Lowest Decile Of Risk Research Articles

Heterogeneous Treatment Effects of Therapeutic-Dose Heparin in Patients Hospitalized for COVID-19

Randomized clinical trials (RCTs) of therapeutic-dose heparin in patients hospitalized with COVID-19 produced conflicting results, possibly due to heterogeneity of treatment effect (HTE) across individuals. Better understanding of HTE could facilitate individualized clinical decision-making. To evaluate HTE of therapeutic-dose heparin for patients hospitalized for COVID-19 and to compare approaches to assessing HTE. Exploratory analysis of a multiplatform adaptive RCT of therapeutic-dose heparin vs usual care pharmacologic thromboprophylaxis in 3320 patients hospitalized for COVID-19 enrolled in North America, South America, Europe, Asia, and Australia between April 2020 and January 2021. Heterogeneity of treatment effect was assessed 3 ways: using (1) conventional subgroup analyses of baseline characteristics, (2) a multivariable outcome prediction model (risk-based approach), and (3) a multivariable causal forest model (effect-based approach). Analyses primarily used bayesian statistics, consistent with the original trial. Participants were randomized to therapeutic-dose heparin or usual care pharmacologic thromboprophylaxis. Organ support-free days, assigning a value of -1 to those who died in the hospital and the number of days free of cardiovascular or respiratory organ support up to day 21 for those who survived to hospital discharge; and hospital survival. Baseline demographic characteristics were similar between patients randomized to therapeutic-dose heparin or usual care (median age, 60 years; 38% female; 32% known non-White race; 45% Hispanic). In the overall multiplatform RCT population, therapeutic-dose heparin was not associated with an increase in organ support-free days (median value for the posterior distribution of the OR, 1.05; 95% credible interval, 0.91-1.22). In conventional subgroup analyses, the effect of therapeutic-dose heparin on organ support-free days differed between patients requiring organ support at baseline or not (median OR, 0.85 vs 1.30; posterior probability of difference in OR, 99.8%), between females and males (median OR, 0.87 vs 1.16; posterior probability of difference in OR, 96.4%), and between patients with lower body mass index (BMI <30) vs higher BMI groups (BMI ≥30; posterior probability of difference in ORs >90% for all comparisons). In risk-based analysis, patients at lowest risk of poor outcome had the highest propensity for benefit from heparin (lowest risk decile: posterior probability of OR >1, 92%) while those at highest risk were most likely to be harmed (highest risk decile: posterior probability of OR <1, 87%). In effect-based analysis, a subset of patients identified at high risk of harm (P = .05 for difference in treatment effect) tended to have high BMI and were more likely to require organ support at baseline. Among patients hospitalized for COVID-19, the effect of therapeutic-dose heparin was heterogeneous. In all 3 approaches to assessing HTE, heparin was more likely to be beneficial in those who were less severely ill at presentation or had lower BMI and more likely to be harmful in sicker patients and those with higher BMI. The findings illustrate the importance of considering HTE in the design and analysis of RCTs. ClinicalTrials.gov Identifiers: NCT02735707, NCT04505774, NCT04359277, NCT04372589.

Abstract 2256: Validation of an integrated breast cancer risk model with mammographic density in three prospective cohort studies

Abstract Introduction: Incorporation of mammographic density to breast cancer risk models could improve risk stratification to tailor screening and prevention strategies according to risk. However, robust validation of such models in prospective cohort studies is needed to determine their accuracy in identifying women at different risk levels. Methods: We incorporated Breast Imaging and Reporting Data System (BI-RADS) breast density to a literature-based model with questionnaire-based risk factors and a 313-variant polygenic risk score (PRS). The Individualized Coherent Absolute Risk Estimator (iCARE) tool was used to build and validate a 5-year absolute risk model for breast cancer. The model was evaluated for calibration and discrimination in three prospective cohorts of women of European ancestry (1,468 cases, 19,104 controls): US-based Nurses’ Health Study (NHS I and II) and Mayo Mammography Health Study (MMHS); and Sweden-based Karolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA) study. Analyses were done separately for women younger (NHS II, KARMA) and older than 50 years (NHS I, MMHS, KARMA). Improvements in risk stratification were assessed among US non-Hispanic White women aged 50-70 years. Results: For women younger than 50 years, the model with questionnaire-based risk factors, PRS and BI-RADS was well calibrated across risk deciles in NHS II, but overestimated risk at the highest risk decile in KARMA. For women 50 years or older, the model showed good calibration in all studies, with evidence of slight overestimation at the highest risk decile and underestimation at the lowest risk decile. The model with PRS and BI-RADS was well calibrated for women at high-risk in both age groups. Incorporation of BI-RADS to questionnaire-based risk factors and PRS improved risk discrimination: area under the curves (AUC) 67.0% (95% CI: 63.5-70.6%) vs. 65.6% (95% CI: 61.9-69.3%) for models with and without BI-RADS for younger women and 66.1% (95% CI 64.4-67.8%) vs. 65.5% (95% CI: 63.8-67.2%) for older women. The model with BI-RADS identified 18.4% population of non-Hispanic US women 50-70 years old above 3% 5-year risk (used for recommending risk-reducing medication in the US), with 42.4% of future cases expected to occur in this group. Addition of BI-RADS led to the reclassification of ~8.0% of US non-Hispanic White women aged 50-70 years at the high-risk threshold (3% 5-year risk), resulting in identification of 4.2% of additional future cases. Conclusion: Integrating BI-RADS with questionnaire-based risk factors and PRS resulted in improved risk stratification among women of European ancestry, with evidence of slight overestimation of risk for women at elevated risk. Additional validation of the integrated model in diverse populations is needed prior to considering clinical applications. Citation Format: Charlotta V. Mulder, Yon Ho Jee, Xin Yang, Christopher G. Scott, Chi Gao, Amber N. Hurson, Mikael Eriksson, Per Hall, Peter Kraft, Celine M. Vachon, Antonis C. Antoniou, Gretchen Gierach, Montserrat Garcia-Closas, Parichoy Pal Choudhury. Validation of an integrated breast cancer risk model with mammographic density in three prospective cohort studies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2256.

Parkinson’s disease determinants, prediction and gene–environment interactions in the UK Biobank

ObjectiveTo systematically investigate the association of environmental risk factors and prodromal features with incident Parkinson’s disease (PD) diagnosis and the interaction of genetic risk with these factors. To evaluate whether...

Facilitating Versus Inhibiting the Transmission of Drug Abuse from High-Risk Parents to Their Children: A Swedish National Study.

We seek to identify factors that facilitate or inhibit transmission of drug abuse (DA) from high-risk parents to their children. In 44,250 offspring of these parents, ascertained from a Swedish national sample for having a mother and/or father with DA, we explored, using Cox models, how the prevalence of DA was predicted by potentially malleable risk factors in these high-risk parents, their spouses and the rearing environment they provided. Analyses of offspring of discordant high-risk siblings and offspring of discordant sibling-in-laws and step-parents aided causal inference. Risk for DA in the children was associated with high-risk and married-in parental externalizing psychopathology, a range of other features of these parents (e.g., low education and receipt of welfare), and aspects of the rearing environment (e.g., neighborhood deprivation and number of nearby drug dealers). Offspring of discordant high-risk siblings, siblings-in-laws and step-parents suggested that nearly all these associations were partly causal. A multivariate analysis utilizing offspring of discordant high-risk siblings identified the six most significant potentially malleable risk factors for offspring DA: (1) criminal behavior (CB) in married-in parent, (2) community peer deviance, (3) broken family, (4) DA in high-risk parent, (5) CB in high-risk parent and (6) number of family moves. Children in the lowest decile of risk had a 50% reduction in their DA prevalence, similar to that seen in the general population. We conclude that transmission of DA from high-risk parents to children partly results from a range of potentially malleable risk factors that could serve as foci for intervention.

Inclusion of Functional Status Measures in the Risk Adjustment of 30-Day Mortality After Transcatheter Aortic Valve Replacement: A Report From the Society of Thoracic Surgeons/American College of Cardiology TVT Registry

ObjectivesThe aim of this study was to develop and validate a risk adjustment model for 30-day mortality after transcatheter aortic valve replacement (TAVR) that accounted for both standard clinical factors and pre-procedural health status and frailty. BackgroundAssessment of risk for TAVR is important both for patient selection and provider comparisons. Prior efforts for risk adjustment have focused on in-hospital mortality, which is easily obtainable but can be biased because of early discharge of ill patients. MethodsUsing data from patients who underwent TAVR as part of the Society of Thoracic Surgeons/American College of Cardiology TVT (Transcatheter Valve Therapy) Registry (June 2013 to May 2016), a hierarchical logistic regression model to estimate risk for 30-day mortality after TAVR based only on pre-procedural factors and access site was developed and internally validated. The model included factors from the original TVT Registry in-hospital mortality model but added the Kansas City Cardiomyopathy Questionnaire (health status) and gait speed (5-m walk test). ResultsAmong 21,661 TAVR patients at 188 sites, 1,025 (4.7%) died within 30 days. Independent predictors of 30-day death included older age, low body weight, worse renal function, peripheral artery disease, home oxygen, prior myocardial infarction, left main coronary artery disease, tricuspid regurgitation, nonfemoral access, worse baseline health status, and inability to walk. The predicted 30-day mortality risk ranged from 1.1% (lowest decile of risk) to 13.8% (highest decile of risk). The model was able to stratify risk on the basis of patient factors with good discrimination (C = 0.71 [derivation], C = 0.70 [split-sample validation]) and excellent calibration, both overall and in key patient subgroups. ConclusionsA clinical risk model was developed for 30-day death after TAVR that included clinical data as well as health status and frailty. This model will facilitate tracking outcomes over time as TAVR expands to lower risk patients and to less experienced sites and will allow an objective comparison of short-term mortality rates across centers.

Preinfarct Health Status and the Use of Early Invasive Versus Ischemia-Guided Management in Non–ST-Elevation Acute Coronary Syndrome

Early invasive management improves outcomes in non-ST-elevation myocardial infarction (NSTEMI). The association between preinfarct health status and the selecting patients for early invasive management is unknown. The Prospective Registry Evaluating outcomes after Myocardial Infarctions: Events and Recovery and Translational Research Investigating Underlying disparities in acute Myocardial infarction Patients' Health status are consecutive US multicenter registries, in which the associations between preinfarct angina frequency and quality of life (both assessed by the Seattle Angina Questionnaire on admission) and the Global Registry of Acute Coronary Events (GRACE) risk score and referral to early invasive management (coronary angiography within 48 hours) were evaluated using Poisson regression, after adjusting for site, demographics, and clinical and psychosocial variables. Of 3,768 patients with NSTEMI, 2,182 (57.9%) patients were referred for early invasive treatment. Patients with excellent, good, or very good baseline angina-specific quality of life, respectively, were more likely to receive early angiography, even after adjustment, as compared with patients reporting poor baseline quality of life because of angina (62.1.0%, 60.9%, 59.6%, vs 51.2%; adjusted relative risk [RR] = 1.09, 95% confidence interval [CI] 1.04 to 1.16; RR = 1.13, 95% CI 1.01 to 1.27; RR 1.14, 95% CI 0.99 to 1.31, respectively). Finally, patients with a GRACE score in the highest risk decile (199.5 to <321.4) had significantly lower rates of early invasive treatment (42.7%) than patients in the lowest decile of risk (67.6%; adjusted RR for continuous GRACE score per SD [1 SD = 40 points], 0.96, 95% CI 0.92 to 0.99, p = 0.019). In conclusion, in this real-world NSTEMI cohort, patients with the highest mortality risk and worst health status were less likely to be referred for early invasive management. Further work is needed to understand the role of preinfarct health status and in-hospital treatment strategy.

Clinical features of registry-ascertained alcohol use disorders that reflect familial risk

BackgroundAlcohol Use Disorder (AUD) is clinically heterogeneous. Using a large epidemiological sample ascertained via public registries, is it possible to identify clinical and historical features of AUD that reflect familial risk? MethodsUsing registration in national medical, legal or pharmacy registries, we identified four kinds of relative pairs (n=683,223) starting with a proband with AUD: cousins, half-siblings, full-siblings and monozygotic cotwins. Using linear hazard regression, we examined the interaction between five clinical/historical features of AUD in the proband and risk for AUD in these relatives. ResultsIncreased risk for AUD in relatives was predicted by the proband’s early age at first registration, total number of registrations, recurrence, history of drug abuse and ascertainment in the medical versus the legal or pharmacy registry. In multivariate models, age at first registration, number of registrations, recurrence and history of drug abuse remained significant and in aggregate strongly predicted the risk for AUD in relatives. The risk for AUD in siblings of AUD probands in the highest decile of genetic risk predicted by these four indices was more than twice as great as that predicted in siblings of probands in the lowest risk decile. ConclusionsIn an epidemiological sample, familial risk for AUD can be assessed by simple clinical and historical variables.

Thirty-Day Postoperative Mortality Risk Estimates and 1-Year Survival in Veterans Health Administration Surgery Patients.

For more than 2 decades, the Veterans Health Administration (VHA) has relied on risk-adjusted, postoperative, 30-day mortality data as a measure of surgical quality of care. Recently, the use of 30-day mortality data has been criticized based on a theory that health care professionals manage patient care to meet the metric and that other outcome metrics are available. To determine whether postoperative mortality data identify a delay in care to meet a 30-day mortality metric and to evaluate whether 30-day mortality risk score groups stratify survival patterns up to 365 days after surgery in surgical procedures assessed by the Veterans Affairs Surgical Quality Improvement Program (VASQIP). Patients undergoing VASQIP-assessed surgical procedures within the VHA from October 1, 2011, to September 30, 2013, were evaluated. Data on 365-day survival follow-up of 212 733 surgical cases using VHA Vital Status and admission records were obtained with 10 947 mortality events. Data analysis was conducted from September 3, 2014, to November 9, 2015. Survival up to 365 days after surgery for the overall cohort divided into 10 equal groups (deciles). There were 10 947 mortality events identified in a cohort of 212 733 surgical patients. The mean probability of death was 1.03% (95% CI, 1.01%-1.04%). Risk estimate groups in the 212 733 surgical cases analyzed showed significantly different postoperative survival, with consistency beyond the time frame for which they were developed. The lowest risk decile had the highest 365-day survival probability (99.74%; 95% CI, 99.66%-99.80%); the highest risk decile had the lowest 365-day survival probability (72.04%; 95% CI, 71.43%-72.64%). The 9 lowest risk deciles had linear survival curves from 0 to 365 postoperative days, with the highest risk decile having early survival risk and becoming more linear after the first 180 days. Survival curves between 25 and 35 days were consistent for all risk deciles and showed no evidence that mortality rates were affected in the immediate period beyond day 30. The setting of mortality varied by postoperative day ranges, with index hospitalization events declining and deaths outside of the hospital increasing up to 365 days. Deciles of 30-day mortality estimates are associated with significantly different survival outcomes at 365 days even after removing patients who died within the first 30 postoperative days. No evidence of delays in patient care and treatment to meet a 30-day metric were identified. These findings reinforce the usefulness of 30-day mortality risk stratification as a surrogate for long-term outcomes.

Development and Validation of the Neonatal Risk Estimate Score for Children Using Extracorporeal Respiratory Support

To develop and validate the Neonatal Risk Estimate Score for Children Using Extracorporeal Respiratory Support, which estimates the risk of in-hospital death for neonates prior to receiving respiratory extracorporeal membrane oxygenation (ECMO) support. We used an international ECMO registry (2008-2013); neonates receiving ECMO for respiratory support were included. We divided the registry into a derivation sample and internal validation sample, by calendar date. We chose candidate variables a priori based on published evidence of association with mortality; variables independently associated with mortality in logistic regression were included in this parsimonious model of risk adjustment. We evaluated model discrimination with the area under the receiver operating characteristic curve (AUC), and we evaluated calibration with the Hosmer-Lemeshow goodness-of-fit test. During 2008-2013, 4592 neonates received ECMO respiratory support with mortality of 31%. The development dataset contained 3139 patients treated in 2008-2011. The Neo-RESCUERS measure had an AUC of 0.78 (95% CI 0.76-0.79). The validation cohort had an AUC=0.77 (0.75-0.80). Patients in the lowest risk decile had an observed mortality of 7.0% and a predicted mortality of 4.4%, and those in the highest risk decile had an observed mortality of 65.6% and a predicted mortality of 67.5%. Neonatal Risk Estimate Score for Children Using Extracorporeal Respiratory Support offers severity-of-illness adjustment for neonatal patients with respiratory failure receiving ECMO. This score may be used to adjust patient survival to assess hospital-level performance in ECMO-based care.

Development of a cost-effectiveness model for optimisation of the screening interval in diabetic retinopathy screening.

The English NHS Diabetic Eye Screening Programme was established in 2003. Eligible people are invited annually for digital retinal photography screening. Those found to have potentially sight-threatening diabetic retinopathy (STDR) are referred to surveillance clinics or to Hospital Eye Services. To determine whether personalised screening intervals are cost-effective. Risk factors were identified in Gloucestershire, UK using survival modelling. A probabilistic decision hidden (unobserved) Markov model with a misgrading matrix was developed. This informed estimation of lifetime costs and quality-adjusted life-years (QALYs) in patients without STDR. Two personalised risk stratification models were employed: two screening episodes (SEs) (low, medium or high risk) or one SE with clinical information (low, medium-low, medium-high or high risk). The risk factor models were validated in other populations. Gloucestershire, Nottinghamshire, South London and East Anglia (all UK). People with diabetes in Gloucestershire with risk stratification model validation using data from Nottinghamshire, South London and East Anglia. Personalised risk-based algorithm for screening interval; cost-effectiveness of different screening intervals. Data were obtained in Gloucestershire from 12,790 people with diabetes with known risk factors to derive the risk estimation models, from 15,877 people to inform the uptake of screening and from 17,043 people to inform the health-care resource-usage costs. Two stratification models were developed: one using only results from previous screening events and one using previous screening and some commonly available GP data. Both models were capable of differentiating groups at low and high risk of development of STDR. The rate of progression to STDR was 5 per 1000 person-years (PYs) in the lowest decile of risk and 75 per 1000 PYs in the highest decile. In the absence of personalised risk stratification, the most cost-effective screening interval was to screen all patients every 3 years, with a 46% probability of this being cost-effective at a £30,000 per QALY threshold. Using either risk stratification models, screening patients at low risk every 5 years was the most cost-effective option, with a probability of 99-100% at a £30,000 per QALY threshold. For the medium-risk groups screening every 3 years had a probability of 43-48% while screening high-risk groups every 2 years was cost-effective with a probability of 55-59%. The study found that annual screening of all patients for STDR was not cost-effective. Screening this entire cohort every 3 years was most likely to be cost-effective. When personalised intervals are applied, screening those in our low-risk groups every 5 years was found to be cost-effective. Screening high-risk groups every 2 years further improved the cost-effectiveness of the programme. There was considerable uncertainty in the estimated incremental costs and in the incremental QALYs, particularly with regard to implications of an increasing proportion of maculopathy cases receiving intravitreal injection rather than laser treatment. Future work should focus on improving the understanding of risk, validating in further populations and investigating quality issues in imaging and assessment including the potential for automated image grading. Integrated Research Application System project number 118959. The National Institute for Health Research Health Technology Assessment programme.

Ischemic electrocardiographic abnormalities and prognosis in decompensated heart failure.

Identification of coronary ischemia may enable targeted diagnostic and therapeutic strategies for acute heart failure. We determined the risk of 30-day mortality associated with ischemic ECG abnormalities in patients with acute heart failure. Among 8772 patients (53.4% women, median 78 years [Q1, Q3: 68,84]) presenting with acute heart failure to 86 hospital emergency departments in Ontario, Canada, Q-waves, T-wave inversion, or ST-depression were present in 51.8% of subjects. However, presence of ST-depression was the only finding associated with 30-day mortality with adjusted odds ratio 1.24 (95% confidence interval [CI], 1.02-1.50). Using continuous net reclassification improvement, addition of ST-depression to the Emergency Heart failure Mortality Risk Grade model reclassified 16.9% of patients overall, and 29.3% of those with a history of ischemic heart disease (both P<0.001). By adding ST-depression to the model, the Emergency Heart failure Mortality Risk Grade was extended to predict 30-day death with high discrimination (c-statistic 0.801), with 0.57% mortality rate in the lowest risk decile. Adjusted odds ratios for 30-day mortality were 2.81 (95% CI, 1.48-5.31; P=0.002) in quintile 2, 7.41 (95% CI, 4.13-13.30; P<0.001) in quintile 3, and 14.47 (95% CI, 8.20-25.54; P<0.001) in quintile 4 compared with the lowest risk quintile. When the highest risk quintile was subdivided into 2 equally sized risk strata (deciles 9 and 10), the adjusted odds ratios for 30-day mortality were 27.20 (95% CI, 15.33-48.27; P<0.001) in decile 9 and 58.96 (95% CI, 33.54-103.65; P<0.001) in highest risk decile 10. Presence of ST-depression on the ECG reclassified risk of 30-day mortality in patients with acute heart failure, identifying both high- and low-risk subsets.

Prediction of Costs and Length of Stay in Coronary Artery Bypass Grafting

Although more than 200,000 bypass operations are performed in the United States annually, few data exist on the predictors of costs and resource use for this procedure. Questions related to clinical outcomes, costs, and resource use in coronary artery bypass grafting (CABG) were addressed. In a multiinstitutional statewide database, patient level data from 42,839 patients undergoing isolated CABG were combined with cost data. After adjustment for cost-to-charge ratios and inflation, the association of length of stay and costs with the Society ofThoracic Surgeons-Predicted Risk of Mortality (STS-PROM) was analyzed. Patients were randomly divided into development (60%) and validation (40%) cohorts. Regression models were developed to analyze the impact of patient characteristics, comorbidities, and adverse events on postoperative length of stay and total costs. Postoperative length of stay and total direct costs for CABG averaged 6.9 days and $38,847. Length of stay and costs increased from 5.4 days and $33,275 in the lowest-risk decile (mean STS-PROM of 0.6%) to 13.8 days and $69,122 in the highest-risk decile (mean STS-PROM 19%). Compared with adverse events, patient characteristics had little impact on length of stay and costs. onvalidation, the models that combined preoperative and postoperative variables explained variance better (R(2)=0.51 for length of stay; R(2)= 0.47 for costs) and were better calibrated than the preoperative models (R(2)= 0.10 for length of stay; R(2)= 0.14 for costs). The STS-PROM and preoperative regression models are useful for preoperative prediction of costs and length of stay for groups of patients, case-mix adjustment in hospital benchmarking, and pay for performance measures. The combined preoperative and postoperative models identify incremental costs and length of stay associated with adverse events and are more suitable for prioritizing quality improvement efforts.

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 &amp;lt;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 &amp;lt;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.

174 Do Hospital Occupancy Levels Affect Initial Inpatient Level of Care for Emergency Department Admissions?

174 Do Hospital Occupancy Levels Affect Initial Inpatient Level of Care for Emergency Department Admissions?

The use of Meta‐Analysis Risk Estimates for Candidate Genes in Combination to Predict Coronary Heart Disease Risk

Although the risk for coronary heart disease (CHD) associated with single SNPs is modest it has been suggested that, in combination, several common risk-associated alleles could lead to a substantially better heart disease risk prediction. We have modelled this using 10 SNPs in ten candidate genes (APOB, NOS3, APOE, ACE, SERPINE1, MTHFR, ITGA2B, PON 1, LPL, and CETP) and their predicted summary risk estimates from meta-analysis. Based on published allele frequencies, approximately 29% of the general population would be expected to carry less than three risk alleles, approximately 55% would carry 3 or 4 risk alleles, 4% would have 6 and 1% 7 or more risk alleles. Compared to the mean of those with 3 or 4 risk associated genotypes, those with 6 and 7-or-more alleles have a significantly higher risk odds ratio (OR) of CHD (mean OR (95% Confidence Intervals), 1.70 (1.14 to 2.55); and 4.51 (2.89 to 7.04) respectively), while compared to those in the lowest decile of risk, those in the highest decile have a CHD odds ratio in the range of 3.05 (2.24 to 4.14). Taking into account age and the risk alleles carried, the mean 10 year probability for developing CHD for a 55 year old man was calculated to be 15% (8.6% to 24.8%), with nearly 1 in 5 having more than 20% risk. Whether this particular group of 10 SNPs will improve the accuracy of CHD predictions over the combination of classical risk factors in clinical use requires further experimental evidence.

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.

Determinants of all causes of death in samples of Italian middle-aged men followed up for 25 years.

A total of 1712 men aged 40 to 59 years in two rural cohorts of northern and central Italy have been followed up for 25 years after an entry examination in 1960. Forty one individual characteristics have been considered as possible predictors of death in the next 25 years. After exclusion of 55 men with life threatening diseases (cardiovascular and cancer) and of 161 men because of missing measurements, 1495 men have been analysed for relation between entry factors and subsequent death (n = 670). Twelve factors eventually emerged as powerful predictors of future death: in hierarchical order, age, blood pressure, forced expiratory volume, cigarette smoking, xanthelasma, mother life-status, arm circumference, father life-status, shoulder-pelvis ratio, vital capacity, arcus senilis, and serum cholesterol. Discrimination as provided by logistic modelling placed 19.6% of all cases in the upper decile of the estimated risk, 36.8% in the upper quintile, 2.5% in the lowest decile, and 7.1% in the lowest quintile. Out of those located in the lowest decile of risk, 11.4% died within 25 years while the corresponding percentage in the upper decile was 87.3%. Use of the Cox model yielded slightly better coefficients than logistic function.

Predicting Coronary Heart Disease in Middle-Aged and Older Persons

Of 1,025 men and 1,445 women, aged 49 to 82 years, who were free of coronary heart disease (CHD), 79 men and 63 women subsequently had CHD. Using a risk function based on cholesterol in the high density and low density lipoproteins, systolic blood pressure, left ventricular hypertrophy, and diabetes, less than 2% of the subsequent CHD cases were found in the lowest decile of risk, whereas 25% of the cases for men and 37% for women were found in the highest decile. Predictability held for each specific age group. This predictability was at least as good as that obtained by the usual CHD risk profile at younger ages.