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Background: The American College of Cardiology Reduce the Risk: PCI Bleed Campaign was a hospital-based quality improvement campaign designed to reduce post-percutaneous coronary intervention (PCI) bleeding events. The aim of the campaign was to provide actionable evidence-based tools for participants to review, adapt, and adopt, depending upon hospital resources and engagement., Methods: We used data from 8 757 737 procedures in the National Cardiovascular Data Registry between 2015 and 2021 to compare patient and hospital characteristics and bleeding outcomes among campaign participants (n=195 hospitals) and noncampaign participants (n=1384). Post-PCI bleeding risk was compared before and after campaign participation. Multivariable hierarchical logistic regression was used to determine the adjusted association between campaign participation and post-PCI bleeding events. Prespecified subgroups were examined., Results: Campaign hospitals were more often higher volume teaching facilities located in urban or suburban locations. After adjustment, campaign participation was associated with a significant reduction in the rate of bleeding (bleeding: adjusted odds ratio, 0.61 [95% CI, 0.53-0.71]). Campaign hospitals had a greater decrease in bleeding events than noncampaign hospitals. In a subgroup analysis, the reduction in bleeding was noted in non-ST-segment-elevation acute coronary syndrome and ST-segment-elevation myocardial infarction patients, but no significant reduction was seen in patients without acute coronary syndrome., Conclusions: Participation in the American College of Cardiology Reduce the Risk: PCI Bleed Campaign was associated with a significant reduction in post-PCI bleeding. Our results underscore that national quality improvement efforts can be associated with a significant impact on PCI outcomes., Competing Interests: Dr Amin has institutional grant support (modest) from GE Healthcare and Chiesi. Dr Abbott has the following relationships with industry: research: Boston Scientific and Microport; consulting: Abbott, Medtronic, Penumbra, Shockwave, and Philips. Dr Masoudi had a contract with the American College of Cardiology for his role as the Chief Scientific Advisor, National Cardiovascular Data Registry. The other authors report no conflicts.

The American College of Cardiology (ACC) collaborated with the American Heart Association, American Society of Echocardiography, Heart Rhythm Society, International Society for Adult Congenital Heart Disease, Society for Cardiovascular Angiography and Interventions, Society of Cardiovascular Computed Tomography, Society for Cardiovascular Magnetic Resonance, and the Society of Pediatric Echocardiography to develop Appropriate Use Criteria (AUC) for multimodality imaging during the follow-up care of patients with congenital heart disease (CHD). This is the first AUC to address cardiac imaging in adult and pediatric patients with established CHD. A number of common patient scenarios (also termed "indications") and associated assumptions and definitions were developed using guidelines, clinical trial data, and expert opinion in the field of CHD. 1 The indications relate primarily to evaluation before and after cardiac surgery or catheter-based intervention, and they address routine surveillance as well as evaluation of new-onset signs or symptoms. The writing group developed 324 clinical indications, which they separated into 19 tables according to the type of cardiac lesion. Noninvasive cardiac imaging modalities that could potentially be used for these indications were incorporated into the tables, resulting in a total of 1,035 unique scenarios. These scenarios were presented to a separate, independent panel for rating, with each being scored on a scale of 1 to 9, with 1 to 3 categorized as "Rarely Appropriate," 4 to 6 as "May Be Appropriate," and 7 to 9 as "Appropriate." Forty-four percent of the scenarios were rated as Appropriate, 39% as May Be Appropriate, and 17% as Rarely Appropriate. This AUC document will provide guidance to clinicians in the care of patients with established CHD by identifying the reasonable imaging modality options available for evaluation and surveillance of such patients. It will also serve as an educational and quality improvement tool to identify patterns of care and reduce the number of Rarely Appropriate tests in clinical practice., (Copyright © 2020 The American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Despite recent progress on estimating the heritability explained by genotyped SNPs (h(2)g), a large gap between h(2)g and estimates of total narrow-sense heritability (h(2)) remains. Explanations for this gap include rare variants or upward bias in family-based estimates of h(2) due to shared environment or epistasis. We estimate h(2) from unrelated individuals in admixed populations by first estimating the heritability explained by local ancestry (h(2)γ). We show that h(2)γ = 2FSTCθ(1 - θ)h(2), where FSTC measures frequency differences between populations at causal loci and θ is the genome-wide ancestry proportion. Our approach is not susceptible to biases caused by epistasis or shared environment. We applied this approach to the analysis of 13 phenotypes in 21,497 African-American individuals from 3 cohorts. For height and body mass index (BMI), we obtained h(2) estimates of 0.55 ± 0.09 and 0.23 ± 0.06, respectively, which are larger than estimates of h(2)g in these and other data but smaller than family-based estimates of h(2).

Motivation: Inference of ancestry using genetic data is motivated by applications in genetic association studies, population genetics and personal genomics. Here, we provide methods and software for improved ancestry inference using genome-wide single nucleotide polymorphism (SNP) weights from external reference panels. This approach makes it possible to leverage the rich ancestry information that is available from large external reference panels, without the administrative and computational complexities of re-analyzing the raw genotype data from the reference panel in subsequent studies., Results: We extensively validate our approach in multiple African American, Latino American and European American datasets, making use of genome-wide SNP weights derived from large reference panels, including HapMap 3 populations and 6546 European Americans from the Framingham Heart Study. We show empirically that our approach provides much greater accuracy than either the prevailing ancestry-informative marker (AIM) approach or the analysis of genome-wide target genotypes without a reference panel. For example, in an independent set of 1636 European American genome-wide association study samples, we attained prediction accuracy (R(2)) of 1.000 and 0.994 for the first two principal components using our method, compared with 0.418 and 0.407 using 150 published AIMs or 0.955 and 0.003 by applying principal component analysis directly to the target samples. We finally show that the higher accuracy in inferring ancestry using our method leads to more effective correction for population stratification in association studies., Availability: The SNPweights software is available online at http://www.hsph.harvard.edu/faculty/alkes-price/software/., Supplementary Information: Supplementary data are available at Bioinformatics online.

Bipolar disorders are common, disabling, recurrent mental health conditions of variable severity. Onset is often in late childhood or early adolescence. Patients with bipolar disorders have higher rates of other mental health disorders and general medical conditions. Early recognition and treatment of bipolar disorders improve outcomes. Treatment of mood episodes depends on the presenting phase of illness: mania, hypomania, mixed state, depression, or maintenance. Psychotherapy and mood stabilizers, such as lithium, anticonvulsants, and antipsychotics, are first-line treatments that should be continued indefinitely because of the risk of relapse. Monotherapy with antidepressants is contraindicated in mixed states, manic episodes, and bipolar I disorder. Maintenance therapy for patients involves screening for suicidal ideation and substance abuse, evaluating adherence to treatment, and recognizing metabolic complications of pharmacotherapy. Active management of body weight reduces complications and improves lipid control. Patients and their support systems should be educated about mood relapse, suicidal ideation, and the effectiveness of early intervention to reduce complications.

The study of recent natural selection in human populations has important applications to human history and medicine. Positive natural selection drives the increase in beneficial alleles and plays a role in explaining diversity across human populations. By discovering traits subject to positive selection, we can better understand the population level response to environmental pressures including infectious disease. Our study examines unusual population differentiation between three large data sets to detect natural selection. The populations examined, African Americans, Nigerians, and Gambians, are genetically close to one another (F(ST) < 0.01 for all pairs), allowing us to detect selection even with moderate changes in allele frequency. We also develop a tree-based method to pinpoint the population in which selection occurred, incorporating information across populations. Our genome-wide significant results corroborate loci previously reported to be under selection in Africans including HBB and CD36. At the HLA locus on chromosome 6, results suggest the existence of multiple, independent targets of population-specific selective pressure. In addition, we report a genome-wide significant (p = 1.36 × 10(-11)) signal of selection in the prostate stem cell antigen (PSCA) gene. The most significantly differentiated marker in our analysis, rs2920283, is highly differentiated in both Africa and East Asia and has prior genome-wide significant associations to bladder and gastric cancers., (Copyright © 2011 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

Aims/hypothesis: Type 2 diabetes is more prevalent in US American minority populations of African or Native American descent than it is in European Americans. However, the proportion of this epidemiological difference that can be ascribed to genetic or environmental factors is unknown. To determine whether genetic ancestry is correlated with diabetes risk in Latinos, we estimated the proportion of European ancestry in case-control samples from Mexico and Colombia in whom socioeconomic status had been carefully ascertained., Methods: We genotyped 67 ancestry-informative markers in 499 participants with type 2 diabetes and 197 controls from Medellín (Colombia), as well as in 163 participants with type 2 diabetes and 72 controls from central Mexico. Each participant was assigned a socioeconomic status scale via various measures., Results: Although European ancestry was associated with lower diabetes risk in Mexicans (OR [95% CI] 0.06 [0.02-0.21], p = 2.0 x 10(-5)) and Colombians (OR 0.26 [0.08-0.78], p = 0.02), adjustment for socioeconomic status eliminated the association in the Colombian sample (OR 0.64 [0.19-2.12], p = 0.46) and significantly attenuated it in the Mexican sample (OR 0.17 [0.04-0.71], p = 0.02). Adjustment for BMI did not change the results., Conclusions/interpretation: The proportion of non-European ancestry is associated with both type 2 diabetes and lower socioeconomic status in admixed Latino populations from North and South America. We conclude that ancestry-directed search for genetic markers associated with type 2 diabetes in Latinos may benefit from information involving social factors, as these factors have a quantitatively important effect on type 2 diabetes risk relative to ancestry effects.

Competing Interests: Competing interests. The authors have declared that no competing interests exist.

European Americans are often treated as a homogeneous group, but in fact form a structured population due to historical immigration of diverse source populations. Discerning the ancestry of European Americans genotyped in association studies is important in order to prevent false-positive or false-negative associations due to population stratification and to identify genetic variants whose contribution to disease risk differs across European ancestries. Here, we investigate empirical patterns of population structure in European Americans, analyzing 4,198 samples from four genome-wide association studies to show that components roughly corresponding to northwest European, southeast European, and Ashkenazi Jewish ancestry are the main sources of European American population structure. Building on this insight, we constructed a panel of 300 validated markers that are highly informative for distinguishing these ancestries. We demonstrate that this panel of markers can be used to correct for stratification in association studies that do not generate dense genotype data., Competing Interests: Competing interests. The authors have declared that no competing interests exist.