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PubMed2003

Application of breast cancer risk prediction models in clinical practice.

Breast cancer risk assessment provides an estimation of disease risk that can be used to guide management for women at all levels of risk. In addition, the likelihood that breast cancer risk is due to specific genetic susceptibility (such as BRCA1 or BRCA2 mutations) can be determined. Recent developments have reinforced the clinical importance of breast cancer risk assessment. Tamoxifen chemoprevention as well as prevention studies such as the Study of Tamoxifen and Raloxifene are available to women at increased risk of developing breast cancer. In addition, specific management strategies are now defined for BRCA1 and BRCA2 mutation carriers. Risk may be assessed as the likelihood of developing breast cancer (using risk assessment models) or as the likelihood of detecting a BRCA1 or BRCA2 mutation (using prior probability models). Each of the models has advantages and disadvantages, and all need to be interpreted in context. We review available risk assessment tools and discuss their application. As illustrated by clinical examples, optimal counseling may require the use of several models, as well as clinical judgment, to provide the most accurate and useful information to women and their families.

Breast Neoplasms↗
PubMed1984

Intent to participate in worksite health promotion activities: a model of risk factors and psychosocial variables.

A model of risk factors and psychosocial variables that should enhance the prediction of participation in worksite health promotion programs was evaluated by a sample survey (10% random sample) of approximately 13,000 State employees in South Carolina. The response rate for the survey was 66.3%, n = 854. A split sample strategy was employed to develop and then validate the psychosocial model. Degree of satisfaction with one's current status and intent to change it were assessed in six health areas: weight, nutrition, exercise, cigarette smoking, alcohol consumption, and the handling of stress and tension. In each area, a known risk factor was used to predict degree of satisfaction and intent to change. Variables from the psychosocial model were added in a step-wise regression procedure. The key variables in the psychosocial model were Personal Efficacy (specific to each of five areas), Job Stress, Trait Anxiety, and Health Knowledge. Risk factors contribute significantly to the prediction of dissatisfaction and intent to change in all areas except cigarette smoking and nutrition. The model of psychosocial factors contributed significantly to the prediction of dissatisfaction and intent to change in all areas. Personal Efficacy was significantly related both to dissatisfaction and intent to change, whereas the Job Stress and Anxiety variables were more effective in predicting dissatisfaction than intent to change. Health Knowledge had little direct or interactive effects on dissatisfaction or intent to participate. The implications of these findings for health promotion programs were examined.

Adolescent↗
PubMed1999

The use of fractional polynomials to model continuous risk variables in epidemiology.

BACKGROUND: The traditional method of analysing continuous or ordinal risk factors by categorization or linear models may be improved. METHODS: We propose an approach based on transformation and fractional polynomials which yields simple regression models with interpretable curves. We suggest a way of presenting the results from such models which involves tabulating the risks estimated from the model at convenient values of the risk factor. We discuss how to incorporate several continuous risk and confounding variables within a single model. The approach is exemplified with data from the Whitehall I study of British Civil Servants. We discuss the approach in relation to categorization and non-parametric regression models. RESULTS: We show that non-linear risk models fit the data better than linear models. We discuss the difficulties introduced by categorization and the advantages of the new approach. CONCLUSIONS: Our approach based on fractional polynomials should be considered as an important alternative to the traditional approaches for the analysis of continuous variables in epidemiological studies.

Coronary Disease↗
PubMed1995

Relative-risk regression models using cases and their parents.

Relative-risk regression models are presented for studies of the association of genetic markers with disease status when the study design uses affected cases and their parents, with or without unaffected sibs. These models generalize the "haplotype relative-risk" method and allow for censored unaffected sibs; in this sense, these models resemble proportional hazards models that are commonly used in survival analysis. A critical distinction between these models and the usual Cox proportional hazards model is that the frequencies of the genotypes of the cases are compared to controls based on Mendelian expectations, and not simply to the genotypes of the sib controls who are at risk of disease. These models allow modeling of the contribution of specific alleles to the relative risk of disease, as well as interactions of allelic effects with environmental risk factors. To demonstrate the application of these models, we have fit them to the binary affection status of the Problem 2 data set. Four candidate-gene loci were found to have a significant association with affection status, after allowance for relative risks that decrease with age; two of these associations correctly identified two of the major gene loci, and the other two were false-positive associations.

Aging↗
PubMed2005

Cancer risk prediction models: a workshop on development, evaluation, and application.

Cancer researchers, clinicians, and the public are increasingly interested in statistical models designed to predict the occurrence of cancer. As the number and sophistication of cancer risk prediction models have grown, so too has interest in ensuring that they are appropriately applied, correctly developed, and rigorously evaluated. On May 20-21, 2004, the National Cancer Institute sponsored a workshop in which experts identified strengths and limitations of cancer and genetic susceptibility prediction models that were currently in use and under development and explored methodologic issues related to their development, evaluation, and validation. Participants also identified research priorities and resources in the areas of 1) revising existing breast cancer risk assessment models and developing new models, 2) encouraging the development of new risk models, 3) obtaining data to develop more accurate risk models, 4) supporting validation mechanisms and resources, 5) strengthening model development efforts and encouraging coordination, and 6) promoting effective cancer risk communication and decision-making.

Breast Neoplasms↗
PubMed2005

Sensitivity analysis of a two-dimensional probabilistic risk assessment model using analysis of variance.

This article demonstrates application of sensitivity analysis to risk assessment models with two-dimensional probabilistic frameworks that distinguish between variability and uncertainty. A microbial food safety process risk (MFSPR) model is used as a test bed. The process of identifying key controllable inputs and key sources of uncertainty using sensitivity analysis is challenged by typical characteristics of MFSPR models such as nonlinearity, thresholds, interactions, and categorical inputs. Among many available sensitivity analysis methods, analysis of variance (ANOVA) is evaluated in comparison to commonly used methods based on correlation coefficients. In a two-dimensional risk model, the identification of key controllable inputs that can be prioritized with respect to risk management is confounded by uncertainty. However, as shown here, ANOVA provided robust insights regarding controllable inputs most likely to lead to effective risk reduction despite uncertainty. ANOVA appropriately selected the top six important inputs, while correlation-based methods provided misleading insights. Bootstrap simulation is used to quantify uncertainty in ranks of inputs due to sampling error. For the selected sample size, differences in F values of 60% or more were associated with clear differences in rank order between inputs. Sensitivity analysis results identified inputs related to the storage of ground beef servings at home as the most important. Risk management recommendations are suggested in the form of a consumer advisory for better handling and storage practices.

Analysis of Variance↗
PubMed2006

Risk prediction models for familial breast cancer.

A positive family history of breast cancer, reflecting genetic susceptibility, is one of the strongest risk factors for the disease. A number of breast cancer susceptibility genes have been identified to date, with the most important being BRCA1 and BRCA2. Risk prediction models can be used to identify individuals likely to carry BRCA1 and BRCA2 mutations and individuals at high risk of developing the disease. This information can then be used to target genetic testing, screening and interventions more effectively. In this article, the authors review the risk models that have been developed for familial breast cancer and discuss their applicability, strengths and weaknesses, and present examples of classifying women into risk categories according to the predictions by the various models. The review concludes with a discussion of the ways in which risk models could be improved in the immediate- and long-term future.

BRCA2 Protein↗
PubMed2001

A simple benchmark for evaluating quality of care of patients following acute myocardial infarction.

OBJECTIVE: To develop a simple risk model as a basis for evaluating care of patients admitted with acute myocardial infarction. METHODS: From coronary care registers, biochemistry records and hospital management systems, 2153 consecutive patients with confirmed acute myocardial infarction were identified. With 30 day all cause mortality as the end point, a multivariable logistic regression model of risk was constructed and validated in independent patient cohorts. The areas under receiver operating characteristic curves were calculated as an assessment of sensitivity and specificity. The model was reapplied to a number of commonly studied subgroups for further assessment of robustness. RESULTS: A three variable model was developed based on age, heart rate, and systolic blood pressure on admission. This produced an individual probability of death by 30 days (P(30)) where P(30) = 1/(1 + exp(-L(30))) and L(30) = -5.624 + (0.085 x age) + (0.014 x heart rate) - (0.022 x systolic blood pressure). The areas under the receiver operating characteristic curves for the reference and test cohorts were 0.79 (95% CI 0.76 to 0.82) and 0.76 (95% CI 0.72 to 0.79), respectively. To aid application of the model to routine clinical audit, a normogram relating observed mortality and sample size to the likelihood of a significant deviation from the expected 30 day mortality rate was constructed. CONCLUSIONS: This risk model is simple, reproducible, and permits quality of care of acute myocardial infarction patients to be reliably evaluated both within and between centres.

Adult↗
PubMed2003

Impact of cardiovascular risk factors in an urban sample of Portuguese adults according to the Framingham risk prediction models.

OBJECTIVES: To estimate the 10-year risk of coronary heart disease (CHD) according to the Framingham risk prediction models and to assess the impact of major risk factors in an urban sample of Portuguese adults. METHODS: We surveyed 938 non-institutionalized adults aged 40 to 74 years, recruited by random digit dialing. A fasting venous blood sample was collected to measure glucose, total cholesterol and high-density-lipoprotein (HDL) cholesterol. Blood pressure was measured and a resting 12-lead ECG registered. Sixty-eight individuals (7.2%) were excluded due to prevalent CHD, according to self-reported information and/or ECG. Data on the remaining 532 women and 338 men at risk were used for analysis. For each participant we calculated the 10-year risk using the Framingham risk tables. RESULTS: In this community-based sample, the expected 10-year cumulative incidence was estimated to be on average 19.3% for men and 9.5% for women. The factor with greatest impact was hypertension. Had this factor been altered such that individuals whose blood pressure was > or = 140/90 mmHg instead had high-normal blood pressure (130-139/85-89 mmHg), the risk of CHD would have been reduced to 15.7% in men and 7.3% in women. Thus, 18.6% and 23.2% of cases would have been avoided, respectively. The number of individuals in whom hypertension should be prevented to avoid one case of CHD was 15 among men and 25 among women. The impact of other risk factors expressed as a proportion of attributable cases was, in men and women respectively, 5.7% and 7.4% if none had diabetes; 12.4% and 3.2% if none smoked; 23.8% and 14.7% if total cholesterol were between 160 and 199 mg/dl instead of over 200 mg/dl; and 10.9% and 5.3% if HDL were above 45 mg/dl. If all participants did not smoke, did not have diabetes, had total cholesterol between 160 and 199 instead of over 200 mg/dl, HDL over 45 mg/dl and high-normal blood pressure instead of hypertension, the expected cumulative incidence would be 8.1% for men and 5.1% for women. This means that respectively 58.0% and 46.3% of cases would have been avoided. CONCLUSION: Blood pressure control in this population is the intervention with greatest impact on CHD prevention. Given the tendency for risk factors to cluster and to interact, we can expect to achieve even greater success with multifactorial interventions.

Adult↗
PubMed1997

Development of a quantitative risk assessment model for Salmonella enteritidis in pasteurized liquid eggs.

The performance of hazard analyses and the establishment of critical limits by the food industry are both hampered by the inability to directly relate food processing operations from farm-to-table with their public health impact. Using a 'unit operations' and stochastic simulation approach, data on the frequency of pathogens in raw ingredients, predictive microbiology models for growth and inactivation (thermal and non-thermal), and dose-response models for infectivity were integrated to create a quantitative risk assessment model for a Salmonella enteritidis infection from thermally processed liquid whole eggs made into mayonnaise in the home. The risk assessment indicated pasteurization provides sufficient consumer protection from a high incidence of infected birds and from temperature abuse between the farm and the egg breakers. However scenarios showed how inadequate pasteurization temperatures and/or temperature abuse during storage leads to a hazardous product. This dynamic approach to modeling risk should aid in identification and setting critical control points and assessing the impact of altering food formulations or processes.

Eggs↗
PubMed1998

Can low accuracy disease risk predictor models improve health care using decision support systems?

A prototype decision support system has been designed for managing dental caries using a risk assessment model. Caries is a multifactorial disease with risk prediction models having low sensitivity (65%) and moderate specificity (80%) for 2 or more new lesions. These models are inaccurate for targeting resources at high risk people. However, low risk individuals can be more accurately identified. If the activity of early tooth decay lesions, in low risk people, are monitored over time and only lesions beyond 1/3 of the dentin depth are filled, the number of annual fillings may be reduced by 50%. Currently, most US dental schools do not teach risk assessment for caries and encourage early treatment of lesions leading to a repair destruction cycle. The combination of a decision support system with a moderate accuracy specificity risk model for predicting low risk individuals may produce a significant improvement in caries management.

Decision Support Systems, Clinical↗
PubMedDate not supplied

Reanalysis of the Harvard Six Cities Study, part II: sensitivity analysis.

Following the validation and replication of the Harvard Six Cities Study (Krewski et al., this issue), we conducted a wide range of sensitivity analyses to explore the observed associations between long-term exposure to fine particle or sulfate air pollution and mortality. We examined the impact of alternative risk models on estimates of risk, taking into account covariates not included in the original analyses. These risk models provided a basis for identifying covariates that may confound or modify the association between fine particle or sulfate air pollution and mortality, and for identifying sensitive population subgroups. The possibility of confounding due to occupational exposures was also investigated. Residence histories were coded for the study subjects and were used to examine temporal patterns of exposure and risk. Our sensitivity analyses showed the mortality risk estimates for fine particle and sulfate air pollution to be highly robust against alternative risk models of the Cox proportional hazards family, including models with additional covariates from the original questionnaires not included in the original published analyses. There was limited evidence of departures from the proportional hazards assumption. Flexible exposure-response models provided some evidence of departures from linearity at both low and high sulfate concentrations. Incorporating information on changes over time in cigarette smoking and body mass index had little effect on the association between fine particles and mortality. There was limited evidence of variation in risk with attained age, gender, smoking status, occupational exposure to dust and fumes, marital status, heart or lung diseases, or lung function. However, air pollution risk did appear to decreasing with increasing educational attainment. Extensive adjustment for occupation using aggregate indices of occupational "dirtiness" and occupational exposure to known lung carcinogens had little impact on the mortality risks associated with particulate air pollution. Our evaluation of population mobility indicated that relatively few subjects moved from their original city of residence. Attempts to identify critical exposure time windows were limited by the lack of marked interindividual variation in temporal exposure patterns throughout the study period. Overall, this extensive sensitivity analysis both supported the conclusions reached by the original investigators and demonstrated the robustness of these conclusions to alternative analytic approaches.

Age Factors↗
PubMed2000

Determinants of infant feeding method in relation to risk factors for breast cancer.

BACKGROUND: Breastfeeding is considered to be an important factor for maternal and children's health. However, the epidemiological findings related to the effect of breastfeeding on women's health, especially with respect to breast cancer development, are inconsistent. Determinants of infant feeding method may contribute to the inconsistency. METHODS: A total of 24,769 women aged 40-64 in Miyagi Prefecture, Japan, responded to a self-administered questionnaire survey in 1990. Using the data obtained from 22,085 parous women, we calculated odds ratios (ORs) for the choice of "breastfeeding only" during reproductive period. RESULTS: Late age at menarche (> or = 16 years, OR = 1.57) and high body mass index (BMI) at 20 years of age (> or = 24, OR = 1.31) were associated with the choice of breastfeeding only. Late age at birth of first child (> or = 28 years, OR = 0.29), history of breast cancer in mother (OR = 0.68), and high educational level (more than a high school education, OR = 0.53) reduced the possibility of choosing breastfeeding only. CONCLUSION: The results indicate that the choice of infant feeding method is associated with several breast cancer risk factors. Based on this finding, we should construct appropriate breast cancer risk models for parous women and investigate the changes in the effects of breastfeeding and other breast cancer risk factors among these risk models. Especially in a risk model controlling for breastfeeding, the effects of other breast cancer risk factors should be reevaluated. Through comparisons among different risk models, we may find the best-fitted risk model and identify the true effect of breastfeeding.

Adolescent↗
PubMed1995

Prospective evaluation of a model of risk for HIV infection among injecting drug users.

Data on 3016 out-of-treatment injecting drug users (IDUs) were analyzed in order to replicate findings from an earlier study on risk factors for HIV infection (Iguchi et al., 1992) and evaluate a model for estimating probability of infection. Logistic regression analyses yielded a set of risk factors highly consistent with previous findings. A logistic function was used to estimate subjects' probabilities of infection, and these estimates were strongly correlated with actual HIV prevalence in both the original and current samples. The current study represents a successful replication of earlier findings and supports the validity of the risk model.

Adult↗
PubMed2002

A comparison of the Framingham and European Society of Cardiology coronary heart disease risk prediction models in the normative aging study.

BACKGROUND: A number of prediction models have been developed in an attempt to accurately identify patients at increased risk of a first coronary heart disease event. We sought to determine the ten-year incidence of coronary heart disease events in a healthy cohort with measurable risk factors, and to compare these results with the predicted number of events by use of both the Framingham and European Society of Cardiology risk prediction models. METHODS: We compared the predicted and observed number of events in 5 risk categories in 1393 subjects aged 30 to 74 years who were enrolled in the Normative Aging Study. RESULTS: The risk prediction models reliably stratify populations with regards to relative risk of coronary heart disease events and there is reasonable agreement between the 2 models (weighted kappa = 0.46, P <.01). The Framingham model underestimated the absolute risk of coronary heart disease events in the low-risk group, and both risk prediction models overestimated the absolute risk of events in the high- or very-high-risk groups (Framingham c-statistic = 0.60, European Society of Cardiology c-statistic = 0.58). CONCLUSIONS: Despite simplification, the accuracy of the European model was not significantly different from the Framingham model. But the accuracy of absolute risk prediction, particularly at the extremes of risk, is imperfect. Refinement and validation of these risk prediction models is important because they affect the management of individual patients and the allocation of community resources.

Adult↗
PubMed2002

Are received risk perception models alive and well?

Two recent papers on risk perception models are discussed. In these papers, quantitative analyses are presented of risk perception in relation to risk characteristics as specified in the Psychometric Model, and to cultural biases according to Cultural Theory. This comment points out that the data quality of these two studies is doubtful, with a very small convenience sample and a very low response rate. More importantly, the analyses show the same low levels of explained variance of risk perception as other researchers have found previously, but the authors still draw optimistic conclusions from their data. Such conclusions are unjustified.

Culture↗
PubMed2003

Limitations of current risk-adjustment models in the era of coronary stenting.

BACKGROUND: Several risk-adjustment models have been developed to compare outcomes of conventional coronary angioplasty across physicians and institutions. Yet the accuracy of these models in contemporary interventional practice--characterized by the widespread use of stents and novel adjuvant pharmacotherapies--has not been sufficiently studied. METHODS: The principal published predictive models for inhospital mortality after angioplasty were validated in 11,681 patients undergoing coronary stenting and 6475 patients undergoing balloon-only procedures in the Society for Cardiac Angiography and Interventions registry from July 1996 to December 1998. We examined the 2 components of model accuracy: discrimination, as determined by the c-index; and calibration, as measured by the Hosmer-Lemeshow statistic and predicted-versus-observed probability plots. RESULTS: The discriminative properties of the models were preserved in the validation cohort and did not differ statistically from one another (c-indexes 0.85-0.89). Hosmer-Lemeshow statistics, however, showed poor fit (P <.001), with all 3 models substantially overestimating the risk of adverse outcomes. Although recalibration of the models achieved satisfactory goodness of fit, laboratory-specific ratings differed depending on the model applied. CONCLUSIONS: Predictive models developed in the era of conventional angioplasty cannot be applied directly to current interventional practice. Although recalibration restores model fit, application of different recalibrated models yields inconsistent assessment of laboratory performance. Development of new, widely generalizable models is warranted, but such models will require continued reassessment as medical technology evolves and practice patterns change.

Aged↗