scholarly journals Personalized Decision Making in Early Stage Breast Cancer: Applying Clinical Prediction Models for Anthracycline Cardiotoxicity and Breast Cancer Mortality Demonstrates Substantial Heterogeneity of Benefit-Harm Trade-off

2019 ◽  
Vol 19 (4) ◽  
pp. 259-267.e1 ◽  
Author(s):  
Jenica N. Upshaw ◽  
Robin Ruthazer ◽  
Kathy D. Miller ◽  
Susan K. Parsons ◽  
John K. Erban ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Decision Making ◽  
Prediction Models ◽  
Early Stage ◽  
Breast Cancer Mortality ◽  
Clinical Prediction ◽  
Clinical Prediction Models ◽  
Anthracycline Cardiotoxicity ◽  
Trade Off ◽  
Substantial Heterogeneity

scholarly journals Clinical Prediction Models for Primary Prevention of Cardiovascular Disease: Validity in Independent Cohorts

2021 ◽  
Author(s):  
Gaurav Gulati ◽  
Riley J Brazil ◽  
Jason Nelson ◽  
David van Klaveren ◽  
Christine M. Lundquist ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Decision Making ◽  
Primary Prevention ◽  
Prediction Models ◽  
Model Updating ◽  
Clinical Prediction ◽  
Net Benefit ◽  
Clinical Prediction Models ◽  
C Statistic ◽  
Prevention Of Cardiovascular Disease

AbstractBackgroundClinical prediction models (CPMs) are used to inform treatment decisions for the primary prevention of cardiovascular disease. We aimed to assess the performance of such CPMs in fully independent cohorts.Methods and Results63 models predicting outcomes for patients at risk of cardiovascular disease from the Tufts PACE CPM Registry were selected for external validation on publicly available data from up to 4 broadly inclusive primary prevention clinical trials. For each CPM-trial pair, we assessed model discrimination, calibration, and net benefit. Results were stratified based on the relatedness of derivation and validation cohorts, and net benefit was reassessed after updating model intercept, slope, or complete re-estimation. The median c statistic of the CPMs decreased from 0.77 (IQR 0.72-0.78) in the derivation cohorts to 0.63 (IQR 0.58-0.66) when externally validated. The validation c-statistic was higher when derivation and validation cohorts were considered related than when they were distantly related (0.67 vs 0.60, p < 0.001). The calibration slope was also higher in related cohorts than distantly related cohorts (0.69 vs 0.58, p < 0.001). Net benefit analysis suggested substantial likelihood of harm when models were externally applied, but this likelihood decreased after model updating.ConclusionsDiscrimination and calibration decrease significantly when CPMs for primary prevention of cardiovascular disease are tested in external populations, particularly when the population is only distantly related to the derivation population. Poorly calibrated predictions lead to poor decision making. Model updating can reduce the likelihood of harmful decision making, and is needed to realize the full potential of risk-based decision making in new settings.

scholarly journals Clinical prediction models to inform individualized decision-making in subfertile couples: a stratified medicine approach

2014 ◽  
Vol 29 (9) ◽  
pp. 1851-1858 ◽  
Author(s):  
D. J. McLernon ◽  
E. R. te Velde ◽  
E. W. Steyerberg ◽  
B. W. J. Mol ◽  
S. Bhattacharya
Keyword(s):  
Decision Making ◽  
Prediction Models ◽  
Stratified Medicine ◽  
Clinical Prediction ◽  
Clinical Prediction Models ◽  
Individualized Decision

scholarly journals Application of clinical prediction modeling in pediatric neurosurgery: a case study

2021 ◽  
Author(s):  
Hendrik-Jan Mijderwijk ◽  
Thomas Beez ◽  
Daniel Hänggi ◽  
Daan Nieboer
Keyword(s):  
Decision Making ◽  
Prediction Models ◽  
Pediatric Neurosurgery ◽  
Medical Decision ◽  
Clinical Prediction ◽  
Clinical Prediction Models ◽  
Current Form ◽  
Related Risk ◽  
Decision Making Processes

AbstractThere has been an increasing interest in articles reporting on clinical prediction models in pediatric neurosurgery. Clinical prediction models are mathematical equations that combine patient-related risk factors for the estimation of an individual’s risk of an outcome. If used sensibly, these evidence-based tools may help pediatric neurosurgeons in medical decision-making processes. Furthermore, they may help to communicate anticipated future events of diseases to children and their parents and facilitate shared decision-making accordingly. A basic understanding of this methodology is incumbent when developing or applying a prediction model. This paper addresses this methodology tailored to pediatric neurosurgery. For illustration, we use original pediatric data from our institution to illustrate this methodology with a case study. The developed model is however not externally validated, and clinical impact has not been assessed; therefore, the model cannot be recommended for clinical use in its current form.

A literature review of treatment-specific clinical prediction models in patients with breast cancer

2020 ◽  
Vol 148 ◽  
pp. 102908
Author(s):  
Natansh D. Modi ◽  
Michael J. Sorich ◽  
Andrew Rowland ◽  
Jessica M. Logan ◽  
Ross A. McKinnon ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Literature Review ◽  
Prediction Models ◽  
Clinical Prediction ◽  
Clinical Prediction Models

scholarly journals Personalized Decision Making on Genomic Testing in Early Breast Cancer: Expanding the MINDACT Trial with Decision-Analytic Modeling

2021 ◽  
pp. 0272989X2199117
Author(s):  
Ewout W. Steyerberg ◽  
Liesbeth C. de Wreede ◽  
David van Klaveren ◽  
Patrick M. M. Bossuyt
Keyword(s):  
Breast Cancer ◽  
Decision Making ◽  
Genetic Testing ◽  
Early Stage ◽  
Pragmatic Trial ◽  
Genomic Signature ◽  
Genomic Testing ◽  
Analytic Modeling ◽  
Clinical Risk ◽  
Genomic Test

Background Genomic tests may improve upon clinical risk estimation with traditional prognostic factors. We aimed to explore how evidence on the prognostic strength of a genomic signature (clinical validity) can contribute to individualized decision making on starting chemotherapy for women with breast cancer (clinical utility). Methods The MINDACT trial was a randomized trial that enrolled 6693 women with early-stage breast cancer. A 70-gene signature (Mammaprint) was used to estimate genomic risk, and clinical risk was estimated by a dichotomized version of the Adjuvant!Online risk calculator. Women with discordant risk results were randomized to the use of chemotherapy. We simulated the full risk distribution of these women and estimated individual benefit, assuming a constant relative effect of chemotherapy. Results The trial showed a prognostic effect of the genomic signature (adjusted hazard ratio 2.4). A decision-analytic modeling approach identified far fewer women as candidates for genetic testing (4% rather than 50%) and fewer benefiting from chemotherapy (3% rather than 27%) as compared with the MINDACT trial report. The selection of women benefitting from genetic testing and chemotherapy depended strongly on the required benefit from treatment and the assumed therapeutic effect of chemotherapy. Conclusions A high-quality pragmatic trial was insufficient to directly inform clinical practice on the utility of a genomic test for individual women. The indication for genomic testing may be far more limited than suggested by the MINDACT trial.

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