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Abstract P6-09-03: Development of a cardiac toxicity prediction tool for HER2 (+) breast cancer patients receiving trastuzumab

2015· article· en· W1563983159 on OpenAlexaffabout
Jeffrey Sulpher, George Dranitsaris, Freya Crawley, Franco Dattilo, Maya Kovacs, Christopher Johnson, Susan Dent

Bibliographic record

VenueCancer Research · 2015
Typearticle
Languageen
FieldMedicine
TopicCancer Treatment and Pharmacology
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineTrastuzumabEjection fractionInternal medicineBreast cancerDiscontinuationOncologyCancerCardiotoxicityCardiologyMetastatic breast cancerConcomitantHeart failureToxicity

Abstract

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Abstract Background: In HER2 positive breast cancer, the addition of trastuzumab prolongs overall survival in both early stage and metastatic disease. However, moderate to severe cardiac toxicity is a potentially serious complication which can lead to dose reductions, delays, hospitalizations, and premature discontinuation of treatment. Patient care could be substantially improved if such cardiac events are accurately predicted through the use of validated and easy-to-use mathematical models. In this study, the development of a model to predict the risk of cardiac toxicity prior to initiation of trastuzumab therapy is described. Methods: Medical records of 498 HER2 positive breast cancer patients who received trastuzumab at the Ottawa Hospital Cancer Centre were identified. Charts were reviewed for potential cardiac toxicity risk factors and cardiac events. Potential cardiac toxicity variables included: cardiac risk factors, medications, previous chemotherapy/radiation, baseline left ventricular ejection fraction (LVEF), and exposure to anthracyclines. Cardiac toxicity was defined as: decreased LVEF greater or equal to 10% and to less than 50%, referral to the cardiac oncology service, or clinical symptoms of heart failure. General linear modeling for a discrete bivariate outcome was used to identify risk factors for cardiac toxicity using a backwards elimination process. Internal validation of the final regression coefficients was done using nonparametric bootstrapping. A risk scoring algorithm (range 0-100) was then derived from the final model coefficients. A receiver operating characteristic (ROC) curve analysis was then undertaken to measure the predictive accuracy of the final scoring algorithm. Results: Baseline LVEF, concomitant use of any cardiac medication or lipid lower drugs and doxorubicin based chemotherapy were identified as being important predictors for cardiac toxicity. The ROC curve analysis indicated good predictive accuracy with an area under the curve of 0.69 (95%CI: 0.64 to 0.74). Prior to the initiation of trastuzumab, patients with risk scores greater than 45 units would be considered at high risk for developing cardiac toxicity (likelihood ratio = 2.9). Conclusions: Risk of cardiac toxicity with trastuzumab is increased in patients with a low baseline LVEF, history of coronary artery disease and hyperlipidemia, as well as those receiving doxorubicin. The planned external validation and eventual clinical application of this prediction tool will be an important source of risk information for the practicing oncologist, and may enhance patient care by optimizing preventative therapies and selecting high risk patients for cardiac imaging and cardiology follow-up. Citation Format: Jeffrey A Sulpher, George Dranitsaris, Freya Crawley, Franco Dattilo, Maya Kovacs, Christopher Johnson, Susan Dent. Development of a cardiac toxicity prediction tool for HER2 (+) breast cancer patients receiving trastuzumab [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P6-09-03.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.112
GPT teacher head0.436
Teacher spread0.324 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2015
Admission routes2
Has abstractyes

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