Life tables adjusted for comorbidity more accurately estimate noncancer survival for recently diagnosed cancer patients

Abstract

Objective To provide cancer patients and clinicians with more accurate estimates of a patient’s life expectancy with respect to non-cancer mortality, we estimated comorbidity-adjusted life tables and health-adjusted age. Study Design and Setting Using data from the SEER-Medicare database, we estimated comorbidity scores that reflect the health status of people 66 years of age and older in the year prior to cancer diagnosis. Non-cancer survival by comorbidity score was estimated for each age, race and sex. Health-adjusted age was estimated by systematically comparing the non-cancer survival models with US life tables. Results Comorbidity, cancer status, sex and race are all important predictors of non-cancer survival; however, their relative impact on non-cancer survival decreases as age increases. Survival models by comorbidity better predicted non-cancer survival than the US life tables. The health-adjusted age and national life tables can be consulted to provide an approximate estimate of a person’s life expectancy, e.g., the health-adjusted age of a black man aged 75 with no comorbidities is 67, giving him a life expectancy of 13 years. Conclusions The health-adjusted age and the life tables adjusted by age, race, sex and comorbidity, can provide important information to facilitate decision-making about treatment for cancer and other conditions.

Self-Reported Health Status Predicts Other-Cause Mortality in Men with Localized Prostate Cancer: Results from the Prostate Cancer Outcomes Study

“…6,7 Adjusting life tables for comorbidity scores obtained from the SEER-Medicare database improves non-cancer survival estimates for recently diagnosed cancer patients. 16 Comorbidity-adjusted life expectancy varied considerably among Medicare beneficiaries, suggesting the importance of considering comorbidity in tailoring treatment decisions in older patients. 15 While life table and comorbidity counts can accurately estimate life expectancy, these measures have not been widely used in clinical practice for men with prostate cancer.…”

“…12 Indeed, life tables have been shown to have limited predictive value for men undergoing radiation therapy 13 or radical prostatectomy. 6 While some multivariate predictive models, particularly those incorporating comorbidity, appear to accurately estimate life expectancy, [14][15][16][17][18] their performance for men with prostate cancer is variable or unknown. 19 Furthermore, many of these measures are not widely used in clinical practice.…”

A Matched Analysis on the Prognostic Impact of Race on Survival Endpoints of Women with Early-Stage Endometrial Cancer

“…Several investigators highlighted the important prognostic impact of comorbidity burden including obesity on survival endpoint of women with EC [17,[28][29][30]. The results of these studies support the theory that comorbidity burden may play a major role in cancer prognosis [31,32] particularly in patients with higher survival rates [33] such as women with early stage EC. We believe that comorbidity burden as shown in the current study could be, at least in part, a potential factor in dictating OS in women with early stage EC of different races.…”

A Systematic Literature Review of Life Expectancy Prediction Tools for Patients with Localized Prostate Cancer

“…After screening the retrieved articles and scanning the reference lists of those meeting our inclusion criteria, we identified a total of 14 articles. Three used life tables to assist predicting life expectancy [3–5], ten presented prediction models [4–13], and three presented risk scores [14–16]. Of the three papers that used life tables, two used the United States life tables [3, 5] while the other used the United Kingdom life tables [4].…”

“…Three used life tables to assist predicting life expectancy [3–5], ten presented prediction models [4–13], and three presented risk scores [14–16]. Of the three papers that used life tables, two used the United States life tables [3, 5] while the other used the United Kingdom life tables [4]. The remaining studies used SEER or Medicare data [5–8], came from US institutions [9, 11–16] or a Canadian Health insurance database [10].…”