Search

Background: Little is known about the frequency, hospital-level variation, predictors, and outcomes of antibiotic de-escalation in suspected sepsis., Methods: We retrospectively analyzed adults admitted to 236 US hospitals from 2017-2021 with suspected sepsis (defined by blood culture draw, lactate measurement, and intravenous antibiotic administration) who were initially treated with ≥2 days of anti-methicillin-resistant Staphylococcus aureus (MRSA) and anti-pseudomonal antibiotics but had no resistant organisms that required these agents identified through hospital day 4. De-escalation was defined as stopping anti-MRSA and anti-pseudomonal antibiotics or switching to narrower antibiotics by day 4. We created a propensity score for de-escalation using 82 hospital and clinical variables; matched de-escalated to non-de-escalated patients; and assessed associations between de-escalation and outcomes., Results: Among 124 577 patients, antibiotics were de-escalated in 36 806 (29.5%): narrowing in 27 177 (21.8%), cessation in 9629 (7.7%). De-escalation rates varied between hospitals (median, 29.4%; interquartile range, 21.3%-38.0%). Predictors of de-escalation included less severe disease on day 3-4, positive cultures for nonresistant organisms, and negative/absent MRSA nasal swabs. De-escalation was more common in medium, large, and teaching hospitals in the Northeast and Midwest. De-escalation was associated with lower adjusted risks for acute kidney injury (AKI) (odds ratio [OR], 0.80; 95% confidence interval [CI], .76-.84), intensive-care unit (ICU) admission after day 4 (OR, 0.59; 95% CI, .52-.66), and in-hospital mortality (OR, 0.92; 95% CI, .86-.996)., Conclusions: Antibiotic de-escalation in suspected sepsis is infrequent, variable across hospitals, linked with clinical and microbiologic factors, and associated with lower risk for AKI, ICU admission, and in-hospital mortality., (© The Author(s) 2024. Published by Oxford University Press on behalf of Infectious Diseases Society of America. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

We reviewed hospital-onset respiratory viral infections, 2015-2023, in one hospital to determine whether Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) transmission prevention measures prevented non-SARS-CoV-2 respiratory viral infections. Masking, employee symptom attestations, and screening patients and visitors for symptoms were associated with a 44%-53% reduction in hospital-onset influenza and respiratory syncytial virus (RSV), accounting for changes in community incidence., Competing Interests: Potential conflicts of interest . M. K. reports institutional grant funding from CDC, Agency for Healthcare Research and Quality (AHRQ), and Massachusetts Department of Public Health as well as royalties from UpToDate for chapters on pneumonia. C. R. reports institutional grant funding from CDC (for research related to infection prevention and control) and AHRQ (for research related to sepsis treatment and metrics); royalties from UpToDate for chapters on procalcitonin; consulting fees from Cytovale regarding sepsis diagnostics; and payments from the Infectious Diseases Society of America for his role as an associate editor for Clinical Infectious Diseases. M. B. reports grant funding from CDC for ESP VAERS for COVID-19 and a role on the SHEA Guidelines Committee for the IDSA Standards and Practice Guidelines Committee. All other authors report no potential conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed., (© The Author(s) 2023. Published by Oxford University Press on behalf of Infectious Diseases Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Suspected pneumonia is the most common indication for antibiotics in hospitalized patients but is frequently overdiagnosed. We explored whether normal oxygenation could be used as an indicator to support early discontinuation of antibiotics., Methods: We retrospectively identified all patients started on antibiotics for pneumonia in 4 hospitals with oxygen saturations ≥95% on ambient air, May 2017-February 2021. We propensity-matched patients treated 1-2 days vs 5-8 days and compared hospital mortality and time to discharge using subdistribution hazard ratios (SHRs). Secondary outcomes included readmissions, 30-day mortality, Clostridioides difficile infections, hospital-free days, and antibiotic-free days., Results: Among 39 752 patients treated for possible pneumonia, 10 012 had median oxygen saturations ≥95% without supplemental oxygen. Of these, 2871 were treated 1-2 days and 2891 for 5-8 days; 4478 patients were propensity-matched. Patients treated 1-2 vs 5-8 days had similar hospital mortality (2.1% vs 2.8%; SHR, 0.75 [95% confidence interval {CI}, .51-1.09]) but less time to discharge (6.1 vs 6.6 days; SHR, 1.13 [95% CI, 1.07-1.19]) and more 30-day hospital-free days (23.1 vs 22.7; mean difference, 0.44 [95% CI, .09-.78]). There were no significant differences in 30-day readmissions (16.0% vs 15.8%; odds ratio [OR], 1.01 [95% CI, .86-1.19]), 30-day mortality (4.6% vs 5.1%; OR, 0.91 [95% CI, .69-1.19]), or 90-day C. difficile infections (1.3% vs 0.8%; OR, 1.67 [95% CI, .94-2.99])., Conclusions: One-quarter of hospitalized patients treated for pneumonia had oxygenation saturations ≥95% on ambient air. Outcomes were similar with 1-2 vs 5-8 days of antibiotics. Normal oxygenation levels may help identify candidates for early antibiotic discontinuation. Prospective trials are warranted., Competing Interests: Potential conflicts of interest. M. K. has received royalties from UpToDate for articles on pneumonia and has also received grants from the Agency for Healthcare Research and Quality. C. R. has received royalties from UpToDate for articles on procalcitonin; payments for consulting related to sepsis diagnostics from Cytovale; and payments from Pfizer for consulting related to Lyme disease surveillance. All other authors report no potential conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed., (© The Author(s) 2022. Published by Oxford University Press on behalf of Infectious Diseases Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Ending the human immunodeficiency virus (HIV) epidemic requires knowledge of key drivers of spread of HIV infection., Methods: Between 1996 and 2018, 1119 newly and previously diagnosed, therapy-naive persons with HIV (PWH) from San Diego were followed. A genetic distance-based network was inferred using pol sequences, and genetic clusters grew over time through linkage of sequences from newly observed infections. Cox proportional hazards models were used to identify factors associated with the rate of growth. These results were used to predict the impact of a hypothetical intervention targeting PWH with incident infection. Comparison was made to the Centers for Disease Control and Prevention (CDC) Ending the HIV Epidemic (EHE) molecular surveillance strategy, which prioritizes clusters recently linked to all new HIV diagnoses and does not incorporate data on incident infections., Results: Overall, 219 genetic linkages to incident infections were identified over a median follow-up of 8.8 years. Incident cluster growth was strongly associated with proportion of PWH in the cluster who themselves had incident infection (hazard ratio, 44.09 [95% confidence interval, 17.09-113.78]). The CDC EHE molecular surveillance strategy identified 11 linkages to incident infections a genetic distance threshold of 0.5%, and 24 linkages at 1.5%., Conclusions: Over the past 2 decades, incident infections drove incident HIV cluster growth in San Diego. The current CDC EHE molecular detection and response strategy would not have identified most transmission events arising from those with incident infection in San Diego. Molecular surveillance that includes detection of incident cases will provide a more effective strategy for EHE., (© The Author(s) 2021. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.)

Motivated by the need to jointly model the longitudinal trajectories of HIV viral load levels and CD4 counts during the primary infection stage, we propose a joint penalized spline modeling approach that can be used to model the repeated measurements from multiple biomarkers of various types (eg, continuous, binary) simultaneously. This approach allows for flexible trajectories for each marker, accounts for potentially time-varying correlation between markers, and is robust to misspecification of knots. Despite its advantages, the application of multivariate penalized spline models, especially when biomarkers may be of different data types, has been limited in part due to its seemingly complexity in implementation. To overcome this, we describe a procedure that transforms the multivariate setting to the univariate one, and then makes use of the generalized linear mixed effect model representation of a penalized spline model to facilitate its implementation with standard statistical software. We performed simulation studies to evaluate the validity and efficiency through joint modeling of correlated biomarkers measured longitudinally compared to the univariate modeling approach. We applied this modeling approach to longitudinal HIV-1 RNA load and CD4 count data from Southern African cohorts to estimate features of the joint distributions such as the correlation and the proportion of subjects with high viral load levels and high CD4 cell counts over time., (© 2020 The International Biometric Society.)

Standard sample size calculation formulas for stepped wedge cluster randomized trials (SW-CRTs) assume that cluster sizes are equal. When cluster sizes vary substantially, ignoring this variation may lead to an under-powered study. We investigate the relative efficiency of a SW-CRT with varying cluster sizes to equal cluster sizes, and derive variance estimators for the intervention effect that account for this variation under a mixed effects model-a commonly used approach for analyzing data from cluster randomized trials. When cluster sizes vary, the power of a SW-CRT depends on the order in which clusters receive the intervention, which is determined through randomization. We first derive a variance formula that corresponds to any particular realization of the randomized sequence and propose efficient algorithms to identify upper and lower bounds of the power. We then obtain an "expected" power based on a first-order approximation to the variance formula, where the expectation is taken with respect to all possible randomization sequences. Finally, we provide a variance formula for more general settings where only the cluster size arithmetic mean and coefficient of variation, instead of exact cluster sizes, are known in the design stage. We evaluate our methods through simulations and illustrate that the average power of a SW-CRT decreases as the variation in cluster sizes increases, and the impact is largest when the number of clusters is small., (© 2019 The International Biometric Society.)