The risk of chemotherapy

Area of Science:

• Biostatistics
• Gerontology
• Oncology

Background:

• Human survival curves can be categorized by hazard rate derivatives into aging (increasing hazard rate), random death (constant hazard rate), and regeneration (decreasing hazard rate).
• These patterns are observed across various biological contexts, including aging populations, experimental animal studies, and patient recovery from conditions like myocardial infarction or surgical interventions.
• The derivative of the hazard rate (lambda'(t)) is proposed as a measure of morbidity, complementing the hazard rate (lambda(t)) which measures mortality.

Purpose of the Study:

• To classify human survival curves based on hazard rate derivatives, identifying distinct patterns of aging, random death, and regeneration.
• To investigate the underlying mechanisms and biological contexts associated with each survival curve pattern.
• To establish a framework for assessing cancer treatment effectiveness by considering both mortality (hazard rate) and morbidity (hazard rate derivative).

Main Methods:

• Classification of survival curves using the magnitude of their hazard rate derivatives (lambda'(t)).
• Analysis of survival data from population cohorts, experimental animal models (whole body irradiation), and patient outcomes (myocardial infarction, surgery, cancer).
• Interpretation of hazard rate derivatives as indicators of morbidity and hazard rates as indicators of mortality.

Main Results:

• Survival curves were classified into three elementary shapes: aging (lambda'(t) > 0), random death (lambda'(t) = 0), and regeneration (lambda'(t) < 0).
• The aging pattern is prevalent in human populations from age 15 onwards and in animals exposed to irradiation.
• The regeneration pattern is observed in patients recovering from myocardial infarction, surgery, and in cancer survival data, potentially reflecting beneficial effects of neoplasia.
• Current cancer treatments often exhibit increasing morbidity, evidenced by a positive hazard rate derivative.

Conclusions:

• Survival curves can be effectively classified using hazard rate derivatives, providing insights into aging, random death, and regeneration processes.
• Morbidity, as measured by the hazard rate derivative, is a crucial factor in assessing health outcomes and treatment effectiveness, particularly in oncology.
• The observed increase in morbidity associated with many cancer treatments highlights the need for therapeutic strategies that reduce both mortality and morbidity.