Bloodstream infections (BSIs) are a frequent cause of morbidity in patients with acute myeloid leukemia (AML), due in part to the presence of central venous access devices (CVADs) required to deliver therapy.

To determine the differential risk of bacterial BSI during neutropenia by CVAD type in pediatric patients with AML.

We performed a secondary analysis in a cohort of 560 pediatric patients (1,828 chemotherapy courses) receiving frontline AML chemotherapy at 17 US centers. The exposure was CVAD type at course start: tunneled externalized catheter (TEC), peripherally inserted central catheter (PICC), or totally implanted catheter (TIC). The primary outcome was course-specific incident bacterial BSI; secondary outcomes included mucosal barrier injury (MBI)-BSI and non-MBI BSI. Poisson regression was used to compute adjusted rate ratios comparing BSI occurrence during neutropenia by line type, controlling for demographic, clinical, and hospital-level characteristics.

The rate of BSI did not differ by CVAD type: 11 BSIs per 1,000 neutropenic days for TECs, 13.7 for PICCs, and 10.7 for TICs. After adjustment, there was no statistically significant association between CVAD type and BSI: PICC incident rate ratio [IRR] = 1.00 (95% confidence interval [CI], 0.75–1.32) and TIC IRR = 0.83 (95% CI, 0.49–1.41) compared to TEC. When MBI and non-MBI were examined separately, results were similar.

In this large, multicenter cohort of pediatric AML patients, we found no difference in the rate of BSI during neutropenia by CVAD type. This may be due to a risk-profile for BSI that is unique to AML patients.

In March 2020, New York City (NYC) became the epicenter of the COVID-19 pandemic in the United States (US). As healthcare facilities were overwhelmed with patients, the Jacob K. Javits Convention Center was transformed into the nation’s largest alternate care site (ACS): Javits New York Medical Station (Javits). Protecting healthcare workers during a global shortage of personal protective equipment (PPE) in a non-traditional healthcare setting posed unique challenges. We describe components of the healthcare worker safety program implemented at Javits.

Javits, a large convention center transformed into a field hospital, with clinical staff from the US Public Health Service Commissioned Corps (USPHS) and the Department of Defense (DoD).

Key strategies included ensuring one-way flow of traffic on and off the patient floor; developing a matrix detailing PPE required for each work activity and location; PPE extended use and reuse protocols; personnel training; and monitoring adherence to PPE donning/doffing protocols when entering or exiting the patient floor. Javits staff who reported COVID-19 symptoms were immediately isolated, monitored, and offered a SARS-CoV-2 reverse transcriptase polymerase chain reaction (RT-PCR) test.

A well-designed and implemented healthcare worker safety plan can minimize the risk of SARS-CoV-2 infection for healthcare workers. The lessons learned from operating the nation’s largest COVID-19 ACS can be adapted to other environments during public health emergencies.

End-of-life care (EOLC) communication is beneficial but underutilized, particularly in conditions with a variable course such as chronic obstructive pulmonary disease (COPD) and congestive heart failure (CHF). Physicians’ emotional distress intolerance has been identified as a barrier to EOLC communication. However, studies of emotional distress intolerance in EOLC have largely relied on anecdotal reports, qualitative data, or observational studies of physician–patient communication. A free-standing measure of multiple dimensions of distress tolerance is warranted to enable the identification of individuals experiencing distress intolerance and to facilitate the effective targeting of interventions to improve distress tolerance.

This study provides preliminary data on the reliability and validity of the Physician Distress Intolerance (PDI) scale. We examine potential subdimensions of emotional distress intolerance.

Family medicine and internal medicine physicians completed the PDI, read vignettes describing patients with COPD or CHF, and indicated whether they initiated or delayed EOLC communication with their patients with similar conditions.

Exploratory and confirmatory factor analyses were performed on separate samples. Confirmatory factor analysis confirmed that a three-factor solution was superior to a two- or one-factor solution. Three subscales were created: Anticipating Negative Emotions, Intolerance of Uncertainty, and Iatrogenic Harm. The full scale and subscales had adequate internal consistency and demonstrated evidence of validity. Higher scores on the PDI, indicating greater distress intolerance, were negatively associated with initiation and positively associated with delay of EOLC communication. Subscales provided unique information.

The PDI can contribute to research investigating and addressing emotional barriers to EOLC communication.

Methicillin-resistant Staphylococcus aureus (MRSA) is an important pathogen in neonatal intensive care units (NICU) that confers significant morbidity and mortality.

Improving our understanding of MRSA transmission dynamics, especially among high-risk patients, is an infection prevention priority.

We investigated a cluster of clinical MRSA cases in the NICU using a combination of epidemiologic review and whole-genome sequencing (WGS) of isolates from clinical and surveillance cultures obtained from patients and healthcare personnel (HCP).

Phylogenetic analysis identified 2 genetically distinct phylogenetic clades and revealed multiple silent-transmission events between HCP and infants. The predominant outbreak strain harbored multiple virulence factors. Epidemiologic investigation and genomic analysis identified a HCP colonized with the dominant MRSA outbreak strain who cared for most NICU patients who were infected or colonized with the same strain, including 1 NICU patient with severe infection 7 months before the described outbreak. These results guided implementation of infection prevention interventions that prevented further transmission events.

Silent transmission of MRSA between HCP and NICU patients likely contributed to a NICU outbreak involving a virulent MRSA strain. WGS enabled data-driven decision making to inform implementation of infection control policies that mitigated the outbreak. Prospective WGS coupled with epidemiologic analysis can be used to detect transmission events and prompt early implementation of control strategies.

Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment.

To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.

This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework.

The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data.

Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.

Clinical high-risk states for psychosis (CHR) are associated with functional impairments and depressive disorders. A previous PRONIA study predicted social functioning in CHR and recent-onset depression (ROD) based on structural magnetic resonance imaging (sMRI) and clinical data. However, the combination of these domains did not lead to accurate role functioning prediction, calling for the investigation of additional risk dimensions. Role functioning may be more strongly associated with environmental adverse events than social functioning.

We aimed to predict role functioning in CHR, ROD and transdiagnostically, by adding environmental adverse events-related variables to clinical and sMRI data domains within the PRONIA sample.

Baseline clinical, environmental and sMRI data collected in 92 CHR and 95 ROD samples were trained to predict lower versus higher follow-up role functioning, using support vector classification and mixed k-fold/leave-site-out cross-validation. We built separate predictions for each domain, created multimodal predictions and validated them in independent cohorts (74 CHR, 66 ROD).

Models combining clinical and environmental data predicted role outcome in discovery and replication samples of CHR (balanced accuracies: 65.4% and 67.7%, respectively), ROD (balanced accuracies: 58.9% and 62.5%, respectively), and transdiagnostically (balanced accuracies: 62.4% and 68.2%, respectively). The most reliable environmental features for role outcome prediction were adult environmental adjustment, childhood trauma in CHR and childhood environmental adjustment in ROD.

Findings support the hypothesis that environmental variables inform role outcome prediction, highlight the existence of both transdiagnostic and syndrome-specific predictive environmental adverse events, and emphasise the importance of implementing real-world models by measuring multiple risk dimensions.