Highest Risk Decile Research Articles

Abstract P4-12-05: Enrichment of aldosterone synthase (CYP11B2) gene C-allele carriers in women at high risk in the OncoVue® polyfactorial risk model

Abstract Background: Models for accurately assessing breast cancer risk are utilized for guiding clinical decisions regarding early detection and prevention. We developed a polyfactorial risk model (PFRM; OncoVue) from analysis of a large case-control database including both functional gene polymorphism and clinical factor data. A multivariate logistic regression based statistical protocol was used to derive a single risk model consisting of 22 single nucleotide polymorphisms (SNPs) in 19 genes and 5 traditional risk factors. Several studies have shown that the PFRM is significantly improved in individualized risk estimation compared to the Breast Cancer Risk Assessment Tool (BCRAT) or Gail model. Here we have examined the genotype frequencies of women placed in the highest decile of risk by the PFRM compared to both the overall control population and to women in the control population placed at the lowest decile of risk. Materials and Methods: Analyses were performed on 1671 breast cancer cases and 3351 controls that enrolled in a case-control study conducted in six regions of the United States. DNAs were genotyped for the 22 genetic polymorphisms and combined with clinical risk factor information to calculate PFRM risk estimates for the individual participants. The PFRM lifetime scores were used to stratify the upper and lower deciles of risk. The genotype frequencies for these stratified risk groups were determined and compared to the overall control population and to each other (upper vs. lower decile). Odds Ratios (OR) were determined for the CYP11B2 genotypes because they exhibited the largest differences in frequency among the groups. Results: For CYP11B2 comparison of the genotype frequencies in the highest decile risk group to the overall genotype frequencies of controls identified significant enrichment of the C-allele carriers in the highest risk group. The OR for C-allele carriers (C/C+C/T) compared to the most common T/T genotype was 3.0 (1.9, 4.8; 95% C.I.) with a p-value <0.0001. Similarly for the upper decile risk group compared to the lower decile risk group the association of C-allele carriers was even stronger with OR = 11.0 (6.6,18.4; p <0.0001). In the upper decile of risk 86% of the individuals were C-allele carriers compared to only 38% in the lower decile of risk. Conclusions: Our previous studies have shown an age dependent association of the CYP11B2 C/C and C/T genotypes with breast cancer risk. The enrichment of these genotypes in women in the upper decile risk group as determined by the PFRM further reinforces the role of this genotype in influencing breast cancer risk and suggests that intervention in the aldosterone synthase pathway be explored as a method of breast cancer prevention. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P4-12-05.

Azithromycin and the Risk of Cardiovascular Death

BackgroundAlthough several macrolide antibiotics are proarrhythmic and associated with an increased risk of sudden cardiac death, azithromycin is thought to have minimal cardiotoxicity. However, published reports of arrhythmias suggest that azithromycin may increase the risk of cardiovascular death.MethodsWe studied a Tennessee Medicaid cohort designed to detect an increased risk of death related to short-term cardiac effects of medication, excluding patients with serious noncardiovascular illness and person-time during and shortly after hospitalization. The cohort included patients who took azithromycin (347,795 prescriptions), propensity-score–matched persons who took no antibiotics (1,391,180 control periods), and patients who took amoxicillin (1,348,672 prescriptions), ciprofloxacin (264,626 prescriptions), or levofloxacin (193,906 prescriptions).ResultsDuring 5 days of therapy, patients taking azithromycin, as compared with those who took no antibiotics, had an increased risk of cardiovascular death (hazard ratio, 2.88; 95% confidence interval [CI], 1.79 to 4.63; P<0.001) and death from any cause (hazard ratio, 1.85; 95% CI, 1.25 to 2.75; P=0.002). Patients who took amoxicillin had no increase in the risk of death during this period. Relative to amoxicillin, azithromycin was associated with an increased risk of cardiovascular death (hazard ratio, 2.49; 95% CI, 1.38 to 4.50; P=0.002) and death from any cause (hazard ratio, 2.02; 95% CI, 1.24 to 3.30; P=0.005), with an estimated 47 additional cardiovascular deaths per 1 million courses; patients in the highest decile of risk for cardiovascular disease had an estimated 245 additional cardiovascular deaths per 1 million courses. The risk of cardiovascular death was significantly greater with azithromycin than with ciprofloxacin but did not differ significantly from that with levofloxacin.ConclusionsDuring 5 days of azithromycin therapy, there was a small absolute increase in cardiovascular deaths, which was most pronounced among patients with a high baseline risk of cardiovascular disease. (Funded by the National Heart, Lung, and Blood Institute and the Agency for Healthcare Quality and Research Centers for Education and Research on Therapeutics.)

Enhanced Prediction of Mortality After Percutaneous Coronary Intervention by Consideration of General and Neurological Indicators

This study sought to improve methodology for predicting post-percutaneous coronary intervention (PCI) mortality. Recently, an increased proportion of post-PCI deaths caused by noncardiac causes has been suggested, often in rapidly triaged patients resuscitated from sudden cardiac death or presenting with cardiogenic shock. Older risk adjustment algorithms may not adequately reflect these issues. Consecutive patients undergoing PCI from 2000 to 2009 were randomly divided into training (n = 8,966) and validation (n = 8,891) cohorts. The 2010 ACC-NCDR (American College of Cardiology-National Cardiovascular Data Registry) mortality algorithm was applied to the training cohort and its highest risk decile, separately. Variables describing general and neurological status at admission were then tested for their additional predictive capability and new algorithms developed. These were tested in the validation cohort, using receiver-operator characteristic curve, Hosmer-Lemeshow, and reclassification measures as principal outcome measures. In-hospital mortality was 1.0%, of which 52.2% had noncardiac causes or major contributions. Baseline model C-statistics for the total and upper decile training cohorts were 0.904 and 0.830. The Aldrete score (addressing consciousness, respiration, skin color, muscle function, and circulation) and neurology scores added incremental information, resulting in improved validation cohort C-statistics (entire group: 0.883 to 0.914, p < 0.001; high-risk decile: 0.829 to 0.874, p < 0.001). Reclassification of the ACC-NCDR <90th and ≥90th risk percentiles by the new score yielded improved mortality prediction (p < 0.001 and p = 0.033, respectively). Half of in-hospital deaths in this series were of noncardiac causation. Prediction of in-hospital mortality after PCI can be considerably improved over conventional models by the inclusion of variables describing general and neurological status.

Risk Factors and Individual Probabilities of Melanoma for Whites

Incidence and mortality of cutaneous melanoma is rising rapidly in the United States; therefore, identifying risk factors for melanoma and integrating them into a clinical and population risk estimation tool may help guide prevention efforts and identify participants for preventive interventions. We examined risk factors for melanoma in three large prospective studies of women and men. We observed 152,949 women and 25,206 men free of cancer at baseline for up to 14 years. A total of 535 incident cases of invasive melanoma (444 women and 91 men) were included in the analysis. We combined the three studies to examine risk factors and to build a risk model to calculate melanoma risk score. Older age, male sex, family history of melanoma, higher number of nevi, history of severe sunburn, and light hair color were each associated with significantly elevated risk of melanoma and were included in the final risk prediction. Participants at the highest decile of risk had a more than three-fold increase in risk of melanoma compared with those in the lowest decile (observed relative risk, 3.61; expected relative risk, 4.20). The measure of discriminatory accuracy as summarized by an age-and sex-adjusted concordance statistic of 0.62 (95% CI, 0.58 to 0.65) indicated that the model had reasonable ability to differentiate those who will develop melanoma and those who will remain free from the disease. We identified several risk factors for melanoma and developed statistical models with adequate performance and discriminatory accuracy.

Relationship of clinical factors to the use of Cox-2 selective NSAIDs within an arthritis population in a large HMO.

To investigate the degree to which physicians use clinical factors to focus use of Cox-2 selective NSAIDs within an arthritis population. Diagnostic codes in the medical records of a large group-model HMO in northern California with approximately 3 million members were examined to identify patients with either rheumatoid arthritis (RA) or non-RA (osteoarthritis or degenerative joint disease). RA and non-RA patients were stratified in deciles of relative risk for gastrointestinal (GI) complications according to patient characteristics identified on the Standardized Calculator of Risk for Events (SCORE) that were associated with use of Cox-2 selective NSAIDs. (The SCORE tool stratifies patients by risk of serious GI complications using patient characteristics that are assigned points during an office visit, including age, health status, diagnosis of rheumatoid arthritis, corticosteroid use, and history of GI ulcer or bleed.) The second stage of analysis examined the percentage of arthritis patients in each SCORE-risk decile who received a Cox-2 selective NSAID, lower-risk NSAID, or traditional NSAID during calendar year 1999. The study population consisted of 144,360 members with an arthritis diagnosis, approximately 4.8% of members in this HMO. The mean age was 62.8 years (SD = 14.1), 61% were female, 10,449 (7%) had rheumatoid arthritis (RA), and 133,911 (93%) had non-rheumatoid arthritis. A diagnosis of RA was the most significant predictor of Cox-2 NSAID use (OR=2.4; 95% CI=1.6-3.5), followed by a history of GI problems (OR=1.5; 95% CI=1.4- 1.6). Female gender, chronic steroid use, and age each increased the odds of receiving a Cox-2 selective NSAID by about 35% (P<0.001 for all). Approximately 8.3% of patients in the highest decile of risk and 1.5% of patients in the lowest decile of risk received a Cox-2 selective NSAID. Clinical characteristics of patients identified on the SCORE (GI-risk) tool were strongly associated with use of Cox-2-selective NSAIDs in this HMO. A 5.5-fold difference in utilization of Cox-2 selective NSAIDs was found among patients determined to be in the highest-risk decile versus patients in the lowest-risk decile. Future research should investigate how nonclinical factors play a role in the treatment decisions made by physicians.

Significance of genetic information in risk assessment and individual classification using silicosis as a case model.

Over the last decade the role of genetic data in epidemiological research has expanded considerably. We recently published a case-control study that evaluated the interaction between silica exposure and minor variants in the genes coding for interleukin-1alpha (IL-1alpha), interleukin-1 receptor antagonist (IL-1RA) and tumor necrosis factor alpha (TNFalpha) as risk factors associated with silicosis, a fibrotic lung disease. In contrast, this report uses data generated from these studies to illustrate the utility of genetic information for the purposes of risk assessment and clinical prediction. Specifically, this study will address how, given a known exposure, genetic information affects the characterization of risk groups. Relative operating characteristic (ROC) curves were then used to determine the impact of genetic information on individual classification. Logistic regression modeling procedures were used to estimate the predicted probability of developing silicosis. This probability was then used to construct predicted risk deciles, first for a model with occupational exposure only and then for a model containing occupational exposure and genetic main effects and interactions. Results indicate that the exposure-only model effectively captures an increasing relationship between predicted risk deciles and prevalence of observed silicosis cases. Individuals comprising the highest risk decile were almost four times as likely to have silicosis as opposed to the lowest risk decile. The addition of genetic data, however, substantially improved characterization of risk categories; the proportion of cases in the highest risk decile was almost eight times that in the lowest risk decile. However, the ROC curve and classification analysis demonstrated that the addition of genetic main effects and interactions did not significantly impact on prediction of the individual's case status. These results indicate that genetic information plays a valuable role in effectively characterizing risk groups and mechanisms of disease operating in a substantial proportion of the population. However, in the case of fibrotic lung disease caused by silica exposure, information about the presence or absence of the minor variants of IL-1alpha, IL-1RA and TNFalpha is unlikely to be a useful tool for individual classification.

The relationship between parental history of vascular disease and cardiovascular disease risk factors in children: the Bogalusa Heart Study.

The relationship between parental history of vascular disease (heart attack, stroke, diabetes mellitus, and hypertension) and risk factor variables for cardiovascular disease was assessed in 3,312 offspring aged 5-17 years during the 1981-1982 school year in the biracial community of Bogalusa, Louisiana. Risk factors studied included systolic blood pressure, diastolic blood pressure, serum total cholesterol, triglycerides, and individual lipoprotein cholesterol (beta-, low density lipoprotein (LDL) cholesterol; pre-beta, very low density lipoprotein (VLDL) cholesterol; and alpha-, high density lipoprotein (HDL) cholesterol). Risk factors were adjusted for age, race, sex, and height (blood pressure only) prior to testing parental history effects. Univariate comparisons between risk factors in children and vascular disease in parents resulted in statistically significant increases in systolic and diastolic pressures associated with the presence of maternal or paternal hypertension (p less than 0.001). Paternal heart attack was also associated with elevations in diastolic pressure (p less than 0.01) of children. Maternal diabetes mellitus was associated with an increase in serum total cholesterol (p less than 0.05). Paternal diabetes mellitus and maternal heart attack (for female progeny only) were associated with increases in mean triglyceride levels of children. VLDL cholesterol results were similar to those for triglycerides. For HDL cholesterol, paternal diabetes mellitus was associated with a small decrease in mean levels (p less than 0.05). Dramatic increases to the highest decile of risk were found in association with the following parental disease combinations: paternal heart attack-paternal diabetes for serum total cholesterol (p less than 0.0001), maternal heart attack-paternal diabetes (p less than 0.001) and paternal stroke-maternal diabetes (p less than 0.0001) for LDL cholesterol. Multivariate analysis detected no significant effects of single parental vascular disease. However, paternal heart attack in combination with either diabetes mellitus or hypertension was statistically significant in their relationship to the risk factors overall.

Factors influencing long-term prognosis after recovery from myocardial infarction—Three-year findings of the Coronary Drug Project

1. (1) Data from the 2789 placebo patients in the CDP were analyzed to evaluate the relationship of 40 baseline characteristics to 3-yr mortality. 2. (2) Examination of the 40 variables by univariate linear regression analyses showed that many variables were apparently associated with an increased risk of mortality. 3. (3) Sets of variables were also examined simultaneously using both linear and logistic models. Similar results were obtained with the two models. A smaller number of variables appeared to be independently and additively associated with 3-yr mortality. 4. (4) Attempts to classify the men with respect to estimated risk were successful. The men were grouped into quantiles of estimated risk which correspond closely to observed mortality in those quantiles. Analyses using 10 variables classified patients according to risk about as well as did analyses with 20 or 40 variables. Using 10 baseline characteristics and a logistic risk function, the ratio of observed mortality in the highest decile of estimated risk relative to that in the lowest decile was 12.3. Of all deaths, 30.7 per cent occurred among the patients in the upper 10 per cent of estimated risk. 5. (5) In order to evaluate the generalizability of the results of these analyses, two predictive analyses were also done in which a risk function was derived from the data for one subgroup of patients and used to predict mortality for an independent group of patients. Patients classified into quantiles of predicted risk by this procedure showed a wide range of observed mortality. In one case, the ratio of observed mortality in the highest decile to the lowest was 7.2 with 21.0 per cent of all deaths in the highest decile of predicted risk. In the second case, the ratio of relative risk was 5.3, and 22.5 per cent of all deaths occurred in the highest decile of risk. In conclusion, these analyses have shown that it is possible, using as few as 10 measured characteristics, to predict prognosis after recovery from myocardial infarction.

Practice in a Health Centre

<h3>Background</h3> Patients with myeloma experience substantial delays in their diagnosis, which can adversely affect their prognosis. <h3>Aim</h3> To generate a clinical prediction rule to identify primary care patients who are at highest risk of myeloma. <h3>Design and setting</h3> Retrospective open cohort study using electronic health records data from the UK’s Clinical Practice Research Datalink (CPRD) between 1 January 2000 and 1 January 2014. <h3>Method</h3> Patients from the CPRD were included in the study if they were aged ≥40 years, had two full blood counts within a year, and had no previous diagnosis of myeloma. Cases of myeloma were identified in the following 2 years. Derivation and external validation datasets were created based on geographical region. Prediction equations were estimated using Cox proportional hazards models including patient characteristics, symptoms, and blood test results. Calibration, discrimination, and clinical utility were evaluated in the validation set. <h3>Results</h3> Of 1 281 926 eligible patients, 737 (0.06%) were diagnosed with myeloma within 2 years. Independent predictors of myeloma included: older age; male sex; back, chest and rib pain; nosebleeds; low haemoglobin, platelets, and white cell count; and raised mean corpuscular volume, calcium, and erythrocyte sedimentation rate. A model including symptoms and full blood count had an area under the curve of 0.84 (95% CI = 0.81 to 0.87) and sensitivity of 62% (95% CI = 55% to 68%) at the highest risk decile. The corresponding statistics for a second model, which also included calcium and inflammatory markers, were an area under the curve of 0.87 (95% CI = 0.84 to 0.90) and sensitivity of 72% (95% CI = 66% to 78%). <h3>Conclusion</h3> The implementation of these prediction rules would highlight the possibility of myeloma in patients where GPs do not suspect myeloma. Future research should focus on the prospective evaluation of further external validity and the impact on clinical practice.