Garrison Keillor’s fictional mid-Western town of Lake Wobegon “all the ladies are strong all the men are good looking and all the children are above normal”. for those smokers. Statisticians have created prediction models giving lung malignancy risk in terms of age gender pack years and current smoking status.2 The math underlying these prediction models is such that the risk distribution will almost always be skewed to the right when the overall outcome rate is <50%. The number shows the distribution of risk from a typical risk prediction model. With this hypothetical example 10 of individuals develop disease and so the mean risk is definitely 10%. The median risk is definitely closer to 7% and in fact about two-thirds of individuals possess a risk less than the mean. Number The distribution of risk for a typical risk prediction model. The mean risk is definitely 10% the median risk is definitely 7%. The dark gray area constitutes the 50% of individuals with risk less than the median; the light grey represents the individuals with risk greater than ... The implications of this “Lake Wobegon effect” are far from trivial. Take a standard randomized trial taught in an introductory evidence-based medicine class MK-4827 with cardiovascular event rates of 10% in the control arm compared to 5% in the MK-4827 drug group. This is a 5% complete risk reduction and a number-needed-to-treat of 20. Let us assume that most clinicians believe that the burdens costs side-effects and risks of the drug are low plenty of that it would in fact become worth treating 20 individuals although no more than 20 in order to prevent one cardiovascular event. As such the trial is deemed a success and the drug widely prescribed. But let us further assume that a prediction model is definitely available that can be used to determine the risk of an individual patient based on risk factors such as blood pressure and cholesterol and that the prediction model offers related properties to the one demonstrated in the number. If relative risk is definitely roughly constant across different levels of complete risk (50%) and complete risk of drug harms is also approximately constant then it can be seen that only about a third of individuals benefit sufficiently from your drug to outweigh its costs burdens and harms. Or MK-4827 to put it another way although the treatment is definitely worthwhile normally it is not worthwhile in the average patient. We have examined this effect empirically in individuals with ST-elevation myocardial infarction. Using a previously developed MK-4827 risk model we found that individuals in the highest risk quartile have about 16-collapse the risk of mortality compared to the least expensive risk quartile. The typical patient on the other hand has a risk only about half the average. When we reanalyzed the GUSTO trial by using this risk model we found that the more potent and expensive thrombolytic therapy (tPA) was indeed effective normally but for most individuals the degree of benefit likely did not warrant the extra risks and costs compared to the less effective but safer and less expensive alternate streptokinase.3 MK-4827 In a similar study we found that main angioplasty ITSN2 saves lives normally compared to thrombolytic therapy but not in typical risk individuals: up to 75% of ST-elevation MI individuals derive no mortality benefit from angioplasty.1 4 5 Because such risk-stratified analyses are rare the emphasis placed on the average summary results can lead to low value care and attention and overtreatment in many particularly for treatments that carry substantial hazards or costs. Perhaps the “floor zero” of overtreatment in contemporary medicine is definitely testing for prostate malignancy. Population data display that following a introduction of screening with prostate-specific antigen (PSA) mortality fell somewhat but incidence increased dramatically. We believe that standard approaches to prostate malignancy screening which presume all men are at average risk are a major cause of overdiagnosis. In fact risk can be very strongly separated depending on PSA. Men in the top quartile of PSA at age 60 equivalent to a PSA of 2 ng/ml or above have a risk of prostate malignancy mortality more than 20 instances greater than those with lower PSAs and 90% of deaths by age 85 occur with this group 6 a definite example of the Lake Wobegon effect. We recently shown that screening only males at high risk rather than testing all men drastically reduced testing harms – in terms of overdiagnosis – but retained 100% of the screening benefits – in terms of mortality reductions – because screening did not reduce prostate malignancy deaths in the low PSA group 7 Of course the distribution of risk is definitely a model-dependent house. While.