Analysis of multiple bacterial species and antibiotic classes reveals large variation in the association between seasonal antibiotic use and resistance.

Understanding how antibiotic use drives resistance is crucial for guiding effective strategies to limit the spread of resistance, but the use-resistance relationship across pathogens and antibiotics remains unclear. We applied sinusoidal models to evaluate the seasonal use-resistance relationship across 3 species (Staphylococcus aureus, Escherichia coli, and Klebsiella pneumoniae) and 5 antibiotic classes (penicillins, macrolides, quinolones, tetracyclines, and nitrofurans) in Boston, Massachusetts. Outpatient use of all 5 classes and resistance in inpatient and outpatient isolates in 9 of 15 species-antibiotic combinations showed statistically significant amplitudes of seasonality (false discovery rate (FDR) < 0.05). While seasonal peaks in use varied by class, resistance in all 9 species-antibiotic combinations peaked in the winter and spring. The correlations between seasonal use and resistance thus varied widely, with resistance to all antibiotic classes being most positively correlated with use of the winter peaking classes (penicillins and macrolides). These findings challenge the simple model of antibiotic use independently selecting for resistance and suggest that stewardship strategies will not be equally effective across all species and antibiotics. Rather, seasonal selection for resistance across multiple antibiotic classes may be dominated by use of the most highly prescribed antibiotic classes, penicillins and macrolides.

Morbidity and Mortality of Hospital-Onset SARS-CoV-2 Infections Due to Omicron Versus Prior Variants : A Propensity-Matched Analysis.

BACKGROUND: Many hospitals have scaled back measures to prevent nosocomial SARS-CoV-2 infection given large decreases in the morbidity and mortality of SARS-CoV-2 infections for most people. Little is known, however, about the morbidity and mortality of nosocomial SARS-CoV-2 infections for hospitalized patients in the Omicron era. OBJECTIVE: To estimate the effect of nosocomial SARS-CoV-2 infection on hospitalized patients' outcomes during the pre-Omicron and Omicron periods. DESIGN: Retrospective matched cohort study. SETTING: 5 acute care hospitals in Massachusetts, December 2020 to April 2023. PATIENTS: Adults testing positive for SARS-CoV-2 on or after hospital day 5, after negative SARS-CoV-2 test results on admission and on hospital day 3, were matched to control participants by hospital, service, time period, days since admission, and propensity scores that incorporated demographics, comorbid conditions, vaccination status, primary diagnosis category, vital signs, and laboratory test values. MEASUREMENTS: Primary outcomes were hospital mortality and time to discharge. Secondary outcomes were intensive care unit (ICU) admission, need for advanced oxygen support, discharge destination, hospital-free days, and 30-day readmissions. RESULTS: There were 274 cases of hospital-onset SARS-CoV-2 infection during the pre-Omicron period and 1037 cases during the Omicron period (0.17 vs. 0.49 cases per 100 admissions). Patients with hospital-onset SARS-CoV-2 infection were older and had more comorbid conditions than those without. During the pre-Omicron period, hospital-onset SARS-CoV-2 infection was associated with increased risk for ICU admission, increased need for high-flow oxygen, longer time to discharge (median difference, 4.7 days [95% CI, 2.9 to 6.6 days]), and higher mortality (risk ratio, 2.0 [CI, 1.1 to 3.8]) versus matched control participants. During the Omicron period, hospital-onset SARS-CoV-2 infection remained associated with increased risk for ICU admission and increased time to discharge (median difference, 4.2 days [CI, 3.6 to 5.0 days]). The association with increased hospital mortality was attenuated but still significant (risk ratio, 1.6 [CI, 1.2 to 2.3]). LIMITATION: Residual confounding may be present. CONCLUSION: Hospital-onset SARS-CoV-2 infection during the Omicron period remains associated with increased morbidity and mortality. PRIMARY FUNDING SOURCE: Harvard Medical School Department of Population Medicine.

Socioeconomic Disparities and the Prevalence of Antimicrobial Resistance.

BACKGROUND: The increased prevalence of antimicrobial resistant (AMR) infections is a significant global health threat, resulting in increased morbidity, mortality, and costs. The drivers of AMR are complex and potentially impacted by socioeconomic factors. We investigated the relationships between geographic and socioeconomic factors and AMR. METHODS: We collected select patient bacterial culture results from 2015 to 2020 from electronic health records (EHR) of two expansive healthcare systems within the Dallas-Fort Worth, TX (DFW) metropolitan area. Among individuals with EHR records who resided in the four most populus counties in DFW, culture data were aggregated. Case counts for each organism studied were standardized per 1,000 persons per area population. Using residential addresses, the cultures were geocoded and linked to socioeconomic index values. Spatial autocorrelation tests identified geographic clusters of high and low AMR organism prevalence and correlations with established socioeconomic indices. RESULTS: We found significant clusters of AMR organisms in areas with high levels of deprivation, as measured by the Area Deprivation Index (ADI). We found a significant spatial autocorrelation between ADI and the prevalence of AMR organisms, particularly for AmpC and MRSA with 14% and 13%, respectively, of the variability in prevalence rates being attributable to their relationship with the ADI values of the neighboring locations. CONCLUSIONS: We found that areas with a high ADI are more likely to have higher rates of AMR organisms. Interventions that improve socioeconomic factors such as poverty, unemployment, decreased access to healthcare, crowding, and sanitation in these areas of high prevalence may reduce the spread of AMR.

The modern alchemy of clinical pathology: turning the output of microbiology laboratory operations into gold.

The clinical microbiology laboratory generates a huge amount of high-quality data that play a vital role in clinical care. With proper extraction, cleaning, analysis, and validation pipelines, these data can serve multiple other purposes that include supporting laboratory operations, understanding local epidemiology, informing hospital-specific policies, and public health surveillance. In this review, I use one of the core activities of the microbiology laboratory, antimicrobial susceptibility testing (AST), to illustrate several potential applications of next-generation data analytics. The first involves continuous monitoring of commercial AST systems using comparisons of minimum inhibitory concentration (MIC) distributions over time to trigger re-verification when statistically significant differences are detected. An extension of this is temporal analysis of joint MIC distributions to understand performance for multidrug-resistant organisms. More sophisticated analyses involve linking microbiologic data to clinical metadata to gain insight into the clinical validity of AST data and to inform treatment policies. The elements of a robust, validated analysis engine using routine data streams already exist, but numerous challenges must be overcome to make it a reality. Most importantly, it will require the sustained collaboration and advocacy of hospital leadership, microbiologists, clinicians, antimicrobial stewardship, data scientists, and regulatory agencies. Though no small feat, achieving this vision would provide an important resource for microbiology laboratories facing a rapidly evolving practice landscape and further cement its role as an integral part of a learning health system.

Hidden in plain sight: urinary Cryptococcus neoformans missed by routine diagnostics in a patient with acute leukemia.

Cryptococcuria is a rare manifestation of localized cryptococcal disease. We present a case of Cryptococcus neoformans urinary tract infection in an immunocompromised host missed by routine laboratory workup. The patient had negative blood cultures, a negative serum cryptococcal antigen (CrAg), and "non-Candida yeast" growing in urine culture that was initially dismissed as non-pathogenic. The diagnosis was ultimately made by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) from a repeat urine culture after transfer to a tertiary care center. Cryptococcus should be considered in the differential of refractory urinary tract infections growing non-Candida yeast.

Duration of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Infectivity: When Is It Safe to Discontinue Isolation?

Defining the duration of infectivity of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has major implications for public health and infection control practice in healthcare facilities. Early in the pandemic, most hospitals required 2 negative RT-PCR tests before discontinuing isolation in patients with Covid-19. Many patients, however, have persistently positive RT-PCR tests for weeks to months following clinical recovery, and multiple studies now indicate that these generally do not reflect replication-competent virus. SARS-CoV-2 appears to be most contagious around the time of symptom onset, and infectivity rapidly decreases thereafter to near-zero after about 10 days in mild-moderately ill patients and 15 days in severely-critically ill and immunocompromised patients. The longest interval associated with replication-competent virus thus far is 20 days from symptom onset. This review summarizes evidence-to-date on the duration of infectivity of SARS-CoV-2, and how this has informed evolving public health recommendations on when it is safe to discontinue isolation precautions.

The Effect of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Mitigation Strategies on Seasonal Respiratory Viruses: A Tale of 2 Large Metropolitan Centers in the United States.

To assess the impact of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic on seasonal respiratory viruses, absolute case counts and viral reproductive rates from 2019-2020 were compared against previous seasons. Our findings suggest that the public health measures implemented to reduce SARS-CoV-2 transmission significantly reduced the transmission of other respiratory viruses.

Applications of Machine Learning to the Problem of Antimicrobial Resistance: an Emerging Model for Translational Research.

Antimicrobial resistance (AMR) remains one of the most challenging phenomena of modern medicine. Machine learning (ML) is a subfield of artificial intelligence that focuses on the development of algorithms that learn how to accurately predict outcome variables using large sets of predictor variables that are typically not hand selected and are minimally curated. Models are parameterized using a training data set and then applied to a test data set on which predictive performance is evaluated. The application of ML algorithms to the problem of AMR has garnered increasing interest in the past 5 years due to the exponential growth of experimental and clinical data, heavy investment in computational capacity, improvements in algorithm performance, and increasing urgency for innovative approaches to reducing the burden of disease. Here, we review the current state of research at the intersection of ML and AMR with an emphasis on three domains of work. The first is the prediction of AMR using genomic data. The second is the use of ML to gain insight into the cellular functions disrupted by antibiotics, which forms the basis for understanding mechanisms of action and developing novel anti-infectives. The third focuses on the application of ML for antimicrobial stewardship using data extracted from the electronic health record. Although the use of ML for understanding, diagnosing, treating, and preventing AMR is still in its infancy, the continued growth of data and interest ensures it will become an important tool for future translational research programs.

Diagnostic Testing for Severe Acute Respiratory Syndrome-Related Coronavirus 2: A Narrative Review.

Diagnostic testing to identify persons infected with severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) infection is central to control the global pandemic of COVID-19 that began in late 2019. In a few countries, the use of diagnostic testing on a massive scale has been a cornerstone of successful containment strategies. In contrast, the United States, hampered by limited testing capacity, has prioritized testing for specific groups of persons. Real-time reverse transcriptase polymerase chain reaction-based assays performed in a laboratory on respiratory specimens are the reference standard for COVID-19 diagnostics. However, point-of-care technologies and serologic immunoassays are rapidly emerging. Although excellent tools exist for the diagnosis of symptomatic patients in well-equipped laboratories, important gaps remain in screening asymptomatic persons in the incubation phase, as well as in the accurate determination of live viral shedding during convalescence to inform decisions to end isolation. Many affluent countries have encountered challenges in test delivery and specimen collection that have inhibited rapid increases in testing capacity. These challenges may be even greater in low-resource settings. Urgent clinical and public health needs currently drive an unprecedented global effort to increase testing capacity for SARS-CoV-2 infection. Here, the authors review the current array of tests for SARS-CoV-2, highlight gaps in current diagnostic capacity, and propose potential solutions.

Serodiagnostics for Severe Acute Respiratory Syndrome-Related Coronavirus 2 : A Narrative Review.

Accurate serologic tests to detect host antibodies to severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) will be critical for the public health response to the coronavirus disease 2019 pandemic. Many use cases are envisaged, including complementing molecular methods for diagnosis of active disease and estimating immunity for individuals. At the population level, carefully designed seroepidemiologic studies will aid in the characterization of transmission dynamics and refinement of disease burden estimates and will provide insight into the kinetics of humoral immunity. Yet, despite an explosion in the number and availability of serologic assays to test for antibodies against SARS-CoV-2, most have undergone minimal external validation to date. This hinders assay selection and implementation, as well as interpretation of study results. In addition, critical knowledge gaps remain regarding serologic correlates of protection from infection or disease, and the degree to which these assays cross-react with antibodies against related coronaviruses. This article discusses key use cases for SARS-CoV-2 antibody detection tests and their application to serologic studies, reviews currently available assays, highlights key areas of ongoing research, and proposes potential strategies for test implementation.

Diagnostic Approach for Arboviral Infections in the United States.

Domestic arthropod-borne viruses (arboviruses) are single-stranded RNA viruses, the most common of which include the mosquito-borne West Nile virus, St. Louis encephalitis virus, La Crosse virus, Jamestown Canyon virus, and eastern equine encephalitis virus, as well as the tick-borne Powassan virus. Previously considered rare infections, they have been detected with increasing frequency over the past 2 decades. Here, we present an overview of the domestic arboviruses listed above and describe the modalities employed to diagnose infection. Global arboviruses, including dengue virus, Zika virus, and chikungunya virus, have also been increasingly detected in the United States within the last 5 years but are not a focus of this minireview. Typical manifestations of arbovirus infection range from no symptoms, to meningitis or encephalitis, to death. Serologies are the standard means of diagnosis in the laboratory, since most viruses have a short period of replication, limiting the utility of molecular tests. The interpretation of serologies is confounded by antibody cross-reactivity with viruses belonging to the same serogroup and by long-lasting antibodies from prior infections. Next-generation assays have improved performance by increasing antigen purity, selecting optimal epitopes, and improving interpretive algorithms, but challenges remain. Due to cross-reactivity, a positive first-line serology test requires confirmation by either a plaque reduction neutralization test or detection of seroconversion or a 4-fold rise in virus-specific IgM or IgG antibody titers from acute- and convalescent-phase sera. The use of molecular diagnostics, such as reverse transcription PCR or unbiased metagenomic sequencing, is limited to the minority of patients who present with ongoing viremia or central nervous system replication. With the continued expansion of vector range, the diagnosis of domestic arboviruses will become an increasingly important task for generalists and specialists alike.

In situ detection of SARS-CoV-2 in lungs and airways of patients with COVID-19.

Coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 has led to a global public health crisis. In elderly individuals and those with comorbidities, COVID-19 is associated with high mortality, frequently caused by acute respiratory distress syndrome. We examine in situ expression of SARS-CoV-2 in airways and lung obtained at autopsy of individuals with confirmed COVID-19 infection. Seven autopsy cases (male, N = 5; female, N = 2) with reverse transcriptase-polymerase chain reaction (RT-PCR)-confirmed SARS-CoV-2 infection and a median age of 66 years (range, 50-77 years) were evaluated using a rabbit polyclonal antibody against SARS Nucleocapsid protein in correlation with clinical parameters. The median time from symptom onset to death was 9 days (range, 6-31 days), from hospitalization 7 days (range, 1-21 days), from positive RT-PCR 7 days (range, 0-18 days), and from intensive care unit admission defining onset of respiratory failure 3 days (range, 1-18 days). Chest imaging identified diffuse airspace disease in all patients corresponding to acute and (N = 5) or organizing (N = 2) diffuse alveolar damage (DAD) on histologic examination. Among five patients with acute-phase DAD (≤7 days from onset of respiratory failure), SARS-CoV-2 was detected in pulmonary pneumocytes and ciliated airway cells (N = 5), and in upper airway epithelium (N = 2). In two patients with organizing DAD (>14 days from onset of respiratory failure), no virus was detected in lungs or airways. No endothelial cell infection was observed. The findings suggest that SARS-CoV-2 infection of epithelial cells in lungs and airways of patients with COVID-19 who developed respiratory failure can be detected during the acute phase of lung injury and is absent in the organizing phase.

Severe Herpes Zoster Requiring Intravenous Antiviral Treatment in Allogeneic Hematopoietic Cell Transplantation Recipients on Standard Acyclovir Prophylaxis.

Allogeneic hematopoietic cell transplantation (HCT) recipients are at increased risk for varicella zoster virus (VZV) reactivation and associated complications. The incidence, timing, and risk factors for severe herpes zoster (HZ) are not well described in the era of acyclovir (ACV) prophylaxis. We performed a retrospective cohort study of all patients who underwent first allogeneic HCT between October 2006 and December 2015 at our institution. Patients were followed until December 2017 for the development of severe HZ, defined as necessitating administration of i.v. antiviral medication. Out of 2163 patients who underwent allogeneic HCT, 22 (1.0%) developed severe HZ at a rate of 1 per 228 person-years, including dermatomal/multidermatomal disease (n = 5), disseminated skin disease (n = 5), HZ ophthalmicus (n = 4), meningitis/encephalitis (n = 4), pneumonia (n = 2), viremia (n = 1), and erythema multiforme (n = 1). Severe HZ infection occurred in a bimodal distribution during the early peri-HCT period and at 12 to 24 months post-HCT (median, 12.7 months). Twelve patients (54.5%) were compliant with ACV prophylaxis at the time of HZ diagnosis. Eleven patients (50%) died during the study period, only 2 of whom (9.1%) with active VZV infection. Mortality was higher in patients on immunosuppressive therapy (62.5% versus 16.7%; P = .045) and with concurrent graft-versus-host disease (75.0% versus 35.7%; P= .044). These data suggest that severe HZ remains an important consideration despite ACV prophylaxis.

Diagnostic Testing in Central Nervous System Infection.

Patients with central nervous system (CNS) infection experience very high levels of morbidity and mortality, in part because of the many challenges inherent to the diagnosis of CNS infection and identification of a causative pathogen. The clinical presentation of CNS infection is nonspecific, so clinicians must often order and interpret many diagnostic tests in parallel. This can be a daunting task given the large number of potential pathogens and the availability of different testing modalities. Here, we review traditional diagnostic techniques including Gram stain and culture, serology, and polymerase chain reaction (PCR). We highlight which of these are recommended for the pathogens most commonly tested among U.S. patients with suspected CNS infection. Finally, we describe the newer broad-range diagnostic approaches, multiplex PCR and metagenomic sequencing, which are increasingly used in clinical practice.

Rapid Detection of Powassan Virus in a Patient With Encephalitis by Metagenomic Sequencing.

We describe a patient with severe and progressive encephalitis of unknown etiology. We performed rapid metagenomic sequencing from cerebrospinal fluid and identified Powassan virus, an emerging tick-borne flavivirus that has been increasingly detected in the United States.

Trends in Antibiotic Susceptibility in Staphylococcus aureus in Boston, Massachusetts, from 2000 to 2014.

The rate of infection by methicillin-resistant Staphylococcus aureus (MRSA) has declined over the past decade, but it is unclear whether this represents a decline in S. aureus infections overall. To evaluate the trends in the annual rates of infection by S. aureus subtypes and mean antibiotic resistance, we conducted a 15-year retrospective observational study at two tertiary care institutions in Boston, MA, of 31,753 adult inpatients with S. aureus isolated from clinical specimens. We inferred the gain and loss of methicillin resistance through genome sequencing of 180 isolates from 2016. The annual rates of infection by S. aureus declined from 2003 to 2014 by 4.2% (2.7% to 5.6%), attributable to an annual decline in MRSA of 10.9% (9.3% to 12.6%). Penicillin-susceptible S. aureus (PSSA) increased by 6.1% (4.2% to 8.1%) annually, and rates of methicillin-susceptible penicillin-resistant S. aureus (MSSA) did not change. Resistance in S. aureus decreased from 2000 to 2014 by 0.8 antibiotics (0.7 to 0.8). Within common MRSA clonal complexes, 3/14 MSSA and 2/21 PSSA isolates arose from the loss of resistance-conferring genes. Overall, in two tertiary care institutions in Boston, MA, a decline in S. aureus infections has been accompanied by a shift toward increased antibiotic susceptibility. The rise in PSSA makes penicillin an increasingly viable treatment option.