Following the Annals of Internal Medicine's publication of the latest recommendations of the USPTSP regarding breast cancer screening, was an editorial by Dr. Karla Kerlikowske discussing the need for "individual risk assessment" of breast cancer.See here for excerpt, subscription required for full text
Previously it was a commentary in the same journal regarding breast cancer and the need for "better" (more accurate?) risk assessment in the context of whether women in the 40-49 age group should be advised to get a mammogram. I wrote this entry making the claim that at the core it is debatable if the concept of individual risk risk makes any sense at all as opposed to speaking of the risk of an event in a group of people.
Dr. Kerlikowske begin her final paragraph with this sentence:
We can improve primary and secondary breast cancer prevention effectiveness by implementing risk assessment in primary care and mammography facilities and providing tailored recommendations for prevention based on individual risk.
So what is this thing called individual risk and how do we determine it?
What follows is a re-write of my 2007 blog entry on this issue with the addition of skeptical comments by my brother, Jarrad ,who is a radiologist considering retirement.
Risk assessment for various medical conditions has become an everyday part of the activities of primary care physicians. Risk assessment involves the identification of something called risk factors, personal characteristics or test findings that are associated with increased incidence of a given disease. This term was coined by the researchers in the Framingham study when they spoke of factors that were associated with an increased risk of coronary artery disease. As the "practice model" of internist practices changes from hospital based consultation type to office outpatient, more attention is given to preventive medicine which is a world of risk factor identification and risk assessment exercises as well as recitation of various guidelines and targets or as Jarrad says treating folks who have no demonstrable diseases.
Here is an example of risk assessment using the equation from the National Cholesterol Panel's (NCEP) web site.A 67 year old non-smoking man, Mr. Jones,with a history of hypertension under control and systolic blood pressure of 120, with a total cholesterol of 170 and an HDL cholesterol of 75 would have a risk estimate of 9 % according to their risk equation.
This means that if we consider the 10 year health outcomes of 100 men from the Framingham data base with this particular set of characteristics, 9 would have a coronary event. (A so-called hard end point of either a myocardial infarction or coronary death.) Of course, we do not know who the 9 will be until the event occurs and we cannot tell Mr. Jones if he will be one of the nine or not raising the question of in what sense is this number "his" individual risk.
What does this" risk "of 9% for Mr Jones mean? Maybe the following mind experiment will shed some light on that. Let's pretend we can clone Mr. Jones and we do so 100 times and consider the question of what will be the outcomes of these 100 Joneses. Will 9% have a coronary artery event or will it be the case that either all will be fine or all will have a coronary event. (My gratitude again to Dr. Goodman and his memorable article in the Annals of Internal Medicine for this line of though that I blogged about here.)
If we believe in medical determinism- that clinical outcomes are determined by a causal chain of events-we believe that either all will be fine or all will have a heart attack. They will all be fine if they and the original Mr. Jones do not posses the factor(s) that sum up or interact to bring on an event or all will have an event if Mr. Jones had-as will all his clones have-whatever factor(s) known and unknown which determine a coronary artery event. If we believe in a cosmic dice roll then some 9% will have an event and medical science will never know ahead of the event who will because it is simply random.
Another consideration is that while we have placed Mr. Jones in this set of men with these particular features , we could have -if we had the data available-place him into a different set or as a member of as many sets as the imagination allows. We might consider him as a member of a set defined by his age, his c-reactive protein value, his performance on a stress test, his calcium score on a heart CT scan and his triglyceride level and if we consider the event rate in a group of men with these features we may well arrive a different value which could be 22%. So what is his risk- 9% or 22% or any of the multitude of other numbers that we could construct in a similar manner and are any of these numbers his individual risk? With the publication of the 2013 Guidelines from AHA and ACC we have a new prediction tool.Is the new tool better than Framigham and how do we make that determination? What if the expert panel who uses their new rule give recommendations and risk level cut points that differ from what the old expert panel with their predictions and rules? Doctor,were you wrong then or are you wrong now?
That type of consideration led the imminent German statistical theorist, Richard Von Mises to say in his book " Probability,Statistics and Truth" that it is only possible to speak of probability in terms of a collective (or in more modern terms -a set or a group) and that to say, for example, that a given person has the probability 0.10 of dying in the next year is nonsense. Yet, isn't this is exactly what we do when we we punch in a person's numbers into the Framingham equation and announce to the patient that his risk of a cardiac event in the next 10 years is 9%. ?
Jarrad,offers this: "But wait,if you believe that "determination" of individual risk is nonsense why is it that seemingly there are a number of very useful prediction models used by physicians for such things as risk or likelihood of pulmonary embolus given several clinical variables?In what way does the use of those prediction model equation differ from telling Mr. Jones that he has a risk of 9 % of a heart attack in the next ten years?"
Well, I'm not sure but one thing is usually those prediction models classify patients into low, moderate and high risk of the disease at issue and based on that certain further testing is or is not done and those strategies seemed to have bee shown to work out reasonably well in clinical trials.Further the determination of risk in general terms ( low, medium, high) has at times been proven to be of clinical value in diagnosing pulmonary embolism or whatever, but telling someone their risk is 9% of a future disease is not per se an actionable item.We can link the numbers to some recommendations for statins or whatever but.... I am not sure there is any practical or useful outcome from that. Trying to figure out the best way to clinically manage someone with a given clinical picture is one thing, advising someone to take or not take statins or bisphophonates based on some "determination" of her risk based on some expert panel's judgment is another matter entirely.Although I spent a number of years doing just that sort thing for many hours per week,now I am much less certain about the validity of the entire enterprise and whether I was doing my patients good or harm.