TY - JOUR
T1 - Machine learning and patient-reported outcomes for longitudinal monitoring of disease progression in metastatic breast cancer
T2 - a multicenter, retrospective analysis
AU - Deutsch, Thomas M.
AU - Pfob, André
AU - Brusniak, Katharina
AU - Riedel, Fabian
AU - Bauer, Armin
AU - Dijkstra, Tjeerd
AU - Engler, Tobias
AU - Brucker, Sara Y.
AU - Hartkopf, Andreas D.
AU - Schneeweiss, Andreas
AU - Sidey-Gibbons, Chris
AU - Wallwiener, Markus
N1 - Funding Information:
This study was funded by Tschira-Stiftung . The funding party had no role in the study conduction, data analysis, and results interpretation.
Publisher Copyright:
© 2023 Elsevier Ltd
PY - 2023/7
Y1 - 2023/7
N2 - Background: Assessments of health-related quality of life (HRQoL) play an important role in transition to palliative care for women with metastatic breast cancer. We developed machine learning (ML) algorithms to analyse longitudinal HRQoL data and identify patients who may benefit from palliative care due to disease progression. Methods: We recruited patients from two institutions and administered the EuroQoL Visual Analog Scale (EQ-VAS) via an online platform over a 6-month period. We trained a regularised regression algorithm using 10-fold cross-validation to determine if a patient was at high or low risk of disease progression based on changes in the EQ-VAS scores using data of one institution and validated the performance on data of the other institution. Progression-free survival (PFS) was the end-point. We conducted Kaplan-Meier and Cox regression analysis adjusted for clinical risk factors. Results: Of 179 patients, 98 (54.7%) had progressive disease after a median follow-up of 14 weeks. Using EQ-VAS scores collected at weeks 1–6 to predict disease progression at week 12, in the validation set (n = 63), PFS was significantly lower in the intelligent EQ-VAS high-risk versus low-risk group: median PFS 7 versus 10 weeks, log-rank P < 0.038). Intelligent EQ-VAS had the strongest association with PFS (adjusted hazard ratio 2.69, 95% confidence interval 1.17–6.18, P = 0.02). Conclusion: ML algorithms can analyse changes in longitudinal HRQoL data to identify patients with disease progression earlier than standard follow-up methods. Intelligent EQ-VAS scores were identified as independent prognostic factor. Future studies may validate these results to remotely monitor patients.
AB - Background: Assessments of health-related quality of life (HRQoL) play an important role in transition to palliative care for women with metastatic breast cancer. We developed machine learning (ML) algorithms to analyse longitudinal HRQoL data and identify patients who may benefit from palliative care due to disease progression. Methods: We recruited patients from two institutions and administered the EuroQoL Visual Analog Scale (EQ-VAS) via an online platform over a 6-month period. We trained a regularised regression algorithm using 10-fold cross-validation to determine if a patient was at high or low risk of disease progression based on changes in the EQ-VAS scores using data of one institution and validated the performance on data of the other institution. Progression-free survival (PFS) was the end-point. We conducted Kaplan-Meier and Cox regression analysis adjusted for clinical risk factors. Results: Of 179 patients, 98 (54.7%) had progressive disease after a median follow-up of 14 weeks. Using EQ-VAS scores collected at weeks 1–6 to predict disease progression at week 12, in the validation set (n = 63), PFS was significantly lower in the intelligent EQ-VAS high-risk versus low-risk group: median PFS 7 versus 10 weeks, log-rank P < 0.038). Intelligent EQ-VAS had the strongest association with PFS (adjusted hazard ratio 2.69, 95% confidence interval 1.17–6.18, P = 0.02). Conclusion: ML algorithms can analyse changes in longitudinal HRQoL data to identify patients with disease progression earlier than standard follow-up methods. Intelligent EQ-VAS scores were identified as independent prognostic factor. Future studies may validate these results to remotely monitor patients.
KW - Artificial Intelligence
KW - Digital medicine
KW - Machine learning
KW - Metastatic breast cancer
KW - Palliative care
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U2 - 10.1016/j.ejca.2023.04.019
DO - 10.1016/j.ejca.2023.04.019
M3 - Article
C2 - 37229835
AN - SCOPUS:85160077255
SN - 0959-8049
VL - 188
SP - 111
EP - 121
JO - European Journal of Cancer
JF - European Journal of Cancer
ER -