Evidence for necrotising otitis externa (NOE) diagnosis and management is limited, and outcome reporting is heterogeneous. International best practice guidelines were used to develop consensus diagnostic criteria and a core outcome set (COS).

The study was pre-registered on the Core Outcome Measures in Effectiveness Trials (COMET) database. Systematic literature review identified candidate items. Patient-centred items were identified via a qualitative study. Items and their definitions were refined by multidisciplinary stakeholders in a two-round Delphi exercise and subsequent consensus meeting.

The final COS incorporates 36 items within 12 themes: Signs and symptoms; Pain; Advanced Disease Indicators; Complications; Survival; Antibiotic regimes and side effects; Patient comorbidities; Non-antibiotic treatments; Patient compliance; Duration and cessation of treatment; Relapse and readmission; Multidisciplinary team management.

Consensus diagnostic criteria include 12 items within 6 themes: Signs and symptoms (oedema, otorrhoea, granulation); Pain (otalgia, nocturnal otalgia); Investigations (microbiology [does not have to be positive], histology [malignancy excluded], positive CT and MRI); Persistent symptoms despite local and/or systemic treatment for at least two weeks; At least one risk factor for impaired immune response; Indicators of advanced disease (not obligatory but mut be reported when present at diagnosis). Stakeholders were unanimous that there is no role for secondary, graded, or optional diagnostic items. The consensus meeting identified themes for future research.

The adoption of consensus-defined diagnostic criteria and COS facilitates standardised research reporting and robust data synthesis. Inclusion of patient and professional perspectives ensures best practice stakeholder engagement.

Cardiometabolic disease risk factors are disproportionately prevalent in bipolar disorder (BD) and are associated with cognitive impairment. It is, however, unknown which health risk factors for cardiometabolic disease are relevant to cognition in BD. This study aimed to identify the cardiometabolic disease risk factors that are the most important correlates of cognitive impairment in BD; and to examine whether the nature of the relationships vary between mid and later life.

Data from the UK Biobank were available for 966 participants with BD, aged between 40 and 69 years. Individual cardiometabolic disease risk factors were initially regressed onto a global cognition score in separate models for the following risk factor domains; (1) health risk behaviors (physical activity, sedentary behavior, smoking, and sleep) and (2) physiological risk factors, stratified into (2a) anthropometric and clinical risk (handgrip strength, body composition, and blood pressure), and (2b) cardiometabolic disease risk biomarkers (CRP, lipid profile, and HbA1c). A final combined multivariate regression model for global cognition was then fitted, including only the predictor variables that were significantly associated with cognition in the previous models.

In the final combined model, lower mentally active and higher passive sedentary behavior, higher levels of physical activity, inadequate sleep duration, higher systolic and lower diastolic blood pressure, and lower handgrip strength were associated with worse global cognition.

Health risk behaviors, as well as blood pressure and muscular strength, are associated with cognitive function in BD, whereas other traditional physiological cardiometabolic disease risk factors are not.

Performance validity (PVT) and symptom validity tests (SVT) have become standard practice in assessing credibility of neuropsychological profiles and symptom report. While PVTs assess cognitive task engagement, SVTs assess credibility of patient symptom report. Although prior research aimed to conceptualize the relationship between the two validity measure types, it generally focused on SVTs from the Minnesota Multiphasic Personality Inventory (MMPI-2 &RF) and the Structured Inventory of Malingered Symptoms (SIMS; Ord et al., 2021, MMPI-2; Van Dyke et al., 2013). Further studies have demonstrated mixed results, with many studies concluding that symptom and performance validity are separate but related constructs. The current study aimed to assess the relationship between PVTs and SVTs utilizing symptom validity measures from the Personality Assessment Inventory (PAI) across three samples, including neurodevelopmental, psychiatric, and traumatic brain injury groups.

Participants included 634 individuals consecutively referred for neuropsychological assessment who completed the Test of Memory Malingering (TOMM) and the PAI (mean Age = 41.7, SD = 15.7; mean Education = 13.7, SD = 2.7; 53% female; 89% Caucasian). Participants were divided into three groups based on referral, including neurodevelopmental (mean Age = SD = 10.7; mean Education = 13.4, SD = 2.5; 39% female; 79% Caucasian), psychiatric (mean Age = 44.7, SD = 15.0; mean Education = 13.8, SD = 2.8; 58% female; 90% Caucasian), and traumatic brain injury samples (mean Age = SD = 15.5; mean Education = 13.3, SD = 2.3; 50% female; 91% Caucasian). Four structural equation models (latent variable models) were constructed. The first model was fit across the entire sample while the remaining three were fit for the aforementioned subsamples. TOMM trials modeled the performance validity latent variable while SVTs from the PAI modeled the symptom validity latent variable (Positive Impression Management and Defensiveness Index modeled underreporting; Negative Impression Management, Malingering Index, and Cognitive Bias Scale modeled overreporting).

In the full sample model overreporting significantly predicted performance validity (p < 0.001, r = -0.31), indicating higher symptom overreporting related to poorer performance validity while symptom underreporting did not significantly predict performance validity (p = 0.09, r = 0.08). In the neurodevelopmental model overreporting did not significantly predict performance validity (p = 0.44, r = 0.10). Further, symptom underreporting did not significantly predict performance validity (p = 0.40, r = 0.10). Similarly, for the TBI model, overreporting did not significantly predict performance validity (p = 0.82, r = -0.02) and symptom underreporting did not significantly predict performance validity (p = 0.50, r = -0.08). For the psychiatric sample symptom underreporting did not significantly predict performance validity (p = 0.06, r = 0.11); however, symptom overreporting significantly predicted performance validity (p < 0.001, r = - 0.39).

The current study expands on prior research comparing the relationship between SVTs and PVTs in neuropsychological evaluation utilizing SVTs from the PAI. Results of the present study suggest the relationship between the SVTs and PVTs varies by referral type and further supports using both PVTs and SVTs in neuropsychological assessment.

Social determinants of health (SDoH) are structural elements of our living and working environments that fundamentally shape health risks and outcomes. The Healthy People 2030 campaign delineated SDoH into five distinct categories that include: economic stability, education access/quality, healthcare access, neighborhood and built environment, and social and community contexts. Recent research has demonstrated that minoritized individuals have greater disadvantage across SDoH domains, which has been linked to poorer cognitive performance in older adulthood. However, the independent effects of SDoH on everyday functioning across and within racial groups remains less clear. The current project explored the association between SDoH factors and 10-year change in everyday functioning in a large sample of community-dwelling Black and White older adults.

Data from 2,505 participants without dementia enrolled in the Advanced Cognitive Training for Independent and Vital Elderly (ACTIVE) study (age M=73.5; 76% women; 28% Black/African American). Sociodemographic, census, and industry classification data were reduced into five SDoH factors: economic stability, education access and quality, healthcare access and quality, neighborhood and built environment, and social and community contexts. The Observed Tasks of Daily Living, a performance-based measure of everyday functioning with tasks involving medication management, finances, and telephone use, was administered at baseline, 1-, 2-, 3-, 5, and 10-year follow up visits. Mixed-effects models with age as the timescale tested (1) racial group differences in OTDL trajectories, (2) race x SDOH interactions on OTDL trajectories, and (3) associations between SDoH and OTDL trajectories stratified within Black and White older adults. Covariates included sex/gender, vocabulary score, Mini-Mental Status Examination, depressive symptoms, visual acuity, general health, training group status, booster status, testing site, and recruitment wave.

Black older adults had a steeper decline of OTDL performance compared to Whites (linear: b = -.25, quadratic b=-.009, ps < .001). There was a significant race x social and community context interaction on linear OTDL trajectories (b =.06, p=.01), but no other significant race x SDoH interactions were observed (bs =-.007-.05, ps=.73-.11). Stratified analyses revealed lower levels of social and community context were associated with steeper age-related linear declines in OTDL performance in Black (b = .08, p=.001), but not White older adults (b =.004, p=.64). Additionally, lower levels of economic stability were associated with steeper age-related linear declines in OTDL performance in Black (b =.07, p=.04), but not White older adults (b =.01, p=.35). Finally, no significant associations between other SDoH and OTDL trajectories were observed in Black (bs = -.04-.01, ps =.09-.80) or White (bs = -.02-.003, ps=.07-.96) older adults.

SDoH, which measure aspects of structural racism, play an important role in accelerating age-related declines in everyday functioning. Lower levels of economic and community-level social resources are two distinct SDoH domains associated with declines in daily functioning that negatively impact Black, but not White, older adults. It is imperative that future efforts focus on both identifying and acting upon upstream drivers of SDoH-related inequities. Within the United States, this will require addressing more than a century of antiBlack sentiment, White supremacy, and unjust systems of power and policies designed to intentionally disadvantage minoritized groups.

In the United States, Black individuals have suffered from 300 years of racism, bias, segregation and have been systematically and intentionally denied opportunities to accrue wealth. These disadvantages have resulted in disparities in health outcomes. Over the last decade there has been a growing interest in examining social determinants of health as upstream factors that lead to downstream health disparities. It is of vital importance to quantify the contribution of SDH factors to racial disparities in order to inform policy and social justice initiatives. This demonstration project uses years of education and white matter hyperintensities (WMH) to illustrate two methods of quantifying the role of a SDH in producing health disparities.

The current study is a secondary data analysis of baseline data from a subset of the National Alzheimer's Coordinating Center database with neuroimaging data collected from 2002-2019. Participants were 997 cognitively diverse, Black and White (10.4% Black) individuals, aged 60-94 (mean=73.86, 56.5% female), mean education of 15.18 years (range= 0-23, SD=3.55). First, mediation, was conducted in the SEM framework using the R package lavaan. Black/White race was the independent variable, education was the mediator, WMH volume was the dependent variable, and age/sex were the covariates. Bootstrapped standard errors were calculated using 1000 iterations. The indirect effect was then divided by the total effect to determine the proportion of the total effect attributable to education. Second, a population attributable fraction (PAF) or the expected reduction in WMH if we eliminated low education and structural racism for which Black serves as a proxy was calculated. Two logistic regressions with dichotomous (median split) WMH as the dependent variable, first with low (less than high school) versus high education, and second with Black/White race added as predictors. Age/sex were covariates. PAF of education, and then of Black/White race controlling for education were obtained. Subsequently, a combined PAF was calculated.

In the lavaan model, the total effect of Black/White race on WMH was not significant (B=.040, se=.113, p=.246); however, Black/White race significantly predicted education (B= -.108, se=.390, p=.001) and education significantly predicted WMH burden (B=-.084, se=.008, p=.002). This resulted in a significant indirect effect (effect=.009, se=.014, p=.032). 22.6 % of the relationship between Black/White race and WMH was mediated by education. In the logistic models, the PAF of education was 5.3% and the additional PAF of Black/White race was 2.7%. The combined PAF of Black race and low education was 7.8%.

From our mediation we can conclude that 22.6% of the relationship between Black/White race and WMH volume is explained by education. Our PAF analysis suggests that we could reduce 7.8% of the cases with high WMH burden if we eliminated low education and the structural racism for which Black serves as a proxy. This is an under estimation of the role that education and structural racism play in WMH burden due to our positively selected sample and crude measure of education. However, these methods can help researchers quantify the contribution of SDH to disparities in older adulthood and provide targets for policy change.

Inductive reasoning training has been found to be particularly effective at improving inductive reasoning, with some evidence of improved everyday functioning and driving. Telehealth may be useful for increasing access to, reducing time and travel burdens of, and reducing the need for physical spaces for cognitive training. On the other hand, telehealth increases technology burden. The present study investigated the feasibility and effectiveness of implementing an inductive reasoning training program, designed to mimic the inductive reasoning arm used in a large multi-site clinical trial (Advanced Cognitive Training for Independent and Vital Elderly (ACTIVE)), via telehealth (using Zoom and Canvas as delivery platforms).

31 older adult participants (mean age = 71.2, range = 65-85; mean education = 15.5, range = 13-18; 64.5% female; 87.1% white) received 10-sessions of telehealth-delivered inductive reasoning training over 5 weeks. Comparison groups (inductive reasoning trained and no-contact controls) were culled from the in-person ACTIVE trial via propensity matching. All participants completed three pretest and posttest inductive reasoning measures (Word Series, Letter Series, Letter Sets), as well as a posttest measure assessing participant perceptions of the telehealth intervention. In addition, at the end of each of the ten training sessions, participants received a final inductive reasoning assessment.

Telehealth participants provided high levels of endorsement suggesting that the telehealth training program was useful, reliable, easy to use and interact on, and employed a useable interface. Participants were generally satisfied with the training program. With regard to performance, telehealth participants demonstrated greater gains than untrained controls on Letter Series [F(1, 116) = 9.81, p = 0.002, partial eta-squared = 0.084] and Letter Sets [F(1, 116) = 8.69, p = 0.004, partial eta-squared = 0.074], but did not differ in improvement on Word Series [F(1, 116) = 1.145, p = 0.287, partial eta-squared = 0.010]. Furthermore, telehealth participants evinced similar inductive reasoning gains as matched inperson inductive reasoning trained participants on Letter Series [F(1, 116) = 1.24, p = 0.226, partial eta-squared = 0.01] and Letter Sets [F(1, 116) = 1.29, p = 0.259, partial eta-squared = 0.01], but demonstrated fewer gains in Word Series performance [F(1, 116) = 25.681, p = < 0.001, partial eta-squared = 0.181]. On the end-of-session reasoning tests, telehealth-trained participants showed a similar general pattern of improvement across the ten training sessions and did not differ significantly from in-person trained comparison participants.

Cognitive training via telehealth evinced similar gains across nearly all measures as its in-person counterpart. However, telehealth also led to substantial challenges regarding the telehealth training platform. Despite these challenges, participants reported perceiving increased competence with computer use, peripherals (mice, trackpad), and videoconferencing. These may be ancillary benefits of such training and may be maximized if more age-friendly learning management systems are investigated. Overall, this study suggests that telehealth delivery may be a viable form of cognitive training in inductive reasoning, and future studies could increase performance gains by optimizing the online training platform for older adults.

Current psychiatric diagnoses, although heritable, have not been clearly mapped onto distinct underlying pathogenic processes. The same symptoms often occur in multiple disorders, and a substantial proportion of both genetic and environmental risk factors are shared across disorders. However, the relationship between shared symptoms and shared genetic liability is still poorly understood.

Well-characterised, cross-disorder samples are needed to investigate this matter, but few currently exist. Our aim is to develop procedures to purposely curate and aggregate genotypic and phenotypic data in psychiatric research.

As part of the Cardiff MRC Mental Health Data Pathfinder initiative, we have curated and harmonised phenotypic and genetic information from 15 studies to create a new data repository, DRAGON-Data. To date, DRAGON-Data includes over 45 000 individuals: adults and children with neurodevelopmental or psychiatric diagnoses, affected probands within collected families and individuals who carry a known neurodevelopmental risk copy number variant.

We have processed the available phenotype information to derive core variables that can be reliably analysed across groups. In addition, all data-sets with genotype information have undergone rigorous quality control, imputation, copy number variant calling and polygenic score generation.

DRAGON-Data combines genetic and non-genetic information, and is available as a resource for research across traditional psychiatric diagnostic categories. Algorithms and pipelines used for data harmonisation are currently publicly available for the scientific community, and an appropriate data-sharing protocol will be developed as part of ongoing projects (DATAMIND) in partnership with Health Data Research UK.

Copy number variants (CNVs) have been associated with the risk of schizophrenia, autism and intellectual disability. However, little is known about their spectrum of psychopathology in adulthood.

We investigated the psychiatric phenotypes of adult CNV carriers and compared probands, who were ascertained through clinical genetics services, with carriers who were not. One hundred twenty-four adult participants (age 18–76), each bearing one of 15 rare CNVs, were recruited through a variety of sources including clinical genetics services, charities for carriers of genetic variants, and online advertising. A battery of psychiatric assessments was used to determine psychopathology.

The frequencies of psychopathology were consistently higher for the CNV group compared to general population rates. We found particularly high rates of neurodevelopmental disorders (NDDs) (48%), mood disorders (42%), anxiety disorders (47%) and personality disorders (73%) as well as high rates of psychiatric multimorbidity (median number of diagnoses: 2 in non-probands, 3 in probands). NDDs [odds ratio (OR) = 4.67, 95% confidence interval (CI) 1.32–16.51; p = 0.017) and psychotic disorders (OR = 6.8, 95% CI 1.3–36.3; p = 0.025) occurred significantly more frequently in probands (N = 45; NDD: 39[87%]; psychosis: 8[18%]) than non-probands (N = 79; NDD: 20 [25%]; psychosis: 3[4%]). Participants also had somatic diagnoses pertaining to all organ systems, particularly conotruncal cardiac malformations (in individuals with 22q11.2 deletion syndrome specifically), musculoskeletal, immunological, and endocrine diseases.

Adult CNV carriers had a markedly increased rate of anxiety and personality disorders not previously reported and high rates of psychiatric multimorbidity. Our findings support in-depth psychiatric and medical assessments of carriers of CNVs and the establishment of multidisciplinary clinical services.

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.