Citations

Time and again, hospital code teams are dispatched to a patient in cardiac arrest only to find their resuscitative efforts unsuccessful. Are there earlier interventions that may have saved the patient? Do any warning signs exist before the patient actually goes into a full code or cardiac arrest event? The answer is a repeated yes, and the solution is a rapid response team. J Contin Educ Nurs 2008;39(11):488–489.

This column discusses how to manage challenging patient behaviors. J Contin Educ Nurs 2008;39(9):390–391.

This column discusses the etiology and clinical manifestation of and postexposure prophylaxis for rabies in humans. J Contin Educ Nurs 2008;39(7):294–295.

This column discusses the prevalence, symptoms, and treatment of Crohn's disease and ulcerative colitis and the role nurses play for these patients. J Contin Educ Nurs 2008;39(4):151–152.

Due to the increasing prevalence of autism, nurses need to become more informed about the disease. This column reviews the genetics, diagnosis, and treatment of autism. J Contin Educ Nurs 2008;39(3):102–103.

Autism is a neurodevelopmental disorder that is gaining the attention of public health, educators, and health care providers. This column reviews the deficits, prevalence, symptoms, and environmental factors. J Contin Educ Nurs 2008;39(2):55–56.

Abstract Family history is a strong predictor of colorectal cancer risk; however, a diagnosis of colorectal cancer among first-degree relatives has not been systematically investigated as a function of the colorectal cancer molecular subtypes related to tumor microsatellite instability (MSI) status. We investigated whether the observable familial colorectal cancer risks differed according to tumor MSI subtypes, stratified as MSI-High (>30% instability), MSI-Low (<30% instability), and MSS (no instability). Data from 3,143 population-based colorectal cancer cases from five institutions were assessed for family history according to the Amsterdam criteria and the Bethesda guidelines, age at diagnosis, sex, tumor location, and MSI status. The distribution of patient characteristics by MSI status was compared using polytomous logistic regression. Overall, 2.8% colorectal cancer cases met the Amsterdam criteria and 37% met the Bethesda guidelines. There were 14% MSI-High, 13% MSI-Low, and 73% MSS colorectal cancers. MSI-High (P < 0.0001) and MSI-Low tumors (P = 0.01) were more proximally located than MSS tumors. MSI-High tumors were more common among females (P < 0.001). The highest proportion of MSI-High tumors occurred in cases <40 years of age whereas the age-dependent distribution of MSI-Low tumors was unchanged. MSI-High tumors showed a statistically significant association with increasing numbers of first-degree relatives with colorectal cancer (P = 0.002); this association disappeared, however, when MSI-High cases meeting Amsterdam criteria were removed from the analysis. MSI-Low tumors did not show a similar association with family history of colorectal cancer. Familial risk associated with MSI-High tumors is primarily driven by the Amsterdam-criteria patients. MSI-Low tumors may represent a distinct subtype of colorectal cancer with respect to certain epidemiologic variables studied here. (Cancer Epidemiol Biomarkers Prev 2009;18(3):967–75)

Abstract Polymorphisms in genes critical to cell cycle control are outstanding candidates for association with ovarian cancer risk; numerous genes have been interrogated by multiple research groups using differing tagging single-nucleotide polymorphism (SNP) sets. To maximize information gleaned from existing genotype data, we conducted a combined analysis of five independent studies of invasive epithelial ovarian cancer. Up to 2,120 cases and 3,382 controls were genotyped in the course of two collaborations at a variety of SNPs in 11 cell cycle genes (CDKN2C, CDKN1A, CCND3, CCND1, CCND2, CDKN1B, CDK2, CDK4, RB1, CDKN2D, and CCNE1) and one gene region (CDKN2A-CDKN2B). Because of the semi-overlapping nature of the 123 assayed tagging SNPs, we performed multiple imputation based on fastPHASE using data from White non-Hispanic study participants and participants in the international HapMap Consortium and National Institute of Environmental Health Sciences SNPs Program. Logistic regression assuming a log-additive model was done on combined and imputed data. We observed strengthened signals in imputation-based analyses at several SNPs, particularly CDKN2A-CDKN2B rs3731239; CCND1 rs602652, rs3212879, rs649392, and rs3212891; CDK2 rs2069391, rs2069414, and rs17528736; and CCNE1 rs3218036. These results exemplify the utility of imputation in candidate gene studies and lend evidence to a role of cell cycle genes in ovarian cancer etiology, suggest a reduced set of SNPs to target in additional cases and controls. (Cancer Epidemiol Biomarkers Prev 2009;18(3):935–44)

AbstractMammographic percent density (PD) is a strong risk factor for breast cancer, but there has been relatively little systematic evaluation of other features in mammographic images that might additionally predict breast cancer risk. We evaluated the association of a large number of image texture features with risk of breast cancer using a clinic-based case-control study of digitized film mammograms, all with screening mammograms before breast cancer diagnosis. The sample was split into training (123 cases and 258 controls) and validation (123 cases and 264 controls) data sets. Age-adjusted and body mass index (BMI)–adjusted odds ratios (OR) per SD change in the feature, 95% confidence intervals, and the area under the receiver operator characteristic curve (AUC) were obtained using logistic regression. A bootstrap approach was used to identify the strongest features in the training data set, and results for features that validated in the second half of the sample were reported using the full data set. The mean age at mammography was 64.0 ± 10.2 years, and the mean time from mammography to breast cancer was 3.7 ± 1.0 (range, 2.0-5.9 years). PD was associated with breast cancer risk (OR, 1.49; 95% confidence interval, 1.25-1.78). The strongest features that validated from each of several classes (Markovian, run length, Laws, wavelet, and Fourier) showed similar ORs as PD and predicted breast cancer at a similar magnitude (AUC = 0.58-0.60) as PD (AUC = 0.58). All of these features were automatically calculated (unlike PD) and measure texture at a coarse scale. These features were moderately correlated with PD (r = 0.39-0.76), and after adjustment for PD, each of the features attenuated only slightly and retained statistical significance. However, simultaneous inclusion of these features in a model with PD did not significantly improve the ability to predict breast cancer. (Cancer Epidemiol Biomarkers Prev 2009;18(3):837–45)