Development and Validation of a Risk Prediction Model for Breast Cancer Prognosis Based on Depression-Related Genes

2021 ◽  
Author(s):  
Xuan Wang ◽  
Neng Wang ◽  
Linda Zhong ◽  
Shengqi Wang ◽  
Yifeng Zheng ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Breast Cancer Prognosis ◽  
Cancer Prognosis ◽  
Development And Validation

Development and Validation of a Risk Prediction Model for Patients with COVID-19 Using Linked Population Health Data (Preprint)

2021 ◽  
Author(s):  
Nikolaos Mastellos ◽  
Richard Betteridge ◽  
Prasanth Peddaayyavarla ◽  
Andrew Moran ◽  
Jurgita Kaubryte ◽  
...  
Keyword(s):  
Health Care ◽  
Prediction Model ◽  
Population Health ◽  
Risk Prediction ◽  
Validation Cohort ◽  
Risk Prediction Model ◽  
Adult Patients ◽  
Internal Validation ◽  
Temporal Validation ◽  
Development And Validation

BACKGROUND The impact of the COVID-19 pandemic on health care utilisation and associated costs has been significant, with one in ten patients becoming severely ill and being admitted to hospital with serious complications during the first wave of the pandemic. Risk prediction models can help health care providers identify high-risk patients in their populations and intervene to improve health outcomes and reduce associated costs. OBJECTIVE To develop and validate a hospitalisation risk prediction model for adult patients with laboratory confirmed Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). METHODS The model was developed using pre-linked and standardised data of adult patients with laboratory confirmed SARS-CoV-2 from Cerner’s population health management platform (HealtheIntent®) in the London Borough of Lewisham. A total of 14,203 patients who tested positive for SARS-CoV-2 between 1st March 2020 and 28th February 2021 were included in the development and internal validation cohorts. A second temporal validation cohort covered the period between 1st March 2021 to 30th April 2021. The outcome variable was hospital admission in adult patients with laboratory confirmed SARS-CoV-2. A generalised linear model was used to train the model. The predictive performance of the model was assessed using the area under the receiver operator characteristic curve (ROC-AUC). RESULTS Overall, 14,203 patients were included. Of those, 9,755 (68.7%) were assigned to the development cohort, 2,438 (17.2%) to the internal validation cohort, and 2,010 (14.1%) to the temporal validation cohort. A total of 917 (9.4%) patients were admitted to hospital in the development cohort, 210 (8.6%) in the internal validation cohort, and a further 204 (10.1%) in the temporal validation cohort. The model had a ROC-AUC of 0.85 in both the development and validation cohorts. The most predictive factors were older age, male sex, Asian or Other ethnic minority background, obesity, chronic kidney disease, hypertension and diabetes. CONCLUSIONS The COVID-19 hospitalisation risk prediction model demonstrated very good performance and can be used to stratify risk in the Lewisham population to help providers reduce unnecessary hospital admissions and associated costs, improve patient outcomes, and target those at greatest risk to ensure full vaccination against SARS-CoV-2. Further research may examine the external validity of the model in other populations.

A Validated Risk Prediction Model for Breast Cancer in US Black Women

2021 ◽  
Author(s):  
Julie R. Palmer ◽  
Gary Zirpoli ◽  
Kimberly A. Bertrand ◽  
Tracy Battaglia ◽  
Leslie Bernstein ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Estrogen Receptor ◽  
Black Women ◽  
Prediction Model ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Prediction Model ◽  
Risk Prediction Models ◽  
Relative Risks ◽  
Discriminatory Accuracy

PURPOSE Breast cancer risk prediction models are used to identify high-risk women for early detection, targeted interventions, and enrollment into prevention trials. We sought to develop and evaluate a risk prediction model for breast cancer in US Black women, suitable for use in primary care settings. METHODS Breast cancer relative risks and attributable risks were estimated using data from Black women in three US population-based case-control studies (3,468 breast cancer cases; 3,578 controls age 30-69 years) and combined with SEER age- and race-specific incidence rates, with incorporation of competing mortality, to develop an absolute risk model. The model was validated in prospective data among 51,798 participants of the Black Women's Health Study, including 1,515 who developed invasive breast cancer. A second risk prediction model was developed on the basis of estrogen receptor (ER)–specific relative risks and attributable risks. Model performance was assessed by calibration (expected/observed cases) and discriminatory accuracy (C-statistic). RESULTS The expected/observed ratio was 1.01 (95% CI, 0.95 to 1.07). Age-adjusted C-statistics were 0.58 (95% CI, 0.56 to 0.59) overall and 0.63 (95% CI, 0.58 to 0.68) among women younger than 40 years. These measures were almost identical in the model based on estrogen receptor–specific relative risks and attributable risks. CONCLUSION Discriminatory accuracy of the new model was similar to that of the most frequently used questionnaire-based breast cancer risk prediction models in White women, suggesting that effective risk stratification for Black women is now possible. This model may be especially valuable for risk stratification of young Black women, who are below the ages at which breast cancer screening is typically begun.

Development and Validation of a Risk Prediction Model for Cesarean Delivery After Labor Induction

2020 ◽  
Vol 29 (5) ◽  
pp. 656-669 ◽  
Author(s):  
Valery A. Danilack ◽  
Jennifer A. Hutcheon ◽  
Elizabeth W. Triche ◽  
David D. Dore ◽  
Janet H. Muri ◽  
...  
Keyword(s):  
Prediction Model ◽  
Cesarean Delivery ◽  
Risk Prediction ◽  
Labor Induction ◽  
Risk Prediction Model ◽  
Development And Validation

Development and validation of an individualized risk prediction model for oropharynx cancer in the US population

Cancer ◽  
2019 ◽  
Vol 125 (24) ◽  
pp. 4407-4416 ◽  
Author(s):  
Joseph E. Tota ◽  
Maura L. Gillison ◽  
Hormuzd A. Katki ◽  
Lisa Kahle ◽  
Robert K. Pickard ◽  
...  
Keyword(s):  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Oropharynx Cancer ◽  
The Us ◽  
Development And Validation

scholarly journals Validation of a Breast Cancer Risk Prediction Model Developed for Black Women

2013 ◽  
Vol 105 (5) ◽  
pp. 361-367 ◽  
Author(s):  
Deborah A. Boggs ◽  
Lynn Rosenberg ◽  
Michael J. Pencina ◽  
Lucile L. Adams-Campbell ◽  
Julie R. Palmer
Keyword(s):  
Breast Cancer ◽  
Black Women ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model
Sign in / Sign up

Export Citation Format

Share Document