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Comparing survival of older ovarian cancer patients treated with neoadjuvant chemotherapy versus primary cytoreductive surgery: Reducing bias through machine learning.
- Source :
-
Gynecologic oncology [Gynecol Oncol] 2024 Jul; Vol. 186, pp. 9-16. Date of Electronic Publication: 2024 Mar 29. - Publication Year :
- 2024
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Abstract
- Objective: To develop and evaluate a multidimensional comorbidity index (MCI) that identifies ovarian cancer patients at risk of early mortality more accurately than the Charlson Comorbidity Index (CCI) for use in health services research.<br />Methods: We utilized SEER-Medicare data to identify patients with stage IIIC and IV ovarian cancer, diagnosed in 2010-2015. We employed partial least squares regression, a supervised machine learning algorithm, to develop the MCI by extracting latent factors that optimally captured the variation in health insurance claims made in the year preceding cancer diagnosis, and 1-year mortality. We assessed the discrimination and calibration of the MCI for 1-year mortality and compared its performance to the commonly-used CCI. Finally, we evaluated the MCI's ability to reduce confounding in the association of neoadjuvant chemotherapy (NACT) and all-cause mortality.<br />Results: We included 4723 patients in the development cohort and 933 in the validation cohort. The MCI demonstrated good discrimination for 1-year mortality (c-index: 0.75, 95% CI: 0.72-0.79), while the CCI had poor discrimination (c-index: 0.59, 95% CI: 0.56-0.63). Calibration plots showed better agreement between predicted and observed 1-year mortality risk for the MCI compared with CCI. When comparing all-cause mortality between NACT with primary cytoreductive surgery, NACT was associated with a higher hazard of death (HR: 1.13, 95% CI: 1.04-1.23) after controlling for tumor characteristics, demographic factors, and the CCI. However, when controlling for the MCI instead of the CCI, there was no longer a significant difference (HR: 1.05, 95% CI: 0.96-1.14).<br />Conclusions: The MCI outperformed the conventional CCI in predicting 1-year mortality, and reducing confounding due to differences in baseline health status in comparative effectiveness analysis of NACT versus primary surgery.<br />Competing Interests: Declaration of competing interest Dr.Thomas H. McCoy has received unrelated honoraria from Springer Nature and the Massachusetts General Hospital Psychiatry Academy as well as grants to his institution from Koa Health, InterSystems, NIMH, NINR, NIA, and NHGRI. Dr.Jason D. Wright has received unrelated honoraria from UpToDate and research grant from Merck, received payment for medicolegal consulting, served as journal editor for American College of Obstetricians and Gynecologists. Dr. Alexander Melamed has served on an advisory board for AstraZeneca, consulted for Axena Health, received payment for medicolegal consulting, and received research funding from the Department of Defense, the Conquer Cancer Foundation, and the National Cancer Institute outside this work. The other authors have no financial conflicts of interest to disclose.<br /> (Copyright © 2024 Elsevier Inc. All rights reserved.)
- Subjects :
- Humans
Female
Aged
Aged, 80 and over
United States epidemiology
Chemotherapy, Adjuvant
Bias
Carcinoma, Ovarian Epithelial mortality
Carcinoma, Ovarian Epithelial surgery
Carcinoma, Ovarian Epithelial drug therapy
Carcinoma, Ovarian Epithelial pathology
Neoplasm Staging
Medicare statistics & numerical data
Cytoreduction Surgical Procedures methods
Neoadjuvant Therapy
Ovarian Neoplasms mortality
Ovarian Neoplasms drug therapy
Ovarian Neoplasms surgery
Ovarian Neoplasms pathology
Machine Learning
SEER Program
Subjects
Details
- Language :
- English
- ISSN :
- 1095-6859
- Volume :
- 186
- Database :
- MEDLINE
- Journal :
- Gynecologic oncology
- Publication Type :
- Academic Journal
- Accession number :
- 38554626
- Full Text :
- https://doi.org/10.1016/j.ygyno.2024.03.016