Multicenter evaluation of the BIOFIRE Joint Infection Panel for the detection of bacteria, yeast, and AMR genes in synovial fluid samples.

The bioMérieux BIOFIRE Joint Infection (JI) Panel is a multiplex in vitro diagnostic test for the simultaneous and rapid (~1 h) detection of 39 potential pathogens and antimicrobial resistance (AMR) genes directly from synovial fluid (SF) samples. Thirty-one species or groups of microorganisms are included in the kit, as well as several AMR genes. This study, performed to evaluate the BIOFIRE JI Panel for regulatory clearance, provides data from a multicenter evaluation of 1,544 prospectively collected residual SF samples with performance compared to standard-of-care (SOC) culture for organisms or polymerase chain reaction (PCR) and sequencing for AMR genes. The BIOFIRE JI Panel demonstrated a sensitivity of 90.9% or greater for all but six organisms and a positive percent agreement (PPA) of 100% for all AMR genes. The BIOFIRE JI Panel demonstrated a specificity of 98.5% or greater for detection of all organisms and a negative percent agreement (NPA) of 95.7% or greater for all AMR genes. The BIOFIRE JI Panel provides an improvement over SOC culture, with a substantially shorter time to result for both organisms and AMR genes with excellent sensitivity/PPA and specificity/NPA, and is anticipated to provide timely and actionable diagnostic information for joint infections in a variety of clinical scenarios.

Comparing Clinical Characteristics of Influenza and Common Coronavirus Infections Using Electronic Health Records.

BACKGROUND: We compared outcomes in inpatients and outpatients, pre-COVID-19, who were infected with either coronavirus or influenza. METHODS: Using deidentified electronic health records data from the Geisinger-Regeneron partnership, we compared patients with RT-PCR-positive tests for the 4 common coronaviruses (229E, HKU1, NL63, OC43) or influenza (A and B) from June 2016 to February 2019. RESULTS: Overall, 52 833 patients were tested for coronaviruses and influenza. For patients ≥21 years old, 1555 and 3991 patient encounters had confirmed positive coronavirus and influenza tests, respectively. Both groups had similar intensive care unit (ICU) admission rates (7.2% vs 6.1%, P = .12), although patients with coronavirus had significantly more pneumonia (15% vs 7.4%, P < .001) and higher death rate within 30 days (4.9% vs 3.0%, P < .001). After controlling for other covariates, coronavirus infection still had a higher risk of death and pneumonia than influenza (odds ratio, 1.64 and 2.05, P < .001), with no significant difference in ICU admission rates. CONCLUSIONS: Common coronaviruses cause significant morbidity, with potentially worse outcomes than influenza. Identifying a subset of patients who are more susceptible to poor outcomes from common coronavirus infections may help plan clinical interventions in patients with suspected infections.

A Multicenter Clinical Study To Demonstrate the Diagnostic Accuracy of the GenMark Dx ePlex Blood Culture Identification Gram-Negative Panel.

Bacteremia can progress to septic shock and death without appropriate medical intervention. Increasing evidence supports the role of molecular diagnostic panels in reducing the clinical impact of these infections through rapid identification of the infecting organism and associated antimicrobial resistance genes. We report the results of a multicenter clinical study assessing the performance of the GenMark Dx ePlex investigational-use-only blood culture identification Gram-negative panel (BCID-GN), a rapid diagnostic assay for detection of bloodstream pathogens in positive blood culture (PBC) bottles. Prospective, retrospective, and contrived samples were tested. Results from the BCID-GN were compared to standard-of-care bacterial identification methods. Antimicrobial resistance genes (ARGs) were identified using PCR and sequence analysis. The final BCID-GN analysis included 2,444 PBC samples, of which 926 were clinical samples with negative Gram stain results. Of these, 109 samples had false-negative and/or -positive results, resulting in an overall sample accuracy of 88.2% (817/926). After discordant resolution, overall sample accuracy increased to 92.9% (860/926). Pre- and postdiscordant resolution sample accuracy excludes 37 Gram-negative organisms representing 20 uncommon genera, 10 Gram-positive organisms, and 1 Candida species present in 5% of samples that are not targeted by the BCID-GN. The overall weighted positive percent agreement (PPA), which averages the individual PPAs from the 27 targets (Gram-negative and ARG), was 94.9%. The limit of detection ranged from 104 to 107 CFU/ml, except for one strain of Fusobacterium necrophorum at 108 CFU/ml.

Performance of the T2Bacteria Panel for Diagnosing Bloodstream Infections: A Diagnostic Accuracy Study.

Background: Blood cultures, the gold standard for diagnosing bloodstream infections (BSIs), are insensitive and limited by prolonged time to results. The T2Bacteria Panel (T2 Biosystems) is a direct-from-blood, nonculture test that identifies the most common ESKAPE bacteria (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Escherichia coli). Objective: To assess performance of the T2Bacteria Panel in diagnosing suspected BSIs in adults. Design: Prospective patient enrollment (8 December 2015 through 4 August 2017). Setting: Eleven U.S. hospitals. Patients: 1427 patients for whom blood cultures were ordered as standard of care. Intervention: Paired blood culture and T2Bacteria testing. Measurements: Performance of T2Bacteria compared with a single set of blood cultures in diagnosing proven, probable, and possible BSIs caused by T2Bacteria-targeted organisms. Results: Blood culture and T2Bacteria results were positive for targeted bacteria in 3% (39 of 1427) and 13% (181 of 1427) of patients, respectively. Mean times from start of blood culture incubation to positivity and species identification were 38.5 (SD, 32.8) and 71.7 (SD, 39.3) hours, respectively. Mean times to species identification with T2Bacteria were 3.61 (SD, 0.2) to 7.70 (SD, 1.38) hours, depending on the number of samples tested. Per-patient sensitivity and specificity of T2Bacteria for proven BSIs were 90% (95% CI, 76% to 96%) and 90% (CI, 88% to 91%), respectively; the negative predictive value was 99.7% (1242 of 1246). The rate of negative blood cultures with a positive T2Bacteria result was 10% (146 of 1427); 60% (88 of 146) of such results were associated with probable (n = 62) or possible (n = 26) BSIs. If probable BSIs and both probable and possible BSIs were assumed to be true positives missed by blood culture, per-patient specificity of T2Bacteria was 94% and 96%, respectively. Limitation: Low prevalence of positive blood cultures, collection of a single set of culture specimens, and inability of T2Bacteria to detect nontargeted pathogens. Conclusion: The T2Bacteria Panel rapidly and accurately diagnoses BSIs caused by 5 common bacteria. Primary Funding Source: T2 Biosystems.

Multicenter Evaluation of the Xpert MRSA NxG Assay for Detection of Methicillin-Resistant Staphylococcus aureus in Nasal Swabs.

Health care-associated methicillin-resistant Staphylococcus aureus (MRSA) infections are a burden on the health care system. Clinical laboratories play a key role in reducing this burden, as the timely identification of MRSA colonization or infection facilitates infection control practices that are effective at limiting invasive MRSA infections. The Xpert MRSA NxG assay recently received FDA clearance for the direct detection of MRSA from nasal swabs. This multicenter study evaluated the clinical performance characteristics of the Xpert MRSA NxG assay with prospectively collected rayon nasal swabs (n = 1,103) and flocked swab (ESwab) nasal specimens (n = 846). Culture-based identification methods and antimicrobial susceptibility testing were used as the reference standards for comparison. According to the reference method, the positivity rates for MRSA in the population evaluated were 11.1% (122/1,103) for rayon swabs and 11.6% (98/846) for flocked swabs. The overall sensitivity and specificity of the rayon swabs were 91.0% (95% confidence interval [CI], 84.6 to 94.9%) and 96.9% (95% CI, 95.7 to 97.8%), respectively, across eight testing sites. The flocked swab specimens were 92.9% sensitive (95% CI, 86.0 to 96.5%) and 97.6% specific (95% CI, 96.2 to 98.5%) for MRSA detection across six testing sites. The sensitivity and specificity of the combined flocked and rayon swab data were 91.8% (95% CI, 87.4 to 94.8%) and 97.2% (95% CI, 96.3 to 97.9%), respectively. The positive predictive value (PPV) for rayon swabs was 78.7%, versus 83.5% for ESwabs. The negative predictive values (NPVs) for rayon swabs and ESwab specimens were 98.9% and 99.1%, respectively. In conclusion, the Xpert MRSA NxG assay is a sensitive and specific assay for the direct detection of MRSA from nasal swab specimens.

Multicenter Evaluation of the Accelerate PhenoTest BC Kit for Rapid Identification and Phenotypic Antimicrobial Susceptibility Testing Using Morphokinetic Cellular Analysis.

We describe results from a multicenter study evaluating the Accelerate Pheno system, a first of its kind diagnostic system that rapidly identifies common bloodstream pathogens from positive blood cultures within 90 min and determines bacterial phenotypic antimicrobial susceptibility testing (AST) results within ∼7 h. A combination of fresh clinical and seeded blood cultures were tested, and results from the Accelerate Pheno system were compared to Vitek 2 results for identification (ID) and broth microdilution or disk diffusion for AST. The Accelerate Pheno system accurately identified 14 common bacterial pathogens and two Candida spp. with sensitivities ranging from 94.6 to 100%. Of fresh positive blood cultures, 89% received a monomicrobial call with a positive predictive value of 97.3%. Six common Gram-positive cocci were evaluated for ID. Five were tested against eight antibiotics, two resistance phenotypes (methicillin-resistant Staphylococcus aureus and Staphylococcus spp. [MRSA/MRS]), and inducible clindamycin resistance (MLSb). From the 4,142 AST results, the overall essential agreement (EA) and categorical agreement (CA) were 97.6% and 97.9%, respectively. Overall very major error (VME), major error (ME), and minor error (mE) rates were 1.0%, 0.7%, and 1.3%, respectively. Eight species of Gram-negative rods were evaluated against 15 antibiotics. From the 6,331 AST results, overall EA and CA were 95.4% and 94.3%, respectively. Overall VME, ME, and mE rates were 0.5%, 0.9%, and 4.8%, respectively. The Accelerate Pheno system has the unique ability to identify and provide phenotypic MIC and categorical AST results in a few hours directly from positive blood culture bottles and support accurate antimicrobial adjustment.

Effectiveness of Practices To Increase Timeliness of Providing Targeted Therapy for Inpatients with Bloodstream Infections: a Laboratory Medicine Best Practices Systematic Review and Meta-analysis.

BACKGROUND: Bloodstream infection (BSI) is a major cause of morbidity and mortality throughout the world. Rapid identification of bloodstream pathogens is a laboratory practice that supports strategies for rapid transition to direct targeted therapy by providing for timely and effective patient care. In fact, the more rapidly that appropriate antimicrobials are prescribed, the lower the mortality for patients with sepsis. Rapid identification methods may have multiple positive impacts on patient outcomes, including reductions in mortality, morbidity, hospital lengths of stay, and antibiotic use. In addition, the strategy can reduce the cost of care for patients with BSIs. OBJECTIVES: The purpose of this review is to evaluate the evidence for the effectiveness of three rapid diagnostic practices in decreasing the time to targeted therapy for hospitalized patients with BSIs. The review was performed by applying the Centers for Disease Control and Prevention's (CDC's) Laboratory Medicine Best Practices Initiative (LMBP) systematic review methods for quality improvement (QI) practices and translating the results into evidence-based guidance (R. H. Christenson et al., Clin Chem 57:816-825, 2011, http://dx.doi.org/10.1373/clinchem.2010.157131). SEARCH STRATEGY: A comprehensive literature search was conducted to identify studies with measurable outcomes. A search of three electronic bibliographic databases (PubMed, Embase, and CINAHL), databases containing "gray" literature (unpublished academic, government, or industry evidence not governed by commercial publishing) (CIHI, NIHR, SIGN, and other databases), and the Cochrane database for English-language articles published between 1990 and 2011 was conducted in July 2011. DATES OF SEARCH: The dates of our search were from 1990 to July 2011. SELECTION CRITERIA: Animal studies and non-English publications were excluded. The search contained the following medical subject headings: bacteremia; bloodstream infection; time factors; health care costs; length of stay; morbidity; mortality; antimicrobial therapy; rapid molecular techniques, polymerase chain reaction (PCR); in situ hybridization, fluorescence; treatment outcome; drug therapy; patient care team; pharmacy service, hospital; hospital information systems; Gram stain; pharmacy service; and spectrometry, mass, matrix-assisted laser desorption-ionization. Phenotypic as well as the following key words were searched: targeted therapy; rapid identification; rapid; Gram positive; Gram negative; reduce(ed); cost(s); pneumoslide; PBP2; tube coagulase; matrix-assisted laser desorption/ionization time of flight; MALDI TOF; blood culture; EMR; electronic reporting; call to provider; collaboration; pharmacy; laboratory; bacteria; yeast; ICU; and others. In addition to the electronic search being performed, a request for unpublished quality improvement data was made to the clinical laboratory community. MAIN RESULTS: Rapid molecular testing with direct communication significantly improves timeliness compared to standard testing. Rapid phenotypic techniques with direct communication likely improve the timeliness of targeted therapy. Studies show a significant and homogeneous reduction in mortality associated with rapid molecular testing combined with direct communication. AUTHORS' CONCLUSIONS: No recommendation is made for or against the use of the three assessed practices of this review due to insufficient evidence. The overall strength of evidence is suggestive; the data suggest that each of these three practices has the potential to improve the time required to initiate targeted therapy and possibly improve other patient outcomes, such as mortality. The meta-analysis results suggest that the implementation of any of the three practices may be more effective at increasing timeliness to targeted therapy than routine microbiology techniques for identification of the microorganisms causing BSIs. Based on the included studies, results for all three practices appear applicable across multiple microorganisms, including methicillin-resistant Staphylococcus aureus (MRSA), methicillin-sensitive S. aureus (MSSA), Candida species, and Enterococcus species.

Multicenter Evaluation of MRSASelect II Chromogenic Agar for Identification of Methicillin-Resistant Staphylococcus aureus from Wound and Nasal Specimens.

Hospitals strive to reduce methicillin-resistant Staphylococcus aureus (MRSA) prevalence via active surveillance of inpatient populations. Rapid and inexpensive screening methods are utilized when molecular methods are not operationally feasible. In this multisite clinical trial, the utility of Bio-Rad's MRSASelect II was evaluated for MRSA identification from remnant nares and wound swabs. The prevalence of MRSA was 11.1% (n = 1,384) from nares samples and 18.1% (n = 842) from wound samples. MRSASelect II had an overall concordance of 95.4% (confidence interval [CI] = 94.5% to 96.2%) compared to a broth-enriched reference standard. Comparisons between results, stratified by examination times, exhibited a nonsignificant trend toward increased positivity at prolonged incubation times. Cefoxitin screening of colonies directly from MRSASelect II was 96.7% (95.8% to 97.3%) concordant compared to testing of colonies following broth enrichment. A comparison of MRSASelect and MRSASelect II revealed no statistical differences; however, the latter exhibited earlier positivity, greater selectivity, and more intense indicator staining, which resulted in facilitated differentiation of positive results. MRSASelect II agar is a simple, rapid, and robust method to routinely screen patients for MRSA colonization without the need for additional testing.

Matrix-assisted laser desorption ionization-time of flight mass spectrometry: a fundamental shift in the routine practice of clinical microbiology.

Within the past decade, clinical microbiology laboratories experienced revolutionary changes in the way in which microorganisms are identified, moving away from slow, traditional microbial identification algorithms toward rapid molecular methods and mass spectrometry (MS). Historically, MS was clinically utilized as a high-complexity method adapted for protein-centered analysis of samples in chemistry and hematology laboratories. Today, matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) MS is adapted for use in microbiology laboratories, where it serves as a paradigm-shifting, rapid, and robust method for accurate microbial identification. Multiple instrument platforms, marketed by well-established manufacturers, are beginning to displace automated phenotypic identification instruments and in some cases genetic sequence-based identification practices. This review summarizes the current position of MALDI-TOF MS in clinical research and in diagnostic clinical microbiology laboratories and serves as a primer to examine the "nuts and bolts" of MALDI-TOF MS, highlighting research associated with sample preparation, spectral analysis, and accuracy. Currently available MALDI-TOF MS hardware and software platforms that support the use of MALDI-TOF with direct and precultured specimens and integration of the technology into the laboratory workflow are also discussed. Finally, this review closes with a prospective view of the future of MALDI-TOF MS in the clinical microbiology laboratory to accelerate diagnosis and microbial identification to improve patient care.

Would earlier microbe identification alter antibiotic therapy in bacteremic emergency department patients?

BACKGROUND: Although debate exists about the treatment of sepsis, few disagree about the benefits of early, appropriately targeted antibiotic administration. STUDY OBJECTIVES: To determine the appropriateness of empiric antimicrobial therapy and the extent to which therapy would be altered if the causative organism for sepsis was known at the time of administration. METHODS: This was a retrospective cohort study, conducted in an academic Emergency Department (ED), on consecutive positive blood cultures between November 1, 2008 and February 1, 2009. Blood cultures and the appropriateness of administered antimicrobial therapy were evaluated. Therapy choices were categorized based on whether or not a physician, complying with antimicrobial guidelines, would have made changes to empiric antibiotic therapy had the causative organism initially been known. RESULTS: There were 90 positive blood cultures obtained from 84 patients. Of these, 21.1% (n=19) were considered contaminants. The final categorization of empiric antibiotics given in the ED for the remaining blood culture results were: 1) therapy would be changed to narrower-spectrum antibiotics (n=34, 55.7%); 2) therapy would be changed because the organism was not covered (n=13, 21.3%); and 3) therapy would remain the same (n=14, 23.0%). There was 90.2% inter-rater agreement for these classifications (p<0.0001), with a kappa of 0.84. Polymerase chain reaction analysis had a statistically significant advantage (p<0.0001) over Infectious Disease Society of America protocols in facilitating accurate antimicrobial therapies. CONCLUSION: This study confirms the need for more rapid and accurate laboratory methods for bloodstream pathogen identification.

An integrated pipeline for prediction of Clostridioides difficile infection.

With the expansion of electronic health records(EHR)-linked genomic data comes the development of machine learning-enable models. There is a pressing need to develop robust pipelines to evaluate the performance of integrated models and minimize systemic bias. We developed a prediction model of symptomatic Clostridioides difficile infection(CDI) by integrating common EHR-based and genetic risk factors(rs2227306/IL8). Our pipeline includes (1) leveraging phenotyping algorithm to minimize temporal bias, (2) performing simulation studies to determine the predictive power in samples without genetic information, (3) propensity score matching to control for the confoundings, (4) selecting machine learning algorithms to capture complex feature interactions, (5) performing oversampling to address data imbalance, and (6) optimizing models and ensuring proper bias-variance trade-off. We evaluate the performance of prediction models of CDI when including common clinical risk factors and the benefit of incorporating genetic feature(s) into the models. We emphasize the importance of building a robust integrated pipeline to avoid systemic bias and thoroughly evaluating genetic features when integrated into the prediction models in the general population and subgroups.

Prediction of Influenza Complications: Development and Validation of a Machine Learning Prediction Model to Improve and Expand the Identification of Vaccine-Hesitant Patients at Risk of Severe Influenza Complications.

Influenza vaccinations are recommended for high-risk individuals, but few population-based strategies exist to identify individual risks. Patient-level data from unvaccinated individuals, stratified into retrospective cases (n = 111,022) and controls (n = 2,207,714), informed a machine learning model designed to create an influenza risk score; the model was called the Geisinger Flu-Complications Flag (GFlu-CxFlag). The flag was created and validated on a cohort of 604,389 unique individuals. Risk scores were generated for influenza cases; the complication rate for individuals without influenza was estimated to adjust for unrelated complications. Shapley values were used to examine the model's correctness and demonstrate its dependence on different features. Bias was assessed for race and sex. Inverse propensity weighting was used in the derivation stage to correct for biases. The GFlu-CxFlag model was compared to the pre-existing Medial EarlySign Flu Algomarker and existing risk guidelines that describe high-risk patients who would benefit from influenza vaccination. The GFlu-CxFlag outperformed other traditional risk-based models; the area under curve (AUC) was 0.786 [0.783−0.789], compared with 0.694 [0.690−0.698] (p-value < 0.00001). The presence of acute and chronic respiratory diseases, age, and previous emergency department visits contributed most to the GFlu-CxFlag model's prediction. When higher numerical scores were assigned to more severe complications, the GFlu-CxFlag AUC increased to 0.828 [0.823−0.833], with excellent discrimination in the final model used to perform the risk stratification of the population. The GFlu-CxFlag can better identify high-risk individuals than existing models based on vaccination guidelines, thus creating a population-based risk stratification for individual risk assessment and deployment in vaccine hesitancy reduction programs in our health system.

Cepheid Xpert Xpress Flu/RSV evaluation performed by minimally trained non-laboratory operators in a CLIA-waived environment.

The COVID-19 pandemic highlighted the significance of readily available and easily performed viral testing for surveillance during future infectious pandemics. The objectives of this study were: to assess the performance of the Xpert Xpress Flu and/or RSV test, a multiplex PCR assay for detecting influenza A and B virus and respiratory syncytial virus nucleic acids in respiratory tract specimens, relative to the Quidel Lyra Influenza A+B assay and the Prodesse ProFlu+ assay, and the system's ease of use by minimally trained operators. Overall, the Xpert Xpress Flu/RSV test demonstrated a high positive and negative percent agreement with the comparator assays, and was easy to use and interpret results, based on the operators' feedback. We concluded that the Xpert Xpress Flu/RSV test is sensitive, specific, and easy to use for the diagnosis of influenza and RSV by minimally trained operators and can be a valuable tool in future infectious clusters or pandemics.

Use of PCR coupled with electrospray ionization mass spectrometry for rapid identification of bacterial and yeast bloodstream pathogens from blood culture bottles.

Sepsis is among the top 10 causes of mortality in the United States. Rapid administration of antibiotics is one of the most important contributors to patient survival, yet only a limited number of methods exist for rapid identification of microbes cultivated from bloodstream infections, which can lead to sepsis. While traditional single-target molecular methods have been shown to greatly improve survival for septic patients by enabling rapid deescalation of broad-spectrum antibiotics, multiplex methods offer even greater possibilities. A novel multiplex method, PCR coupled to electrospray ionization mass spectrometry (PCR/ESI-MS), was used to identify the genus and species of microorganisms found to cause human bloodstream infections. DNA was directly extracted from 234 BacT-Alert blood culture bottles, and results were compared to those obtained by clinical reference standard methods. The study results demonstrated 98.7% and 96.6% concordance at the genus and species levels, respectively. Mixtures of microbes were identified in 29 blood culture bottles, including mixed species of the same genus, as well as mixtures containing Gram-positive and Gram-negative organisms, exemplifying the PCR/ESI-MS capability to identify multiple organisms simultaneously without the need for cultivation. This study demonstrates high analytical accuracy in comparison to routine subculture of blood culture bottles and phenotypic identification of microbes. Without foreknowledge of the microorganisms potentially present, the PCR/ESI-MS methods can deliver accurate results in as little as 5 to 6 h after a positive alarm from the automated blood culture system; however, current batch mode testing limits the method's clinical utility at this time.

Comparative analysis of PCR-electrospray ionization/mass spectrometry (MS) and MALDI-TOF/MS for the identification of bacteria and yeast from positive blood culture bottles.

BACKGROUND: Emerging technologies for rapid identification of microbes demonstrate a shift from traditional biochemical and molecular testing algorithms toward methods using mass spectrometry (MS) for the semiquantitative analysis of microbial proteins and genetic elements. This study was performed to assess the diagnostic accuracy of 2 such technologies, PCR-electrospray ionization (ESI)/MS and MALDI-TOF/MS, with respect to phenotypic and biochemical profiling as a reference standard method. A positive challenge set of blood culture bottles was used to compare PCR-ESI/MS and MALDI-TOF/MS performance on a matched set of samples. METHODS: We performed characterization of bloodstream infections from blood cultures using the Ibis T5000 PCR-ESI/MS and the Bruker MALDI Biotyper 2.0 (MALDI-TOF/MS) platforms for microbial identification. Diagnostic accuracy was determined by independent comparison of each method to phenotypic and biochemical characterization with Vitek2 analysis as the reference standard identification. RESULTS: The diagnostic accuracy, represented as positive agreement, at the genus level was 0.965 (0.930-0.984) for PCR-ESI/MS and 0.969 (0.935-0.987) for MALDI-TOF/MS, and at the species level was 0.952 (0.912-0.974) with PCR-ESI/MS and 0.943 (0.902-0.968) for MALDI-TOF/MS. No statistically significant difference was found between PCR-ESI/MS and MALDI-TOF/MS in the ability to rapidly identify microorganisms isolated from blood culture. CONCLUSIONS: Our results demonstrate that PCR-ESI/MS and MALDI-TOF/MS are equivalent in their ability to characterize bloodstream infections with respect to the reference standard, and highlight key differences in the methods that allow for each method to have a unique niche as a tool for rapid identification of microbes in blood cultures.

Mapping the Evidence on Rapid Diagnosis of Bloodstream Infections: A Scoping Review.

BACKGROUND: Laboratory and other healthcare professionals participate in developing clinical practice guidelines through systematic review of the evidence. A significant challenge is the identification of areas for analytic focus when the evidence consists of several categories of interventions and outcomes that span both laboratory and clinical processes. The challenge increases when these interventions present as sets of combined interventions. A scoping review may provide a transparent and defensible analytic route forward for systematic reviews challenged in this manner. CONTENT: A scoping review was carried out to characterize the evidence on rapid identification of bloodstream infections. Fifty-five studies previously identified by the supported systematic review were charted in duplicate. Charted records were analyzed using descriptive content analysis and evidence mapping with a 5-step process. SUMMARY: The 5-step analysis culminated in the characterization of 9 different intervention chain configurations that will facilitate the comparison of complex intervention practices across studies. Furthermore, our evidence map indicates that the current evidence base is strongly centered on 3 specific clinical outcomes, and it links these outcomes to the most represented intervention chain configurations. The scoping review effort generated a route forward for the supported systematic review and meta-analysis.

At the Intersection of Gut Microbiome and Stroke: A Systematic Review of the Literature.

Background: Ischemic and hemorrhagic stroke are associated with a high rate of long-term disability and death. Recent investigations focus efforts to better understand how alterations in gut microbiota composition influence clinical outcomes. A key metabolite, trimethylamine N-oxide (TMAO), is linked to multiple inflammatory, vascular, and oxidative pathways. The current biochemical underpinnings of microbial effects on stroke remain largely understudied. The goal of our study is to explore the current literature to explain the interactions between the human gut microbiome and stroke progression, recovery, and outcome. We also provide a descriptive review of TMAO. Methods: A systematic literature search of published articles between January 1, 1990, and March 22, 2020, was performed on the PubMed database to identify studies addressing the role of the microbiome and TMAO in the pathogenesis and recovery of acute stroke. Our initial investigation focused on human subject studies and was further expanded to include animal studies. Relevant articles were included, regardless of study design. The analysis included reviewers classifying and presenting selected articles by study design and sample size in a chart format. Results: A total of 222 titles and abstracts were screened. A review of the 68 original human subject articles resulted in the inclusion of 24 studies in this review. To provide further insight into TMAO as a key player, an additional 40 articles were also reviewed and included. Our findings highlighted that alterations in richness and abundance of gut microbes and increased plasma TMAO play an important role in vascular events and outcomes. Our analysis revealed that restoration of a healthy gut, through targeted TMAO-reducing therapies, could provide alternative secondary prevention for at-risk patients. Discussion: Biochemical interactions between the gut microbiome and inflammation, resulting in metabolic derangements, can affect stroke progression and outcomes. Clinical evidence supports the importance of TMAO in modulating underlying stroke risk factors. Lack of standardization and distinct differences in sample sizes among studies are major limitations.

Informing patients that they are at high risk for serious complications of viral infection increases vaccination rates.

For many vaccine-preventable diseases like influenza, vaccination rates are lower than optimal to achieve community protection. Those at high risk for infection and serious complications are especially advised to be vaccinated to protect themselves. Using influenza as a model, we studied one method of increasing vaccine uptake: informing high-risk patients, identified by a machine learning model, about their risk status. Patients (N=39,717) were evenly randomized to (1) a control condition (exposure only to standard direct mail or patient portal vaccine promotion efforts) or to be told via direct mail, patient portal, and/or SMS that they were (2) at high risk for influenza and its complications if not vaccinated; (3) at high risk according to a review of their medical records; or (4) at high risk according to a computer algorithm analysis of their medical records. Patients in the three treatment conditions were 5.7% more likely to get vaccinated during the 112 days post-intervention (p < .001), and did so 1.4 days earlier (p < .001), on average, than those in the control group. There were no significant differences among risk messages, suggesting that patients are neither especially averse to nor uniquely appreciative of learning their records had been reviewed or that computer algorithms were involved. Similar approaches should be considered for COVID-19 vaccination campaigns.

Early Detection of Septic Shock Onset Using Interpretable Machine Learners.

BACKGROUND: Developing a decision support system based on advances in machine learning is one area for strategic innovation in healthcare. Predicting a patient's progression to septic shock is an active field of translational research. The goal of this study was to develop a working model of a clinical decision support system for predicting septic shock in an acute care setting for up to 6 h from the time of admission in an integrated healthcare setting. METHOD: Clinical data from Electronic Health Record (EHR), at encounter level, were used to build a predictive model for progression from sepsis to septic shock up to 6 h from the time of admission; that is, T = 1, 3, and 6 h from admission. Eight different machine learning algorithms (Random Forest, XGBoost, C5.0, Decision Trees, Boosted Logistic Regression, Support Vector Machine, Logistic Regression, Regularized Logistic, and Bayes Generalized Linear Model) were used for model development. Two adaptive sampling strategies were used to address the class imbalance. Data from two sources (clinical and billing codes) were used to define the case definition (septic shock) using the Centers for Medicare & Medicaid Services (CMS) Sepsis criteria. The model assessment was performed using Area under Receiving Operator Characteristics (AUROC), sensitivity, and specificity. Model predictions for each feature window (1, 3 and 6 h from admission) were consolidated. RESULTS: Retrospective data from April 2005 to September 2018 were extracted from the EHR, Insurance Claims, Billing, and Laboratory Systems to create a dataset for septic shock detection. The clinical criteria and billing information were used to label patients into two classes-septic shock patients and sepsis patients at three different time points from admission, creating two different case-control cohorts. Data from 45,425 unique in-patient visits were used to build 96 prediction models comparing clinical-based definition versus billing-based information as the gold standard. Of the 24 consolidated models (based on eight machine learning algorithms and three feature windows), four models reached an AUROC greater than 0.9. Overall, all the consolidated models reached an AUROC of at least 0.8820 or higher. Based on the AUROC of 0.9483, the best model was based on Random Forest, with a sensitivity of 83.9% and specificity of 88.1%. The sepsis detection window at 6 h outperformed the 1 and 3-h windows. The sepsis definition based on clinical variables had improved performance when compared to the sepsis definition based on only billing information. CONCLUSION: This study corroborated that machine learning models can be developed to predict septic shock using clinical and administrative data. However, the use of clinical information to define septic shock outperformed models developed based on only administrative data. Intelligent decision support tools can be developed and integrated into the EHR and improve clinical outcomes and facilitate the optimization of resources in real-time.

Variants at the MHC Region Associate With Susceptibility to Clostridioides difficile Infection: A Genome-Wide Association Study Using Comprehensive Electronic Health Records.

Background: Clostridioides difficile is a major cause of healthcare-associated and community-acquired diarrhea. Host genetic susceptibility to Clostridioides difficile infection has not been studied on a large-scale. Methods: A total of 1,160 Clostridioides difficile infection cases and 15,304 controls were identified by applying the eMERGE Clostridioides difficile infection algorithm to electronic health record data. A genome-wide association study was performed using a linear mixed model, adjusted for significant covariates in the full dataset and the antibiotic subgroup. Colocalization and MetaXcan were performed to identify potential target genes in Clostridioides difficile infection - relevant tissue types. Results: No significant genome-wide association was found in the meta-analyses of the full Clostridioides difficile infection dataset. One genome-wide significant variant, rs114751021, was identified (OR = 2.42; 95%CI = 1.84-3.11; p=4.50 x 10-8) at the major histocompatibility complex region associated with Clostridioides difficile infection in the antibiotic group. Colocalization and MetaXcan identified MICA, C4A/C4B, and NOTCH4 as potential target genes. Down-regulation of MICA, upregulation of C4A and NOTCH4 was associated with a higher risk for Clostridioides difficile infection. Conclusions: Leveraging the EHR and genetic data, genome-wide association, and fine-mapping techniques, this study identified variants and genes associated with Clostridioides difficile infection, provided insights into host immune mechanisms, and described the potential for novel treatment strategies for Clostridioides difficile infection. Future replication and functional validation are needed.