were performed using a conditional logistic regression model.

RESULTS

The outbreaks contained 26 cases with hospital-acquired CRAB in five different intensive care units. Two exposures were identified to be independent risk factors for nosocomial CRAB acquisition by the multivariable regression analysis: Sharing a patient room with a CRAB patient before availability of the microbiological result was associated with a more than tenfold increase in the risk of nosocomial CRAB acquisition (OR: 10.7, CI: 2.3-50.9), while undergoing bronchoscopy increased the risk more than six times (OR: 6.9, CI: 1.3-38.1).

CONCLUSIONS

The risk factors identified, sharing a patient room with a CRAB patient and undergoing bronchoscopy, could point to an underperformance of basic infection control measure, particularly hand hygiene compliance and handling of medical devices. Both findings reinforce the need for continued promotion of infection control measures. Given that the outbreaks occurred in the first year of the COVID-19 pandemic, our study serves as a reminder that a heightened focus on airborne precautions should not lead to a neglect of other transmission-based precautions.

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.

Analysis of transmission-related third-generation cephalosporin-resistant Enterobacterales by electronic data mining and core genome multi-locus sequence typing

BACKGROUND

To contain intra-hospital transmission of third-generation cephalosporin-resistant Enterobacterales (3GCR-E), contact isolation precautions are recommended.

AIM

To quantify transmissions of 3GCR Escherichia coli and 3GCR Klebsiella pneumoniae within a hospital.

METHODS

An automated outbreak detection system (AODS) was used to identify clusters (N≥2) of 3GCR Enterobacterales for the years 2016, 2018 and 2020. Clusters were defined by phenotypic agreement of microbiological results and spatial and temporal relationship. Core genome multi-locus sequence typing (cgMLST) was used to confirm whether the cluster isolates were transmitted between patients.

FINDINGS

A total of 4343 3GCR E. coli and 1377 K. pneumoniae isolates were analysed. Among the 3GCR E. coli isolates, the AODS identified 304 isolates as cluster isolates, the median cluster size was two (range: 2-5). The cgMLST analysis revealed that a total of 23 (7.5%) 3GCR E. coli cluster isolates were transmission-associated, of which 20 isolates (87%) were detected in intensive care patients. Among the 3GCR K. pneumoniae isolates, the AODS identified 73 isolates as cluster isolates, the median cluster size was two (range: 2-4). CgMLST revealed that 35 (48%) 3GCR K. pneumoniae cluster isolates were transmission associated, of which 27 isolates (77%) were detected in intensive care patients.

CONCLUSION

For 3GCR K. pneumoniae, cgMLST confirmed the AODS results more frequently than for 3GCR E. coli. Therefore, contact isolation precautions for 3GCR K. pneumoniae may be appropriate on intensive care units, but only in certain circumstances, such as outbreaks, for Enterobacterales with lower transmissibility, such as E. coli.

Split k-mer analysis compared to cgMLST and SNP-based core genome analysis for detecting transmission of vancomycin-resistant enterococci: results from routine outbreak analyses across different hospitals and hospitals networks in Berlin, Germany

The increase of Vancomycin-resistant Enterococcus faecium (VREfm) in recent years has been partially attributed to the rise of specific clonal lineages, which have been identified throughout Germany. To date, there is no gold standard for the interpretation of genomic data for outbreak analyses. New genomic approaches such as split k-mer analysis (SKA) could support cluster attribution for routine outbreak investigation. The aim of this project was to investigate frequent clonal lineages of VREfm identified during suspected outbreaks across different hospitals, and to compare genomic approaches including SKA in routine outbreak investigation. We used routine outbreak laboratory data from seven hospitals and three different hospital networks in Berlin, Germany. Short-read libraries were sequenced on the Illumina MiSeq system. We determined clusters using the published Enterococcus faecium-cgMLST scheme (threshold ≤20 alleles), and assigned sequence and complex types (ST, CT), using the Ridom SeqSphere+ software. For each cluster as determined by cgMLST, we used pairwise core-genome SNP-analysis and SKA at thresholds of ten and seven SNPs, respectively, to further distinguish cgMLST clusters. In order to investigate clinical relevance, we analysed to what extent epidemiological linkage backed the clusters determined with different genomic approaches. Between 2014 and 2021, we sequenced 693 VREfm strains, and 644 (93 %) were associated within cgMLST clusters. More than 74 % (n=475) of the strains belonged to the six largest cgMLST clusters, comprising ST117, ST78 and ST80. All six clusters were detected across several years and hospitals without apparent epidemiological links. Core SNP analysis identified 44 clusters with a median cluster size of three isolates (IQR 2-7, min-max 2-63), as well as 197 singletons (41.4 % of 475 isolates). SKA identified 67 clusters with a median cluster size of two isolates (IQR 2-4, min-max 2-19), and 261 singletons (54.9 % of 475 isolates). Of the isolate pairs attributed to clusters, 7 % (n=3064/45 596) of pairs in clusters determined by standard cgMLST, 15 % (n=1222/8500) of pairs in core SNP-clusters and 51 % (n=942/1880) of pairs in SKA-clusters showed epidemiological linkage. The proportion of epidemiological linkage differed between sequence types. For VREfm, the discriminative ability of the widely used cgMLST based approach at ≤20 alleles difference was insufficient to rule out hospital outbreaks without further analytical methods. Cluster assignment guided by core genome SNP analysis and the reference free SKA was more discriminative and correlated better with obvious epidemiological linkage, at least recently published thresholds (ten and seven SNPs, respectively) and for frequent STs. Besides higher overall discriminative power, the whole-genome approach implemented in SKA is also easier and faster to conduct and requires less computational resources.

Vancomycin-resistant Enterococcus faecium: admission prevalence, sequence types and risk factors-a cross-sectional study in seven German university hospitals from 2014 to 2018

OBJECTIVES

Assessment of vancomycin-resistant Enterococcus faecium (VREfm) prevalence upon hospital admission and analysis of risk factors for colonization.

METHODS

From 2014 to 2018, patients were recruited within 72 hours of admission to seven participating German university hospitals, screened for VREfm and questioned for potential risk factors (prior multidrug-resistant organism detection, current/prior antibiotic consumption, prior hospital, rehabilitation or long-term care facility stay, international travel, animal contact and proton pump inhibitor [PPI]/antacid therapy). Genotype analysis was done using cgMLST typing. Multivariable analysis was performed.

RESULTS

In 5 years, 265 of 17,349 included patients were colonized with VREfm (a prevalence of 1.5%). Risk factors for VREfm colonization were age (adjusted OR [aOR], 1.02; 95% CI, 1.01-1.03), previous (aOR, 2.71; 95% CI, 1.87-3.92) or current (aOR, 2.91; 95% CI, 2.60-3.24) antibiotic treatment, prior multidrug-resistant organism detection (aOR, 2.83; 95% CI, 2.21-3.63), prior stay in a long-term care facility (aOR, 2.19; 95% CI, 1.62-2.97), prior stay in a hospital (aOR, 2.91; 95% CI, 2.05-4.13) and prior consumption of PPI/antacids (aOR, 1.29; 95% CI, 1.18-1.41). Overall, the VREfm admission prevalence increased by 33% each year and 2% each year of life. 250 of 265 isolates were genotyped and 141 (53.2%) of the VREfm were the emerging ST117. Multivariable analysis showed that ST117 and non-ST117 VREfm colonized patients differed with respect to admission year and prior multidrug-resistant organism detection.

DISCUSSION

Age, healthcare contacts and antibiotic and PPI/antacid consumption increase the individual risk of VREfm colonization. The VREfm admission prevalence increase in Germany is mainly driven by the emergence of ST117.

Corticosteroids as risk factor for COVID-19-associated pulmonary aspergillosis in intensive care patients

PURPOSE

Corticosteroids, in particular dexamethasone, are one of the primary treatment options for critically ill COVID-19 patients. However, there are a growing number of cases that involve COVID-19-associated pulmonary aspergillosis (CAPA), and it is unclear whether dexamethasone represents a risk factor for CAPA. Our aim was to investigate a possible association of the recommended dexamethasone therapy with a risk of CAPA.

METHODS

We performed a study based on a cohort of COVID-19 patients treated in 2020 in our 13 intensive care units at Charité Universitätsmedizin Berlin. We used ECMM/ISHM criteria for the CAPA diagnosis and performed univariate and multivariable analyses of clinical parameters to identify risk factors that could result in a diagnosis of CAPA.

RESULTS

Altogether, among the n = 522 intensive care patients analyzed, n = 47 (9%) patients developed CAPA. CAPA patients had a higher simplified acute physiology score (SAPS) (64 vs. 53, p < 0.001) and higher levels of IL-6 (1,005 vs. 461, p < 0.008). They more often had severe acute respiratory distress syndrome (ARDS) (60% vs. 41%, p = 0.024), renal replacement therapy (60% vs. 41%, p = 0.024), and they were more likely to die (64% vs. 48%, p = 0.049). The multivariable analysis showed dexamethasone (OR 3.110, CI95 1.112-8.697) and SAPS (OR 1.063, CI95 1.028-1.098) to be independent risk factors for CAPA.

CONCLUSION

In our study, dexamethasone therapy as recommended for COVID-19 was associated with a significant three times increase in the risk of CAPA.

TRIAL REGISTRATION

Registration number DRKS00024578, Date of registration March 3rd, 2021.

Implementation of an automated cluster alert system into the routine work of infection control and hospital epidemiology: experiences from a tertiary care university hospital

Background

Early detection of clusters of pathogens is crucial for infection prevention and control (IPC) in hospitals. Conventional manual cluster detection is usually restricted to certain areas of the hospital and multidrug resistant organisms. Automation can increase the comprehensiveness of cluster surveillance without depleting human resources. We aimed to describe the application of an automated cluster alert system (CLAR) in the routine IPC work in a hospital. Additionally, we aimed to provide information on the clusters detected and their properties.

Methods

CLAR was continuously utilized during the year 2019 at Charité university hospital. CLAR analyzed microbiological and patient-related data to calculate a pathogen-baseline for every ward. Daily, this baseline was compared to data of the previous 14 days. If the baseline was exceeded, a cluster alert was generated and sent to the IPC team. From July 2019 onwards, alerts were systematically categorized as relevant or non-relevant at the discretion of the IPC physician in charge.

Results

In one year, CLAR detected 1,714 clusters. The median number of isolates per cluster was two. The most common cluster pathogens were Enterococcus faecium (n = 326, 19 %), Escherichia coli (n = 274, 16 %) and Enterococcus faecalis (n = 250, 15 %). The majority of clusters (n = 1,360, 79 %) comprised of susceptible organisms. For 906 alerts relevance assessment was performed, with 317 (35 %) alerts being classified as relevant.

Conclusions

CLAR demonstrated the capability of detecting small clusters and clusters of susceptible organisms. Future improvements must aim to reduce the number of non-relevant alerts without impeding detection of relevant clusters. Digital solutions to IPC represent a considerable potential for improved patient care. Systems such as CLAR could be adapted to other hospitals and healthcare settings, and thereby serve as a means to fulfill these potentials.

Transformation of microbiology data into a standardised data representation using OpenEHR

The spread of multidrug resistant organisms (MDRO) is a global healthcare challenge. Nosocomial outbreaks caused by MDRO are an important contributor to this threat. Computer-based applications facilitating outbreak detection can be essential to address this issue. To allow application reusability across institutions, the various heterogeneous microbiology data representations needs to be transformed into standardised, unambiguous data models. In this work, we present a multi-centric standardisation approach by using openEHR as modelling standard. Data models have been consented in a multicentre and international approach. Participating sites integrated microbiology reports from primary source systems into an openEHR-based data platform. For evaluation, we implemented a prototypical application, compared the transformed data with original reports and conducted automated data quality checks. We were able to develop standardised and interoperable microbiology data models. The publicly available data models can be used across institutions to transform real-life microbiology reports into standardised representations. The implementation of a proof-of-principle and quality control application demonstrated that the new formats as well as the integration processes are feasible. Holistic transformation of microbiological data into standardised openEHR based formats is feasible in a real-life multicentre setting and lays the foundation for developing cross-institutional, automated outbreak detection systems.

Prevalence and risk factors of colonisation with vancomycin-resistant Enterococci faecium upon admission to Germany's largest university hospital

Background: Hospital-acquired infections due to vancomycin-resistant enterococci (VRE) are emerging globally. The aims of our study were to estimate VRE colonisation prevalence in patients upon admission, to determine possible risk factors for VR E. faecium acquisition that already exist in the outpatient setting, and to monitor whether VRE-colonised patients developed a VRE infection during their current hospital stay.

Methods: In 2014 and 2015, patients admitted to non-intensive care units were screened for rectal VRE carriage. The study patients filled out a questionnaire on potential risk factors. Analyses were restricted to VR E. faecium carriage. All patients with VRE colonisation were retrospectively monitored for infections with VRE during their current hospital stay.

Results: In 4,013 enrolled patients, the VRE colonisation prevalence upon admission was 1.2% (n=48), and colonisation prevalence was 1.1% (n=45) for VR E. faecium. Only one VRE-colonised patient developed an infection with the detection of a VRE, among others. Colonisation with VR E. faecium was associated with current antibiotic use. Risk factors of VR E. faecium colonisation upon admission were increasing age, previous colonisation or infection with multidrug resistant organisms, sampling year 2015, and, within the previous six months, antibiotic exposure, a stay at a rehabilitation center, and a hospital stay.

Conclusions: We observed that antibiotic treatment which occurred prior admission influenced VR E. faecium prevalence upon admission. Thus, wise antibiotic use in outpatient settings plays a major role in the prevention of VR E. faecium acquisition.

An aberration detection-based approach for sentinel syndromic surveillance of COVID-19 and other novel influenza-like illnesses

Coronavirus Disease 2019 has emerged as a significant global concern, triggering harsh public health restrictions in a successful bid to curb its exponential growth. As discussion shifts towards relaxation of these restrictions, there is significant concern of second-wave resurgence. The key to managing these outbreaks is early detection and intervention, and yet there is a significant lag time associated with usage of laboratory confirmed cases for surveillance purposes. To address this, syndromic surveillance can be considered to provide a timelier alternative for first-line screening. Existing syndromic surveillance solutions are however typically focused around a known disease and have limited capability to distinguish between outbreaks of individual diseases sharing similar syndromes. This poses a challenge for surveillance of COVID-19 as its active periods tend to overlap temporally with other influenza-like illnesses. In this study we explore performing sentinel syndromic surveillance for COVID-19 and other influenza-like illnesses using a deep learning-based approach. Our methods are based on aberration detection utilizing autoencoders that leverages symptom prevalence distributions to distinguish outbreaks of two ongoing diseases that share similar syndromes, even if they occur concurrently. We first demonstrate that this approach works for detection of outbreaks of influenza, which has known temporal boundaries. We then demonstrate that the autoencoder can be trained to not alert on known and well-managed influenza-like illnesses such as the common cold and influenza. Finally, we applied our approach to 2019-2020 data in the context of a COVID-19 syndromic surveillance task to demonstrate how implementation of such a system could have provided early warning of an outbreak of a novel influenza-like illness that did not match the symptom prevalence profile of influenza and other known influenza-like illnesses.

An Aberration Detection-Based Approach for Sentinel Syndromic Surveillance of COVID-19 and Other Novel Influenza-Like Illnesses

Coronavirus Disease 2019 (COVID-19) has emerged as a significant global concern, triggering harsh public health restrictions in a successful bid to curb its exponential growth. As discussion shifts towards relaxation of these restrictions, there is significant concern of second-wave resurgence. The key to managing these outbreaks is early detection and intervention, and yet there is significant lag time associated with usage of laboratory confirmed cases for surveillance purposes. To address this, syndromic surveillance can be considered to provide a timelier alternative for first-line screening. Existing syndromic surveillance solutions are however typically focused around a known disease and have limited capability to distinguish between outbreaks of individual diseases sharing similar syndromes. This poses a challenge for surveillance of COVID-19 as its active periods are tend to overlap temporally with other influenza-like illnesses. In this study we explore performing sentinel syndromic surveillance for COVID-19 and other influenza-like illnesses using a deep learning-based approach. Our methods are based on aberration detection utilizing autoencoders that leverages symptom prevalence distributions to distinguish outbreaks of two ongoing diseases that share similar syndromes, even if they occur concurrently. We first demonstrate that this approach works for detection of outbreaks of influenza, which has known temporal boundaries. We then demonstrate that the autoencoder can be trained to not alert on known and well-managed influenza-like illnesses such as the common cold and influenza. Finally, we applied our approach to 2019-2020 data in the context of a COVID-19 syndromic surveillance task to demonstrate how implementation of such a system could have provided early warning of an outbreak of a novel influenza-like illness that did not match the symptom prevalence profile of influenza and other known influenza-like illnesses.