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Personalizing Medicine: Considering Preferences and Values

Clare Marantz

Clare Marantz, (photo courtesy of Andrew Janke)

If you’re interested in heart disease—and who over the age of 40 isn’t?—you may have read an excellent series of articles by reporter Gina Kolata recently published in the New York Times. If you haven’t seen it, the series includes pieces on blood pressure, stents, heart attack treatment and a new approach to aortic valve replacement. The heart valve article especially caught my eye, as this is a story I’ve been watching with personal interest: my 90-year-old mother has aortic stenosis for which surgery has been recommended.

In fact, surgery was first recommended for my mom at least six years ago. I know that timing is correct because my sister Robin, an accomplished science and health journalist, described the decision-making process in writing in 2009. It’s a thought-provoking piece, and I recommend it to all. (I show up in that piece in the role of “brother.”) And the ensuing years—which I will describe here—provide a window on both scientific progress and the challenge of translating the best available evidence to make personal choices.

As Robin noted, our mom was told in 2009 that if she didn’t have the surgery, she had a 50 percent chance of dying within two years. At the time, I was skeptical of that prognosis—not because it wasn’t based on the best available evidence, but because the best available evidence wasn’t any good.

The only way to know the “natural history” of a condition such as aortic stenosis is to systematically record outcomes in everyone without offering any treatment. The only systematic records of untreated outcomes came from the 1950s, when patients in their mid-80s were rare and life expectancy in general was much shorter. Today, “natural history” could be gleaned by following individuals with surgically treatable disease who don’t undergo surgery, but in that case you’d have a biased sample, for a simple reason: patients who don’t get operated on tend to be sicker (and likely to die sooner) than those who do, so “natural history” outcomes would look particularly bad. Now that we are six years past my mother’s grim prognosis, we think she beat the odds, but perhaps the odds makers were wrong.

What we knew at the time was that there was a new, nonsurgical “transcatheter” approach being used experimentally—the one now in more common use, which Gina Kolata discusses in her article. At first, our mom was, ironically, “too healthy” for the randomized controlled trial (RCT) being conducted. This made scientific sense: since open-heart valve replacement was known to be effective, it was deemed ethical to study the experimental procedure only in patients who were too sick for the more invasive surgery. The initial trials demonstrated the efficacy of the transcatheter valve, and its use was approved for people too sick to undergo surgery. Soon came RCTs enrolling “healthier” patients, such as our mom, but this meant a 50-50 chance that she’d be randomized into the group that received the open-heart procedure instead, which was the operation she had already decided she didn’t want. She decided not to participate.

A couple of years ago, as Mom’s symptoms worsened (which mostly involved breathlessness when walking up hills, which eventually became breathlessness when walking at all), we met with some cardiologists  who deemed her eligible for the transcatheter valve – which was no longer considered experimental – and who recommended it for her. In re-pondering the pros and cons, we discussed that the available data at that time showed an increased risk of stroke in those who had the procedure. This possibility, along with her general preference for an error of omission over an error of commission, led her to decline the operation yet again. A recent large RCT now shows a lower risk of stroke, but as she nears 91, kinahora, and deals with other age-related problems, she is no longer considering interventionist options. Her activity level is minimal—her valve disease severely limits how far she can walk—but it’s good enough for her.

What can we learn from this story? Well, if we’re looking for generalizable knowledge about treatment efficacy, nothing at all. If my mother had opted for the procedure and had died on the table, we’d be pretty sure the decision was a mistake for her; if she had it and was now hopping onto buses and walking around museums with ease, we’d be pretty sure it had worked wonders for her; but in neither case would we know whether the procedure is bad or good in general. Nor does her being alive and (sort-of)-kicking three times longer (so far) than she was told to expect militate against the procedure for anyone else. It troubles me deeply when patients, and even doctors, draw inappropriate conclusions from their personal experiences, as I’ve written about in an earlier blog post.

So why tell this story at all? Because large studies and population-level data must ultimately be translated to an individual patient, and each patient’s story uniquely contributes to our understanding of that process. Coming from a family that is flush with writers, my mother’s stories have been chronicled to the extent that she may be overrepresented in the media. In addition to the piece by my sister, there are items about her by my son, a niece, and another niece. And now by me.

While my mother’s story can’t teach us anything about the efficacy of treatment (or no treatment), it describes an example of rapid scientific advances, and has a generalizable message about the challenge of using evidence to make personal healthcare decisions. As we talk more about personalized medicine, let’s remember that “personal” involves more than just genes. It also involves preferences and values – values like my mother’s, who told us that when she dies, “I don’t want it to be because of something I decided, something I did.”

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  • Jordan July 14, 2015, 10:45 AM

    Some very important points here. It’s true that each patient is an individual, and needs to be treated as such. It’s part of the duty of the doctor to translate the big data there into something the patient can use to to be treated.