Comparing survival of older ovarian cancer patients treated with neoadjuvant chemotherapy versus primary cytoreductive surgery: Reducing bias through machine learning

Yongmei Huang, J. Alejandro Rauh-Hain, Thomas H. McCoy, June Y. Hou, Grace Hillyer, Jennifer S. Ferris, Dawn Hershman, Jason D. Wright, Alexander Melamed

Research output: Contribution to journalArticlepeer-review

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. 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. 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). 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.

Original languageEnglish (US)
Pages (from-to)9-16
Number of pages8
JournalGynecologic oncology
Volume186
DOIs
StatePublished - Jul 2024

Keywords

  • All-cause mortality
  • Machine learning
  • Multidimensional comorbidity index
  • Neoadjuvant chemotherapy
  • Primary cytoreductive surgery

ASJC Scopus subject areas

  • Oncology
  • Obstetrics and Gynecology

Fingerprint

Dive into the research topics of 'Comparing survival of older ovarian cancer patients treated with neoadjuvant chemotherapy versus primary cytoreductive surgery: Reducing bias through machine learning'. Together they form a unique fingerprint.

Cite this