Citations

Background Tools for the prediction of atrial fibrillation ( AF ) may identify high‐risk individuals more likely to benefit from preventive interventions and serve as a benchmark to test novel putative risk factors. Methods and Results Individual‐level data from 3 large cohorts in the U nited S tates (Atherosclerosis Risk in Communities [ ARIC ] study, the Cardiovascular Health Study [ CHS ], and the Framingham Heart Study [ FHS ]), including 18 556 men and women aged 46 to 94 years (19% A frican A mericans, 81% whites) were pooled to derive predictive models for AF using clinical variables. Validation of the derived models was performed in 7672 participants from the Age, Gene and Environment—Reykjavik study ( AGES ) and the Rotterdam Study ( RS ). The analysis included 1186 incident AF cases in the derivation cohorts and 585 in the validation cohorts. A simple 5‐year predictive model including the variables age, race, height, weight, systolic and diastolic blood pressure, current smoking, use of antihypertensive medication, diabetes, and history of myocardial infarction and heart failure had good discrimination (C‐statistic, 0.765; 95% CI , 0.748 to 0.781). Addition of variables from the electrocardiogram did not improve the overall model discrimination (C‐statistic, 0.767; 95% CI , 0.750 to 0.783; categorical net reclassification improvement, −0.0032; 95% CI , −0.0178 to 0.0113). In the validation cohorts, discrimination was acceptable ( AGES C‐statistic, 0.664; 95% CI , 0.632 to 0.697 and RS C‐statistic, 0.705; 95% CI , 0.664 to 0.747) and calibration was adequate. Conclusion A risk model including variables readily available in primary care settings adequately predicted AF in diverse populations from the U nited S tates and E urope.

BACKGROUND Maintaining consistency of results over time is a challenge in laboratory medicine. Lot-to-lot reagent changes are a major threat to consistency of results. METHODS For the period October 2007 through July 2012, we reviewed lot validation data for each new lot of insulin-like growth factor 1 (IGF-1) reagents (Siemens Healthcare Diagnostics) at Mayo Clinic, Rochester, MN, and the University of Virginia, Charlottesville, VA. Analyses of discarded patient samples were used for comparison of lots. For the same period, we determined the distributions of reported patient results for each lot of reagents at the 2 institutions. RESULTS Lot-to-lot validation studies identified no reagent lot as significantly different from the preceding lot. By contrast, significant lot-to-lot changes were seen in the means and medians of 105 668 reported patient IGF-I results during the period. The frequency of increased results increased nearly 2-fold to a high of 17%, without detectable changes in the underlying patient demographics. Retrospective statistical analysis indicated that lot-to-lot comparison protocols were underpowered and that validation studies for this assay required testing >100 samples to achieve 90% power to detect reagent lots that would significantly alter the distributions of patient results. CONCLUSIONS The number of test samples required for adequate lot-to-lot validation protocols is high and may be prohibitively large, especially for low-volume or complex assays. Monitoring of the distributions of patient results has the potential to detect lot-to-lot inconsistencies relatively quickly. We recommend that manufacturers implement remote monitoring of patient results from analyzers in multiple institutions to allow rapid identification of between-lot result inconsistency.

Abstract Background: There are several well-established environmental risk factors for ovarian cancer, and recent genome-wide association studies have also identified six variants that influence disease risk. However, the interplay between such risk factors and susceptibility loci has not been studied. Methods: Data from 14 ovarian cancer case–control studies were pooled, and stratified analyses by each environmental risk factor with tests for heterogeneity were conducted to determine the presence of interactions for all histologic subtypes. A genetic “risk score” was created to consider the effects of all six variants simultaneously. A multivariate model was fit to examine the association between all environmental risk factors and genetic risk score on ovarian cancer risk. Results: Among 7,374 controls and 5,566 cases, there was no statistical evidence of interaction between the six SNPs or genetic risk score and the environmental risk factors on ovarian cancer risk. In a main effects model, women in the highest genetic risk score quartile had a 65% increased risk of ovarian cancer compared with women in the lowest [95% confidence interval (CI), 1.48–1.84]. Analyses by histologic subtype yielded risk differences across subtype for endometriosis (Phet < 0.001), parity (Phet < 0.01), and tubal ligation (Phet = 0.041). Conclusions: The lack of interactions suggests that a multiplicative model is the best fit for these data. Under such a model, we provide a robust estimate of the effect of each risk factor that sets the stage for absolute risk prediction modeling that considers both environmental and genetic risk factors. Further research into the observed differences in risk across histologic subtype is warranted. Cancer Epidemiol Biomarkers Prev; 22(5); 880–90. ©2013 AACR.

RAS is an oncogene frequently mutated in human cancer. RAS mutations have been reported in 10–15% of cases of acute myeloid leukemia (AML) but they appear to be less frequent among patients with myelodysplastic syndrome (MDS). The impact of RAS mutations in patients with MDS is unclear. We conducted a retrospective study in 1,067 patients with newly diagnosed MDS for whom RAS mutational analysis was available. Overall, 4% of patients carried mutant RAS alleles. Notably, FLT3 mutations, which were found in 2% of patients, were mutually exclusive with RAS mutations. Patients with RAS mutations had a higher white blood cell count as well as bone marrow blasts compared with patients carrying wild‐type RAS. However, no differences were observed between both groups regarding the risk of AML transformation (9% vs. 7%) and overall survival (395 days vs. 500 days, P = 0.057). In summary, RAS mutations are infrequent in patients with MDS and do not appear to negatively impact their outcome. Am. J. Hematol. 88:365–369, 2013. © 2013 Wiley Periodicals, Inc.

Purpose To identify the criteria for hematologic and cardiac response to treatment in immunoglobulin light chain (AL) amyloidosis based on survival analysis of a large patient population. Patients and Methods We gathered for analysis 816 patients with AL amyloidosis from seven referral centers in the European Union and the United States. A different cohort of 374 patients prospectively evaluated at the Pavia Amyloidosis Research and Treatment Center was used for validation. Data was available for all patients before and 3 and/or 6 months after initiation of first-line therapy. The prognostic relevance of different criteria for hematologic and cardiac response was assessed. Results There was a strong correlation between the extent of reduction of amyloidogenic free light chains (FLCs) and improvement in survival. This allowed the identification of four levels of response: amyloid complete response (normal FLC ratio and negative serum and urine immunofixation), very good partial response (difference between involved and uninvolved FLCs [dFLC] < 40 mg/L), partial response (dFLC decrease > 50%), and no response. Cardiac involvement is the major determinant of survival, and changes in cardiac function after therapy can be reliably assessed using the cardiac biomarker N-terminal natriuretic peptide type B (NT-proBNP). Changes in FLC and NT-proBNP predicted survival as early as 3 months after treatment initiation. Conclusion This study identifies and validates new criteria for response to first-line treatment in AL amyloidosis, based on their association with survival in large patient populations, and offers surrogate end points for clinical trials.

Objective Obstructive sleep apnoea (OSA) and obesity are both associated with endothelial dysfunction, which precedes the development of atherosclerosis. As obesity is highly prevalent in OSA, we wanted to test the hypothesis that OSA is associated with endothelial dysfunction independently of obesity. Design Cross-sectional, population-based study. Setting Norwegian university hospital. Patients Seventy-one subjects (median age 44 years, 35% female) were recruited from a population-based study in Norway. Participants were categorised as obese (body mass index (BMI) ≥30 kg/m2), non-obese (BMI<30 kg/m2) with OSA (apnoea–hypopnoea index (AHI)≥10), or non-obese without OSA (AHI<5). Interventions None. Main outcome measures Endothelial function measured by brachial artery ultrasound and expressed as percentage of flow-mediated dilation (FMD%). Results When non-obese subjects without OSA were used as the reference (FMD% (mean±SD) 10.1±6.3), endothelial function was found to be impaired in subjects with OSA (FMD% 6.4±3.2) (p=0.003). FMD% did not differ between obese (6.0±3.4) and non-obese (6.7±3.1) OSA subjects (p=0.3). By univariate linear regression analysis, AHI, BMI, gender and baseline brachial artery diameter were significantly associated with FMD%. When these variables were entered into a multivariate model, only AHI was significantly associated with FMD%. Conclusions OSA is associated with endothelial dysfunction independently of obesity and conventional risk factors.