Modeling the life expectancy benefits of active surveillance

An article in today’s issue of the Journal of the American Medical Association, and the accompanying editorial in the same issue, are getting a lot of media attention — despite the fact that the article is “only” a mathematical analysis of the possible quality of life benefits of active surveillance in low-risk patients compared to active intervention.

The article by Hayes et al. reports the results of a sophisticated attempt to assess the quality-adjusted life expectancy (QALE) of hypothetical groups (“cohorts”) of 65-year-old men newly diagnosed with clinically localized, low-risk prostate cancer (defined by a PSA level <10 ng/ml, a clinical stage of T2a disease or less, and a Gleason score of 6 or less). Let us be quite clear … These are not “real” patients. They are hypothetical patients.

What Hayes and her colleagues have done is to use data from a variety of sources to construct a model of the way that their hypothetical cohorts of patients might reasonably be expected to respond, over time, to a variety of management strategies, specifically including active surveillance, radical prostatectomy, intensity modulated radiation therapy, and brachytherapy.

Here are their basic results, which are given in terms of quality-adjusted life expectancy (QALE), which is measured in quality-adjusted life-years (QALYs):

  • For the active surveillance cohort, QALE = 11.07 QALYs.
  • For the brachytherapy cohort, QALE = 10.57 QALYs.
  • For the intensity-modulated radiation therapy cohort, QALE = 10.51 QALYs.
  • For the radical prostatectomy cohort, QALE = 10.23 QALYs.

In other words, based on the model constructed by Hayes and her colleagues, active surveillance offers the best long-term outcome compared to any form of active intervention for these hypothetical patient cohorts.

And for those who are interested in gaining a better understanding of what is meant by a quality-adjusted life-year or QALY, we refer you to the explanation on Wikipedia.

But let us be very clear:

  • This is a mathematical model that can not be applied specifically to an individual patient.
  • The model is based on data that is (at least in some cases) open to question.
  • Many assumptions have to be made in constructing a model of this type, and those assumptions may not be accepted by others.
  • The personal choices and priorities of the patients have been completely ignored in  constructing this model.
  • The precise clinical characteristics of individual patients have also been ignored in this model.

Hayes and her colleagues have done an excellent job of demonstrating the potential value of active surveillance compared to active intervention in a well-defined and increasingly common set of prostate cancer patients. Their data may be helpful in allowing clinicians to explain this potential value to newly diagnosed patients. However, Hayes and her colleagues — and Thompson and Klotz, in their editorial comments on the paper by Hayes et al., are all extremely careful to note the continuing importance of patient choice in decisions about prostate cancer treatment.

In their conclusion, Hayes et al. clearly state that, “Under a wide range of assumptions, for a 65-year-old man, active surveillance is a reasonable approach to low-risk prostate cancer based on QALE compared with initial treatment. However, individual preferences play a central role in the decision whether to treat or to pursue active surveillance.”

Because of the wide media “pick-up” of this story, we have provided links to a series of other articles based specifically on the Hayes et al. study:

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