Ten Clostridioides difficile isolates representing the top 10 ribotypes collected in 2016 through the Emerging Infections Program underwent long-read sequencing to obtain high-quality reference genome assemblies. These isolates are publicly available through the CDC & FDA Antibiotic Resistance Isolate Bank.

Venous thromboembolism (VTE) is the leading cause of preventable death in hospitalized patients. Artificial intelligence (AI) and machine learning (ML) can support guidelines recommending an individualized approach to risk assessment and prophylaxis. We conducted electronic surveys asking clinician and healthcare informaticians about their perspectives on AI/ML for VTE prevention and management. Of 101 respondents to the informatician survey, most were 40 years or older, male, clinicians and data scientists, and had performed research on AI/ML. Of the 607 US-based respondents to the clinician survey, most were 40 years or younger, female, physicians, and had never used AI to inform clinical practice. Most informaticians agreed that AI/ML can be used to manage VTE (56.0%). Over one-third were concerned that clinicians would not use the technology (38.9%), but the majority of clinicians believed that AI/ML probably or definitely can help with VTE prevention (70.1%). The most common concern in both groups was a perceived lack of transparency (informaticians 54.4%; clinicians 25.4%). These two surveys revealed that key stakeholders are interested in AI/ML for VTE prevention and management, and identified potential barriers to address prior to implementation.

BACKGROUND: Understanding the epidemiology of carbapenem-resistant A. baumannii complex (CRAB) and the patients impacted is an important step towards informing better infection prevention and control practices and improving public health response. METHODS: Active, population-based surveillance was conducted for CRAB in 9 U.S. sites from January 1-December 31, 2019. Medical records were reviewed, isolates were collected and characterized including antimicrobial susceptibility testing and whole genome sequencing. RESULTS: Among 136 incident cases in 2019, 66 isolates were collected and characterized; 56.5% were from cases who were male, 54.5% were from persons of Black or African American race with non-Hispanic ethnicity, and the median age was 63.5 years. Most isolates, 77.2%, were isolated from urine, and 50.0% were collected in the outpatient setting; 72.7% of isolates harbored an acquired carbapenemase gene (aCP), predominantly bla(OXA-23) or bla(OXA-24/40); however, an isolate with bla(NDM) was identified. The antimicrobial agent with the most in vitro activity was cefiderocol (96.9% of isolates were susceptible). CONCLUSIONS: Our surveillance found that CRAB isolates in the U.S. commonly harbor an aCP, have an antimicrobial susceptibility profile that is defined as difficult-to-treat resistance, and epidemiologically are similar regardless of the presence of an aCP.

Venous thromboembolism (VTE) is a leading cause of preventable in-hospital mortality. Monitoring VTE cases is limited by the challenges of manual chart review and diagnosis code interpretation. Natural language processing (NLP) can automate the process. Rule-based NLP methods are effective but time consuming. Machine learning (ML)-NLP methods present a promising solution. We conducted a systematic review and meta-analysis of studies published before May 2023 that use ML-NLP to identify VTE diagnoses in the electronic health records. Four reviewers screened all manuscripts, excluding studies that only used a rule-based method. A meta-analysis evaluated the pooled performance of each study's best performing model that evaluated for pulmonary embolism (PE) and/or deep vein thrombosis (DVT). Pooled sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) with confidence interval (CI) were calculated by DerSimonian and Laird method using a random-effects model. Study quality was assessed using an adapted TRIPOD tool. Thirteen studies were included in the systematic review and 8 had data available for meta-analysis. Pooled sensitivity was 0.931 (95% CI 0.881-0.962), specificity 0.984 (95% CI 0.967-0.992), PPV 0.910 (95% CI 0.865-0.941) and NPV 0.985 (95% CI 0.977-0.990). All studies met at least 13 of the 21 NLP-modified TRIPOD items, demonstrating fair quality. The highest performing models used vectorization rather than bag-of-words, and deep learning techniques such as convolutional neural networks. There was significant heterogeneity in the studies and only four validated their model on an external dataset. Further standardization of ML studies can help progress this novel technology towards real-world implementation.

Hospital-associated venous thromboembolism (VTE) is a major public health challenge, and while thromboprophylaxis is known to be effective, it remains misused [1]. Clinicians face enormous complexity when determining who should receive thromboprophylaxis. To better understand current practices around VTE prophylaxis in adult hospitalized patients, we previously surveyed 607 clinicians across the United States between 2021 and 2022 [2]. Overall, 48% of respondents reported patients at their institution are not on appropriate VTE prophylaxis almost all the time. The majority reported that technology such as artificial intelligence (AI) may help improve rates of appropriate prophylaxis. However, only 35% reported using existing risk assessment models (RAMs); 68% reported using their own clinical assessment instead. Therefore, we invited survey respondents to participate in focus groups to better understand how they approach VTE prophylaxis, with a focus on their perspectives regarding using AI decision support.

We thank Gonzales-Luna and colleagues [1] for their comments. We agree that laboratories must have access to accurate and standardized methods for antimicrobial susceptibility testing (AST) results to be clinically meaningful. The reference method for performing Clostridioides difficile AST is agar dilution according to Clinical and Laboratory Standards Institute (CLSI) guidelines [2]. The CLSI method for performing AST for anaerobic bacteria recommends that 5 μg/mL of hemin be incorporated into agar dilution plates and that the hemin stock solution should be protected from light and stored at 4°C–8°C for no longer than 1 month [2]. The susceptibility testing done by Gargis et al [3] was performed according to these guidelines, and the hemin stock solution was protected from light. | | Nevertheless, we read with interest the research in recent years [4–6] related to heme-dependent metronidazole resistance, including the reported association between isolates characterized as heme dependent and metronidazole resistant and the presence of a T to G mutation (PnimBG) in the −10 promoter region of the nitroimidazole reductase gene, nimB [5]. While Olaitan et al [5] found that not all heme-dependent metronidazole-resistant isolates contained the PnimBG mutation, Olaitan et al [5] indicate that most do; therefore, the presence of PnimBG may be predictive of resistance. We determined that the nimB mutation was present in 20% of our study isolates (116 of 593), of which 99% (115 of 116) belonged to RT027 (Table 1). The remaining isolate was RT014, the only RT014 isolate containing the PnimBG mutation among the 65 evaluated.

BACKGROUND: Accurate diagnostic and prognostic predictions of venous thromboembolism (VTE) are crucial for VTE management. Artificial intelligence (AI) enables autonomous identification of the most predictive patterns from large complex data. Although evidence regarding its performance in VTE prediction is emerging, a comprehensive analysis of performance is lacking. AIMS: To systematically review the performance of AI in the diagnosis and prediction of VTE and compare it to clinical risk assessment models (RAMs) or logistic regression models. METHODS: A systematic literature search was performed using PubMed, MEDLINE, EMBASE, and Web of Science from inception to April 20, 2021. Search terms included "artificial intelligence" and "venous thromboembolism." Eligible criteria were original studies evaluating AI in the prediction of VTE in adults and reporting one of the following outcomes: sensitivity, specificity, positive predictive value, negative predictive value, or area under receiver operating curve (AUC). Risks of bias were assessed using the PROBAST tool. Unpaired t-test was performed to compare the mean AUC from AI versus conventional methods (RAMs or logistic regression models). RESULTS: A total of 20 studies were included. Number of participants ranged from 31 to 111 888. The AI-based models included artificial neural network (six studies), support vector machines (four studies), Bayesian methods (one study), super learner ensemble (one study), genetic programming (one study), unspecified machine learning models (two studies), and multiple machine learning models (five studies). Twelve studies (60%) had both training and testing cohorts. Among 14 studies (70%) where AUCs were reported, the mean AUC for AI versus conventional methods were 0.79 (95% CI: 0.74-0.85) versus 0.61 (95% CI: 0.54-0.68), respectively (p < .001). However, the good to excellent discriminative performance of AI methods is unlikely to be replicated when used in clinical practice, because most studies had high risk of bias due to missing data handling and outcome determination. CONCLUSION: The use of AI appears to improve the accuracy of diagnostic and prognostic prediction of VTE over conventional risk models; however, there was a high risk of bias observed across studies. Future studies should focus on transparent reporting, external validation, and clinical application of these models.

BACKGROUND: Venous thromboembolism (VTE) is a leading cause of preventable mortality among hospitalized patients, but appropriate risk assessment and thromboprophylaxis remain underutilized or misapplied. OBJECTIVES: We conducted an electronic survey of US health care providers to explore attitudes, practices, and barriers related to thromboprophylaxis in adult hospitalized patients and at discharge. RESULTS: A total of 607 US respondents completed the survey: 63.1% reported working in an academic hospital, 70.7% identified as physicians, and hospital medicine was the most frequent specialty (52.1%). The majority of respondents agreed that VTE prophylaxis is important (98.8%; 95% CI: 97.6%-99.5%) and that current measures are safe (92.6%; 95% CI: 90.2%-94.5%) and effective (93.8%; 95% CI: 91.6%-95.6%), but only half (52.0%; 95% CI: 47.9%-56.0%) believed that hospitalized patients at their institution are on appropriate VTE prophylaxis almost all the time. One-third (35.4%) reported using a risk assessment model (RAM) to determine VTE prophylaxis need; 44.9% reported unfamiliarity with RAMs. The most common recommendation for improving rates of appropriate thromboprophylaxis was to leverage technology. A majority of respondents (84.5%) do not reassess a patient's need for VTE prophylaxis at discharge, and a minority educates patients about the risk (16.2%) or symptoms (18.9%) of VTE at discharge. CONCLUSION: Despite guideline recommendations to use RAMs, the majority of providers in our survey do not use them. A majority of respondents believed that technology could help improve VTE prophylaxis rates. A majority of respondents do not reassess the risk of VTE at discharge or educate patients about this risk of VTE at discharge.

BACKGROUND: Antimicrobial susceptibility testing (AST) is not routinely performed for Clostridioides difficile and data evaluating minimum inhibitory concentrations (MICs) are limited. We performed AST and whole genome sequencing (WGS) for 593 C. difficile isolates collected between 2012-2017 through the Centers for Disease Control and Prevention's Emerging Infections Program. METHODS: MICs to six antimicrobial agents (ceftriaxone, clindamycin, meropenem, metronidazole, moxifloxacin, and vancomycin) were determined using the reference agar dilution method according to Clinical and Laboratory Standards Institute guidelines. WGS was performed on all isolates to detect the presence of genes or mutations previously associated with resistance. RESULTS: Among all isolates, 98.5% displayed a vancomycin MIC ≤ 2 μg/mL and 97.3% displayed a metronidazole MIC ≤ 2 μg/mL. Ribotype 027 (RT027) isolates displayed higher vancomycin MICs (MIC50: 2 μg/mL; MIC90: 2 μg/mL) than non-RT027 isolates (MIC50: 0.5 μg/mL; MIC90: 1 μg/mL) (P < 0.01). No vanA/B genes were detected. RT027 isolates also showed higher MICs to clindamycin and moxifloxacin and were more likely to harbor associated resistance genes or mutations. CONCLUSIONS: Elevated MICs to antibiotics used for treatment of C. difficile infection were rare and there was no increase in MICs over time. The lack of vanA/B genes or mutations consistently associated with elevated vancomycin MICs suggests there are multifactorial mechanisms of resistance. Ongoing surveillance of C. difficile using reference AST and WGS to monitor MIC trends and the presence of antibiotic resistance mechanisms is essential.

Healthcare-associated carbapenem-resistant Acinetobacter baumannii (CRAB) infections are a serious threat associated with global epidemic clones and a variety of carbapenemase gene classes. In this study, we describe the molecular epidemiology, including whole-genome sequencing analysis and antimicrobial susceptibility profiles of 92 selected, nonredundant CRAB collected through public health efforts in the United States from 2013 to 2017. Among the 92 isolates, the Oxford (OX) multilocus sequence typing scheme identified 30 sequence types (STs); the majority of isolates (n = 59, 64%) represented STs belonging to the international clonal complex 92 (CC92(OX)). Among these, ST208(OX) (n = 21) and ST281(OX) (n = 20) were the most common. All isolates carried an OXA-type carbapenemase gene, comprising 20 alleles. Ninety isolates (98%) encoded an intrinsic OXA-51-like enzyme; 67 (73%) harbored an additional acquired bla(OXA) gene, most commonly bla(OXA-23) (n = 45; 49%). Compared with isolates harboring only intrinsic oxacillinase genes, acquired bla(OXA) gene presence was associated with higher prevalence of resistance and a higher median minimum inhibitory concentration to the carbapenem imipenem (64 μg/mL vs. 8 μg/mL), and antibiotics from other drug classes, including penicillin, aminoglycosides, cephalosporins, and polymyxins. These data illustrate the wide distribution of CC92(OX) and high prevalence of acquired bla(OXA) carbapenemase genes among CRAB in the United States.

OBJECTIVES: In December 2020, CDC launched the Pharmacy Partnership for Long-Term Care Program to facilitate COVID-19 vaccination of residents and staff in long-term care facilities (LTCFs), including assisted living (AL) and other residential care (RC) communities. We aimed to assess vaccine uptake in these communities and identify characteristics that might impact uptake. DESIGN: Cross-sectional study. SETTING AND PARTICIPANTS: AL/RC communities in the Pharmacy Partnership for Long-Term Care Program that had ≥1 on-site vaccination clinic during December 18, 2020-April 21, 2021. METHODS: We estimated uptake by using the cumulative number of doses of COVID-19 vaccine administered and normalizing by the number of AL/RC community beds. We estimated the percentage of residents vaccinated in 3 states using AL census counts. We linked community vaccine administration data with county-level social vulnerability index (SVI) measures to calculate median vaccine uptake by SVI tertile. RESULTS: In AL communities, a median of 67 residents [interquartile range (IQR): 48-90] and 32 staff members (IQR: 15-60) per 100 beds received a first dose of COVID-19 vaccine at the first on-site clinic; in RC, a median of 8 residents (IQR: 5-10) and 5 staff members (IQR: 2-12) per 10 beds received a first dose. Among 3 states with available AL resident census data, median resident first-dose uptake at the first clinic was 93% (IQR: 85-108) in Connecticut, 85% in Georgia (IQR: 70-102), and 78% (IQR: 56-91) in Tennessee. Among both residents and staff, cumulative first-dose vaccine uptake increased with increasing social vulnerability related to housing type and transportation. CONCLUSIONS AND IMPLICATIONS: COVID-19 vaccination of residents and staff in LTCFs is a public health priority. On-site clinics may help to increase vaccine uptake, particularly when transportation may be a barrier. Ensuring steady access to COVID-19 vaccine in LTCFs following the conclusion of the Pharmacy Partnership is critical to maintaining high vaccination coverage among residents and staff.

MOTIVATION: Tools used to identify genes in microbial sequences using a reference database generally report matches as a percent identity, which can be difficult to interpret in cases with <100%sequence identity, as changes to specific amino acids can have dramatic effects on protein function, such as when they occur in substrate binding regions or enzyme active sites, which in turn can have dramatic effects on phenotypes like antimicrobial resistance or virulence. RESULTS: Here we present GAMMA, an open-source tool for Gene Allele Mutation Microbial Assessment, which uses protein coding-level identity to make gene calls from any gene database and generates a classification (e.g., mutant, truncation) and translated annotation (e.g., Y190S mutation, truncation at residue 110) for these calls. GAMMA accurately called antimicrobial resistance genes from a large set of genomes faster than three other tools. It can also be used with any gene database, as we demonstrated by identifying virulence genes in the same genome set. Because of its speed and flexibility, GAMMA can be used to rapidly find and annotate any gene matches of interest in microbial sequencing data. AVAILABILITY: GAMMA is freely available as a Bioconda package (https://bioconda.github.io/recipes/gamma/README.html) and as a command line script (https://github.com/rastanton/GAMMA). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

We identified a cluster of extensively drug-resistant, carbapenemase gene-positive, carbapenem-resistant Acinetobacter baumannii (CP-CRAB) at a teaching hospital in Kansas City. Extensively drug-resistant CRAB was identified from eight patients and 3% of environmental cultures. We used patient cohorting and targeted environmental disinfection to stop transmission. After implementation of these measures, no additional cases were identified.

Thirty Clostridioides difficile isolates collected in 2016 through the Centers for Disease Control and Prevention Emerging Infections Program were selected for reference antimicrobial susceptibility testing and whole-genome sequencing. Here, we present the genetic characteristics of these isolates and announce their availability in the CDC & FDA Antibiotic Resistance Isolate Bank.

The coronavirus disease 2019 (COVID-19) pandemic has highlighted the vulnerability of residents and staff members in long-term care facilities (LTCFs) (1). Although skilled nursing facilities (SNFs) certified by the Centers for Medicare & Medicaid Services (CMS) have federal COVID-19 reporting requirements, national surveillance data are less readily available for other types of LTCFs, such as assisted living facilities (ALFs) and those providing similar residential care. However, many state and territorial health departments publicly report COVID-19 surveillance data across various types of LTCFs. These data were systematically retrieved from health department websites to characterize COVID-19 cases and deaths in ALF residents and staff members. Limited ALF COVID-19 data were available for 39 states, although reporting varied. By October 15, 2020, among 28,623 ALFs, 6,440 (22%) had at least one COVID-19 case among residents or staff members. Among the states with available data, the proportion of COVID-19 cases that were fatal was 21.2% for ALF residents, 0.3% for ALF staff members, and 2.5% overall for the general population of these states. To prevent the introduction and spread of SARS-CoV-2, the virus that causes COVID-19, in their facilities, ALFs should 1) identify a point of contact at the local health department; 2) educate residents, families, and staff members about COVID-19; 3) have a plan for visitor and staff member restrictions; 4) encourage social (physical) distancing and the use of masks, as appropriate; 5) implement recommended infection prevention and control practices and provide access to supplies; 6) rapidly identify and properly respond to suspected or confirmed COVID-19 cases in residents and staff members; and 7) conduct surveillance of COVID-19 cases and deaths, facility staffing, and supply information (2).

We describe transmission of Klebsiella pneumoniae carbapenemase-producing Escherichia coli sequence type (ST) 1193 in a group home. E. coli ST1193 is an emerging multidrug-resistant clone not previously shown to carry carbapenemases in the United States. Our investigation illustrates the potential of residential group homes to amplify rare combinations of pathogens and resistance mechanisms.

Pseudomonas aeruginosa is an opportunistic human pathogen that frequently causes healthcare-associated infections (HAIs). Due to its metabolic diversity and ability to form biofilms, this gram negative, non-fermenter can persist in the healthcare environment, which can lead to prolonged HAI outbreaks. We describe the creation of a core genome MLST (cgMLST) scheme to provide a stable platform for the rapid comparison of P. aeruginosa isolates using whole genome sequencing (WGS) data. We used a diverse set of 58 complete P. aeruginosa genomes to curate a set of 4400 core genes found in each isolate, representing approximately 65% of the average genome size. We then expanded the alleles for each gene using 1991 contig-level genome sequences. The scheme was used to analyze genomes from four historical HAI outbreaks to compare the phylogenies generated using cgMLST to those of other means (traditional MLST, PFGE, and SNV analysis). The cgMLST scheme provides sufficient resolution for analyzing individual outbreaks, as well as the stability for comparisons across a variety of isolates encountered in surveillance studies, making it a valuable tool for the rapid analysis of P. aeruginosa genomes.

Background: Klebsiella pneumoniae carbapenemase-producing organisms (KPCOs) are often multidrug-resistant, and the KPC resistance determinant can be transmitted between bacteria. KPCOs are associated with healthcare facility exposures; identification in community-based, residential care settings is uncommon. In September 2018, the Washoe County Health District was notified of a KPC-producing Escherichia coli from a group home (GH) resident. We investigated the source of this KPCO and evaluated transmission in the GH. Methods: A case was defined as detection of KPCO from a GH resident or staff from June 1 to November 30, 2018. Staff included caregivers who provided daily care (including toileting, bathing, feeding) and visiting healthcare workers. Residents and staff were offered KPCO screening to assess colonization status. Exposures were assessed by medical record review and interviews. Genetic relatedness of KPCOs was evaluated by whole-genome sequencing (WGS). Infection prevention and control (IPC) practices were reviewed. Results: Overall, six cases were identified, including the index, two of seven staff screened and three of six residents screened. Three residents with KPCOs had recent hospitalizations and shared a bathroom in the GH; one overlapped on the same hospital unit as a patient with KPC-producing Klebsiella oxytoca. Staff with KPCOs were caregivers who had extensive contact with residents and their environment and no IPC training. Gaps in hand hygiene and environmental cleaning were observed. Organism was recovered from 4 positive screening tests as well as from blood cultures from the index case; all were KPC-producing E. coli. WGS showed that the five E. coli isolates were closely related, consistent with transmission, and harbored the same KPC variant as the K. oxytoca. No new cases occurred after IPC was improved. Conclusion: A GH resident likely acquired KPCOs during a recent hospitalization, and extensive transmission among GH residents and staff occurred. Factors contributing to transmission included resident dependence on caregivers for daily care and minimal IPC knowledge among caregivers. Facilities with similar populations should increase IPC training to prevent transmission of resistant pathogens. Disclosures: All authors: No reported disclosures.

During August 2017, two separate clusters of platelet transfusion-associated bacterial sepsis were reported in Utah and California. In Utah, two patients died after platelet transfusions from the same donation. Clostridium perfringens isolates from one patient's blood, the other patient's platelet bag, and donor skin swabs were highly related by whole genome sequencing (WGS). In California, one patient died after platelet transfusion; Klebsiella pneumoniae isolates from the patient's blood and platelet bag residuals and a nontransfused platelet unit were matched using WGS. Investigation revealed no deviations in blood supplier or hospital procedures. Findings in this report highlight that even when following current procedures, the risk for transfusion-related infection and fatality persists, making additional interventions necessary. Clinicians need to be vigilant in monitoring for platelet-transmitted bacterial infections and report adverse reactions to blood suppliers and hemovigilance systems. Blood suppliers and hospitals could consider additional evidence-based bacterial contamination risk mitigation strategies, including pathogen inactivation, rapid detection devices, and modified screening of bacterial culture protocols.