Prediction of QT Prolongation in Advanced Breast Cancer Patients Using Survival Modelling Algorithms

Asmir Vodenčarević, Julia Kreuzeder, Achim Wöckel, Peter Fasching

2023

Abstract

Advanced breast cancer includes locally advanced disease and metastatic breast cancer with distant metastasis in other organs like lung, liver, brain and bone. While it cannot be cured, its progression can be controlled by modern treatments including targeted therapies. However, these therapies as well as certain risk factors like advanced age can facilitate toxicities such as prolongation of the time interval between the start of the Q wave and the end of the T wave in patient’s electrocardiogram. This could lead to serious life-threatening issues like cardiac arrhythmia. In this paper we addressed the issue of individual, patient-level prediction of QT prolongation in advanced breast cancer patients treated with the CDK4/6-inhibitor ribociclib. By formulating the prediction task as a survival analysis problem, we were able to apply five conventional statistical and machine learning survival modelling algorithms to both clinical trial and real-world data in order to train and externally validate prediction models. Cox proportional hazards model regularized by elastic net reached external, cross-study validation performance (c-index based on inverse probability of censoring weights) of 0.63 on the real-world data and 0.71 on the clinical trial data. The most important predictive factors included baseline electrocardiogram features and patient quality of life.

Paper Citation

in Harvard Style

Vodenčarević A., Kreuzeder J., Wöckel A. and Fasching P. (2023). Prediction of QT Prolongation in Advanced Breast Cancer Patients Using Survival Modelling Algorithms. In Proceedings of the 12th International Conference on Data Science, Technology and Applications - Volume 1: DATA; ISBN 978-989-758-664-4, SciTePress, pages 164-172. DOI: 10.5220/0012130900003541

in Bibtex Style

@conference{data23,
author={Asmir Vodenčarević and Julia Kreuzeder and Achim Wöckel and Peter Fasching},
title={Prediction of QT Prolongation in Advanced Breast Cancer Patients Using Survival Modelling Algorithms},
booktitle={Proceedings of the 12th International Conference on Data Science, Technology and Applications - Volume 1: DATA},
year={2023},
pages={164-172},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012130900003541},
isbn={978-989-758-664-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Data Science, Technology and Applications - Volume 1: DATA
TI - Prediction of QT Prolongation in Advanced Breast Cancer Patients Using Survival Modelling Algorithms
SN - 978-989-758-664-4
AU - Vodenčarević A.
AU - Kreuzeder J.
AU - Wöckel A.
AU - Fasching P.
PY - 2023
SP - 164
EP - 172
DO - 10.5220/0012130900003541
PB - SciTePress