Title: Use of Next-Generation Sequencing to Rule Out Cluster of Pseudomonas aeruginosa in a Cardiac Critical Care Unit
Background: In spring of 2019, 2 positive sputum cases of Pseudomonas aeruginosa in the cardiac critical care unit (CCU) were reported to the UFHJ infection prevention (IP) department. The initial 2 cases, detected within 3 days of each other, were followed shortly by a third case. Epidemiological evidence was initially consistent with a hospital-acquired infection (HAI): 2 of the 3 patients roomed next to each other, and all 3 patients were ventilated, 2 of whom shared the same respiratory therapist. However, no other changes in routine or equipment were noted. The samples were cultured and processed using Illumina NGS technology, generating 1–2 million short (ie, 250-bp) reads across the P. aeruginosa genome. As an additional positive control, 8 P . aeruginosa NGS data sets, previously shown to be from a single outbreak in a UK facility, were included. Reads were mapped back to a reference sequence, and single-nucleotide polymorphisms (SNPs) between each sample and the reference were extracted. Genetic distances (ie, the number of unshared SNPs) between all UFHJ and UK samples were calculated. Genetic linkage was determined using hierarchical clustering, based on a commonly used threshold of 40 SNPs. All UFHJ patient samples were separated by >18,000 SNPs, indicating genetically distinct samples from separate sources. In contrast, UK samples were separated from each other by <16 SNPs, consistent with genetic linkage and a single outbreak. Furthermore, the UFHJ samples were separated from the UK samples by >17,000 SNPs, indicating a lack of geographical distinction of the UFHJ samples (Fig. 1). These results demonstrated that while the initial epidemiological evidence pointed towards a single HAI, the high-precision and relatively inexpensive (more »« less
Background: The CDC considers methicillin-resistant Staphylococcus aureus (MRSA) one of the most important hospital-acquired infections (HAIs) in the United States. However, infection control departments (ICDs) often rely on subjective data to determine whether multiple MRSA cases are a true outbreak and whether the hospital is responsible (community- vs hospital-acquired). Objective: Our objective was to determine whether whole-genome sequencing (WGS) of MRSA provided new insights into on transmission dynamics at large, inner-city hospital in Jacksonville, Florida. Methods: Over a 4-month period, MRSA samples were obtained from 44 infected patients at 3 campuses within a single hospital system. Limited nonpatient identifying information was obtained, including date of collection, campus, unit, reason for admission, and days post admission (DPA) of MRSA diagnosis. Whole-genome sequences were generated using the Illumina platform. Raw reads were processed, and genetic distances were calculated and used to identify genetically linked bacterial infections using FoxSeq version 1.0 software. Results: Based on their length of stay, 10 patients were reported by the ICD as obtaining an HAI. Three distinct “episodes” were evident in which >5 MRSA cases were observed within a 3–5-day period. Genomic analysis identified 5 clusters of linked infections: 4 clusters contained 2 patients and another contained 3. Of these clusters, only 1 contained multiple cases that were reported as HAIs; however, because these case were separated by 2 weeks, it is unlikely that they came from a source in the hospital. The results suggest that HAIs were overreported and that most MRSA present in the hospital likely came from community sources. Conclusions: WGS provided clear evidence that temporally clustered MRSA cases do not imply an outbreak is occurring. Furthermore, ongoing detection of the same community-acquired infections over several months is indicative of a shared source outside of the hospital, which could be uncovered through examination of clinical records. Considering the implications of HAIs, best approaches to combat them should include identifying their sources. As molecular surveillance approaches to infection control are rapidly becoming easier and less expensive, the methods can be used to bring objective clarity to the ICD. Funding: None Disclosures: Susanna L. Lamers reports salary from BioInfoExperts and contract research for the NIH, the University of California - San Francisco, and UMASS Medical School.
Krishnan, Gayathri; Udaondo, Zulema; Jun, Se-Ran; Kothari, Atul(
, Open Forum Infectious Diseases)
null
(Ed.)
Abstract Background VREfm is a major cause of Hospital Acquired Infection in the United States. We analyzed all the VREfm infections that occurred in our institution between 2018 and 2019 using Whole Genome Sequencing (WGS) to understand epidemiological relationship between previously unidentified clusters. In this study we describe a cluster in our hematology oncology unit. Methods A total of 109 discrete VREfm isolates from 66 patients were analyzed. VREfm isolates used in this study were identified from positive blood and urine cultures. Genomic deoxyribonucleic acid (DNA) was extracted from pure cultures. The purity and integrity of extracted DNA were determined using appropriate assays. Library construction and sequencing were conducted and Multi Locust Sequence Typing (MLST) obtained (image 1). Phylogenomic tree was plotted using the Interactive Tree of Life (image 2). Image 1 - methods Image 2 - Tree of Life Results Total of 7 clusters were identified. Here we describe one cluster (image 3) with the highest genetic similarity which showed maximum difference of 5 Single Nucleotide Polymorphisms (zero between patient 1 and 2, image 4). The cluster is composed of 24 clinical strains of VREfm from 6 patients, over a 9 month time period (Image 5). All patients had hematologic malignancies; 4/6 patients had received recent chemotherapy and 5/6 patients were neutropenic. 4 patients were admitted in a single unit (labelled E7), 1 patient was on a sister unit (labelled F7); and 1 patient was in the cancer infusion center. All patients had central venous access placed by radiology at the time of diagnosis of infection and had visited our outpatient infusion center multiple times during this time frame. Image 3 - Close look at cluster 1 Image 4 - Dendrogram of 106 isolates performed with coreSNP(Single Nucleotide Polymorphisms) pairwise distances. • Dendogram shows different patients (same color for isolates that belong to the same patient) and the patient numbers. • Besides the patient number, the number of largest number SNPS that separate those isolates is shown. • Branches represent the number of coreSNPs that differ strains from that branch. As you see isolates from cluster 1 differ in a maximum of 5 SNPs but isolates of patient 1 and patient 2 differ in 0 SNPs between them. Cluster 1 is represented by a green square. Image 5 - Time period of infections Conclusion The prolonged period in our cluster argues in favor of an environmental niche in the hospital unit. We are unable to elucidate pattern of transmission in a cluster of infections without knowing patient colonization of VREfm; because we are likely looking at the tip of the iceberg when analyzing infected cases. It is difficult to ascribe causality to any one of these exposures without concomitant surveillance cultures of environment and personnel. Retrospective WGS is of limited value in infection control. We now have third generation sequencing with the MinION device to do real time sequencing with which we also validated some of our samples. Disclosures Atul Kothari, MD, Ansun Biopharma (Consultant)
Wevodau, Z.; Doshna, B.; Jhala, N.; Akhtar, I.; Obeid, I.; Picone, J.(
, Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB))
Obeid, I.
(Ed.)
The Neural Engineering Data Consortium (NEDC) is developing the Temple University Digital Pathology Corpus (TUDP), an open source database of high-resolution images from scanned pathology samples [1], as part of its National Science Foundation-funded Major Research Instrumentation grant titled “MRI: High Performance Digital Pathology Using Big Data and Machine Learning” [2]. The long-term goal of this project is to release one million images. We have currently scanned over 100,000 images and are in the process of annotating breast tissue data for our first official corpus release, v1.0.0. This release contains 3,505 annotated images of breast tissue including 74 patients with cancerous diagnoses (out of a total of 296 patients). In this poster, we will present an analysis of this corpus and discuss the challenges we have faced in efficiently producing high quality annotations of breast tissue.
It is well known that state of the art algorithms in machine learning require vast amounts of data. Fields such as speech recognition [3], image recognition [4] and text processing [5] are able to deliver impressive performance with complex deep learning models because they have developed large corpora to support training of extremely high-dimensional models (e.g., billions of parameters). Other fields that do not have access to such data resources must rely on techniques in which existing models can be adapted to new datasets [6]. A preliminary version of this breast corpus release was tested in a pilot study using a baseline machine learning system, ResNet18 [7], that leverages several open-source Python tools.
The pilot corpus was divided into three sets: train, development, and evaluation. Portions of these slides were manually annotated [1] using the nine labels in Table 1 [8] to identify five to ten examples of pathological features on each slide. Not every pathological feature is annotated, meaning excluded areas can include focuses particular to these labels that are not used for training. A summary of the number of patches within each label is given in Table 2. To maintain a balanced training set, 1,000 patches of each label were used to train the machine learning model. Throughout all sets, only annotated patches were involved in model development.
The performance of this model in identifying all the patches in the evaluation set can be seen in the confusion matrix of classification accuracy in Table 3. The highest performing labels were background, 97% correct identification, and artifact, 76% correct identification. A correlation exists between labels with more than 6,000 development patches and accurate performance on the evaluation set. Additionally, these results indicated a need to further refine the annotation of invasive ductal carcinoma (“indc”), inflammation (“infl”), nonneoplastic features (“nneo”), normal (“norm”) and suspicious (“susp”).
This pilot experiment motivated changes to the corpus that will be discussed in detail in this poster presentation. To increase the accuracy of the machine learning model, we modified how we addressed underperforming labels. One common source of error arose with how non-background labels were converted into patches. Large areas of background within other labels were isolated within a patch resulting in connective tissue misrepresenting a non-background label. In response, the annotation overlay margins were revised to exclude benign connective tissue in non-background labels.
Corresponding patient reports and supporting immunohistochemical stains further guided annotation reviews. The microscopic diagnoses given by the primary pathologist in these reports detail the pathological findings within each tissue site, but not within each specific slide. The microscopic diagnoses informed revisions specifically targeting annotated regions classified as cancerous, ensuring that the labels “indc” and “dcis” were used only in situations where a micropathologist diagnosed it as such. Further differentiation of cancerous and precancerous labels, as well as the location of their focus on a slide, could be accomplished with supplemental immunohistochemically (IHC) stained slides. When distinguishing whether a focus is a nonneoplastic feature versus a cancerous growth, pathologists employ antigen targeting stains to the tissue in question to confirm the diagnosis. For example, a nonneoplastic feature of usual ductal hyperplasia will display diffuse staining for cytokeratin 5 (CK5) and no diffuse staining for estrogen receptor (ER), while a cancerous growth of ductal carcinoma in situ will have negative or focally positive staining for CK5 and diffuse staining for ER [9]. Many tissue samples contain cancerous and non-cancerous features with morphological overlaps that cause variability between annotators. The informative fields IHC slides provide could play an integral role in machine model pathology diagnostics.
Following the revisions made on all the annotations, a second experiment was run using ResNet18. Compared to the pilot study, an increase of model prediction accuracy was seen for the labels indc, infl, nneo, norm, and null. This increase is correlated with an increase in annotated area and annotation accuracy. Model performance in identifying the suspicious label decreased by 25% due to the decrease of 57% in the total annotated area described by this label. A summary of the model performance is given in Table 4, which shows the new prediction accuracy and the absolute change in error rate compared to Table 3.
The breast tissue subset we are developing includes 3,505 annotated breast pathology slides from 296 patients. The average size of a scanned SVS file is 363 MB. The annotations are stored in an XML format. A CSV version of the annotation file is also available which provides a flat, or simple, annotation that is easy for machine learning researchers to access and interface to their systems. Each patient is identified by an anonymized medical reference number. Within each patient’s directory, one or more sessions are identified, also anonymized to the first of the month in which the sample was taken. These sessions are broken into groupings of tissue taken on that date (in this case, breast tissue). A deidentified patient report stored as a flat text file is also available. Within these slides there are a total of 16,971 total annotated regions with an average of 4.84 annotations per slide. Among those annotations, 8,035 are non-cancerous (normal, background, null, and artifact,) 6,222 are carcinogenic signs (inflammation, nonneoplastic and suspicious,) and 2,714 are cancerous labels (ductal carcinoma in situ and invasive ductal carcinoma in situ.)
The individual patients are split up into three sets: train, development, and evaluation. Of the 74 cancerous patients, 20 were allotted for both the development and evaluation sets, while the remain 34 were allotted for train. The remaining 222 patients were split up to preserve the overall distribution of labels within the corpus. This was done in hope of creating control sets for comparable studies. Overall, the development and evaluation sets each have 80 patients, while the training set has 136 patients.
In a related component of this project, slides from the Fox Chase Cancer Center (FCCC) Biosample Repository (https://www.foxchase.org/research/facilities/genetic-research-facilities/biosample-repository -facility) are being digitized in addition to slides provided by Temple University Hospital. This data includes 18 different types of tissue including approximately 38.5% urinary tissue and 16.5% gynecological tissue. These slides and the metadata provided with them are already anonymized and include diagnoses in a spreadsheet with sample and patient ID. We plan to release over 13,000 unannotated slides from the FCCC Corpus simultaneously with v1.0.0 of TUDP. Details of this release will also be discussed in this poster.
Few digitally annotated databases of pathology samples like TUDP exist due to the extensive data collection and processing required. The breast corpus subset should be released by November 2021. By December 2021 we should also release the unannotated FCCC data. We are currently annotating urinary tract data as well. We expect to release about 5,600 processed TUH slides in this subset. We have an additional 53,000 unprocessed TUH slides digitized. Corpora of this size will stimulate the development of a new generation of deep learning technology. In clinical settings where resources are limited, an assistive diagnoses model could support pathologists’ workload and even help prioritize suspected cancerous cases.
ACKNOWLEDGMENTS
This material is supported by the National Science Foundation under grants nos. CNS-1726188 and 1925494. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
REFERENCES
[1] N. Shawki et al., “The Temple University Digital Pathology Corpus,” in Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications, 1st ed., I. Obeid, I. Selesnick, and J. Picone, Eds. New York City, New York, USA: Springer, 2020, pp. 67 104. https://www.springer.com/gp/book/9783030368432.
[2] J. Picone, T. Farkas, I. Obeid, and Y. Persidsky, “MRI: High Performance Digital Pathology Using Big Data and Machine Learning.” Major Research Instrumentation (MRI), Division of Computer and Network Systems, Award No. 1726188, January 1, 2018 – December 31, 2021. https://www. isip.piconepress.com/projects/nsf_dpath/.
[3] A. Gulati et al., “Conformer: Convolution-augmented Transformer for Speech Recognition,” in Proceedings of the Annual Conference of the International Speech Communication Association (INTERSPEECH), 2020, pp. 5036-5040. https://doi.org/10.21437/interspeech.2020-3015.
[4] C.-J. Wu et al., “Machine Learning at Facebook: Understanding Inference at the Edge,” in Proceedings of the IEEE International Symposium on High Performance Computer Architecture (HPCA), 2019, pp. 331–344. https://ieeexplore.ieee.org/document/8675201.
[5] I. Caswell and B. Liang, “Recent Advances in Google Translate,” Google AI Blog: The latest from Google Research, 2020. [Online]. Available: https://ai.googleblog.com/2020/06/recent-advances-in-google-translate.html. [Accessed: 01-Aug-2021].
[6] V. Khalkhali, N. Shawki, V. Shah, M. Golmohammadi, I. Obeid, and J. Picone, “Low Latency Real-Time Seizure Detection Using Transfer Deep Learning,” in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB), 2021, pp. 1 7. https://www.isip. piconepress.com/publications/conference_proceedings/2021/ieee_spmb/eeg_transfer_learning/.
[7] J. Picone, T. Farkas, I. Obeid, and Y. Persidsky, “MRI: High Performance Digital Pathology Using Big Data and Machine Learning,” Philadelphia, Pennsylvania, USA, 2020. https://www.isip.piconepress.com/publications/reports/2020/nsf/mri_dpath/.
[8] I. Hunt, S. Husain, J. Simons, I. Obeid, and J. Picone, “Recent Advances in the Temple University Digital Pathology Corpus,” in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB), 2019, pp. 1–4. https://ieeexplore.ieee.org/document/9037859.
[9] A. P. Martinez, C. Cohen, K. Z. Hanley, and X. (Bill) Li, “Estrogen Receptor and Cytokeratin 5 Are Reliable Markers to Separate Usual Ductal Hyperplasia From Atypical Ductal Hyperplasia and Low-Grade Ductal Carcinoma In Situ,” Arch. Pathol. Lab. Med., vol. 140, no. 7, pp. 686–689, Apr. 2016. https://doi.org/10.5858/arpa.2015-0238-OA.
Formalin‐fixed tissues provide the medical and forensic communities with alternative and often last resort sources of DNA for identification or diagnostic purposes. The DNA in these samples can be highly degraded and chemically damaged, making downstream genotyping using short tandem repeats (STRs) challenging. Therefore, the use of alternative genetic markers, methods that pre‐amplify the low amount of good quality DNA present, or methods that repair the damaged DNA template may provide more probative genetic information. This study investigated whether whole genome amplification (WGA) and DNA repair could improve STR typing of formaldehyde‐damaged (FD) tissues from embalmed cadavers. Additionally, comparative genotyping success using bi‐allelic markers, including INDELs and SNPs, was explored. Calculated random match probabilities (RMPs) using traditional STRs, INDEL markers, and two next generation sequencing (NGS) panels were compared across all samples. Overall, results showed that neither WGA nor DNA repair substantially improved STR success rates from formalin‐fixed tissue samples. However, when DNA from FD samples was genotyped using INDEL and SNP‐based panels, the RMP of each sample was markedly lower than the RMPs calculated from partial STR profiles. Therefore, the results of this study suggest that rather than attempting to improve the quantity and quality of severely damaged and degraded DNA prior to STR typing, a more productive approach may be to target smaller amplicons to provide more discriminatory DNA identifications. Furthermore, an NGS panel with less loci may yield better results when examining FD samples, due to more optimized chemistries that result in greater allelic balance and amplicon coverage.
The World Health Organization (WHO) declared coronavirus disease-2019 (COVID-19) a global pandemic on 11 March 2020. In Ecuador, the first case of COVID-19 was recorded on 29 February 2020. Despite efforts to control its spread, SARS-CoV-2 overran the Ecuadorian public health system, which became one of the most affected in Latin America on 24 April 2020. The Hospital General del Sur de Quito (HGSQ) had to transition from a general to a specific COVID-19 health center in a short period of time to fulfill the health demand from patients with respiratory afflictions. Here, we summarized the implementations applied in the HGSQ to become a COVID-19 exclusive hospital, including the rearrangement of hospital rooms and a triage strategy based on a severity score calculated through an artificial intelligence (AI)-assisted chest computed tomography (CT). Moreover, we present clinical, epidemiological, and laboratory data from 75 laboratory tested COVID-19 patients, which represent the first outbreak of Quito city. The majority of patients were male with a median age of 50 years. We found differences in laboratory parameters between intensive care unit (ICU) and non-ICU cases considering C-reactive protein, lactate dehydrogenase, and lymphocytes. Sensitivity and specificity of the AI-assisted chest CT were 21.4% and 66.7%, respectively, when considering a score >70%; regardless, this system became a cornerstone of hospital triage due to the lack of RT-PCR testing and timely results. If health workers act as vectors of SARS-CoV-2 at their domiciles, they can seed outbreaks that might put 1,879,047 people at risk of infection within 15 km around the hospital. Despite our limited sample size, the information presented can be used as a local example that might aid future responses in low and middle-income countries facing respiratory transmitted epidemics.
Neilsen, Chad, Rose, Rebecca, Nolan, David, McCarter, Yvette Simone, Rollini, Fabiana, Lamers, Susanna, Sands, Michael Lee, and Hoadley, Thuyvi. Use of Next-Generation Sequencing to Rule Out Cluster of Pseudomonas aeruginosa in a Cardiac Critical Care Unit. Retrieved from https://par.nsf.gov/biblio/10218301. Infection Control & Hospital Epidemiology 41.S1 Web. doi:10.1017/ice.2020.1185.
Neilsen, Chad, Rose, Rebecca, Nolan, David, McCarter, Yvette Simone, Rollini, Fabiana, Lamers, Susanna, Sands, Michael Lee, & Hoadley, Thuyvi. Use of Next-Generation Sequencing to Rule Out Cluster of Pseudomonas aeruginosa in a Cardiac Critical Care Unit. Infection Control & Hospital Epidemiology, 41 (S1). Retrieved from https://par.nsf.gov/biblio/10218301. https://doi.org/10.1017/ice.2020.1185
Neilsen, Chad, Rose, Rebecca, Nolan, David, McCarter, Yvette Simone, Rollini, Fabiana, Lamers, Susanna, Sands, Michael Lee, and Hoadley, Thuyvi.
"Use of Next-Generation Sequencing to Rule Out Cluster of Pseudomonas aeruginosa in a Cardiac Critical Care Unit". Infection Control & Hospital Epidemiology 41 (S1). Country unknown/Code not available. https://doi.org/10.1017/ice.2020.1185.https://par.nsf.gov/biblio/10218301.
@article{osti_10218301,
place = {Country unknown/Code not available},
title = {Use of Next-Generation Sequencing to Rule Out Cluster of Pseudomonas aeruginosa in a Cardiac Critical Care Unit},
url = {https://par.nsf.gov/biblio/10218301},
DOI = {10.1017/ice.2020.1185},
abstractNote = {Background: In spring of 2019, 2 positive sputum cases of Pseudomonas aeruginosa in the cardiac critical care unit (CCU) were reported to the UFHJ infection prevention (IP) department. The initial 2 cases, detected within 3 days of each other, were followed shortly by a third case. Epidemiological evidence was initially consistent with a hospital-acquired infection (HAI): 2 of the 3 patients roomed next to each other, and all 3 patients were ventilated, 2 of whom shared the same respiratory therapist. However, no other changes in routine or equipment were noted. The samples were cultured and processed using Illumina NGS technology, generating 1–2 million short (ie, 250-bp) reads across the P. aeruginosa genome. As an additional positive control, 8 P . aeruginosa NGS data sets, previously shown to be from a single outbreak in a UK facility, were included. Reads were mapped back to a reference sequence, and single-nucleotide polymorphisms (SNPs) between each sample and the reference were extracted. Genetic distances (ie, the number of unshared SNPs) between all UFHJ and UK samples were calculated. Genetic linkage was determined using hierarchical clustering, based on a commonly used threshold of 40 SNPs. All UFHJ patient samples were separated by >18,000 SNPs, indicating genetically distinct samples from separate sources. In contrast, UK samples were separated from each other by <16 SNPs, consistent with genetic linkage and a single outbreak. Furthermore, the UFHJ samples were separated from the UK samples by >17,000 SNPs, indicating a lack of geographical distinction of the UFHJ samples (Fig. 1). These results demonstrated that while the initial epidemiological evidence pointed towards a single HAI, the high-precision and relatively inexpensive (},
journal = {Infection Control & Hospital Epidemiology},
volume = {41},
number = {S1},
author = {Neilsen, Chad and Rose, Rebecca and Nolan, David and McCarter, Yvette Simone and Rollini, Fabiana and Lamers, Susanna and Sands, Michael Lee and Hoadley, Thuyvi},
editor = {null}
}
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