Patients with posttraumatic stress disorder (PTSD) exhibit smaller regional brain volumes in commonly reported regions including the amygdala and hippocampus, regions associated with fear and memory processing. In the current study, we have conducted a voxel-based morphometry (VBM) meta-analysis using whole-brain statistical maps with neuroimaging data from the ENIGMA-PGC PTSD working group.

T1-weighted structural neuroimaging scans from 36 cohorts (PTSD n = 1309; controls n = 2198) were processed using a standardized VBM pipeline (ENIGMA-VBM tool). We meta-analyzed the resulting statistical maps for voxel-wise differences in gray matter (GM) and white matter (WM) volumes between PTSD patients and controls, performed subgroup analyses considering the trauma exposure of the controls, and examined associations between regional brain volumes and clinical variables including PTSD (CAPS-4/5, PCL-5) and depression severity (BDI-II, PHQ-9).

PTSD patients exhibited smaller GM volumes across the frontal and temporal lobes, and cerebellum, with the most significant effect in the left cerebellum (Hedges’ g = 0.22, pcorrected = .001), and smaller cerebellar WM volume (peak Hedges’ g = 0.14, pcorrected = .008). We observed similar regional differences when comparing patients to trauma-exposed controls, suggesting these structural abnormalities may be specific to PTSD. Regression analyses revealed PTSD severity was negatively associated with GM volumes within the cerebellum (pcorrected = .003), while depression severity was negatively associated with GM volumes within the cerebellum and superior frontal gyrus in patients (pcorrected = .001).

PTSD patients exhibited widespread, regional differences in brain volumes where greater regional deficits appeared to reflect more severe symptoms. Our findings add to the growing literature implicating the cerebellum in PTSD psychopathology.

In response to the COVID-19 pandemic, we rapidly implemented a plasma coordination center, within two months, to support transfusion for two outpatient randomized controlled trials. The center design was based on an investigational drug services model and a Food and Drug Administration-compliant database to manage blood product inventory and trial safety.

A core investigational team adapted a cloud-based platform to randomize patient assignments and track inventory distribution of control plasma and high-titer COVID-19 convalescent plasma of different blood groups from 29 donor collection centers directly to blood banks serving 26 transfusion sites.

We performed 1,351 transfusions in 16 months. The transparency of the digital inventory at each site was critical to facilitate qualification, randomization, and overnight shipments of blood group-compatible plasma for transfusions into trial participants. While inventory challenges were heightened with COVID-19 convalescent plasma, the cloud-based system, and the flexible approach of the plasma coordination center staff across the blood bank network enabled decentralized procurement and distribution of investigational products to maintain inventory thresholds and overcome local supply chain restraints at the sites.

The rapid creation of a plasma coordination center for outpatient transfusions is infrequent in the academic setting. Distributing more than 3,100 plasma units to blood banks charged with managing investigational inventory across the U.S. in a decentralized manner posed operational and regulatory challenges while providing opportunities for the plasma coordination center to contribute to research of global importance. This program can serve as a template in subsequent public health emergencies.

To describe utilization of at-home coronavirus disease 2019 (COVID-19) testing among healthcare workers (HCW).

Serial cross-sectional study.

HCWs in the Chicago area.

Serial surveys were conducted from the Northwestern Medicine (NM HCW SARS-CoV-2) Serology Cohort Study. In April 2022, participants reflected on the past 30 days to complete an online survey regarding COVID-19 home testing. Surveys were repeated in June and November 2022. The percentage of completed home tests and ever-positive tests were reported. Multivariable Poisson regression was used to calculate prevalence rate ratios (PRR) and univariate analysis was used for association between participant characteristics with home testing and positivity.

Overall, 2,226 (62.4%) of 3,569 responded to the survey in April. Home testing was reported by 26.6% of respondents and 5.9% reported having at least one positive home test. Testing was highest among those 30–39 years old (35.9%) and nurses (28.3%). A positive test was associated (P < .001) with exposure to people, other than patients with known or suspected COVID-19. Home testing increased in June to 36.4% (positivity 19.9%) and decreased to 25% (positivity 13.5%) by November.

Our cohort findings show the overall increase in both home testing and ever positivity from April to November – a period where changes in variants of concern of SARS-CoV-2 were reported nationwide. Having an exposure to people, other than patients with known or suspected COVID-19 was significantly associated with both, higher home testing frequency and ever-test positivity.

To describe the genomic analysis and epidemiologic response related to a slow and prolonged methicillin-resistant Staphylococcus aureus (MRSA) outbreak.

Prospective observational study.

Neonatal intensive care unit (NICU).

We conducted an epidemiologic investigation of a NICU MRSA outbreak involving serial baby and staff screening to identify opportunities for decolonization. Whole-genome sequencing was performed on MRSA isolates.

A NICU with excellent hand hygiene compliance and longstanding minimal healthcare-associated infections experienced an MRSA outbreak involving 15 babies and 6 healthcare personnel (HCP). In total, 12 cases occurred slowly over a 1-year period (mean, 30.7 days apart) followed by 3 additional cases 7 months later. Multiple progressive infection prevention interventions were implemented, including contact precautions and cohorting of MRSA-positive babies, hand hygiene observers, enhanced environmental cleaning, screening of babies and staff, and decolonization of carriers. Only decolonization of HCP found to be persistent carriers of MRSA was successful in stopping transmission and ending the outbreak. Genomic analyses identified bidirectional transmission between babies and HCP during the outbreak.

In comparison to fast outbreaks, outbreaks that are “slow and sustained” may be more common to units with strong existing infection prevention practices such that a series of breaches have to align to result in a case. We identified a slow outbreak that persisted among staff and babies and was only stopped by identifying and decolonizing persistent MRSA carriage among staff. A repeated decolonization regimen was successful in allowing previously persistent carriers to safely continue work duties.

Healthcare workers (HCWs) are a high-priority group for coronavirus disease 2019 (COVID-19) vaccination and serve as sources for public information. In this analysis, we assessed vaccine intentions, factors associated with intentions, and change in uptake over time in HCWs.

A prospective cohort study of COVID-19 seroprevalence was conducted with HCWs in a large healthcare system in the Chicago area. Participants completed surveys from November 25, 2020, to January 9, 2021, and from April 24 to July 12, 2021, on COVID-19 exposures, diagnosis and symptoms, demographics, and vaccination status.

Of 4,180 HCWs who responded to a survey, 77.1% indicated that they intended to get the vaccine. In this group, 23.2% had already received at least 1 dose of the vaccine, 17.4% were unsure, and 5.5% reported that they would not get the vaccine. Factors associated with intention or vaccination were being exposed to clinical procedures (vs no procedures: adjusted odds ratio [AOR], 1.39; 95% confidence interval [CI], 1.16–1.65) and having a negative serology test for COVID-19 (vs no test: AOR, 1.46; 95% CI, 1.24–1.73). Nurses (vs physicians: AOR, 0.24; 95% CI, 0.17–0.33), non-Hispanic Black (vs Asians: AOR, 0.35; 95% CI, 0.21–0.59), and women (vs men: AOR, 0.38; 95% CI, 0.30–0.50) had lower odds of intention to get vaccinated. By 6-months follow-up, >90% of those who had previously been unsure were vaccinated, whereas 59.7% of those who previously reported no intention of getting vaccinated, were vaccinated.

COVID-19 vaccination in HCWs was high, but variability in vaccination intention exists. Targeted messaging coupled with vaccine mandates can support uptake.

Cardiac intensivists frequently assess patient readiness to wean off mechanical ventilation with an extubation readiness trial despite it being no more effective than clinician judgement alone. We evaluated the utility of high-frequency physiologic data and machine learning for improving the prediction of extubation failure in children with cardiovascular disease.

This was a retrospective analysis of clinical registry data and streamed physiologic extubation readiness trial data from one paediatric cardiac ICU (12/2016-3/2018). We analysed patients’ final extubation readiness trial. Machine learning methods (classification and regression tree, Boosting, Random Forest) were performed using clinical/demographic data, physiologic data, and both datasets. Extubation failure was defined as reintubation within 48 hrs. Classifier performance was assessed on prediction accuracy and area under the receiver operating characteristic curve.

Of 178 episodes, 11.2% (N = 20) failed extubation. Using clinical/demographic data, our machine learning methods identified variables such as age, weight, height, and ventilation duration as being important in predicting extubation failure. Best classifier performance with this data was Boosting (prediction accuracy: 0.88; area under the receiver operating characteristic curve: 0.74). Using physiologic data, our machine learning methods found oxygen saturation extremes and descriptors of dynamic compliance, central venous pressure, and heart/respiratory rate to be of importance. The best classifier in this setting was Random Forest (prediction accuracy: 0.89; area under the receiver operating characteristic curve: 0.75). Combining both datasets produced classifiers highlighting the importance of physiologic variables in determining extubation failure, though predictive performance was not improved.

Physiologic variables not routinely scrutinised during extubation readiness trials were identified as potential extubation failure predictors. Larger analyses are necessary to investigate whether these markers can improve clinical decision-making.

To determine the changes in severe acute respiratory coronavirus virus 2 (SARS-CoV-2) serologic status and SARS-CoV-2 infection rates in healthcare workers (HCWs) over 6-months of follow-up.

Prospective cohort study.

HCWs in the Chicago area.

Cohort participants were recruited in May and June 2020 for baseline serology testing (Abbott anti-nucleocapsid IgG) and were then invited for follow-up serology testing 6 months later. Participants completed monthly online surveys that assessed demographics, medical history, coronavirus disease 2019 (COVID-19), and exposures to SARS-CoV-2. The electronic medical record was used to identify SARS-CoV-2 polymerase chain reaction (PCR) positivity during follow-up. Serologic conversion and SARS-CoV-2 infection or possible reinfection rates (cases per 10,000 person days) by antibody status at baseline and follow-up were assessed.

In total, 6,510 HCWs were followed for a total of 1,285,395 person days (median follow-up, 216 days). For participants who had baseline and follow-up serology checked, 285 (6.1%) of the 4,681 seronegative participants at baseline seroconverted to positive at follow-up; 138 (48%) of the 263 who were seropositive at baseline were seronegative at follow-up. When analyzed by baseline serostatus alone, 519 (8.4%) of 6,194 baseline seronegative participants had a positive PCR after baseline serology testing (4.25 per 10,000 person days). Of 316 participants who were seropositive at baseline, 8 (2.5%) met criteria for possible SARS-CoV-2 reinfection (ie, PCR positive >90 days after baseline serology) during follow-up, a rate of 1.27 per 10,000 days at risk. The adjusted rate ratio for possible reinfection in baseline seropositive compared to infection in baseline seronegative participants was 0.26 (95% confidence interval, 0.13–0.53).

Seropositivity in HCWs is associated with moderate protection from future SARS-CoV-2 infection.

The coronavirus disease 2019 (COVID-19) pandemic has resulted in shortages of personal protective equipment (PPE), underscoring the urgent need for simple, efficient, and inexpensive methods to decontaminate masks and respirators exposed to severe acute respiratory coronavirus virus 2 (SARS-CoV-2). We hypothesized that methylene blue (MB) photochemical treatment, which has various clinical applications, could decontaminate PPE contaminated with coronavirus.

The 2 arms of the study included (1) PPE inoculation with coronaviruses followed by MB with light (MBL) decontamination treatment and (2) PPE treatment with MBL for 5 cycles of decontamination to determine maintenance of PPE performance.

MBL treatment was used to inactivate coronaviruses on 3 N95 filtering facepiece respirator (FFR) and 2 medical mask models. We inoculated FFR and medical mask materials with 3 coronaviruses, including SARS-CoV-2, and we treated them with 10 µM MB and exposed them to 50,000 lux of white light or 12,500 lux of red light for 30 minutes. In parallel, integrity was assessed after 5 cycles of decontamination using multiple US and international test methods, and the process was compared with the FDA-authorized vaporized hydrogen peroxide plus ozone (VHP+O3) decontamination method.

Overall, MBL robustly and consistently inactivated all 3 coronaviruses with 99.8% to >99.9% virus inactivation across all FFRs and medical masks tested. FFR and medical mask integrity was maintained after 5 cycles of MBL treatment, whereas 1 FFR model failed after 5 cycles of VHP+O3.

MBL treatment decontaminated respirators and masks by inactivating 3 tested coronaviruses without compromising integrity through 5 cycles of decontamination. MBL decontamination is effective, is low cost, and does not require specialized equipment, making it applicable in low- to high-resource settings.

The Institute of Health Economics offers a suite of analyses that provide developers an understanding of the expected commercial viability of an early stage health technology. In combination, these analyses form the Value-Engineered Translation framework. These methods incorporate innovative methods to manage uncertainty in early economic evaluations, in particular, moving beyond current stochastic assessments of headroom to account for inter-market variability in value hurdles, as well as incorporating social value premia considerations. An illustration of these methods is demonstrated using the example of a non-invasive diagnostic test (called DCRSHP) at an early stage of development, compared to current practice of cystoscopy in the diagnosis of bladder cancer.

Competing technologies were identified to inform the headroom assessment based on price and effectiveness. Then, a model-based cost-effectiveness analysis was undertaken incorporating headroom analysis, stochastic one-way sensitivity analysis, and value of information analysis using data from secondary sources.

Currently there are a number of non-invasive tests available, but none have sufficient test accuracy to be suitable for bladder cancer diagnosis alone. From the headroom analysis, DCRSHP can be priced at up to CAD 790 (i.e. USD 588) and still be cost-effective compared to the current practice of cystoscopy. Interestingly this price can be increased for patient groups that have lower levels of bladder cancer prevalence.

The requirements of economic evaluations depend on the stage of technology development, and analysis approaches must reflect this. The results here indicate that DCRSHP clears the value hurdle in terms of being cost-effective, and thus provides the opportunity to make a commercial return on future investment. Future analysis of DCRSHP could consider the cost drivers for development of the technology, including the regulatory pathways, costs associated with the intellectual asset management for the technology, and alternative manufacturing costs. All of which contribute to the research-to-practice continuum.