We reviewed outcomes in all 36 consecutive children <5 kg supported with the Berlin Heart pulsatile ventricular assist device at the University of Florida, comparing those with acquired heart disease (n = 8) to those with congenital heart disease (CHD) (n = 28).

The primary outcome was mortality. The Kaplan-Meier method and log-rank tests were used to assess group differences in long-term survival after ventricular assist device insertion. T-tests using estimated survival proportions were used to compare groups at specific time points.

Of 82 patients supported with the Berlin Heart at our institution, 49 (49/82 = 59.76%) weighed <10 kg and 36 (36/82 = 43.90%) weighed <5 kg. Of 36 patients <5 kg, 26 (26/36 = 72.22%) were successfully bridged to transplantation. (The duration of support with ventricular assist device for these 36 patients <5 kg was [days]: median = 109, range = 4–305.) Eight out of 36 patients <5 kg had acquired heart disease, and all eight [8/8 = 100%] were successfully bridged to transplantation. (The duration of support with ventricular assist device for these 8 patients <5 kg with acquired heart disease was [days]: median = 50, range = 9–130.) Twenty-eight of 36 patients <5 kg had congenital heart disease. Eighteen of these 28 [64.3%] were successfully bridged to transplantation. (The duration of support with ventricular assist device for these 28 patients <5 kg with congenital heart disease was [days]: median = 136, range = 4–305.) For all 36 patients who weighed <5 kg: 1-year survival estimate after ventricular assist device insertion = 62.7% (95% confidence interval = 48.5–81.2%) and 5-year survival estimate after ventricular assist device insertion = 58.5% (95% confidence interval = 43.8–78.3%). One-year survival after ventricular assist device insertion = 87.5% (95% confidence interval = 67.3–99.9%) in acquired heart disease and 55.6% (95% confidence interval = 39.5–78.2%) in CHD, P = 0.036. Five-year survival after ventricular assist device insertion = 87.5% (95% confidence interval = 67.3–99.9%) in acquired heart disease and 48.6% (95% confidence interval = 31.6–74.8%) in CHD, P = 0.014.

Pulsatile ventricular assist device facilitates bridge to transplantation in neonates and infants weighing <5 kg; however, survival after ventricular assist device insertion in these small patients is less in those with CHD in comparison to those with acquired heart disease.

Publication bias has the potential to adversely impact clinical decision making and patient health if alternative decisions would have been made had there been complete publication of evidence.

The objective of our analysis was to determine if earlier publication of the complete evidence on rosiglitazone’s risk of myocardial infarction (MI) would have changed clinical decision making at an earlier point in time. We tested several methods for adjustment of publication bias to assess the impact of potential time delays to identifying the MI effect. We then performed a cumulative meta-analysis (CMA) for both published studies (published-only data set) and all studies performed (comprehensive data set). We then created an adjusted data set using existing methods of adjustment for publication bias (Harbord regression, Peter’s regression, and the nonparametric trim and fill method) applied to the limited data set. Finally, we compared the time to the decision threshold for each data set using CMA.

Although published-only and comprehensive data sets did not provide notably different final summary estimates [OR = 1.4 (95 percent confidence interval [CI]: .95–2.05) and 1.42 (95 percent CI: 1.03–1.97)], the comprehensive data set reached the decision threshold 36 months earlier than the published-only data set. All three adjustment methods tested did not show a differential time to decision threshold versus the published-only data set.

Complete access to studies capturing MI risk for rosiglitazone would have led to the evidence reaching a clinically meaningful decision threshold 3 years earlier.

The coronavirus disease 2019 (COVID-19) pandemic has significantly increased depression rates, particularly in emerging adults. The aim of this study was to examine longitudinal changes in depression risk before and during COVID-19 in a cohort of emerging adults in the U.S. and to determine whether prior drinking or sleep habits could predict the severity of depressive symptoms during the pandemic.

Participants were 525 emerging adults from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA), a five-site community sample including moderate-to-heavy drinkers. Poisson mixed-effect models evaluated changes in the Center for Epidemiological Studies Depression Scale (CES-D-10) from before to during COVID-19, also testing for sex and age interactions. Additional analyses examined whether alcohol use frequency or sleep duration measured in the last pre-COVID assessment predicted pandemic-related increase in depressive symptoms.

The prevalence of risk for clinical depression tripled due to a substantial and sustained increase in depressive symptoms during COVID-19 relative to pre-COVID years. Effects were strongest for younger women. Frequent alcohol use and short sleep duration during the closest pre-COVID visit predicted a greater increase in COVID-19 depressive symptoms.

The sharp increase in depression risk among emerging adults heralds a public health crisis with alarming implications for their social and emotional functioning as this generation matures. In addition to the heightened risk for younger women, the role of alcohol use and sleep behavior should be tracked through preventive care aiming to mitigate this looming mental health crisis.

We have developed the bispectral electroencephalography (BSEEG) method for detection of delirium and prediction of poor outcomes.

To improve the BSEEG method by introducing a new EEG device.

In a prospective cohort study, EEG data were obtained and BSEEG scores were calculated. BSEEG scores were filtered on the basis of standard deviation (s.d.) values to exclude signals with high noise. Both non-filtered and s.d.-filtered BSEEG scores were analysed. BSEEG scores were compared with the results of three delirium screening scales: the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU), the Delirium Rating Scale-Revised-98 (DRS) and the Delirium Observation Screening Scale (DOSS). Additionally, the 365-day mortalities and the length of stay (LOS) in the hospital were analysed.

We enrolled 279 elderly participants and obtained 620 BSEEG recordings; 142 participants were categorised as BSEEG-positive, reflecting slower EEG activity. BSEEG scores were higher in the CAM-ICU-positive group than in the CAM-ICU-negative group. There were significant correlations between BSEEG scores and scores on the DRS and the DOSS. The mortality rate of the BSEEG-positive group was significantly higher than that of the BSEEG-negative group. The LOS of the BSEEG-positive group was longer compared with that of the BSEEG-negative group. BSEEG scores after s.d. filtering showed stronger correlations with delirium screening scores and more significant prediction of mortality.

We confirmed the usefulness of the BSEEG method for detection of delirium and of delirium severity, and prediction of patient outcomes with a new EEG device.

We evaluated the early impact of a new hospital-based health technology assessment (HB-HTA) program, called Smart Innovation, at the University of Washington Medical Center (UWMC).

We compared the UWMC's utilization trends for two surgical procedures to control hospitals by evaluating the difference before and after adoption decisions: (i) a new filter for transcatheter aortic valve replacement (TAVR) procedures that treat aortic valve stenosis and (ii) microwave ablation (MWA) for treating hepatocellular carcinoma. We used descriptive statistics to assess the difference between the UWMC and controls for TAVR and MWA procedures and multivariate difference-in-differences (DID) analyses to test for statistical significance.

The UWMC experienced a 10 percent reduction in TAVR procedures compared with controls following the implementation of the TAVR Sentinel filter. The DID regression model indicated a 1.5 reduction in the number of TAVR procedures per quarter at the UWMC between the pre- and post period, which was not statistically significant (p-value: .87). The UWMC experienced a 51 percent reduction in utilization when compared with controls for MWA procedures in the pre- and post periods. The DID model for MWA indicated an 18.8 decrease in utilization per quarter during the study period for the UWMC, which was statistically significant (p-value: .0007). For MWA procedures, the UWMC experienced a $647,658 dollar reduction in total costs in the post period compared with controls.

When the UWMC used HB-HTA methods for technology adoption, there was a reduction in utilization and total costs when compared with controls; however, when the UWMC adopted a new technology without using HB-HTA methods, there was no difference in utilization.

We designed, developed, and implemented a new hospital-based health technology assessment (HB-HTA) program called Smart Innovation. Smart Innovation is a decision framework that reviews and makes technology adoption decisions. Smart Innovation was meant to replace the fragmented and complex process of procurement and adoption decisions at our institution. Because use of new medical technologies accounts for approximately 50 percent of the growth in healthcare spending, hospitals and integrated delivery systems are working to develop better processes and methods to sharpen their approach to adoption and management of high cost medical innovations.

The program has streamlined the decision-making process and added a robust evidence review for new medical technologies, aiming to balance efficiency with rigorous evidence standards. To promote system-wide adoption, the program engaged a broad representation of leaders, physicians, and administrators to gain support.

To date, Smart Innovation has conducted eleven HB-HTAs and made clinician-led adoption decisions that have resulted in over $5 million dollars in cost avoidance. These are comprised of five laboratory tests, three software-assisted systems, two surgical devices, and one capital purchase.

Smart Innovation has achieved cost savings, avoided uncertain or low-value technologies, and assisted in the implementation of new technologies that have strong evidence. The keys to its success have been the program's collaborative and efficient decision-making systems, partnerships with clinicians, executive support, and proactive role with vendors.

To describe the relationship between adherence to distinct dietary patterns and nutrition literacy.

We identified distinct dietary patterns using principal covariates regression (PCovR) and principal components analysis (PCA) from the Diet History Questionnaire II. Nutrition literacy was assessed using the Nutrition Literacy Assessment Instrument (NLit). Cross-sectional relationships between dietary pattern adherence and global and domain-specific NLit scores were tested by multiple linear regression. Mean differences in diet pattern adherence among three predefined nutrition literacy performance categories were tested by ANOVA.

Metropolitan Kansas City, USA.

Adults (n 386) with at least one of four diet-related diseases.

Three diet patterns of interest were derived: a PCovR prudent pattern and PCA-derived Western and Mediterranean patterns. After controlling for age, sex, BMI, race, household income, education level and diabetes status, PCovR prudent pattern adherence positively related to global NLit score (P < 0·001, β = 0·36), indicating more intake of prudent diet foods with improved nutrition literacy. Validating the PCovR findings, PCA Western pattern adherence inversely related to global NLit (P = 0·003, β = −0·13) while PCA Mediterranean pattern positively related to global NLit (P = 0·02, β = 0·12). Using predefined cut points, those with poor nutrition literacy consumed more foods associated with the Western diet (fried foods, sugar-sweetened beverages, red meat, processed foods) while those with good nutrition literacy consumed more foods associated with prudent and Mediterranean diets (vegetables, olive oil, nuts).

Nutrition literacy predicted adherence to healthy/unhealthy diet patterns. These findings warrant future research to determine if improving nutrition literacy effectively improves eating patterns.

Most studies underline the contribution of heritable factors for psychiatric disorders. However, heritability estimates depend on the population under study, diagnostic instruments, and study designs that each has its inherent assumptions, strengths, and biases. We aim to test the homogeneity in heritability estimates between two powerful, and state of the art study designs for eight psychiatric disorders.

We assessed heritability based on data of Swedish siblings (N = 4 408 646 full and maternal half-siblings), and based on summary data of eight samples with measured genotypes (N = 125 533 cases and 208 215 controls). All data were based on standard diagnostic criteria. Eight psychiatric disorders were studied: (1) alcohol dependence (AD), (2) anorexia nervosa, (3) attention deficit/hyperactivity disorder (ADHD), (4) autism spectrum disorder, (5) bipolar disorder, (6) major depressive disorder, (7) obsessive-compulsive disorder (OCD), and (8) schizophrenia.

Heritability estimates from sibling data varied from 0.30 for Major Depression to 0.80 for ADHD. The estimates based on the measured genotypes were lower, ranging from 0.10 for AD to 0.28 for OCD, but were significant, and correlated positively (0.19) with national sibling-based estimates. When removing OCD from the data the correlation increased to 0.50.

Given the unique character of each study design, the convergent findings for these eight psychiatric conditions suggest that heritability estimates are robust across different methods. The findings also highlight large differences in genetic and environmental influences between psychiatric disorders, providing future directions for etiological psychiatric research.

Depression contributes to persistent opioid analgesic use (OAU). Treating depression may increase opioid cessation.

To determine if adherence to antidepressant medications (ADMs) v. non-adherence was associated with opioid cessation in patients with a new depression episode after >90 days of OAU.

Patients with non-cancer, non-HIV pain (n = 2821), with a new episode of depression following >90 days of OAU, were eligible if they received ≥1 ADM prescription from 2002 to 2012. ADM adherence was defined as >80% of days covered. Opioid cessation was defined as ≥182 days without a prescription refill. Confounding was controlled by inverse probability of treatment weighting.

In weighted data, the incidence rate of opioid cessation was significantly (P = 0.007) greater in patients who adhered v. did not adhered to taking antidepressants (57.2/1000 v. 45.0/1000 person-years). ADM adherence was significantly associated with opioid cessation (odds ratio (OR) = 1.24, 95% CI 1.05–1.46).

ADM adherence, compared with non-adherence, is associated with opioid cessation in non-cancer pain. Opioid taper and cessation may be more successful when depression is treated to remission.

None.

Race, psychiatric history, and adverse life events have all been independently associated with postpartum depression (PPD). However, the role these play together in Black and Latina women remains inadequately studied. Therefore, we performed a case–control study of PPD, including comprehensive assessments of symptoms and biomarkers, while examining the effects of genetic ancestry.

We recruited our sample (549 cases, 968 controls) at 6 weeks postpartum from obstetrical clinics in North Carolina. PPD status was determined using the MINI-plus. Psychiatric history was extracted from medical records. Participants were administered self-report instruments to assess depression (Edinburgh Postnatal Depression Scale) and adverse life events. Levels of estradiol, progesterone, brain-derived neurotrophic factor, oxytocin, and allopregnanalone were assayed. Principal components from genotype data were used to estimate genetic ancestry and logistic regression was used to identify predictors of PPD.

This population was racially diverse (68% Black, 13% Latina, 18% European). Genetic ancestry was not a predictor of PPD. Case status was predicted by a history of major depression (p = 4.01E-14), lifetime anxiety disorder diagnosis (p = 1.25E-34), and adverse life events (p = 6.06E-06). There were no significant differences between groups in any hormones or neurosteroids.

Psychiatric history and multiple exposures to adverse life events were significant predictors of PPD in a population of minority and low-income women. Genetic ancestry and hormone levels were not predictive of case status. Increased genetic vulnerability in conjunction with risk factors may predict the onset of PPD, whereas genetic ancestry does not appear predictive.