Young people are most vulnerable to suicidal behaviours but least likely to seek help. A more elaborate study of the intrinsic and extrinsic correlates of suicidal ideation and behaviours particularly amid ongoing population-level stressors and the identification of less stigmatising markers in representative youth populations is essential.

Participants (n = 2540, aged 15–25) were consecutively recruited from an ongoing large-scale household-based epidemiological youth mental health study in Hong Kong between September 2019 and 2021. Lifetime and 12-month prevalence of suicidal ideation, plan, and attempt were assessed, alongside suicide-related rumination, hopelessness and neuroticism, personal and population-level stressors, family functioning, cognitive ability, lifetime non-suicidal self-harm, 12-month major depressive disorder (MDD), and alcohol use.

The 12-month prevalence of suicidal ideation, ideation-only (no plan or attempt), plan, and attempt was 20.0, 15.4, 4.6, and 1.3%, respectively. Importantly, multivariable logistic regression findings revealed that suicide-related rumination was the only factor associated with all four suicidal outcomes (all p < 0.01). Among those with suicidal ideation (two-stage approach), intrinsic factors, including suicide-related rumination, poorer cognitive ability, and 12-month MDE, were specifically associated with suicide plan, while extrinsic factors, including coronavirus disease 2019 (COVID-19) stressors, poorer family functioning, and personal life stressors, as well as non-suicidal self-harm, were specifically associated with suicide attempt.

Suicide-related rumination, population-level COVID-19 stressors, and poorer family functioning may be important less-stigmatising markers for youth suicidal risks. The respective roles played by not only intrinsic but also extrinsic factors in suicide plan and attempt using a two-stage approach should be considered in future preventative intervention work.

Deficits in visuospatial attention, known as neglect, are common following brain injury, but underdiagnosed and poorly treated, resulting in long-term cognitive disability. In clinical settings, neglect is often assessed using simple pen-and-paper tests. While convenient, these cannot characterise the full spectrum of neglect. This protocol reports a research programme that compares traditional neglect assessments with a novel virtual reality attention assessment platform: The Attention Atlas (AA).

The AA was codesigned by researchers and clinicians to meet the clinical need for improved neglect assessment. The AA uses a visual search paradigm to map the attended space in three dimensions and seeks to identify the optimal parameters that best distinguish neglect from non-neglect, and the spectrum of neglect, by providing near-time feedback to clinicians on system-level behavioural performance. A series of experiments will address procedural, scientific, patient, and clinical feasibility domains.

Analyses focuses on descriptive measures of reaction time, accuracy data for target localisation, and histogram-based raycast attentional mapping analysis; which measures the individual’s orientation in space, and inter- and intra-individual variation of visuospatial attention. We will compare neglect and control data using parametric between-subjects analyses. We present example individual-level results produced in near-time during visual search.

The development and validation of the AA is part of a new generation of translational neuroscience that exploits the latest advances in technology and brain science, including technology repurposed from the consumer gaming market. This approach to rehabilitation has the potential for highly accurate, highly engaging, personalised care.

Spinal fusion surgery (SFS) is one of the most common operations in the United States, >450,000 SFSs are performed annually, incurring annual costs >$10 billion.

We used a nationwide longitudinal database to accurately assess incidence and payments associated with management of postoperative infection following SFS.

We conducted a retrospective, observational cohort analysis of 210,019 patients undergoing SFS from 2014 to 2018 using IBM MarketScan commercial and Medicaid–Medicare databases. We assessed rates of superficial/deep incisional SSIs, from 3 to 180 days after surgery using Cox proportional hazard regression models. To evaluate adjusted payments for patients with/without SSIs, adjusted for inflation to 2019 Consumer Price Index, we used generalized linear regression models with log-link and γ distribution.

Overall, 6.6% of patients experienced an SSI, 1.7% superficial SSIs and 4.9% deep-incisional SSIs, with a median of 44 days to presentation for superficial SSIs and 28 days for deep-incisional SSIs. Selective risk factors included surgical approach, admission type, payer, and higher comorbidity score. Postoperative incremental commercial payments for patients with superficial SSI were $20,800 at 6 months, $26,937 at 12 months, and $32,821 at 24 months; incremental payments for patients with deep-incisional SSI were $59,766 at 6 months, $74,875 at 12 months, and $93,741 at 24 months. Corresponding incremental Medicare payments for patients with superficial incisional at 6, 12, 24-months were $11,044, $17,967, and $24,096; while payments for patients with deep-infection were: $48,662, $53,757, and $73,803 at 6, 12, 24-months.

We identified a 4.9% rate of deep infection following SFS, with substantial payer burden. The findings suggest that the implementation of robust evidence-based surgical-care bundles to mitigate postoperative SFS infection is warranted.

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.

Asynchronous virtual patient care is increasingly used; however, the effectiveness of virtually delivering guideline-concordant care in conjunction with antibiotic stewardship initiatives remains uncertain. We developed a bundled stewardship intervention to improve antibiotic use in E-visits for upper respiratory tract infections (URTIs).

In this before-and-after study, adult patients who completed E-visits for “cough,” “flu,” or “sinus symptoms” at Michigan Medicine between January 1, 2018, and September 30, 2020, were included. Patient demographics, diagnoses, and antibiotic details were collected. The multifaceted intervention occurred over 6 months. Segmented linear regression was performed to estimate the effect of the intervention on appropriate antibiotic use for URTI diagnoses (defined as no antibiotic prescribed) and sinusitis (defined as guideline-concordant antibiotic selection and duration). Regression lines were fit to data before the bundled intervention (January 2019) and after the bundled intervention (May 2019).

In total, 5,151 E-visits were included. The intervention decreased the number of visits for flu, cough, or sinus symptoms prescribed antibiotics from 43.2% to 28.9% (P < .001). Guideline concordance of antibiotic prescriptions improved following the intervention: first-line amoxicillin-clavulanate rose from 37.9% of prescriptions to 66.1% of prescriptions (P < .001), second-line doxycycline rose from 13.8% to 22.7% (P < .001); and median duration of antibiotics decreased from 10 days to 5 days (P < .001).

A multifaceted stewardship bundle for E-visits involving both changes in the EMR and audit and feedback improved guideline-concordant antibiotic use for URTIs. This approach can aid stewardship efforts in the ambulatory care setting with regard to telemedicine.

The COVID-19 pandemic significantly changed the lives of millions of people in the USA, preventing them from continuing their regular lifestyles. This study examined the manifestation of “Covibesity” in the patient population of the Virginia Commonwealth University (VCU) Health System and explored the effects of the distraction caused by the pandemic in the management of diabetes disease.

This project analyzed body mass index (BMI) rates of the general adult patient population at the VCU Health System during the COVID-19 pandemic compared to prior years. The project also investigated the changes in the severity of diabetes cases treated at the VCU Health System by comparing HbA1c laboratory results and the number of diabetes-related emergency department (ED) visits before and during the pandemic. The results were stratified by age, gender, and race to examine subpopulations.

The mean BMI for the general patient population increased from 2018 to 2019 but decreased in 2020. The mean HbA1c measurements for the diabetic patient population increased from 2018 to 2020, while the number of ED visits declined in 2020 for the same population. When stratified by race, the trends in the outcomes largely reflected those of the overall mean. The African American population had a higher mean BMI, HbA1c, and number of ED visits than other races, but showed the same temporal behavior to the overall mean.