Citations

Background— The use of transcatheter aortic valve replacement (TAVR) to treat aortic stenosis in the United States is growing, yet little is known about the variation in procedural outcomes in community practice. We developed a TAVR in-hospital mortality risk model and used it to quantify variation in mortality rates across United States (US) TAVR centers. Methods and Results— We analyzed data from 22 248 TAVR procedures performed at 318 sites participating in the Society of Thoracic Surgeons/American College of Cardiology Transcatheter Valve Therapy Registry (November 2011 to October 2014). A Bayesian hierarchical model was developed to estimate hospital-specific risk-adjusted mortality rates adjusting for 40 patient baseline factors. A total of 1130 in-hospital deaths (5.1%) were observed. Reliability-adjusted risk-adjusted mortality rate estimates ranged from 3.4% to 7.7% with an interquartile range of 4.8% to 5.4%. A patient’s predicted odds of dying was 80% higher if treated by a hospital 1 standard deviation above the mean compared with a hospital 1 standard deviation below the mean (odds ratio =1.8; 95% credible interval, 1.4%–2.2%). Conclusions— Risk modeling of TAVR in-hospital mortality revealed variation in risk-adjusted mortality rates during the US early commercial experience. Transcatheter Valve Therapy Registry analyses using this model will support research, feedback reporting, and the identification of factors associated with quality.

Background The rarity of mutations in PALB2, CHEK2 and ATM make it difficult to estimate precisely associated cancer risks. Population-based family studies have provided evidence that at least some of these mutations are associated with breast cancer risk as high as those associated with rare BRCA2 mutations. We aimed to estimate the relative risks associated with specific rare variants in PALB2, CHEK2 and ATM via a multicentre case-control study. Methods We genotyped 10 rare mutations using the custom iCOGS array: PALB2 c.1592delT, c.2816T>G and c.3113G>A, CHEK2 c.349A>G, c.538C>T, c.715G>A, c.1036C>T, c.1312G>T, and c.1343T>G and ATM c.7271T>G. We assessed associations with breast cancer risk (42 671 cases and 42 164 controls), as well as prostate (22 301 cases and 22 320 controls) and ovarian (14 542 cases and 23 491 controls) cancer risk, for each variant. Results For European women, strong evidence of association with breast cancer risk was observed for PALB2 c.1592delT OR 3.44 (95% CI 1.39 to 8.52, p=7.1×10−5), PALB2 c.3113G>A OR 4.21 (95% CI 1.84 to 9.60, p=6.9×10−8) and ATM c.7271T>G OR 11.0 (95% CI 1.42 to 85.7, p=0.0012). We also found evidence of association with breast cancer risk for three variants in CHEK2, c.349A>G OR 2.26 (95% CI 1.29 to 3.95), c.1036C>T OR 5.06 (95% CI 1.09 to 23.5) and c.538C>T OR 1.33 (95% CI 1.05 to 1.67) (p≤0.017). Evidence for prostate cancer risk was observed for CHEK2 c.1343T>G OR 3.03 (95% CI 1.53 to 6.03, p=0.0006) for African men and CHEK2 c.1312G>T OR 2.21 (95% CI 1.06 to 4.63, p=0.030) for European men. No evidence of association with ovarian cancer was found for any of these variants. Conclusions This report adds to accumulating evidence that at least some variants in these genes are associated with an increased risk of breast cancer that is clinically important.

Abstract Background: The strongest known risk factor for endometrial cancer is obesity. To determine whether SNPs associated with increased body mass index (BMI) or waist–hip ratio (WHR) are associated with endometrial cancer risk, independent of measured BMI, we investigated relationships between 77 BMI and 47 WHR SNPs and endometrial cancer in 6,609 cases and 37,926 country-matched controls. Methods: Logistic regression analysis and fixed effects meta-analysis were used to test for associations between endometrial cancer risk and (i) individual BMI or WHR SNPs, (ii) a combined weighted genetic risk score (wGRS) for BMI or WHR. Causality of BMI for endometrial cancer was assessed using Mendelian randomization, with BMIwGRS as instrumental variable. Results: The BMIwGRS was significantly associated with endometrial cancer risk (P = 3.4 × 10−17). Scaling the effect of the BMIwGRS on endometrial cancer risk by its effect on BMI, the endometrial cancer OR per 5 kg/m2 of genetically predicted BMI was 2.06 [95% confidence interval (CI), 1.89–2.21], larger than the observed effect of BMI on endometrial cancer risk (OR = 1.55; 95% CI, 1.44–1.68, per 5 kg/m2). The association attenuated but remained significant after adjusting for BMI (OR = 1.22; 95% CI, 1.10–1.39; P = 5.3 × 10−4). There was evidence of directional pleiotropy (P = 1.5 × 10−4). BMI SNP rs2075650 was associated with endometrial cancer at study-wide significance (P < 4.0 × 10−4), independent of BMI. Endometrial cancer was not significantly associated with individual WHR SNPs or the WHRwGRS. Conclusions: BMI, but not WHR, is causally associated with endometrial cancer risk, with evidence that some BMI-associated SNPs alter endometrial cancer risk via mechanisms other than measurable BMI. Impact: The causal association between BMI SNPs and endometrial cancer has possible implications for endometrial cancer risk modeling. Cancer Epidemiol Biomarkers Prev; 25(11); 1503–10. ©2016 AACR.

Background: Traditionally, ultrasound has been used to evaluate musculoskeletal injuries in athletes; however, ultrasound applications extend well beyond musculoskeletal conditions, many of which are pertinent to athletes. Evidence Acquisition: Articles were identified in PubMed using the search terms ultrasound, echocardiogram, preparticipation physical examination, glycogen, focused assessment with sonography of trauma, optic nerve, and vocal cord dysfunction. No date restrictions were placed on the literature search. Study Design: Clinical review. Level of Evidence: Level 4. Results: Several potential applications of nonmusculoskeletal ultrasound in sports medicine are presented, including extended Focused Assessment with Sonography for Trauma (eFAST), limited echocardiographic screening during preparticipation physical examinations, assessment of muscle glycogen stores, optic nerve sheath diameter measurements in athletes with increased intracranial pressure, and assessment of vocal cord dysfunction in athletes. Conclusion: Ultrasound can potentially be used to assist athletes with monitoring their muscle glycogen stores and the diagnosis of multiple nonmusculoskeletal conditions within sports medicine.