Effect of rotavirus vaccination on the burden of rotavirus disease and associated antibiotic use in India: A dynamic agent-based simulation analysis.

BACKGROUND: Rotavirus is a leading cause of diarrhea in infants and young children in many low- and middle-income countries. India launched a childhood immunization program for rotavirus in 2016, starting with four states and expanding it to cover all states by 2019. The objective of this study was to estimate the effects of the rotavirus vaccination program in India on disease burden and antibiotic misuse. METHODS: We built a dynamic agent-based model of rotavirus progression in children under five within each district in India. Simulations were run for various scenarios of vaccination coverage in the context of India's Universal Immunization Programme. Population data were obtained from the National Family Household Surveys and used to calibrate the models. Disease parameters were obtained from published studies. We estimated past and projected future reduction of disease burden and antibiotic misuse due to full vaccination nationwide, by state, and by wealth quintile. RESULTS: We estimate that rotavirus vaccination in India has reduced the prevalence of rotavirus cases by 33.7% (prediction interval: 30.7-36.0%), total antibiotic misuse due to rotavirus by 21.8% (18.6-25.1%), and total deaths due to rotavirus by 38.3% (31.3-44.4%) for children under five. We estimate total antibiotic misuse due to rotavirus infection to be 7.6% (7.5-7.9%) of total antibiotic consumption in this demographic versus 9.6% (9.4-9.9%) in the absence of vaccination. We project rotaviral prevalence to drop to below one case for every 100,000 individuals in those below five if vaccination coverage is increased by 50.3% (45.2-58.5%) to 68.1% (63.1-76.4) nationwide. CONCLUSION: Universal coverage of childhood rotavirus vaccination can substantially reduce inappropriate antibiotic use in India.

Respiratory virus disease and outcomes at a large academic medical center in the United States: a retrospective observational study of the early 2023/2024 respiratory viral season.

Respiratory disease, attributed to influenza, respiratory syncytial virus (RSV), and SARS-CoV-2, was reported nationally during the 2023/2024 respiratory viral season. The emergence of novel SARS-CoV-2 variants was considered a significant factor contributing to the rise in COVID-19 cases. Data from the Johns Hopkins Hospital System (JHHS) showed that enterovirus/rhinovirus had also been circulating at high rates. Analyzing clinical outcomes of the most prevalent respiratory viruses is crucial for understanding the role of circulating viral genotypes. A retrospective cohort of patients who tested positive for SARS-CoV-2, influenza, RSV, or enterovirus/rhinovirus between 1 June and 31 December 2023 was included in the study. Remnant clinical samples were utilized for targeted viral whole-genome sequencing and genotyping. Patients' metadata and outcomes following infection were studied, stratified by viral variants and genotypes. The increase of SARS-CoV-2 positivity in December was associated with the predominance of JN.1. Admissions for patients under 18 years old were primarily associated with enterovirus/rhinovirus and RSV, while older age groups were mainly linked to SARS-CoV-2 and influenza infections. SARS-CoV-2-related admissions increased with the predominance of the JN.1 variant in December. No significant difference in admissions for influenza subtypes, rhinovirus species, or SARS-CoV-2 variants was observed. RSV A was associated with slightly higher odds of admission compared with RSV B. Our data highlight the importance of systematically analyzing respiratory viral infections to inform public health strategies and clinical management, especially as SARS-CoV-2 becomes endemic. The findings highlight the value of expanded genomic surveillance in elucidating the clinical significance of viral evolution.IMPORTANCEThe analysis of the epidemiology and clinical outcomes of multiple co-circulating respiratory viruses in the early 2023/2024 respiratory virus season highlights the emergence of the SARS-CoV-2 JN.1 variant as well as underscores the importance of enterovirus/rhinovirus in respiratory infections. Understanding these dynamics is essential for refining public health strategies and clinical management, especially as SARS-CoV-2 transitions to an endemic status. This work emphasizes the need for ongoing surveillance, robust diagnostic algorithms, and detailed genomic analyses to anticipate and mitigate the burden of respiratory viral infections, ultimately contributing to more informed decision-making in healthcare settings and better patient outcomes.

Genomic Evolution and Surveillance of Respiratory Syncytial Virus during the 2023-2024 Season.

Respiratory syncytial virus (RSV) is a significant cause of morbidity, particularly in infants. This study describes RSV genomic diversity and disease outcomes during the 2023-2024 season in the Johns Hopkins Hospital System (JHHS). Between August and December 2023, 406 patient samples were sequenced, showing that RSV-B GB5.0.5a was the dominant genotype detected. RSV-A genotype GA2.3.5 was detected less frequently. Metadata analysis of patient data revealed that, although RSV-B was more commonly detected, patients with RSV-A infections were more frequently hospitalized. Analysis of both the G- and F-genes revealed multiple amino acid substitutions in both RSV-A and RSV-B, with some positions within the F-protein that could be associated with evasion of antibody responses. Phylogenetic analysis revealed the genetic diversity of circulating GB5.0.5a and GA2.3.5 genotypes. This study serves as an important baseline for genomic surveillance of RSV within the JHHS and will assist in characterizing the impact of the newly approved RSV vaccines on RSV genomic evolution and the emergence of escape mutations.

Genomic evolution of influenza during the 2023-2024 season, the johns hopkins health system.

Influenza, a human disease caused by viruses in the Orthomyxoviridae family, is estimated to infect 5% -10 % of adults and 20% -30 % of children annually. Influenza A (IAV) and Influenza B (IBV) viruses accumulate amino acid substitutions (AAS) in the hemagglutinin (HA) and neuraminidase (NA) proteins seasonally. These changes, as well as the dominating viral subtypes, vary depending on geographical location, which may impact disease prevalence and the severity of the season. Genomic surveillance is crucial for capturing circulation patterns and characterizing AAS that may affect disease outcomes, vaccine efficacy, or antiviral drug activities. In this study, whole-genome sequencing of IAV and IBV was attempted on positive remnant clinical samples (587) collected from 580 patients between June 2023 and February 2024 in the Johns Hopkins Health System (JHHS). Full-length HA segments were obtained from 424 (72.2 %) samples. H1N1pdm09 (71.7 %) was the predominant IAV subtype, followed by H3N2 (16.7 %) and IBV-Victoria clade V1A.3a.2 (11.6 %). Within H1N1pdm09 HA sequences, the 6B1A.5a.2a.1 (60.5 %) clade was the most represented. Full-length NA segments were obtained from 421 (71.7 %) samples. Within H1N1pdm09 and IBV, AAS previously proposed to change susceptibility to NA inhibitors were infrequently detected. Phylogeny of HA and NA demonstrated heterogeneous HA and NA H1N1pdm09 and IBV subclades. No significant differences were observed in admission rates or use of supplemental oxygen between different subtypes or clades. Influenza virus genomic surveillance is essential for understanding the seasonal evolution of influenza viruses and their association with disease prevalence and outcomes.

Identifying Importation and Asymptomatic Spreaders of Multi-drug Resistant Organisms in Hospital Settings.

Healthcare-associated infections (HAIs) due to multi-drug resistant organisms (MDROs) are a significant burden to the healthcare system. Patients are sometimes already infected at the time of admission to the hospital (referred to as "importation"), and additional patients might get infected in the hospital through transmission ("nosocomial infection"). Since many of these importation and nosocomial infection cases may present no symptoms (i.e., "asymptomatic"), rapidly identifying them is difficult since testing is limited and incurs significant delays. Although there has been a lot of work on examining the utility of both mathematical models of transmission and machine learning for identifying patients at risk of MDRO infections in recent years, these methods have limited performance and suffer from different drawbacks: Transmission modeling-based methods do not make full use of rich data contained in electronic health records (EHR), while machine learning-based methods typically lack information about mechanistic processes. In this work, we propose NEURABM, a new framework which integrates both neural networks and agent-based models (ABM) to combine the advantages of both modeling-based and machine learning-based methods. NEURABM simultaneously learns a neural network model for patient-level prediction of importation, as well as the ABM model which is used for identifying infections. Our results demonstrate that NEURABM identifies importation and nosocomial infection cases more accurately than existing methods.

Improving Risk Prediction of Methicillin-Resistant Staphylococcus aureus Using Machine Learning Methods With Network Features: Retrospective Development Study.

BACKGROUND: Health care-associated infections due to multidrug-resistant organisms (MDROs), such as methicillin-resistant Staphylococcus aureus (MRSA) and Clostridioides difficile (CDI), place a significant burden on our health care infrastructure. OBJECTIVE: Screening for MDROs is an important mechanism for preventing spread but is resource intensive. The objective of this study was to develop automated tools that can predict colonization or infection risk using electronic health record (EHR) data, provide useful information to aid infection control, and guide empiric antibiotic coverage. METHODS: We retrospectively developed a machine learning model to detect MRSA colonization and infection in undifferentiated patients at the time of sample collection from hospitalized patients at the University of Virginia Hospital. We used clinical and nonclinical features derived from on-admission and throughout-stay information from the patient's EHR data to build the model. In addition, we used a class of features derived from contact networks in EHR data; these network features can capture patients' contacts with providers and other patients, improving model interpretability and accuracy for predicting the outcome of surveillance tests for MRSA. Finally, we explored heterogeneous models for different patient subpopulations, for example, those admitted to an intensive care unit or emergency department or those with specific testing histories, which perform better. RESULTS: We found that the penalized logistic regression performs better than other methods, and this model's performance measured in terms of its receiver operating characteristics-area under the curve score improves by nearly 11% when we use polynomial (second-degree) transformation of the features. Some significant features in predicting MDRO risk include antibiotic use, surgery, use of devices, dialysis, patient's comorbidity conditions, and network features. Among these, network features add the most value and improve the model's performance by at least 15%. The penalized logistic regression model with the same transformation of features also performs better than other models for specific patient subpopulations. CONCLUSIONS: Our study shows that MRSA risk prediction can be conducted quite effectively by machine learning methods using clinical and nonclinical features derived from EHR data. Network features are the most predictive and provide significant improvement over prior methods. Furthermore, heterogeneous prediction models for different patient subpopulations enhance the model's performance.

Incorporating endogenous human behavior in models of COVID-19 transmission: A systematic scoping review.

Background: During the COVID-19 pandemic there was a plethora of dynamical forecasting models created, but their ability to effectively describe future trajectories of disease was mixed. A major challenge in evaluating future case trends was forecasting the behavior of individuals. When behavior was incorporated into models, it was primarily incorporated exogenously (e.g., fitting to cellphone mobility data). Fewer models incorporated behavior endogenously (e.g., dynamically changing a model parameter throughout the simulation). Methods: This review aimed to qualitatively characterize models that included an adaptive (endogenous) behavioral element in the context of COVID-19 transmission. We categorized studies into three approaches: 1) feedback loops, 2) game theory/utility theory, and 3) information/opinion spread. Findings: Of the 92 included studies, 72% employed a feedback loop, 27% used game/utility theory, and 9% used a model if information/opinion spread. Among all studies, 89% used a compartmental model alone or in combination with other model types. Similarly, 15% used a network model, 11% used an agent-based model, 7% used a system dynamics model, and 1% used a Markov chain model. Descriptors of behavior change included mask-wearing, social distancing, vaccination, and others. Sixty-eight percent of studies calibrated their model to observed data and 25% compared simulated forecasts to observed data. Forty-one percent of studies compared versions of their model with and without endogenous behavior. Models with endogenous behavior tended to show a smaller and delayed initial peak with subsequent periodic waves. Interpretation: While many COVID-19 models incorporated behavior exogenously, these approaches may fail to capture future adaptations in human behavior, resulting in under- or overestimates of disease burden. By incorporating behavior endogenously, the next generation of infectious disease models could more effectively predict outcomes so that decision makers can better prepare for and respond to epidemics. Funding: This study was funded in-part by Centers for Disease Control and Prevention (CDC) MInD-Healthcare Program (1U01CK000536), the National Science Foundation (NSF) Modeling Dynamic Disease-Behavior Feedbacks for Improved Epidemic Prediction and Response grant (2229996), and the NSF PIPP Phase I: Evaluating the Effectiveness of Messaging and Modeling during Pandemics grant (2200256).

A multi-center validation of the electronic health record admission source and discharge location fields against the clinical notes for identifying inpatients with long-term care facility exposure.

Identifying long-term care facility (LTCF)-exposed inpatients is important for infection control research and practice, but ascertaining LTCF exposure is challenging. Across a large validation study, electronic health record data fields identified 76% of LTCF-exposed patients compared to manual chart review. OBJECTIVE: Residence or recent stay in a long-term care facility (LTCF) is an important risk factor for antibiotic-resistant bacterial colonization. However, absent dedicated intake questionnaires or resource-intensive chart review, ascertaining LTCF exposure in inpatients is challenging. We aimed to validate the electronic health record (EHR) admission and discharge location fields against the clinical notes for identifying LTCF-exposed inpatients. METHODS: We conducted a retrospective study of 1020 randomly sampled adult admissions between 2016 and 2021 across 12 University of Maryland Medical System hospitals. Using study-developed guidelines, we categorized the following data for LTCF exposure: each admission's history & physical (H&P) note, each admission's EHR-extracted "Admission Source," and (3) the EHR-extracted admission and discharge locations for previous admissions (≤90 days). We estimated sensitivities, with 95% CIs, of H&P notes and of EHR admission/discharge location fields for detecting "current" and "any recent" (≤90 days, including current) LTCF exposure. RESULTS: For detecting current LTCF exposure, the sensitivity of the index admission's EHR-extracted "Admission Source" was 46% (95% CI: 35%­58%) and of the H&P note was 92% (83%­97%). For detecting any recent LTCF exposure, the sensitivity of "Admission Source" across the index and previous admissions was 32% (24%­41%), "Discharge Location" across previous admission(s) was 57% (47%­66%), and of the H&P note was 68% (59%­76%). The combined sensitivity of admission source and discharge location for detecting any recent LTCF exposure was 76% (67%­83%). CONCLUSIONS: The EHR-obtained admission source and discharge location fields identified 76% of LTCF-exposed patients compared to chart review but disproportionately missed currently exposed patients.

Characterizing Patients Presenting on Hospital Admission With Central Line-Associated Bloodstream Infections: A Multicenter Study.

BACKGROUND: There are no systematic measures of central line-associated bloodstream infections (CLABSIs) in patients maintaining central venous catheters (CVCs) outside acute care hospitals. To clarify the burden of CLABSIs in these patients, we characterized patients with CLABSI present on hospital admission (POA). METHODS: Retrospective cross-sectional analysis of patients with CLABSI-POA in 3 health systems covering 11 hospitals across Maryland, Washington DC, and Missouri from November 2020 to October 2021. CLABSI-POA was defined using an adaptation of the acute care CLABSI definition. Patient demographics, clinical characteristics, and outcomes were collected via record review. Cox proportional hazard analysis was used to assess factors associated with the all-cause mortality rate within 30 days. RESULTS: A total of 461 patients were identified as having CLABSI-POA. CVCs were most commonly maintained in home infusion therapy (32.8%) or oncology clinics (31.2%). Enterobacterales were the most common etiologic agent (29.2%). Recurrent CLABSIs occurred in a quarter of patients (25%). Eleven percent of patients died during the hospital admission. Among patients with CLABSI-POA, mortality risk increased with age (hazard ratio vs age <20 years by age group: 20-44 years, 11.2 [95% confidence interval, 1.46-86.22]; 45-64 years, 20.88 [2.84-153.58]; ≥65 years, 22.50 [2.98-169.93]) and lack of insurance (2.46 [1.08-5.59]), and it decreased with CVC removal (0.57 [.39-.84]). CONCLUSIONS: CLABSI-POA is associated with significant in-hospital mortality risk. Surveillance is required to understand the burden of CLABSI in the community to identify targets for CLABSI prevention initiatives outside acute care settings.

Multisite development and validation of machine learning models to predict severe outcomes and guide decision-making for emergency department patients with influenza.

Objective: Millions of Americans are infected by influenza annually. A minority seek care in the emergency department (ED) and, of those, only a limited number experience severe disease or death. ED clinicians must distinguish those at risk for deterioration from those who can be safely discharged. Methods: We developed random forest machine learning (ML) models to estimate needs for critical care within 24 h and inpatient care within 72 h in ED patients with influenza. Predictor data were limited to those recorded prior to ED disposition decision: demographics, ED complaint, medical problems, vital signs, supplemental oxygen use, and laboratory results. Our study population was comprised of adults diagnosed with influenza at one of five EDs in our university health system between January 1, 2017 and May 18, 2022; visits were divided into two cohorts to facilitate model development and validation. Prediction performance was assessed by the area under the receiver operating characteristic curve (AUC) and the Brier score. Results: Among 8032 patients with laboratory-confirmed influenza, incidence of critical care needs was 6.3% and incidence of inpatient care needs was 19.6%. The most common reasons for ED visit were symptoms of respiratory tract infection, fever, and shortness of breath. Model AUCs were 0.89 (95% CI 0.86-0.93) for prediction of critical care and 0.90 (95% CI 0.88-0.93) for inpatient care needs; Brier scores were 0.026 and 0.042, respectively. Importantpredictors included shortness of breath, increasing respiratory rate, and a high number of comorbid diseases. Conclusions: ML methods can be used to accurately predict clinical deterioration in ED patients with influenza and have potential to support ED disposition decision-making.

Reduced control of SARS-CoV-2 infection associates with lower mucosal antibody responses in pregnancy.

Pregnant patients are at greater risk of hospitalization with severe COVID-19 than non-pregnant people. This was a retrospective observational cohort study of remnant clinical specimens from patients who visited acute care hospitals within the Johns Hopkins Health System in the Baltimore, MD-Washington DC, area between October 2020 and May 2022. Participants included confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected pregnant people and matched non-pregnant people (the matching criteria included age, race/ethnicity, area deprivation index, insurance status, and vaccination status to ensure matched demographics). The primary dependent measures were clinical COVID-19 outcomes, infectious virus recovery, viral RNA levels, and mucosal anti-spike (S) IgG titers from upper respiratory tract samples. A total of 452 individuals (117 pregnant and 335 non-pregnant) were included in the study, with both vaccinated and unvaccinated individuals represented. Pregnant patients were at increased risk of hospitalization (odds ratio [OR] = 4.2; confidence interval [CI] = 2.0-8.6), intensive care unit admittance (OR = 4.5; CI = 1.2-14.2), and being placed on supplemental oxygen therapy (OR = 3.1; CI = 1.3-6.9). Individuals infected during their third trimester had higher mucosal anti-S IgG titers and lower viral RNA levels (P < 0.05) than those infected during their first or second trimesters. Pregnant individuals experiencing breakthrough infections due to the Omicron variant had reduced anti-S IgG compared to non-pregnant patients (P < 0.05). The observed increased severity of COVID-19 and reduced mucosal antibody responses particularly among pregnant participants infected with the Omicron variant suggest that maintaining high levels of SARS-CoV-2 immunity through booster vaccines may be important for the protection of this at-risk population.IMPORTANCEIn this retrospective observational cohort study, we analyzed remnant clinical samples from non-pregnant and pregnant individuals with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections who visited the Johns Hopkins Hospital System between October 2020 and May 2022. Disease severity, including intensive care unit admission, was greater among pregnant than non-pregnant patients. Vaccination reduced recovery of infectious virus and viral RNA levels in non-pregnant patients, but not in pregnant patients. In pregnant patients, increased nasopharyngeal viral RNA levels and recovery of infectious virus were associated with reduced mucosal IgG antibody responses, especially among women in their first trimester of pregnancy or experiencing breakthrough infections from Omicron variants. Taken together, this study provides insights into how pregnant patients are at greater risk of severe COVID-19. The novelty of this study is that it focuses on the relationship between the mucosal antibody response and its association with virus load and disease outcomes in pregnant people, whereas previous studies have focused on serological immunity. Vaccination status, gestational age, and SARS-CoV-2 omicron variant impact mucosal antibody responses and recovery of infectious virus from pregnant patients.

Impact of Blood Culture Contamination on Antibiotic Use, Resource Utilization, and Clinical Outcomes: A Retrospective Cohort Study in Dutch and US Hospitals.

Background: Blood culture contamination (BCC) has been associated with prolonged antibiotic use (AU) and increased health care utilization; however, this has not been widely reevaluated in the era of increased attention to antibiotic stewardship. We evaluated the impact of BCC on AU, resource utilization, and length of stay in Dutch and US patients. Methods: This retrospective observational study examined adults admitted to 2 hospitals in the Netherlands and 5 hospitals in the United States undergoing ≥2 blood culture (BC) sets. Exclusion criteria included neutropenia, no hospital admission, or death within 48 hours of hospitalization. The impact of BCC on clinical outcomes-overall inpatient days of antibiotic therapy, test utilization, length of stay, and mortality-was determined via a multivariable regression model. Results: An overall 22 927 patient admissions were evaluated: 650 (4.1%) and 339 (4.8%) with BCC and 11 437 (71.8%) and 4648 (66.3%) with negative BC results from the Netherlands and the United States, respectively. Dutch and US patients with BCC had a mean ± SE 1.74 ± 0.27 (P < .001) and 1.58 ± 0.45 (P < .001) more days of antibiotic therapy than patients with negative BC results. They also had 0.6 ± 0.1 (P < .001) more BCs drawn. Dutch but not US patients with BCC had longer hospital stays (3.36 days; P < .001). There was no difference in mortality between groups in either cohort. AU remained higher in US but not Dutch patients with BCC in a subanalysis limited to BC obtained within the first 24 hours of admission. Conclusions: BCC remains associated with higher inpatient AU and health care utilization as compared with patients with negative BC results, although the impact on these outcomes differs by country.

Modeling relaxed policies for discontinuation of methicillin-resistant Staphylococcus aureus contact precautions.

OBJECTIVE: To evaluate the economic costs of reducing the University of Virginia Hospital's present "3-negative" policy, which continues methicillin-resistant Staphylococcus aureus (MRSA) contact precautions until patients receive 3 consecutive negative test results, to either 2 or 1 negative. DESIGN: Cost-effective analysis. SETTINGS: The University of Virginia Hospital. PATIENTS: The study included data from 41,216 patients from 2015 to 2019. METHODS: We developed a model for MRSA transmission in the University of Virginia Hospital, accounting for both environmental contamination and interactions between patients and providers, which were derived from electronic health record (EHR) data. The model was fit to MRSA incidence over the study period under the current 3-negative clearance policy. A counterfactual simulation was used to estimate outcomes and costs for 2- and 1-negative policies compared with the current 3-negative policy. RESULTS: Our findings suggest that 2-negative and 1-negative policies would have led to 6 (95% CI, -30 to 44; P < .001) and 17 (95% CI, -23 to 59; -10.1% to 25.8%; P < .001) more MRSA cases, respectively, at the hospital over the study period. Overall, the 1-negative policy has statistically significantly lower costs ($628,452; 95% CI, $513,592-$752,148) annually (P < .001) in US dollars, inflation-adjusted for 2023) than the 2-negative policy ($687,946; 95% CI, $562,522-$812,662) and 3-negative ($702,823; 95% CI, $577,277-$846,605). CONCLUSIONS: A single negative MRSA nares PCR test may provide sufficient evidence to discontinue MRSA contact precautions, and it may be the most cost-effective option.

Understanding the role of antibiotic-associated adverse events in influencing antibiotic decision-making.

Objective: To (1) understand the role of antibiotic-associated adverse events (ABX-AEs) on antibiotic decision-making, (2) understand clinician preferences for ABX-AE feedback, and (3) identify ABX-AEs of greatest clinical concern. Design: Focus groups. Setting: Academic medical center. Participants: Medical and surgical house staff, attending physicians, and advanced practice practitioners. Methods: Focus groups were conducted from May 2022 to December 2022. Participants discussed the role of ABX-AEs in antibiotic decision-making and feedback preferences and evaluated the prespecified categorization of ABX-AEs based on degree of clinical concern. Thematic analysis was conducted using inductive coding. Results: Four focus groups were conducted (n = 15). Six themes were identified. (1) ABX-AE risks during initial prescribing influence the antibiotic prescribed rather than the decision of whether to prescribe. (2) The occurrence of an ABX-AE leads to reassessment of the clinical indication for antibiotic therapy. (3) The impact of an ABX-AE on other management decisions is as important as the direct harm of the ABX-AE. (4) ABX-AEs may be overlooked because of limited feedback regarding the occurrence of ABX-AEs. (5) Clinicians are receptive to feedback regarding ABX-AEs but are concerned about it being punitive. (6) Feedback must be curated to prevent clinicians from being overwhelmed with data. Clinicians generally agreed with the prespecified categorizations of ABX-AEs by degree of clinical concern. Conclusions: The themes identified and assessment of ABX-AEs of greatest clinical concern may inform antibiotic stewardship initiatives that incorporate reporting of ABX-AEs as a strategy to reduce unnecessary antibiotic use.

Racial and Socioeconomic Disparities Evident in Inappropriate Antibiotic Prescribing in the Emergency Department.

STUDY OBJECTIVE: Inappropriate antibiotic prescribing for acute respiratory tract infections is a common source of low-value care in the emergency department (ED). Racial and socioeconomic disparities have been noted in episodes of low-value care, particularly in children. We evaluated whether prescribing rates for acute respiratory tract infections when antibiotics would be inappropriate by guidelines differed by race and socioeconomics. METHODS: A retrospective cross-sectional analysis of adult and pediatric patient encounters in the emergency department (ED) between 2015 and 2023 at 5 hospitals for acute respiratory tract infections that did not require antibiotics by guidelines. Multivariable regression was used to calculate the risk ratio between race, ethnicity, and area deprivation index and inappropriate antibiotic prescribing, controlling for patient age, sex, and relevant comorbidities. RESULTS: A total of 147,401 ED encounters (55% pediatric, 45% adult) were included. At arrival, 4% patients identified as Asian, 50% as Black, 5% as Hispanic, and 23% as White. Inappropriate prescribing was noted in 7.6% of overall encounters, 8% for Asian patients, 6% for Black patients, 5% for Hispanic patients, and 12% for White patients. After adjusting for age, sex, comorbidities, and area deprivation index, White patients had a 1.32 (95% confidence interval, 1.26 to 1.38) higher likelihood of receiving a prescription compared with Black patients. Patients residing in areas of greater socioeconomic deprivation, regardless of race and ethnicity, had a 0.74 (95% confidence interval, 0.70 to 0.78) lower likelihood of receiving a prescription. CONCLUSION: Our results suggest that although overall inappropriate prescribing was relatively low, White patients and patients from wealthier areas were more likely to receive an inappropriate antibiotic prescription.

Examining cefazolin utilization and perioperative anaphylaxis in patients with and without a penicillin allergy label: A cross-sectional study.

STUDY OBJECTIVE: To compare the occurrence of cefazolin perioperative anaphylaxis (POA) in patients with and without a penicillin allergy label (PAL) to determine whether the prevalence of cefazolin POA differs based on the presence of a PAL. DESIGN: Cross-sectional study. SETTING: A large U.S. healthcare system in the Baltimore-D.C. region, July 2017 to July 2020. PATIENTS: 112,817 surgical encounters across inpatient and outpatient settings in various specialties, involving 90,089 patients. Of these, 4876 (4.3%) encounters had a PAL. INTERVENTIONS: Perioperative cefazolin administration within 4 h before surgery to 4 h after the procedure began. MEASUREMENTS: The primary outcome was cefazolin POA in patients with and without PALs. Potential POA cases were identified based on tryptase orders or diphenhydramine administrations within the initial cefazolin administration to 6 h postoperatively. Verification included two validation steps. The first checked for hypersensitivity reaction (HSR) documentation, and the second, led by Allergy specialists, identified POA and the probable culprit. The secondary outcome looked at cefazolin use trends in patients with a PAL, stratified by setting and specialty. MAIN RESULTS: Of 112,817 encounters, 1421 (1.3%) had possible cefazolin HSRs. Of these, 22 (1.5%) had POA, resulting in a 0.02% prevalence. Of these, 13 (59.1%) were linked to cefazolin and 9 (40.9%) attributed to other drugs. Only one cefazolin POA case had a PAL, indicating no significant difference in cefazolin POA prevalence between patients with and without PALs (p = 0.437). Perioperative cefazolin use in patients with PALs steadily increased from 2.6% to 6.0% between 2017 and 2020, specifically in academic settings. CONCLUSIONS: The prevalence of cefazolin POA does not exhibit significant differences between patients with and without PALs, and notably, the incidence remains remarkably low. Based on these findings, it is advisable to view cefazolin as an acceptable choice for prophylaxis in patients carrying a PAL.

Increased circulation of human adenovirus in 2023: an investigation of the circulating genotypes, upper respiratory viral loads, and hospital admissions in a large academic medical center.

IMPORTANCE: The circulation of human adenoviruses (HAdV) increased in 2023. In this manuscript, we show that HAdV-B3 was predominant in 2023 in a cohort characterized by the Johns Hopkins Hospital System. We also show that HAdV-B3 was associated with an increase in viral loads in respiratory samples and provide a correlation with the clinical presentations and outcomes.

Epidemiology and Clinical Outcomes of Community-Acquired Acute Kidney Injury in the Emergency Department: A Multisite Retrospective Cohort Study.

RATIONALE & OBJECTIVE: The prevalence of community-acquired acute kidney injury (CA-AKI) in the United States and its clinical consequences are not well described. Our objective was to describe the epidemiology of CA-AKI and the associated clinical outcomes. STUDY DESIGN: Retrospective cohort study. SETTING & PARTICIPANTS: 178,927 encounters by 139,632 adults at 5 US emergency departments (EDs) between July 1, 2017, and December 31, 2022. PREDICTORS: CA-AKI identified using KDIGO (Kidney Disease: Improving Global Outcomes) serum creatinine (Scr)-based criteria. OUTCOMES: For encounters resulting in hospitalization, the in-hospital trajectory of AKI severity, dialysis initiation, intensive care unit (ICU) admission, and death. For all encounters, occurrence over 180 days of hospitalization, ICU admission, new or progressive chronic kidney disease, dialysis initiation, and death. ANALYTICAL APPROACH: Multivariable logistic regression analysis to test the association between CA-AKI and measured outcomes. RESULTS: For all encounters, 10.4% of patients met the criteria for any stage of AKI on arrival to the ED. 16.6% of patients admitted to the hospital from the ED had CA-AKI on arrival to the ED. The likelihood of AKI recovery was inversely related to CA-AKI stage on arrival to the ED. Among encounters for hospitalized patients, CA-AKI was associated with in-hospital dialysis initiation (OR, 6.2; 95% CI, 5.1-7.5), ICU admission (OR, 1.9; 95% CI, 1.7-2.0), and death (OR, 2.2; 95% CI, 2.0-2.5) compared with patients without CA-AKI. Among all encounters, CA-AKI was associated with new or progressive chronic kidney disease (OR, 6.0; 95% CI, 5.6-6.4), dialysis initiation (OR, 5.1; 95% CI, 4.5-5.7), subsequent hospitalization (OR, 1.1; 95% CI, 1.1-1.2) including ICU admission (OR, 1.2; 95% CI, 1.1-1.4), and death (OR, 1.6; 95% CI, 1.5-1.7) during the subsequent 180 days. LIMITATIONS: Residual confounding. Study implemented at a single university-based health system. Potential selection bias related to exclusion of patients without an available baseline Scr measurement. Potential ascertainment bias related to limited repeat Scr data during follow-up after an ED visit. CONCLUSIONS: CA-AKI is a common and important entity that is associated with serious adverse clinical consequences during the 6-month period after diagnosis. PLAIN-LANGUAGE SUMMARY: Acute kidney injury (AKI) is a condition characterized by a rapid decline in kidney function. There are many causes of AKI, but few studies have examined how often AKI is already present when patients first arrive to an emergency department seeking medical attention for any reason. We analyzed approximately 175,000 visits to Johns Hopkins emergency departments and found that AKI is common on presentation to the emergency department and that patients with AKI have increased risks of hospitalization, intensive care unit admission, development of chronic kidney disease, requirement of dialysis, and death in the first 6 months after diagnosis. AKI is an important condition for health care professionals to recognize and is associated with serious adverse outcomes.

Evolution of Influenza A(H3N2) Viruses in 2 Consecutive Seasons of Genomic Surveillance, 2021-2023.

Background: The circulation and the genomic evolution of influenza A(H3N2) viruses during the 2021/2022 and 2022/2023 seasons were studied and associated with infection outcomes. Methods: Remnant influenza A-positive samples following standard-of-care testing from patients across the Johns Hopkins Health System (JHHS) were used for the study. Samples were randomly selected for whole viral genome sequencing. The sequence-based pEpitope model was used to estimate the predicted vaccine efficacy (pVE) for circulating H3N2 viruses. Clinical data were collected and associated with viral genomic data. Results: A total of 121 683 respiratory specimens were tested for influenza at JHHS between 1 September 2021 and 31 December 2022. Among them, 6071 (4.99%) tested positive for influenza A. Of these, 805 samples were randomly selected for sequencing, with hemagglutinin (HA) segments characterized for 610 samples. Among the characterized samples, 581 were H3N2 (95.2%). Phylogenetic analysis of HA segments revealed the exclusive circulation of H3N2 viruses with HA segments of the 3C.2a1b.2a.2 clade. Analysis of a total of 445 complete H3N2 genomes revealed reassortments; 200 of 227 of the 2022/2023 season genomes (88.1%) were found to have reassorted with clade 3C.2a1b.1a. The pVE was estimated to be -42.53% for the 2021/2022 season and 30.27% for the 2022/2023 season. No differences in clinical presentations or admissions were observed between the 2 seasons. Conclusions: The increased numbers of cases and genomic diversity of influenza A(H3N2) during the 2022/2023 season were not associated with a change in disease severity compared to the previous influenza season.