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Unknown Causes of Death in Cancer Patients.
Chinniah, Siven; Chiam, Mckenzee; Mani, Kyle; Liang, Menglu; Trifiletti, Daniel M; Spratt, Daniel E; Prasad, Vinayak K; Wang, Ming; Tchelebi, Leila T; Zaorsky, Nicholas G.
Afiliación
  • Chinniah S; Department of Radiation Oncology, Mayo Clinic, Jacksonville, FL.
  • Chiam M; Department of Radiation Oncology, Penn State Cancer Institute, Hershey, PA.
  • Mani K; Albert Einstein College of Medicine, The Bronx, NY.
  • Liang M; Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University.
  • Trifiletti DM; Department of Radiation Oncology, Mayo Clinic, Jacksonville, FL.
  • Spratt DE; Department of Radiation Oncology, University Hospitals Seidman Cancer Center, Cleveland, OH.
  • Prasad VK; Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA.
  • Wang M; Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University.
  • Tchelebi LT; Department of Radiation Medicine, Northwell Health, Donald and Barbara Zucker School of Medicine at Hofstra/ Northwell, Lake Success, New York.
  • Zaorsky NG; Department of Radiation Oncology, University Hospitals Seidman Cancer Center, Cleveland, OH.
Am J Clin Oncol ; 46(6): 246-253, 2023 06 01.
Article en En | MEDLINE | ID: mdl-37038261
ABSTRACT

OBJECTIVES:

Deaths from an unknown cause are difficult to adjudicate and oncologic studies of comparative effectiveness often demonstrate inconsistencies in incorporating these deaths and competing events (eg, heart disease and stroke) in their analyses. In this study, we identify cancer patients most at risk for death of an unknown cause.

METHODS:

This retrospective, population-based study used cancer registry data from the Surveillance, Epidemiology, and End Results database (1992-2015). The absolute rate of unknown causes of death (COD) cases stratified by sex, marital status, race, treatment, and cancer site were calculated and a multivariable logistic regression model was applied to obtain adjusted odds ratios with 95% CIs.

RESULTS:

Out of 7,154,779 cancer patients across 22 cancer subtypes extracted from Surveillance, Epidemiology, and End Results, 3,448,927 died during follow-up and 276,068 (7.4%) of these deaths were from unknown causes. Patients with an unknown COD had a shorter mean survival time compared with patients with known COD (36.3 vs 65.7 mo, P < 0.001). The contribution of unknown COD to total mortality was highest in patients with more indolent cancers (eg, prostate [12.7%], thyroid [12.3%], breast [10.7%]) and longer follow-up (eg, >5 to 10 y). One, 3, and 5-year cancer-specific survival (CSS) calculations including unknown COD were significantly decreased compared with CSS estimates excluding cancer patients with unknown COD.

CONCLUSION:

Of the patients, 7.4% died of unknown causes during follow-up and the proportion of death was higher with longer follow-up and among more indolent cancers. The attribution of high percentages of unknown COD to cancer or non-cancer causes could impact population-based cancer registry studies or clinical trial outcomes with respect to measures involving CSS and mortality.
Asunto(s)

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Neoplasias Tipo de estudio: Etiology_studies / Observational_studies / Risk_factors_studies Idioma: En Revista: Am J Clin Oncol Año: 2023 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Neoplasias Tipo de estudio: Etiology_studies / Observational_studies / Risk_factors_studies Idioma: En Revista: Am J Clin Oncol Año: 2023 Tipo del documento: Article