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.

This study aims to outline Clostridioides difficile infection (CDI) trends and outcomes in Mexican healthcare facilities during the COVID-19 pandemic.

Observational study of case series.

Sixteen public hospitals and private academic healthcare institutions across eight states in Mexico from January 2016 to December 2022.

CDI patients.

Demographic, clinical, and laboratory data of CDI patients were obtained from clinical records. Cases were classified as community or healthcare-associated infections, with incidence rates calculated as cases per 10,000 patient days. Risk factors for 30-day all-cause mortality were analyzed by multivariate logistic regression.

We identified 2,356 CDI cases: 2,118 (90%) were healthcare-associated, and 232 (10%) were community-associated. Common comorbidities included hypertension, diabetes, and cancer. Previous high use of proton-pump inhibitors, steroids, and antibiotics was observed. Recurrent infection occurred in 112 (5%) patients, and 30-day mortality in 371 (16%). Risk factors associated with death were a high Charlson score, prior use of steroids, concomitant use of antibiotics, leukopenia, leukocytosis, elevated serum creatine, hypoalbuminemia, septic shock or abdominal sepsis, and SARS-CoV-2 coinfection. The healthcare-associated CDI incidence remained stable at 4.78 cases per 10,000 patient days during the pre-and pandemic periods. However, the incidence was higher in public hospitals.

Our study underscores the need for routine epidemiology surveillance and standardized CDI classification protocols in Mexican institutions. Though CDI rates in our country align with those in some European countries, disparities between public and private healthcare sectors emphasize the importance of targeted interventions.

COVID-19 changed the epidemiology of community-acquired respiratory viruses. We explored patterns of respiratory viral testing to understand which tests are most clinically useful in the postpandemic era.

We conducted a retrospective observational study of discharge data from PINC-AI (formerly Premier), a large administrative database. Use of multiplex nucleic acid amplification respiratory panels in acute care, including small (2–5 targets), medium (6–11), and large panels (>11), were compared between the early pandemic (03/2020–10/2020), late pandemic (11/2020–4/2021), and prepandemic respiratory season (11/2019 - 02/2020) using ANOVA.

A median of 160.5 facilities contributed testing data per quarter (IQR 155.5–169.5). Prepandemic, facilities averaged 103 respiratory panels monthly (sd 138), including 79 large (sd 126), 7 medium (sd 31), and 16 small panels (sd 73). Relative to prepandemic, utilization decreased during the early pandemic (62 panels monthly/facility; sd 112) but returned to the prepandemic baseline by the late pandemic (107 panels monthly/facility; sd 211). Relative to prepandemic, late pandemic testing involved more small panel use (58 monthly/facility, sd 156) and less large panel use (47 monthly/facility, sd 116). Comparisons among periods demonstrated significant differences in overall testing (P < 0.0001), large panel use (P < 0.0001), and small panel use (P < 0.0001).

Postpandemic, clinical use of respiratory panel testing shifted from predominantly large panels to predominantly small panels. Factors driving this change may include resource availability, costs, and the clinical utility of targeting important pathogenic viruses instead of testing “for everything.”

Clostridioides difficile infection (CDI) may be misdiagnosed if testing is performed in the absence of signs or symptoms of disease. This study sought to support appropriate testing by estimating the impact of signs, symptoms, and healthcare exposures on pre-test likelihood of CDI.

A panel of fifteen experts in infectious diseases participated in a modified UCLA/RAND Delphi study to estimate likelihood of CDI. Consensus, defined as agreement by >70% of panelists, was assessed via a REDCap survey. Items without consensus were discussed in a virtual meeting followed by a second survey.

All fifteen panelists completed both surveys (100% response rate). In the initial survey, consensus was present on 6 of 15 (40%) items related to risk of CDI. After panel discussion and clarification of questions, consensus (>70% agreement) was reached on all remaining items in the second survey. Antibiotics were identified as the primary risk factor for CDI and grouped into three categories: high-risk (likelihood ratio [LR] 7, 93% agreement among panelists in first survey), low-risk (LR 3, 87% agreement in first survey), and minimal-risk (LR 1, 71% agreement in first survey). Other major factors included new or unexplained severe diarrhea (e.g., ≥ 10 liquid bowel movements per day; LR 5, 100% agreement in second survey) and severe immunosuppression (LR 5, 87% agreement in second survey).

Infectious disease experts concurred on the importance of signs, symptoms, and healthcare exposures for diagnosing CDI. The resulting risk estimates can be used by clinicians to optimize CDI testing and treatment.

Military Servicemembers and Veterans are at elevated risk for suicide, but rarely self-identify to their leaders or clinicians regarding their experience of suicidal thoughts. We developed an algorithm to identify posts containing suicide-related content on a military-specific social media platform.

Publicly-shared social media posts (n = 8449) from a military-specific social media platform were reviewed and labeled by our team for the presence/absence of suicidal thoughts and behaviors and used to train several machine learning models to identify such posts.

The best performing model was a deep learning (RoBERTa) model that incorporated post text and metadata and detected the presence of suicidal posts with relatively high sensitivity (0.85), specificity (0.96), precision (0.64), F1 score (0.73), and an area under the precision-recall curve of 0.84. Compared to non-suicidal posts, suicidal posts were more likely to contain explicit mentions of suicide, descriptions of risk factors (e.g. depression, PTSD) and help-seeking, and first-person singular pronouns.

Our results demonstrate the feasibility and potential promise of using social media posts to identify at-risk Servicemembers and Veterans. Future work will use this approach to deliver targeted interventions to social media users at risk for suicide.

This study aimed to assess the impact of clinical decision support (CDS) to improve ordering of multiplex gastrointestinal polymerase chain reaction (PCR) testing panel (“GI panel”).

Single-center, retrospective, before-after study.

Tertiary care Veteran’s Affairs (VA) Medical Center provides inpatient, outpatient, and residential care.

All patients tested with a GI panel between June 22, 2022 and April 20, 2023.

We designed a CDS questionnaire in the electronic medical record (EMR) to guide appropriate ordering of the GI panel. A “soft stop” reminder at the point of ordering prompted providers to confirm five appropriateness criteria: 1) documented diarrhea, 2) no recent receipt of laxatives, 3) C. difficile is not the leading suspected cause of diarrhea, 4) time period since a prior test is >14 days or prior positive test is >4 weeks and 5) duration of hospitalization <72 hours. The CDS was implemented in November 2022.

Compared to the pre-implementation period (n = 136), fewer tests were performed post-implementation (n = 92) with an IRR of 0.61 (p = 0.003). Inappropriate ordering based on laxative use or undocumented diarrhea decreased (IRR 0.37, p = 0.012 and IRR 0.25, p = 0.08, respectively). However, overall inappropriate ordering and outcome measures did not significantly differ before and after the intervention.

Implementation of CDS in the EMR decreased testing and inappropriate ordering based on use of laxatives or undocumented diarrhea. However, inappropriate ordering of tests overall remained high post-intervention, signaling the need for continued diagnostic stewardship efforts.

Neuropsychologists have difficulty detecting cognitive decline in high-functioning older adults, in whom substantially greater neurological change may need to occur before performance on cognitive tests are low enough to indicate cognitive impairment. For high-functioning older adults, subjective cognitive concerns (SCC) may indicate decline that is not detected by the presence of low cognitive test scores but may be related to the absence of high scores and the presence of latent neurological changes. We hypothesized that high-functioning older adults with SCC would have fewer high scores than those without concerns, but a comparable number of low scores. These findings would indicate that objective decline has occurred but would not be detected by a traditional focus on low scores. We also hypothesized that SCC would be associated with lower frontoparietal network volume, thickness, and connectivity, indicating latent neurological change underlying subjective cognitive concerns.

Participants from an imaging sub-study of an ongoing longitudinal aging study were selected if they had high estimated premorbid functioning, defined as either (a) estimated intelligence >75th percentile on the North American Adult Reading Test (n=48) or (b) having a college degree (n=62). This resulted in 68 participants subdivided based on SCC, defined as one or more self-reported SCC on the Medical Outcomes Study Cognitive Functioning Scale (MOS-Cog). Participants with SCC (n=35; 73.9 years-old, SD=9.6, range: 60-95; 62.9% female; 94.3% White) and without SCC (n=33; 71.0 years-old, SD=7.2, range: 61-85, 75.8% female; 100% White) completed a neuropsychological test battery of memory and executive functions, including the Rey Auditory Verbal Learning Test, Trail Making Test Parts A and B, Controlled Oral Word Association Test, Digit Span, and Letter-Number Sequencing, and underwent structural MRI. MR images were analyzed for frontoparietal network volume, thickness, and connectivity.

Participants with and without SCC were compared on the number of low test scores (i.e., at or below the 16th percentile) and high test scores (i.e., at or above the 75th percentile), finding a comparable number of low scores, t=1.66, p=.103, d=.40, but a lower number of high scores among participants with SCC, t=2.95, p=.004, d=.71. Participants with SCC had lower bilateral mean frontoparietal network volumes (left: t=2.98, p=.004, d=.74; right: t=2.63, p=.011, d=.66) and cortical thickness (left: t=2.65, p=.010, d=.66; right: t=2.18, p=.033, d=.54), but did not differ from those without SCC in terms of network connectivity.

SCC have been reported as a potential risk factor for dementia in older adults. High-functioning older adults with SCC presented with fewer high scores than those without SCC but had a comparable number of low scores. Among high-functioning older adults, subjective cognitive decline may correspond with objective cognitive change not detected by the traditional emphasis on low scores, but rather the absence of high scores. SCC were also related to underlying changes in the volume and thickness of the frontoparietal network, but not connectivity. In high-functioning older adults, subjective cognitive decline may correspond with a reduction from high average functioning in some domains and underlying neurological changes.

Engaging patients, caregivers, and other stakeholders to help guide the research process is a cornerstone of patient-centered research. Lived expertise may help ensure the relevance of research questions, promote practices that are satisfactory to research participants, improve transparency, and assist with disseminating findings.

Traditionally engagement has been conducted face-to-face in the local communities in which research operates. Decentralized platform trials pose new challenges for the practice of engagement. We used a remote model for stakeholder engagement, relying on Zoom meetings and blog communications.

Here we describe the approach used for research partnership with patients, caregivers, and clinicians in the planning and oversight of the ACTIV-6 trial and the impact of this work. We also present suggestions for future remote engagement.

The ACTIV-6 experience may inform proposed strategies for future engagement in decentralized trials.

Neuropsychologists have difficulty detecting cognitive decline in high-functioning older adults because greater neurological change must occur before cognitive performances are low enough to indicate decline or impairment. For high-functioning older adults, early neurological changes may correspond with subjective cognitive concerns and an absence of high scores. This study compared high-functioning older adults with and without subjective cognitive concerns, hypothesizing those with cognitive concerns would have fewer high scores on neuropsychological testing and lower frontoparietal network volume, thickness, and connectivity.

Participants had high estimated premorbid functioning (e.g., estimated intelligence ≥75th percentile or college-educated) and were divided based on subjective cognitive concerns. Participants with cognitive concerns (n = 35; 74.0 ± 9.6 years old, 62.9% female, 94.3% White) and without cognitive concerns (n = 33; 71.2 ± 7.1 years old, 75.8% female, 100% White) completed a neuropsychological battery of memory and executive function tests and underwent structural and resting-state magnetic resonance imaging, calculating frontoparietal network volume, thickness, and connectivity.

Participants with and without cognitive concerns had comparable numbers of low test scores (≤16th percentile), p = .103, d = .40. Participants with cognitive concerns had fewer high scores (≥75th percentile), p = .004, d = .71, and lower mean frontoparietal network volumes (left: p = .004, d = .74; right: p = .011, d = .66) and cortical thickness (left: p = .010, d = .66; right: p = .033, d = .54), but did not differ in network connectivity.

Among high-functioning older adults, subjective cognitive decline may correspond with an absence of high scores on neuropsychological testing and underlying changes in the frontoparietal network that would not be detected by a traditional focus on low cognitive test scores.

Aberrant reward functioning is implicated in depression. While attention precedes behavior and guides higher-order cognitive processes, reward learning from an attentional perspective – the effects of prior reward-learning on subsequent attention allocation – has been mainly overlooked.

The present study explored the effects of reward-based attentional learning in depression using two separate, yet complimentary, studies. In study 1, participants with high (HD) and low (LD) levels of depression symptoms were trained to divert their gaze toward one type of stimuli over another using a novel gaze-contingent music reward paradigm – music played when fixating the desired stimulus type and stopped when gazing the alternate one. Attention allocation was assessed before, during, and following training. In study 2, using negative reinforcement, the same attention allocation pattern was trained while substituting the appetitive music reward for gazing the desired stimulus type with the removal of an aversive sound (i.e. white noise).

In study 1 both groups showed the intended shift in attention allocation during training (online reward learning), while generalization of learning at post-training was only evident among LD participants. Conversely, in study 2 both groups showed post-training generalization. Results were maintained when introducing anxiety as a covariate, and when using a more powerful sensitivity analysis. Finally, HD participants showed higher learning speed than LD participants during initial online learning, but only when using negative, not positive, reinforcement.

Deficient generalization of learning characterizes the attentional system of HD individuals, but only when using reward-based positive reinforcement, not negative reinforcement.