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.

Diagnostic criteria for major depressive disorder allow for heterogeneous symptom profiles but genetic analysis of major depressive symptoms has the potential to identify clinical and etiological subtypes. There are several challenges to integrating symptom data from genetically informative cohorts, such as sample size differences between clinical and community cohorts and various patterns of missing data.

We conducted genome-wide association studies of major depressive symptoms in three cohorts that were enriched for participants with a diagnosis of depression (Psychiatric Genomics Consortium, Australian Genetics of Depression Study, Generation Scotland) and three community cohorts who were not recruited on the basis of diagnosis (Avon Longitudinal Study of Parents and Children, Estonian Biobank, and UK Biobank). We fit a series of confirmatory factor models with factors that accounted for how symptom data was sampled and then compared alternative models with different symptom factors.

The best fitting model had a distinct factor for Appetite/Weight symptoms and an additional measurement factor that accounted for the skip-structure in community cohorts (use of Depression and Anhedonia as gating symptoms).

The results show the importance of assessing the directionality of symptoms (such as hypersomnia versus insomnia) and of accounting for study and measurement design when meta-analyzing genetic association data.

Diagnosis of acute ischemia typically relies on evidence of ischemic lesions on magnetic resonance imaging (MRI), a limited diagnostic resource. We aimed to determine associations of clinical variables and acute infarcts on MRI in patients with suspected low-risk transient ischemic attack (TIA) and minor stroke and to assess their predictive ability.

We conducted a post-hoc analysis of the Diagnosis of Uncertain-Origin Benign Transient Neurological Symptoms (DOUBT) study, a prospective, multicenter cohort study investigating the frequency of acute infarcts in patients with low-risk neurological symptoms. Primary outcome parameter was defined as diffusion-weighted imaging (DWI)-positive lesions on MRI. Logistic regression analysis was performed to evaluate associations of clinical characteristics with MRI-DWI-positivity. Model performance was evaluated by Harrel’s c-statistic.

In 1028 patients, age (Odds Ratio (OR) 1.03, 95% Confidence Interval (CI) 1.01–1.05), motor (OR 2.18, 95%CI 1.27–3.65) or speech symptoms (OR 2.53, 95%CI 1.28–4.80), and no previous identical event (OR 1.75, 95%CI 1.07–2.99) were positively associated with MRI-DWI-positivity. Female sex (OR 0.47, 95%CI 0.32–0.68), dizziness and gait instability (OR 0.34, 95%CI 0.14–0.69), normal exam (OR 0.55, 95%CI 0.35–0.85) and resolved symptoms (OR 0.49, 95%CI 0.30–0.78) were negatively associated. Symptom duration and any additional symptoms/symptom combinations were not associated. Predictive ability of the model was moderate (c-statistic 0.72, 95%CI 0.69–0.77).

Detailed clinical information is helpful in assessing the risk of ischemia in patients with low-risk neurological events, but a predictive model had only moderate discriminative ability. Patients with clinically suspected low-risk TIA or minor stroke require MRI to confirm the diagnosis of cerebral ischemia.

Blood-based biomarkers represent a scalable and accessible approach for the detection and monitoring of Alzheimer’s disease (AD). Plasma phosphorylated tau (p-tau) and neurofilament light (NfL) are validated biomarkers for the detection of tau and neurodegenerative brain changes in AD, respectively. There is now emphasis to expand beyond these markers to detect and provide insight into the pathophysiological processes of AD. To this end, a reactive astrocytic marker, namely plasma glial fibrillary acidic protein (GFAP), has been of interest. Yet, little is known about the relationship between plasma GFAP and AD. Here, we examined the association between plasma GFAP, diagnostic status, and neuropsychological test performance. Diagnostic accuracy of plasma GFAP was compared with plasma measures of p-tau181 and NfL.

This sample included 567 participants from the Boston University (BU) Alzheimer’s Disease Research Center (ADRC) Longitudinal Clinical Core Registry, including individuals with normal cognition (n=234), mild cognitive impairment (MCI) (n=180), and AD dementia (n=153). The sample included all participants who had a blood draw. Participants completed a comprehensive neuropsychological battery (sample sizes across tests varied due to missingness). Diagnoses were adjudicated during multidisciplinary diagnostic consensus conferences. Plasma samples were analyzed using the Simoa platform. Binary logistic regression analyses tested the association between GFAP levels and diagnostic status (i.e., cognitively impaired due to AD versus unimpaired), controlling for age, sex, race, education, and APOE e4 status. Area under the curve (AUC) statistics from receiver operating characteristics (ROC) using predicted probabilities from binary logistic regression examined the ability of plasma GFAP to discriminate diagnostic groups compared with plasma p-tau181 and NfL. Linear regression models tested the association between plasma GFAP and neuropsychological test performance, accounting for the above covariates.

The mean (SD) age of the sample was 74.34 (7.54), 319 (56.3%) were female, 75 (13.2%) were Black, and 223 (39.3%) were APOE e4 carriers. Higher GFAP concentrations were associated with increased odds for having cognitive impairment (GFAP z-score transformed: OR=2.233, 95% CI [1.609, 3.099], p<0.001; non-z-transformed: OR=1.004, 95% CI [1.002, 1.006], p<0.001). ROC analyses, comprising of GFAP and the above covariates, showed plasma GFAP discriminated the cognitively impaired from unimpaired (AUC=0.75) and was similar, but slightly superior, to plasma p-tau181 (AUC=0.74) and plasma NfL (AUC=0.74). A joint panel of the plasma markers had greatest discrimination accuracy (AUC=0.76). Linear regression analyses showed that higher GFAP levels were associated with worse performance on neuropsychological tests assessing global cognition, attention, executive functioning, episodic memory, and language abilities (ps<0.001) as well as higher CDR Sum of Boxes (p<0.001).

Higher plasma GFAP levels differentiated participants with cognitive impairment from those with normal cognition and were associated with worse performance on all neuropsychological tests assessed. GFAP had similar accuracy in detecting those with cognitive impairment compared with p-tau181 and NfL, however, a panel of all three biomarkers was optimal. These results support the utility of plasma GFAP in AD detection and suggest the pathological processes it represents might play an integral role in the pathogenesis of AD.

Blood-based biomarkers offer a more feasible alternative to Alzheimer’s disease (AD) detection, management, and study of disease mechanisms than current in vivo measures. Given their novelty, these plasma biomarkers must be assessed against postmortem neuropathological outcomes for validation. Research has shown utility in plasma markers of the proposed AT(N) framework, however recent studies have stressed the importance of expanding this framework to include other pathways. There is promising data supporting the usefulness of plasma glial fibrillary acidic protein (GFAP) in AD, but GFAP-to-autopsy studies are limited. Here, we tested the association between plasma GFAP and AD-related neuropathological outcomes in participants from the Boston University (BU) Alzheimer’s Disease Research Center (ADRC).

This sample included 45 participants from the BU ADRC who had a plasma sample within 5 years of death and donated their brain for neuropathological examination. Most recent plasma samples were analyzed using the Simoa platform. Neuropathological examinations followed the National Alzheimer’s Coordinating Center procedures and diagnostic criteria. The NIA-Reagan Institute criteria were used for the neuropathological diagnosis of AD. Measures of GFAP were log-transformed. Binary logistic regression analyses tested the association between GFAP and autopsy-confirmed AD status, as well as with semi-quantitative ratings of regional atrophy (none/mild versus moderate/severe) using binary logistic regression. Ordinal logistic regression analyses tested the association between plasma GFAP and Braak stage and CERAD neuritic plaque score. Area under the curve (AUC) statistics from receiver operating characteristics (ROC) using predicted probabilities from binary logistic regression examined the ability of plasma GFAP to discriminate autopsy-confirmed AD status. All analyses controlled for sex, age at death, years between last blood draw and death, and APOE e4 status.

Of the 45 brain donors, 29 (64.4%) had autopsy-confirmed AD. The mean (SD) age of the sample at the time of blood draw was 80.76 (8.58) and there were 2.80 (1.16) years between the last blood draw and death. The sample included 20 (44.4%) females, 41 (91.1%) were White, and 20 (44.4%) were APOE e4 carriers. Higher GFAP concentrations were associated with increased odds for having autopsy-confirmed AD (OR=14.12, 95% CI [2.00, 99.88], p=0.008). ROC analysis showed plasma GFAP accurately discriminated those with and without autopsy-confirmed AD on its own (AUC=0.75) and strengthened as the above covariates were added to the model (AUC=0.81). Increases in GFAP levels corresponded to increases in Braak stage (OR=2.39, 95% CI [0.71-4.07], p=0.005), but not CERAD ratings (OR=1.24, 95% CI [0.004, 2.49], p=0.051). Higher GFAP levels were associated with greater temporal lobe atrophy (OR=10.27, 95% CI [1.53,69.15], p=0.017), but this was not observed with any other regions.

The current results show that antemortem plasma GFAP is associated with non-specific AD neuropathological changes at autopsy. Plasma GFAP could be a useful and practical biomarker for assisting in the detection of AD-related changes, as well as for study of disease mechanisms.

To examine differences in noticing and use of nutrition information comparing jurisdictions with and without mandatory menu labelling policies and examine differences among sociodemographic groups.

Cross-sectional data from the International Food Policy Study (IFPS) online survey.

IFPS participants from Australia, Canada, Mexico, United Kingdom and USA in 2019.

Adults aged 18–99; n 19 393.

Participants in jurisdictions with mandatory policies were significantly more likely to notice and use nutrition information, order something different, eat less of their order and change restaurants compared to jurisdictions without policies. For noticed nutrition information, the differences between policy groups were greatest comparing older to younger age groups and comparing high education (difference of 10·7 %, 95 % CI 8·9, 12·6) to low education (difference of 4·1 %, 95 % CI 1·8, 6·3). For used nutrition information, differences were greatest comparing high education (difference of 4·9 %, 95 % CI 3·5, 6·4) to low education (difference of 1·8 %, 95 % CI 0·2, 3·5). Mandatory labelling was associated with an increase in ordering something different among the majority ethnicity group and a decrease among the minority ethnicity group. For changed restaurant visited, differences were greater for medium and high education compared to low education, and differences were greater for higher compared to lower income adequacy.

Participants living in jurisdictions with mandatory nutrition information in restaurants were more likely to report noticing and using nutrition information, as well as greater efforts to modify their consumption. However, the magnitudes of these differences were relatively small.

We assessed the magnitude of unidentified coronavirus disease 2019 (COVID-19) in our healthcare personnel (HCP) early in the COVID-19 pandemic, and we evaluated risk factors for infection to identify areas for improvement in infection control practice in a northern California academic medical center.

We reviewed anti–severe acute respiratory coronavirus virus 2 (SARS-CoV-2) receptor-binding domain (RBD) IgG serologic test results and self-reported risk factors for seropositivity among 10,449 asymptomatic HCP who underwent voluntary serology testing between April 20 and May 20, 2020.

In total, 136 employees (1.3%) tested positive for SARS-CoV-2 IgG. This included 41 individuals (30.1%) who had previously tested positive for SARS-CoV-2 by nasopharyngeal reverse-transcription polymerase chain reaction (RT-PCR) between March 13 and April 16, 2020. In multivariable analysis, employees of Hispanic ethnicity (odds ratio [OR], 2.01; 95% confidence interval [CI], 1.22–3.46) and those working in environmental services, food services, or patient transport (OR, 4.81; 95% CI, 2.08–10.30) were at increased risk for seropositivity compared to other groups. Employees reporting a household contact with COVID-19 were also at higher risk for seropositivity (OR, 3.25; 95% CI, 1.47–6.44), but those with a work, exposure alone were not (OR, 1.27; 95% CI, 0.58–2.47). Importantly, one-third of seropositive individuals reported no prior symptoms, no suspected exposures, and no prior positive RT-PCR test.

In this study, SARS-CoV-2 seropositivity among HCP early in the northern California epidemic appeared to be quite low and was more likely attributable to community rather than occupational exposure.

Optimism is associated with reduced cardiovascular disease risk; however, few prospective studies have considered optimism in relation to hypertension risk specifically. We investigated whether optimism was associated with a lower risk of developing hypertension in U.S. service members, who are more likely to develop high blood pressure early in life. We also evaluated race/ethnicity, sex and age as potential effect modifiers of these associations.

Participants were 103 486 hypertension-free U.S. Army active-duty soldiers (mean age 28.96 years, 61.76% White, 20.04% Black, 11.01% Hispanic, 4.09% Asian, and 3.10% others). We assessed optimism, sociodemographic characteristics, health conditions, health behaviours and depression status at baseline (2009–2010) via self-report and administrative records, and ascertained incident hypertension over follow-up (2010–2014) from electronic health records and health assessments. We used Cox proportional hazards regression models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs), and adjusted models for a broad range of relevant covariates.

Over a mean follow-up of 3.51 years, 15 052 incident hypertension cases occurred. The highest v. lowest optimism levels were associated with a 22% reduced risk of developing hypertension, after adjusting for all covariates including baseline blood pressure (HR = 0.78; 95% CI = 0.74–0.83). The difference in hypertension risk between the highest v. lowest optimism was also maintained when we excluded soldiers with hypertension in the first two years of follow-up and, separately, when we excluded soldiers with prehypertension at baseline. A dose–response relationship was evident with higher optimism associated with a lower relative risk (p < 0.001). Higher optimism was consistently associated with a lower risk of developing hypertension across sex, age and most race/ethnicity categories.

In a diverse cohort of initially healthy male and female service members particularly vulnerable to developing hypertension, higher optimism levels were associated with reduced hypertension risk independently of sociodemographic and health factors, a particularly notable finding given the young and healthy population. Results suggest optimism is a health asset and a potential target for public health interventions.

Many institutions are attempting to implement patient-reported outcome (PRO) measures. Because PROs often change clinical workflows significantly for patients and providers, implementation choices can have major impact. While various implementation guides exist, a stepwise list of decision points covering the full implementation process and drawing explicitly on a sociotechnical conceptual framework does not exist.

To facilitate real-world implementation of PROs in electronic health records (EHRs) for use in clinical practice, members of the EHR Access to Seamless Integration of Patient-Reported Outcomes Measurement Information System (PROMIS) Consortium developed structured PRO implementation planning tools. Each institution pilot tested the tools. Joint meetings led to the identification of critical sociotechnical success factors.

Three tools were developed and tested: (1) a PRO Planning Guide summarizes the empirical knowledge and guidance about PRO implementation in routine clinical care; (2) a Decision Log allows decision tracking; and (3) an Implementation Plan Template simplifies creation of a sharable implementation plan. Seven lessons learned during implementation underscore the iterative nature of planning and the importance of the clinician champion, as well as the need to understand aims, manage implementation barriers, minimize disruption, provide ample discussion time, and continuously engage key stakeholders.

Highly structured planning tools, informed by a sociotechnical perspective, enabled the construction of clear, clinic-specific plans. By developing and testing three reusable tools (freely available for immediate use), our project addressed the need for consolidated guidance and created new materials for PRO implementation planning. We identified seven important lessons that, while common to technology implementation, are especially critical in PRO implementation.