Steve Lucas

]]>Thanks for your comment.

Steve,

I am sorry about your father. I agree with your position about the number needed to treat being so high for too many recommended interventions. I remind myself that truly useful developments did not need RTC’s to show their effectiveness, e.g. fire, the wheel, penicillin, clean water, …

]]>Lo and behold the US Preventative Task Force Agrees with me:

http://www.uspreventiveservicestaskforce.org/uspstf/uspscolo.htm

Send your Dad that link and have him show it to the doctor. If he won’t listen to reason, slip Joan Rivers into the conversation.

]]>I have what I call the three places to the right of the decimal point bias. This means any answer to any problem that results in a number requiring thousands of patients or other action to garner one positive result is due to study error. This is why NNT is an important number in my decision making process.

Not being a doctor I then have to look at the financial incentives for prescribing and the pharma driven advertising and mandates. Many drugs do not look good under this light.

Statins are a favorite in that the NNT becomes very high with minimal lipid elevation. Pharma has used a surrogate end point, lower LDL, as a measurable marker. We then see statins being pushed for everything from dementia to preventive care with no indication.

What is not studied is such links as long term use and the possible promotion of dementia, as mentioned recently in an article. People with very low LDL being pushed to even lower levels and the resulting physical problems such as pain and confusion.

One doctor posted some time ago that if these things are so good they should be put in the water.

My reality is statins produce a measurable result, lower LDL. The have a fairly benign profile in lower doses, but as with all drugs not entirely safe, especially when pushed to extremes. We also need to consider drug interaction and safety. Doses given are not a measure of safety.

This is the process by which I measure medical interventions, simply change the drug and add a very high weight to the pharma advertising and promotion. I have found so many drug and other medical interventions lacking as to generate the big question: Who does the doctor work for, me or the drug company/corporate interest they represent.

The real sadness is doctors are unwilling to give up on their ideas of good medical practice in favor of this corporate model. My 83 year old father is fighting his doctor about having another colonoscopy and driving his blood pressure lower when he has had no colon problems and was admitted to a local hospital with no pulse due to over medication.

Doctors need to not look at the big numbers, but the singular patient in front of them, and then decide on the minimal medications and test necessary for them to live the best life they can live given their condition.

Steve Lucas

]]>Thanks for that long comment. It was, indeed, a thought experiment that was a real addition. As was:

]]>“The use of clinical guidelines to mandate treatment can lead to very bad medicine. They may become a procrustean bed upon which individual patients are either stretched to fit or cut down to size. It is the right and the responsibility of the treating physician to weigh the various conditions for each patient and convey their reasoning to the patient so they can make as informed a decision as possible.”

Aside from the numerous problems with the clinical guidelines being influenced by market forces, there is another problem that exists independently of those. Namely, that the guidelines are applying probabilities from a set of study populations to the individual patient in the office. The probability that a patient in a study, no matter how well designed, will benefit from an intervention is not going to be the same as the probability that a patient in my office will benefit from that intervention.

The reason for this is that the relevant probabilities are conditional. They describe the probability of benefit for a person given that the person meets certain conditions. Now in a well-designed study, attempts are made to keep those conditions as consistent as possible across the participants. That necessarily limits the applicability of the results. The patient in my office almost certainly does not meet those conditions, so the study results almost certainly do not apply.

What I need to know as a clinician is: If the probability that a person in a study who meets conditions A,B,C, D, …, M will benefit is 0.6, what is the probability that my patient who meets only condition A and B but also has condition R and V will benefit. The answer to this is not just unknown, but is mathematically unknowable. I am forced to make a guess, and I can’t even say how likely my guess is to be accurate. The best I can do is use my knowledge of how the various conditions influence the effect of the intervention and hope I am not too far off base.

The use of clinical guidelines to mandate treatment can lead to very bad medicine. They may become a procrustean bed upon which individual patients are either stretched to fit or cut down to size. It is the right and the responsibility of the treating physician to weigh the various conditions for each patient and convey their reasoning to the patient so they can make as informed a decision as possible.

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For those interested in how subtle changes in conditions can effect probabilities I offer the following thought experiment:

John and Mary have two children. I want to know the probability that both children are girls.

Condition 1: No information. Answer: Probability that both are girls is 25%.

Condition 2: The oldest child is a girl. Answer: Probability that both are girls is 50%.

Condition 3: At least one child is a girl. Answer 33.33%. Note that changing the condition subtly, i.e. knowing the oldest child is a girl vs only knowing that at least one child is a girl, changes the probability significantly.

It can get even more bizarre. Here is a condition which does not seem like it should influence the probability of both children being girls at all. But it does.

Condition 4: At least one child is a girl with curly hair. Answer: It depends. If we ask question in Taipei the probability that both children will be girls given that at least one is a girl with curly hair is close to 50%. If we ask the question in Nairobi then the probability of the same outcome is close to 33%. (It depends on the frequency of curly haired girls in the population.)

So changes in conditions which we would never expect to have an influence on the probability of an outcome clearly do.

Clinically this could be the change from a study where the medication is paid for to the real world where the cost of the medication is a significant stress on the patient, and on and on.

If anyone wants to have the details for the example I used Mickey can give them my email.

]]>I wonder those same things. But in spite of his weakening at the end, I suspect he was under a lot of pressure. I hope in time, he’ll tell us what happened. I had some communication with him and found him to be a gracious and thoughtful man.

]]>We have gone to France for a number of years and enjoyed a Paris market that had been in service for well over 100 years. The EU decided that this market did not meet current health standards and the only thing to do was issue an immediate shut down order. This would deprive the merchants of an income and the local residents a broader choice of products, but no, the law is the law.

The French being clever installed power outlets that rise from the pavement on market day to supply electric to the refrigeration units in the various stalls. Never mind that people were never getting sick from the fish and other produce, and heaven forbid we sell flowers not kept refrigerated.

Brussels is all about rule and process, common sense takes a back seat to paperwork. Almost sounds like our government.

Steve Lucas

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