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Comparing survival of older ovarian cancer patients treated with neoadjuvant chemotherapy versus primary cytoreductive surgery: Reducing bias through machine learning.
Huang, Yongmei; Rauh-Hain, J Alejandro; McCoy, Thomas H; Hou, June Y; Hillyer, Grace; Ferris, Jennifer S; Hershman, Dawn; Wright, Jason D; Melamed, Alexander.
Afiliação
  • Huang Y; Columbia University Vagelos College of Physicians and Surgeons, Department of Obstetrics and Gynecology, United States of America.
  • Rauh-Hain JA; University of Texas MD Anderson Cancer Center, Division of Surgery, Department of Gynecologic Oncology and Reproductive Medicine, United States of America.
  • McCoy TH; Massachusetts General Hospital, Department of Psychiatry, United States of America.
  • Hou JY; Columbia University Vagelos College of Physicians and Surgeons, Department of Obstetrics and Gynecology, United States of America.
  • Hillyer G; Columbia University Mailman School of Public Health, Department of Epidemiology, United States of America.
  • Ferris JS; Columbia University Mailman School of Public Health, Department of Epidemiology, United States of America.
  • Hershman D; Columbia University Vagelos College of Physicians and Surgeons, Department of Medicine Columbia University Vagelos College of Physicians and Surgeons, Department of Internal Medicine, United States of America.
  • Wright JD; Columbia University Vagelos College of Physicians and Surgeons, Department of Obstetrics and Gynecology, United States of America.
  • Melamed A; Massachusetts General Hospital, Vincent Department of Obstetrics and Gynecology, Meigs Division of Gynecologic Oncology, United States of America. Electronic address: alexander.melamed@mgh.harvard.edu.
Gynecol Oncol ; 186: 9-16, 2024 07.
Article em En | MEDLINE | ID: mdl-38554626
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias Ovarianas / Programa de SEER / Terapia Neoadjuvante / Procedimentos Cirúrgicos de Citorredução / Aprendizado de Máquina Limite: Aged / Aged80 / Female / Humans País/Região como assunto: America do norte Idioma: En Revista: Gynecol Oncol Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias Ovarianas / Programa de SEER / Terapia Neoadjuvante / Procedimentos Cirúrgicos de Citorredução / Aprendizado de Máquina Limite: Aged / Aged80 / Female / Humans País/Região como assunto: America do norte Idioma: En Revista: Gynecol Oncol Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos