By Liam Davenport, medwireNews Reporter
Patients with localized solid kidney tumors may be at greater risk for death from non-cancer-related causes before that from disease progression and cancer-related mortality, say US researchers who developed a comorbidity-based risk prediction model.
"Various forces often conspire to underestimate these competing risks of death, particularly in the aged and/or infirm population, while potentially overestimating patient and physician perceptions of cancer risk in those who are newly diagnosed," the team writes in the Journal of Urology.
They add: "This [model] is an important step toward the goal of comparing treatment choices, toxicities, outcomes and trade-offs as we move from how and when to treat toward why and whether we should."
Roberto Uzzo and colleagues from Temple University School of Medicine in Philadelphia, Pennsylvania, studied 6655 patients aged at least 66 years from the US Surveillance, Epidemiology and End Results-Medicare dataset for 1995-2005 who were diagnosed with localized renal cell carcinoma as their first lifetime cancer diagnosis.
The population was consistent with the majority of those with localized kidney cancer. The median follow-up was 43 months, and 2179 patients had at least 5 years' follow-up. In all, 7.0% of patients died of kidney cancer and 21.2% died of other causes.
Age was strongly associated with mortality, and was most predictive of non-kidney cancer death. Increasing tumor size was positively correlated with kidney cancer death but inversely related to non-kidney cancer death. Race had a greater impact on non-kidney cancer death than kidney cancer death, such that men and Black patients were more likely to die of a non-kidney cancer cause than women and White patients.
In the majority of patients, aside from those with a renal mass over 7 cm in diameter and without comorbidities on the Charlson Comorbidity Index (CCI), the risk for dying from another cause was greater than the risk for dying from kidney cancer, with 5-year probabilities, for example, of 20.1% and 7.5%, respectively. Comorbidity status had a substantial impact on the probability of non-kidney cancer death and a higher comorbidity status adversely affected kidney cancer survival.
Using a Fine and Gray competing risks proportional hazards model, the team built a nomogram to develop a risk model based on patient race, gender, age, CCI score, and tumor size, which they demonstrated to be well-calibrated.
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