Risk models in genetic epidemiology

Venous thromboembolism in cancer patients: a population-based cohort study

The incidence of venous thromboembolism (VTE) in cancer patients may have changed in the past decade, possibly due to novel cancer therapies, improved survival, and high-resolution imaging. Danish medical registries were used to identify 499 092 patients with a first-time cancer diagnosis between 1997 and 2017, who were matched to 1 497 276 comparison individuals without cancer from the general population. We computed cumulative incidences of VTE 6 and 12 months after the diagnosis/index date. Hazard ratios (HRs) were calculated using Cox regression. Risk factors were examined by computing subdistribution hazard ratios (SHRs) in a competing-risk analysis. Cumulative incidence of VTE 12 months after the cancer diagnosis/index date was 2.3% (95% confidence interval [CI], 2.2% to 2.3%) in the cancer cohort and 0.35% (95% CI, 0.34% to 0.36%) in the comparison cohort (HR, 8.5; 95% CI, 8.2-8.8). Important risk factors for cancer patients were prior VTE (SHR, 7.6; 95% CI, 7.2-8.0), distant metastasis (SHR, 3.2; 95% CI, 2.9-3.4), and use of chemotherapy (SHR, 3.4; 95% CI, 3.1-3.7), protein kinase inhibitors (SHR, 4.1; 95% CI, 3.4-4.9), antiangiogenic therapy (SHR, 4.4; 95% CI, 3.8-5.2), and immunotherapy (SHR, 3.6; 2.8-4.6). Twelve-month incidence in the cancer cohort increased from 1.0% (95% CI, 0.9% to 1.2%) in 1997 to 3.4% (95% CI, 2.9% to 4.0%) in 2017, which was paralleled by improved 12-month survival and increased use of computed tomography scans, chemotherapy, and targeted therapies. In conclusion, the risk of VTE in cancer patients is increasing steadily and is ninefold higher than in the general population.

Improving the diagnostic accuracy of a stratified screening strategy by identifying the optimal risk cutoff

BACKGROUND

The American Cancer Society (ACS) suggests using a stratified strategy for breast cancer screening. The strategy includes assessing risk of breast cancer, screening women at high risk with both MRI and mammography, and screening women at low risk with mammography alone. The ACS chose their cutoff for high risk using expert consensus.

METHODS

We propose instead an analytic approach that maximizes the diagnostic accuracy (AUC/ROC) of a risk-based stratified screening strategy in a population. The inputs are the joint distribution of screening test scores, and the odds of disease, for the given risk score. Using the approach for breast cancer screening, we estimated the optimal risk cutoff for two different risk models: the Breast Cancer Screening Consortium (BCSC) model and a hypothetical model with much better discriminatory accuracy. Data on mammography and MRI test score distributions were drawn from the Magnetic Resonance Imaging Screening Study Group.

RESULTS

A risk model with an excellent discriminatory accuracy (c-statistic [Formula: see text]) yielded a reasonable cutoff where only about 20% of women had dual screening. However, the BCSC risk model (c-statistic [Formula: see text]) lacked the discriminatory accuracy to differentiate between women who needed dual screening, and women who needed only mammography.

CONCLUSION

Our research provides a general approach to optimize the diagnostic accuracy of a stratified screening strategy in a population, and to assess whether risk models are sufficiently accurate to guide stratified screening. For breast cancer, most risk models lack enough discriminatory accuracy to make stratified screening a reasonable recommendation.

Principles in genetic risk assessment

Risk assessment constitutes an essential component of genetic counseling and testing, and the genetic risk should be estimated as accurately as possible for individual and family decision making. All relevant information retrieved from population studies and pedigree and genetic testing enhances the accuracy of the assessment of an individual's genetic risk. This review will focus on the following general aspects implicated in risk assessment: the increasing genetic information regarding disease; complex traits versus Mendelian disorders; and the influence of the environment and disease susceptibility. The influence of these factors on risk assessment will be discussed.

Risk modeling: applying evidence-based risk assessment in oncology nursing practice

PURPOSE/OBJECTIVES

To introduce nurses to the concept of evidence-based risk models and their use in practice.

DATA SOURCES

Poster presentations at meetings and published articles and books.

DATA SYNTHESIS

Evidence-based risk models can be used in many clinical situations to identify patients at higher risk for a particular disease or clinical outcome, such as adverse events. These models may be based on molecular, epidemiologic, clinical, or family information obtained from patients. Risk models also may provide information about the cost-effectiveness of prevention, treatment, or support strategies for specific patients.

CONCLUSIONS

Determining the risks of disease- or therapy-related adverse events can help healthcare providers and patients. Risk assessment to identify patients who are most likely to benefit from supportive care can lead to the cost-effective use of these supportive care measures and improved clinical outcomes.

IMPLICATIONS FOR NURSING

Through awareness of relevant evidence-based risk models, nurses can become more effective in actively managing their patients care. Because of their close and ongoing contact with patients with cancer, oncology nurses are in an ideal position to assess risk factors for adverse events and to use appropriate supportive care for those patients who are at greatest risk.