Errors in Statistical Programming for Study About Population-Attributable Risk Proportion of Clinical Risk Factors for Breast Cancer

JAMA Oncology ◽  
2019 ◽  
Vol 5 (11) ◽  
pp. 1637 ◽  
Author(s):  
Natalie J. Engmann
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Population Attributable Risk ◽  
Attributable Risk ◽  
Clinical Risk Factors ◽  
Clinical Risk ◽  
Statistical Programming

scholarly journals Population-Attributable Risk Proportion of Clinical Risk Factors for Breast Cancer

JAMA Oncology ◽  
2017 ◽  
Vol 3 (9) ◽  
pp. 1228 ◽  
Author(s):  
Natalie J. Engmann ◽  
Marzieh K. Golmakani ◽  
Diana L. Miglioretti ◽  
Brian L. Sprague ◽  
Karla Kerlikowske ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Population Attributable Risk ◽  
Attributable Risk ◽  
Clinical Risk Factors ◽  
Clinical Risk

scholarly journals A streamlined model for use in clinical breast cancer risk assessment maintains predictive power and is further improved with inclusion of a polygenic risk score

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245375
Author(s):  
Richard Allman ◽  
Erika Spaeth ◽  
John Lai ◽  
Susan J. Gross ◽  
John L. Hopper
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
African American ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Score ◽  
Clinical Risk Factors ◽  
Polygenic Risk Score ◽  
Gail Model ◽  
Clinical Risk

Five-year absolute breast cancer risk prediction models are required to comply with national guidelines regarding risk reduction regimens. Models including the Gail model are under-utilized in the general population for various reasons, including difficulty in accurately completing some clinical fields. The purpose of this study was to determine if a streamlined risk model could be designed without substantial loss in performance. Only the clinical risk factors that were easily answered by women will be retained and combined with an objective validated polygenic risk score (PRS) to ultimately improve overall compliance with professional recommendations. We first undertook a review of a series of 2,339 Caucasian, African American and Hispanic women from the USA who underwent clinical testing. We first used deidentified test request forms to identify the clinical risk factors that were best answered by women in a clinical setting and then compared the 5-year risks for the full model and the streamlined model in this clinical series. We used OPERA analysis on previously published case-control data from 11,924 Gail model samples to determine clinical risk factors to include in a streamlined model: first degree family history and age that could then be combined with the PRS. Next, to ensure that the addition of PRS to the streamlined model was indeed beneficial, we compared risk stratification using the Streamlined model with and without PRS for the existing case-control datasets comprising 1,313 cases and 10,611 controls of African-American (n = 7421), Caucasian (n = 1155) and Hispanic (n = 3348) women, using the area under the curve to determine model performance. The improvement in risk discrimination from adding the PRS risk score to the Streamlined model was 52%, 46% and 62% for African-American, Caucasian and Hispanic women, respectively, based on changes in log OPERA. There was no statistically significant difference in mean risk scores between the Gail model plus risk PRS compared to the Streamlined model plus PRS. This study demonstrates that validated PRS can be used to streamline a clinical test for primary care practice without diminishing test performance. Importantly, by eliminating risk factors that women find hard to recall or that require obtaining medical records, this model may facilitate increased clinical adoption of 5-year risk breast cancer risk prediction test in keeping with national standards and guidelines for breast cancer risk reduction.

scholarly journals Association between clinical risk factors and left ventricular function in patients with breast cancer following chemotherapy

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
K Yamashita ◽  
H Tanaka ◽  
K Hatazawa ◽  
Y Tanaka ◽  
K Sumimoto ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Cancer Patients ◽  
Previous History ◽  
Left Ventricular ◽  
Clinical Risk Factors ◽  
Lv Function ◽  
Breast Cancer Patients ◽  
Clinical Risk ◽  
History Of

Abstract Background The sequential or concurrent use of two different types of agents such as anthracyclines and trastuzumab may increase myocardial injury and cancer therapeutics-related cardiac dysfunction (CTRCD), which is often the result of the combined detrimental effect of the two therapies for breast cancer patients. For risk stratification to detect the development of CTRCD, the current position paper from the European Society of Cardiology (ESC) lists several factors associated with risk of cardiotoxicity following treatment with chemotherapy. However, the association between clinical risk factors and left ventricular (LV) function in breast cancer patients is currently unclear. Purpose Our purpose was to investigate the impact of baseline risk factors on LV function in patients with preserved LV ejection fraction (LVEF) who have undergone anthracycline or trastuzumab chemotherapy for breast cancer. Methods We studied 86 breast cancer patients treated with anthracyclines, trastuzumab, or both. Mean age was 59±13 years and LVEF was 67±5%. In accordance with the current definition, CTRCD was defined as a decline in LVEF of >10% to an absolute value of <53% after chemotherapy. Based on the 2016 ESC position paper, clinical risk factors for CTRCD were defined as: (1) a cumulative total doxorubicin dose of ≥240 mg/m2, (2) age ≥65-year-old, (3) body mass index ≥30 kg/m2, (4) a previous history of radiation therapy to chest or mediastinum, (5) B-type natriuretic peptide ≥100pg/mL, (6) a previous history of cardiovascular disease, (7) atrial fibrillation, (8) hypertension, (9) diabetes mellitus, (10) current or ex-smoker. Results The relative decrease in LVEF after chemotherapy for patients with more than four risk factors was significantly greater than that for patients without (−9.3±10.8% vs. −2.2±10.2%; p=0.02). However, this finding did not apply to patients with more than one, two or three risk factors. Patients with more than four risk factors also tended to show a higher prevalence of CTRCD than those without (14.3% vs. 2.8%, p=0.12). Moreover, patients with more than four risk factors were more likely to have higher LV mass index (109.3±29.0 g/m2 vs. 83.2±21.0g /m2, p<0.001), lower global longitudinal strain (18.4±2.8% vs. 20.0±2.6%, p=0.06) and higher E/e' (10.4 (8.9–13.0) vs. 9.0 (7.4–10.9), p=0.06) compared to those without. Conclusions Association between clinical risk factors and LV dysfunction following chemotherapy became stronger with an increase in the number of risk factors in breast cancer patients, and was especially strong for patients treated with chemotherapy who had more than four risk factors. Our findings can thus be expected to have clinical implications for better management of patients with breast cancer referred for chemotherapy. Funding Acknowledgement Type of funding source: None

scholarly journals Population Attributable Risk of Modifiable and Nonmodifiable Breast Cancer Risk Factors in Postmenopausal Breast Cancer

2016 ◽  
Vol 184 (12) ◽  
pp. 884-893 ◽  
Author(s):  
Rulla M. Tamimi ◽  
Donna Spiegelman ◽  
Stephanie A. Smith-Warner ◽  
Molin Wang ◽  
Mathew Pazaris ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Population Attributable Risk ◽  
Postmenopausal Breast Cancer ◽  
Attributable Risk ◽  
Cancer Risk Factors ◽  
Breast Cancer Risk Factors

scholarly journals Mammographic Breast Density Assessed with Fully Automated Method and its Risk for Breast Cancer

2019 ◽  
Vol 9 ◽  
pp. 43 ◽  
Author(s):  
Pendem Saikiran ◽  
Ruqiya Ramzan ◽  
Nandish S. ◽  
Phani Deepika Kamineni ◽  
Priyanka ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Cancer Risk ◽  
Breast Density ◽  
Roc Curve ◽  
Clinical Risk Factors ◽  
Mammographic Breast Density ◽  
Risk Models ◽  
Clinical Risk ◽  
Automated Method

Objectives: We evaluated the association between breast cancer and breast density (BD) measured using fully automated software. We also evaluated the performance of cancer risk models such as only clinical risk factors, density related measures, and both clinical risk factors and density-related measures for determining cancer risk. Materials and Methods: This is a retrospective case–control study. The data were collected from August 2015 to December 2018. Two hundred fifty women with breast cancer and 400 control subjects were included in this study. We evaluated the BD qualitatively using breast imaging-reporting and data system density and quantitatively using 3D slicer. We also collected clinical factors such as age, familial history of breast cancer, menopausal status, number of births, body mass index, and hormonal replacement therapy use. We calculated the odds ratio (OR) for BD to determine the risk of breast cancer. We performed receiver operating characteristic (ROC) curve to assess the performance of cancer risk models. Results: The OR for the percentage BD for second, third, and fourth quartiles was 1.632 (95% confidence intervals [CI]: 1.102–2.416), 2.756 (95% CI: 1.704–4.458), and 3.163 (95% CI: 1.356–5.61). The area under ROC curve for clinical risk factors only, mammographic density measures, combined mammographic, and clinical risk factors was 0.578 (95% CI: 0.45, 0.64), 0.684 (95% CI: 0.58, 0.75), and 0.724 (95% CI: 0.64, 0.80), respectively. Conclusion: Mammographic BD was found to be positively associated with breast cancer. The density related measures combined clinical risk factors, and density model had good discriminatory power in identifying the cancer risk.

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