This is an excerpt from Orthopedic Clinical Examination eBook With Web Resource by Michael P. Reiman.
Evidence-Based Practice and Diagnostic Accuracy
Evidence-based practice (EBP) has been defined as "the conscientious and judicious use of current best evidence in making decisions about the care of individual patients." Central to the concept of EBP is the integration of evidence into the diagnoses and management of clients. Today, there is a strong push toward EBP as the conscientious utilization of the strongest and most recent evidence purported in the literature. Clinical expertise is also vital to the practice of EBP, as stated by Sackett and colleagues:
"Good doctors use both individual clinical expertise and the best available external evidence, and neither alone is enough. Without clinical expertise, practice risks becoming tyrannised by evidence, for even excellent external evidence may be inapplicable to or inappropriate for an individual patient. Without current best evidence, practice risks becoming rapidly out of date, to the detriment of patients."
It is possible to have statistical significance without having clinical relevance, to have both statistical significance and clinical relevance together, to have clinical relevance without having statistical significance, or to have neither statistical significance nor clinical relevance. The clinical relevance of research findings is typically not reported in research findings, and it has been suggested as paramount to clinical practice. Current best evidence is therefore a balance of the best available evidence supported in the literature and the clinician's sound clinical reasoning. The practicing clinician must rely on the interweaving of these tenets to make the most conscientious, sound decisions when examining and subsequently treating clients.
For these reasons, this text would like the reader to also think in terms of the terminology of evidence-informed practice (EIP). The purpose of EIP is to make clinical decisions with the information of best evidence. Decisions cannot always be based on evidence alone. This is particularly the case when the evidence supporting or refuting clinical testing is poor. The clinician is referred to the limitations of each component of the examination, particularly special testing (as discussed in chapter 10). Many of these tests have less than good ability to assist with differential diagnoses decisions. All components of the examination, including components not described as standard examination components (e.g., the client's goals, the client's health status), should be used when making clinical diagnosis and treatment decisions.
Diagnostic tests and measures are distinct components of an EBP model of client examination. A diagnostic test and its results are important tools guiding the clinician to the appropriate diagnosis by revealing the likelihood of whether or not a client has a specific disorder. Diagnostic research should evaluate the validity of the complete diagnostic process and study the evidence of the added value of the different tests used. Not all components of the examination process are equal in their ability to differentiate the presence, absence, or severity of a particular disease or condition present in a client. This likely also depends on the particular pathology. Specific components of the examination process have a stronger diagnostic ability depending on several variables including, but not limited to, the prevalence of the disease, the diagnostic accuracy of the examination component, and the strength of the literature investigating the pathology or examination component.
Prior to discussion of the diagnostic accuracy of various musculoskeletal tests and measures, it is necessary to define terminology central to EBP.
Reference standard - The criterion that best defines the condition of interest. The reference standard should have demonstrated validity that justifies its use as a criterion measurement.
Reliability - The degree of consistency with which an instrument or rater measures a particular attribute. Measurements can be affected by random error. In determining the reliability of a measurement, we are determining the proportion of that measurement that is a true representation and the proportion that is the result of measurement error.
- Validity - The degree to which a study or test appropriately measures what it intends to measure. Validity attempts to answer the question: Does the test truly measure what it is designed to measure? A test must be reliable to be valid, but a test does not have to be valid to be reliable. Tests that are valid should measure the abilities vital to the sport, occupation, or aspect of activity of daily living.
- Sensitivity (SN) - The percentage of people who test positive for a specific disease among a group of people who have the disease. The true positive rate.
- Specificity (SP) - The percentage of people who test negative for a specific disease among a group of people who do not have the diagnosis or disorder. The true negative rate.
- Positive likelihood ratio (+LR) - The ratio of a positive (+) test result in people with the pathology to a positive test result in people without the pathology. A +LR identifies the strength of a test in determining the presence of a finding, and it is calculated by the following formula: SN / (1 - SP).
- Negative likelihood ratio (-LR) - The ratio of a negative (-) test result in people with the pathology to a negative test result in people without the pathology. It is calculated by the following formula: (1 - SN) / SP. The higher the +LR and lower the -LR, the more the posttest probability is altered. Posttest probability can be altered to a minimal degree (+LRs of 1-2, or -LRs of 0.5-1), to a small degree (+LRs of 2-5 and -LRs of 0.2-0.5), to a moderated degree (+LRs of 5-10, -LRs of 0.1-0.2), and to a significant and almost conclusive degree (+LRs greater than 10, -LRs less than 0.1).
- Positive predictive value (PPV) - Given a (+) test result, the probability that the client has the condition. Some researchers and clinicians feel that PPV is better than SN since it takes into account the amount of false positives (FP). PPV = TP / (TP + FP), where TP is true positives. Therefore, if the test is (+), the client has X% chance of having the disorder.
Negative predictive value (NPV) - Given a (-) test result, the probability that the client does not have the condition. Again, some believe this is better than SP since it takes into account the number of FNs. Therefore, if the test is (-), the client has X% chance of not having the disorder.
- SN and SP are properties of the measure, while PPV and NPV are properties of both the test and the population that was tested.
- Reading the PPV and NPV from the 2 Ã— 2 contingency table is accurate only if the proportion of diseased clients in the sample is representative of the proportion of the diseased people in the population.
- Overall accuracy - Proportion of clients who are correctly diagnosed.
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