WHAT IS EVIDENCE-BASED MEDICINE?

EVIDENCE-BASED MEDICINE

WHAT IS EVIDENCE-BASED MEDICINE? emember when we used to treat every otitis media with antibiotics? These recommendations came about because we applied logical reasoning to observational studies. If bacteria cause an acute otitis media, then antibiotics should help it resolve sooner, with less morbidity. Yet, when rigorously studied (via a systematic review), we found little benefit to this intervention. The underlying premise of evidence-based medicine (EBM) is the evaluation of medical interventions, and the literature that supports those interventions, in a systematic fashion. EBM hopes to encourage treatments proven to be eective and safe. And when insucient data exists, it hopes to inform you on how to safely proceed. EBM uses as endpoints of real patient outcomes; morbidity, mortality and risk. It focuses less on intermediate outcomes (bone density) and more on patient conditions (hip fractures). Implementing EBM requires 3 components: The best medical evidence, the skill and experience of the provider, and the values of the patients. Should this patient be screened for prostate cancer? It depends on what is known about the test, on what you know of its benets and harms, your ability to communicate that information, and that patient’s informed choice. This book hopes to address the rst EBM component, providing you access to the best information in a quick format. While not every test or treatment has this level of detail, many of the included interventions here use systematic review literature support. The language of medical statistics is useful to interpreting the concepts of EBM. Below is a list of these terms, with examples to help take the confusion and mystery out of their use. Prevalence: Proportion of people in a population who have a disease (in the US, 0.3% [3 in 1,000] people over the age 50 have colon cancer). Incidence: How many new cases of a disease occur in a population during an interval of time; for example, “the estimated incidence of colon cancer in the US is 104,000 in 2005.” Sensitivity (Sn): Percent of people with disease who test positive; for mammography, the sensitivity is 71–96%. Specicity (Sp): Percent of people without disease who test negative; for mammography, the specificity is 94–97%. Now suppose you saw ML, a 53-year-old woman, for a Health Maintenance visit, ordered a screening mammogram, and the report demonstrates an irregular area of microcalcications. She is waiting in your office to receive her test results; what can you tell her? Sensitivity and specicity refer to characteristics of people who are known to have disease (sensitivity) or those that are known not to have disease (specicity). But, what you have is an abnormal test result. To better explain this result to ML, you need to know the positive predictive value. Positive predictive value (PPV): Percent of positive test results that are truly positive; the PPV for a woman aged 50–59 is approximately 22%. That is to say that only 22% of abnormal screening mammograms in this group truly identied cancer. The other 78% are false positives. You can tell ML only 1 out of 5 abnormal mammograms correctly identify cancer; the 4 are false positives, but the only way to know which mammogram is correct is to do further testing. The corollary of the PPV is the Negative predictive value (NPV), which is the percent of negative test results that are truly negative. The PPV and NPV tests are population dependent, while the Sensitivity and Specicity are characteristics of the test, and have little to do with the patient in front of you. So when you receive an abnormal lab result, especially a screening test like mammography, understand their limits based on their PPV and NPV. Treatment Information is a little dierent. In discerning the statistics of randomized, controlled trials of interventions, rst consider an example. The Scandinavian Simvastatin Survival Study (4S) (Lancet. 1994;344[8934]:1383–1389) found using simvastatin in patients at high risk for heart disease for 5 years resulted in deaths in 8% of patients vs. 12% of those on placebo; this results in a relative risk of 0.70, a relative risk reduction (RRR) of 33%, and a number needed to treat of 25. There are two ways of considering the benets of an intervention with respect to a given outcome. The absolute risk reduction (ARR) is the dierence in the percent of people with the condition before and after the intervention. Thus, if the incidence of MI was 12% for the placebo group and 8% for the simvastatin group, the ARR is 4% (12% – 8% = 4%). The RRR reects the improvement in the outcome as a percentage of the original rate and is commonly used to exaggerate the benet of an intervention. Thus, if the risk of MI were reduced by simvastatin from 12% to 8%, then the RRR would be 33% (4%/12%= 33%); 33% may appear better than 4%, the 4% that reflects the true outcome. ARR is usually a better measure of clinical signicance of an intervention. For instance, in one study, the treatment of mild hypertension was been shown to have a RRR of 40% over 5 years (40% fewer strokes in the treated group). However, the ARR was only 1.3%. Because mild hypertension is not strongly associated with strokes, aggressive treatment of mild hypertension yields only a small clinical benefit. Don’t confuse Relative Risk Reduction with Relative Risk. Absolute (or attributable) risk (AR): The percent of people in the placebo or intervention group who reach an end point; in the simvastatin study, the absolute risk of death was 8%. Relative risk (RR): The risk of disease of those treated or exposed to some intervention (i.e., simvastatin) divided by those in the placebo group or who were untreated. –If RR <1 .0="" f="" greater="" it="" number="" reduces="" reduction.="" risk="" rr="" smaller="" the="">1.0, it increases the risk–the greater the number, the greater the risk increase. Relative risk reduction (RRR): The relative decrease in risk of an end point compared to the percent of that end point in the placebo group. If you are still confused, just remember the RRR is an overestimation of the actual effect.Number needed to treat (NNT): This is the number of people who need to be treated by an intervention to prevent one adverse outcome. A “good” NNT can be a large number (>100) if risk of serious outcome is great. If the risk of an outcome is not that dangerous, then lower (