Canadian Medical Association journal
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This paper compares the management of two groups of patients with flail chest. The 25 patients in group 1 had a flail chest without other significant injuries or shock, whereas the 57 in group 2 had a flail chest with multiple injuries, shock or both. The group 1 patients were treated with repeated multiple intercostal nerve blocks or high segmental epidural analgesia, oxygen, intensive chest physiotherapy, fluid restriction, furosemide diuretics, methylprednisolone sodium succinate and colloid infusion in an intensive care unit. ⋯ However, tracheostomy was avoided in the other 21 patients in group 2. There were no deaths in group 1, but 8 (14%) of the patients in group 2 died. These results show that avoidance of tracheostomy and ventilation in selected patients with flail chest is consistent with a low morbidity and mortality.
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We have now shown you how to use decision analysis in making those rare, tough diagnostic decisions that are not soluble through other, easier routes. In summary, to "use more complex maths" the following steps will be useful: Create a decision tree or map of all the pertinent courses of action and their consequences. Assign probabilities to the branches of each chance node. ⋯ That concludes this series of clinical epidemiology rounds. You've come a long way from "doing it with pictures" and are now able to extract most of the diagnostic information that can be provided from signs, symptoms and laboratory investigations. We would appreciate learning whether you have found this series useful and how we can do a better job of presenting these and other elements of "the science of the art of medicine".
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The use of simple maths with the likelihood ratio strategy fits in nicely with our clinical views. By making the most out of the entire range of diagnostic test results (i.e., several levels, each with its own likelihood ratio, rather than a single cut-off point and a single ratio) and by permitting us to keep track of the likelihood that a patient has the target disorder at each point along the diagnostic sequence, this strategy allows us to place patients at an extremely high or an extremely low likelihood of disease. Thus, the numbers of patients with ultimately false-positive results (who suffer the slings of labelling and the arrows of needless therapy) and of those with ultimately false-negative results (who therefore miss their chance for diagnosis and, possibly, efficacious therapy) will be dramatically reduced. ⋯ However, these combinations may not be independent, and convergent diagnostic tests, if treated as independent, will combine to overestimate the final post-test probability of disease. You are now far more sophisticated in interpreting diagnostic tests than most of your teachers. In the last part of our series we will show you some rather complex strategies that combine diagnosis and therapy, quantify our as yet nonquantified ideas about use, and require the use of at least a hand calculator.
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A more complex table is especially useful when a diagnostic test produces a wide range of results and your patient's levels are near one of the extremes. The following guidelines will be useful: Identify the several cut-off points that could be used. Fill in a complex table along the lines of Table I, showing the numbers of patients at each level who have and do not have the target disorder. ⋯ However, if you looked very hard at what was happening, you will probably have noticed that they are not very useful for patients whose test results fall in the middle zones, or for those with just one positive result of two tests; the post-test likelihood of disease in these patients lurches back and forth past 50%, depending on where the cut-off point is. We will show you how to tackle this problem in part 5 of our series. It involves some maths, but you will find that its very powerful clinical application can be achieved with a simple nomogram or with some simple calculations.
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The following guidelines are useful if you want to "do it with a simple table" (Table IV): First, identify the sensitivity and specificity of the sign, symptom or diagnostic test you plan to use. Many are already in the literature, and subspecialists should either know them for their field or be able to track them down for you. Depending on whether you are considering a sign, a symptom or a diagnostic laboratory test, you will want to track down a clinical subspecialist, a radiologist, a pathologist and so on. ⋯ Furthermore, you can already do more than most clinicians, so you may want to stop here, at least for a while. On the other hand, you may want to go further and learn how to handle slightly more complex tables with multiple cut-off points. In the next article you will find more powerful ways to take advantage of the degree of positivity and negativity of diagnostic test results.