To describe the real-world clinical impact of a commercially available plasma cell-free DNA metagenomic next-generation sequencing assay, the Karius test (KT).

We retrospectively evaluated the clinical impact of KT by clinical panel adjudication. Descriptive statistics were used to study associations of diagnostic indications, host characteristics, and KT-generated microbiologic patterns with the clinical impact of KT. Multivariable logistic regression modeling was used to further characterize predictors of higher positive clinical impact.

We evaluated 1000 unique clinical cases of KT from 941 patients between January 1, 2017–August 31, 2023. The cohort included adult (70%) and pediatric (30%) patients. The overall clinical impact of KT was positive in 16%, negative in 2%, and no clinical impact in 82% of the cases. Among adult patients, multivariable logistic regression modeling showed that culture-negative endocarditis (OR 2.3; 95% CI, 1.11–4.53; P .022) and concern for fastidious/zoonotic/vector-borne pathogens (OR 2.1; 95% CI, 1.11–3.76; P .019) were associated with positive clinical impact of KT. Host immunocompromised status was not reliably associated with a positive clinical impact of KT (OR 1.03; 95% CI, 0.83–1.29; P .7806). No significant predictors of KT clinical impact were found in pediatric patients. Microbiologic result pattern was also a significant predictor of impact.

Our study highlights that despite the positive clinical impact of KT in select situations, most testing results had no clinical impact. We also confirm diagnostic indications where KT may have the highest yield, thereby generating tools for diagnostic stewardship.

For young children experiencing an illness, adequate nutrition is critical for recovery and to prevent malnutrition, yet many children do not receive the recommended quantities of food during illness and recuperation. Our research applied a behavioural science lens to identify drivers of feeding behaviours, including barriers inhibiting caregivers from following the feeding guidelines.

In 2021, we conducted qualitative research informed by the behavioural design process. Data from in-depth interviews and observations were analysed for themes.

Research was conducted in South Kivu, Democratic Republic of the Congo.

Research participants included caregivers of young children, other family members, health workers and other community members.

Five key findings about behavioural drivers emerged: (1) poverty and scarcity impose practical constraints and a cognitive and emotional burden on caregivers; (2) health providers are distracted and discouraged from counselling on feeding during sick visits; (3) a focus on quality and hesitations about quantity obscure benefits of feeding greater amounts of available foods; (4) perceptions of inappropriate foods limit caregivers’ choices; and (5) deference to a child’s limited appetite leads to missed opportunities to encourage them to eat.

Each of these behavioural drivers is triggered by one or more addressable features in caregivers’ and health workers’ environment, suggesting concrete opportunities for programmes to support caregivers and health workers to improve feeding of young children during illness and recovery. In other settings where these features of the environment are similar, the insights and programming implications are likely to translate.

The European Union Horizon 2020 research and innovation funding program awarded the NIGHTINGALE grant to develop a toolkit to support first responders engaged in prehospital (PH) mass casualty incident (MCI) response. To reach the projects’ objectives, the NIGHTINGALE consortium adapted the Translational Science (TS) process. The aim of this study is to perform the first TS (T1) phase PRISMA scoping review to extract data that will be used to guide the creation of the initial evidence-based second TS phase (T2) modified Delphi statements for a subsequent study.

The consortium was divided into three work groups (WGs) MCI triage, Prehospital Life Support and Damage Control (PHLSDC) and Prehospital Processes (PHP). Each WG conducted simultaneous literature searches following the PRISMA extension for scoping review with a common research strategy sharing MCI related search terms and then terms specific for each WG. Final included articles went through data extraction based on identified themes and sub-themes from PH MCI response literature to be used to create the future statements.

The initial search yielded 925 total references to be considered for a title and abstract review (PH Triage 311, PHLSDC 329, PHP 285), then 483 articles for a full reference review (MCI Triage 111, PHLSDC 216, PHP 156) and 155 articles for the database extraction process (MCI Triage 27, PHLSDC 38, PHP 90).

The progression of the study of prehospital MCI response enabled NIGHTINGALE partners to methodically obtain information that will contribute to each WG’s creation of initial T2 modified Delphi statements.

To identify preventable factors that contribute to the cross transmission of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) to patients in healthcare facilities.

A case–control study was conducted among inpatients on a coronavirus disease 2019 (COVID-19) outbreak unit.

This study was conducted in a medical-surgical unit of a tertiary-care hospital in Nova Scotia in May 2021.

Patients hospitalized on the unit for at least 12 hours and healthcare workers (HCW) working on the unit within 2 weeks of outbreak declaration were included.

Risk factors for SARS-CoV-2 infection were analyzed using simple and multiple logistic regression. Whole-genome sequencing (WGS) was performed to identify SARS-CoV-2 strain relatedness. Network analysis was used to describe patient accommodation.

SARS-CoV-2 infections were identified in 21 patients (29.6%) and 11 HCWs (6.6%). WGS data revealed 4 distinct clades of related sequences. Several factors likely contributed to the outbreak, including failure to identify SARS-CoV-2, a largely incomplete or unvaccinated population, and patient wandering behaviors. The most significant risk factor for SARS-CoV-2 infection was room sharing with an infectious patient, which was the only factor that remained statistically significant following multivariate analysis (odds ratio [OR], 9.2l; 95% confidence interval [CI], 2.04–41.67; P = .004).

This outbreak likely resulted from admission of 2 patients with COVID-19, with subsequent transmissions to 17 patients and 11 staff. WGS and bioinformatics analyses were critical to identifying previously unrecognized nosocomial transmissions of SARS-CoV-2. This study supports strategies to reduce nosocomial transmissions of SARS-CoV-2, such as single-patient rooms, promotion of COVID-19 vaccination, and infection prevention and control measures including management of wandering behaviors.

Public health authorities recommend symptom monitoring of healthcare personnel (HCP) after defined exposures to monkeypox. We report on the rapid development and implementation of mobile responsive survey solutions for notification of possible exposure, exposure risk assessment and stratification, and symptom monitoring.

An academic health center in Boston, Massachusetts, after admission of first diagnosed case of monkeypox in the United States during the current global outbreak.

Research Electronic Data Capture (REDCap) design and programmers, infection control, occupational health, and emergency preparedness specialists, and HCP with possible exposure to monkeypox.

Design and deployment of REDCap tools to identify HCP with possible exposure to monkeypox, to perform exposure risk assessment and stratification for postexposure prophylaxis (PEP), and to conduct symptom monitoring during the exposure window. Project enhancements included dashboards for HCP tracking and short message service (SMS text) reminders for symptom monitoring.

Tools to support the contact tracing and exposure investigation were deployed within 24 hours of identification of a patient with suspected monkeypox, with the full suite in production within 4 days of confirmation of the monkeypox diagnosis. Clinical follow-up of HCP was integrated into the design, and real-time versioning allowed for improvements in HCP symptom monitoring compliance and enhanced tracking.

During the current monkeypox outbreak, timely and comprehensive evaluation of potential HCP exposures is necessary but presents logistical challenges. Rapid development of monkeypox-specific solutions using REDCap facilitated flexibility in design and approach, and integration of targeted clinical support enhanced functionality.

The Canadian Nosocomial Infection Surveillance Program conducted point-prevalence surveys in acute-care hospitals in 2002, 2009, and 2017 to identify trends in antimicrobial use.

Eligible inpatients were identified from a 24-hour period in February of each survey year. Patients were eligible (1) if they were admitted for ≥48 hours or (2) if they had been admitted to the hospital within a month. Chart reviews were conducted. We calculated the prevalence of antimicrobial use as follows: patients receiving ≥1 antimicrobial during survey period per number of patients surveyed × 100%.

In each survey, 28−47 hospitals participated. In 2002, 2,460 (36.5%; 95% CI, 35.3%−37.6%) of 6,747 surveyed patients received ≥1 antimicrobial. In 2009, 3,566 (40.1%, 95% CI, 39.0%−41.1%) of 8,902 patients received ≥1 antimicrobial. In 2017, 3,936 (39.6%, 95% CI, 38.7%−40.6%) of 9,929 patients received ≥1 antimicrobial. Among patients who received ≥1 antimicrobial, penicillin use increased 36.8% between 2002 and 2017, and third-generation cephalosporin use increased from 13.9% to 18.1% (P < .0001). Between 2002 and 2017, fluoroquinolone use decreased from 25.7% to 16.3% (P < .0001) and clindamycin use decreased from 25.7% to 16.3% (P < .0001) among patients who received ≥1 antimicrobial. Aminoglycoside use decreased from 8.8% to 2.4% (P < .0001) and metronidazole use decreased from 18.1% to 9.4% (P < .0001). Carbapenem use increased from 3.9% in 2002 to 6.1% in 2009 (P < .0001) and increased by 4.8% between 2009 and 2017 (P = .60).

The prevalence of antimicrobial use increased between 2002 and 2009 and then stabilized between 2009 and 2017. These data provide important information for antimicrobial stewardship programs.