We investigated the efficacy and complication profile of intranasal dexmedetomidine for transthoracic echocardiography sedation in patients with single ventricle physiology and shunt-dependent pulmonary blood flow during the high-risk interstage period.

A single-centre, retrospective review identified interstage infants who received dexmedetomidine for echocardiography sedation. Baseline and procedural vitals were reported. Significant adverse events related to sedation were defined as an escalation in care or need for any additional/increased inotropic support to maintain pre-procedural haemodynamics. Minor adverse events were defined as changes from baseline haemodynamics that resolved without intervention. To assess whether sedation was adequate, echocardiogram reports were reviewed for completeness.

From September to December 2020, five interstage patients (age 29–69 days) were sedated with 3 mcg/kg intranasal dexmedetomidine. The median sedation onset time and duration time was 24 minutes (range 12–43 minutes) and 60 minutes (range 33–60 minutes), respectively. Sedation was deemed adequate in all patients as complete echocardiograms were accomplished without a rescue dose. When compared to baseline, three (60%) patients had a >10% reduction in heart rate, one (20%) patient had a >10% reduction in oxygen saturations, and one (20%) patient had a >30% decrease in blood pressure. Amongst all patients, no significant complications occurred and haemodynamic changes from baseline did not result in need for intervention or interruption of study.

Intranasal dexmedetomidine may be a reasonable option for echocardiography sedation in infants with shunt-dependent single ventricle heart disease, and further investigation is warranted to ensure efficacy and safety in an outpatient setting.

American Indian and Alaska Native peoples (AI/AN) have a disproportionately high rate of obesity, but little is known about the social determinants of obesity among older AI/AN. Thus, our study assessed social determinants of obesity in AI/AN aged ≥ 50 years.

We conducted a cross-sectional analysis using multivariate generalised linear mixed models to identify social determinants associated with the risk of being classified as obese (BMI ≥ 30·0 kg/m2). Analyses were conducted for the total study population and stratified by median county poverty level.

Indian Health Service (IHS) data for AI/AN who used IHS services in FY2013.

Totally, 27 696 AI/AN aged ≥ 50 years without diabetes.

Mean BMI was 29·8 ± 6·6 with 43 % classified as obese. Women were more likely to be obese than men, and younger ages were associated with higher obesity risk. While having Medicaid coverage was associated with lower odds of obesity, private health insurance was associated with higher odds. Living in areas with lower rates of educational attainment and longer drive times to primary care services were associated with higher odds of obesity. Those who lived in a county where a larger percentage of people had low access to a grocery store were significantly less likely to be obese.

Our findings contribute to the understanding of social determinants of obesity among older AI/AN and highlight the need to investigate AI/AN obesity, including longitudinal studies with a life course perspective to further examine social determinants of obesity in older AI/AN.

The Canadian Nosocomial Infection Surveillance Program conducted point-prevalence surveys in acute-care hospitals in 2002, 2009, and 2017 to identify trends in antimicrobial use.

Eligible inpatients were identified from a 24-hour period in February of each survey year. Patients were eligible (1) if they were admitted for ≥48 hours or (2) if they had been admitted to the hospital within a month. Chart reviews were conducted. We calculated the prevalence of antimicrobial use as follows: patients receiving ≥1 antimicrobial during survey period per number of patients surveyed × 100%.

In each survey, 28−47 hospitals participated. In 2002, 2,460 (36.5%; 95% CI, 35.3%−37.6%) of 6,747 surveyed patients received ≥1 antimicrobial. In 2009, 3,566 (40.1%, 95% CI, 39.0%−41.1%) of 8,902 patients received ≥1 antimicrobial. In 2017, 3,936 (39.6%, 95% CI, 38.7%−40.6%) of 9,929 patients received ≥1 antimicrobial. Among patients who received ≥1 antimicrobial, penicillin use increased 36.8% between 2002 and 2017, and third-generation cephalosporin use increased from 13.9% to 18.1% (P < .0001). Between 2002 and 2017, fluoroquinolone use decreased from 25.7% to 16.3% (P < .0001) and clindamycin use decreased from 25.7% to 16.3% (P < .0001) among patients who received ≥1 antimicrobial. Aminoglycoside use decreased from 8.8% to 2.4% (P < .0001) and metronidazole use decreased from 18.1% to 9.4% (P < .0001). Carbapenem use increased from 3.9% in 2002 to 6.1% in 2009 (P < .0001) and increased by 4.8% between 2009 and 2017 (P = .60).

The prevalence of antimicrobial use increased between 2002 and 2009 and then stabilized between 2009 and 2017. These data provide important information for antimicrobial stewardship programs.

This study evaluated: (1) apolipoprotein E (APOE) ϵ4 prevalence among Black, Latino, and White older adults, (2) associations of APOE ϵ4 status with baseline level and change over time of cognitive outcomes across groups, and (3) combined impact of APOE ϵ4 prevalence and magnitude of effect on cognitive decline within each racial/ethnic group.

Participants included 297 White, 138 Latino, and 149 Black individuals from the longitudinal UC Davis Diversity Cohort who had APOE genotyping and ≥2 cognitive assessments. Magnitude of associations of ϵ4 with cognitive baseline and change across racial/ethnic groups was tested with multilevel parallel process longitudinal analyses and multiple group models.

ϵ4 prevalence in Black (46%) and White participants (46%) was almost double that of Latino participants (24%). ϵ4 was associated with poorer baseline episodic memory only in White participants (p = .001), but had a moderately strong association with episodic memory change across all racial/ethnic groups (Blacks= −.061 SD/year, Latinos = −.055,Whites= −.055). ϵ4 association with semantic memory change was strongest in White participants (−.071), intermediate in Latino participants (−.041), and weakest in Black participants (−.022).

Calculated cognitive trajectories across racial/ethnic groups were influenced in an additive manner by ϵ4 prevalence and strength of association with cognitive decline within the group. Group differences in ϵ4 prevalences and associations of ϵ4 with cognition may suggest different pathways from APOE to cognitive decline, and, AD possibly having less salient impact on cognitive decline in non-White participants. Differential effects of APOE on episodic memory and non-memory cognition have important implications for understanding how APOE influences late life cognitive decline.

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.

In response to the 2014-2016 West Africa Ebola virus disease (EVD) epidemic, the Centers for Disease Control and Prevention (CDC) designated 56 US hospitals as Ebola treatment centers (ETCs) with high-level isolation capabilities. We aimed to determine ongoing sustainability of ETCs and identify how ETC capabilities have impacted hospital, local, and regional COVID-19 readiness and response.

An electronic survey included both qualitative and quantitative questions and was structured into two sections: operational sustainability and role in the COVID-19 response.

The survey was distributed to site representatives from the 56 originally designated ETCs; 37 (66%) responded.

Data were coded and analyzed using descriptive statistics.

Of the 37 responding ETCs, 33 (89%) reported they were still operating while 4 had decommissioned. ETCs that maintain high-level isolation capabilities incurred a mean of $234,367 in expenses per year. All but one ETC reported that existing capabilities (e.g., trained staff, infrastructure) before COVID-19 positively affected their hospital, local, and regional COVID-19 readiness and response (e.g., ETCs trained staff, donated supplies, and shared developed protocols).

Existing high-level isolation capabilities and expertise developed following the 2014-2016 EVD epidemic were leveraged by ETCs to assist hospital-wide readiness for COVID-19 and support response for other local and regional hospitals However, ETCs face continued challenges in sustaining those capabilities for high-consequence infectious diseases.

Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools.

To examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics.

Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts.

Earlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = −0.34 years, s.e. = 0.08), major depression (β = −0.34 years, s.e. = 0.08), schizophrenia (β = −0.39 years, s.e. = 0.08), and educational attainment (β = −0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO.

AAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.