Which is the right comparison group to use to determine whether she has osteoporosis?

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Which is the right comparison group to use to determine whether she has osteoporosis?

Which is the right comparison group to use to determine whether she has osteoporosis?
Which is the right comparison group to use to determine whether she has osteoporosis?

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FIG 1.2 Representative example of a normal bell curve for a physiologic variable. Many physiologic variables are normally distributed within the population, so the mean ±2 standard deviations include 95% of the normal values in the sample. Approximately 2.5% of values will be above the normal range and 2.5% will be below it. There may be overlap between the values in a normal sample and those in the population with a disease, making interpretation difficult in some cases.

CHAPTER 1 Introduction to Pathophysiology 5

individual. The positive predictive value is an estimate of the probability that disease is present if the test is positive. The negative predictive value is an estimate of the probability that disease is absent if the test is negative. The predictive value of a test depends in part on the sensitivity and specificity of the test and in part on the probability of the disease being present before the test is obtained. Most tests are not perfectly specific and sensitive so the results must be interpreted probabilistically in view of the diagnostic hypotheses being tested.

Sensitivity and specificity are measures of how well a given test can discriminate between persons with and without a given condition. Sensitivity is the probability that the test will be positive when applied to a person with the condition. For example, if a kit for testing a throat swab for the presence of streptococcal infection has a sensitivity of 80%, then 20% of a group of people with streptococcal throat infection would erroneously test negative for the condition (false-negative rate). Another example is the blood test for HIV antibodies, which has a sensitivity of 99% and would fail to detect the condition in only 1% of a group of individuals who had HIV antibodies in their blood. Specificity is the probability that a test will be negative when applied to a person who does not have a given condition. If the streptococcal throat swab kit has a specificity of 95%, then 5% of those tested who do not actually have the condition would erroneously test positive (false-positive rate). The importance of evaluating the accuracy and precision of data is paramount because inappropriate diagnoses and clinical management could occur if decisions are predicated on invalid or unreliable data.

The positive predictive value of a test is improved when sensitiv- ity and specificity are high and the test is applied to individuals who have a high probability of having the condition being tested. If the likelihood of a condition in the population being tested is low (e.g., a 2% prevalence rate), then a positive result in a test with 99% specific- ity and 99% sensitivity would only have a 67% positive predictive value. This means that testing low-likelihood or low-risk individuals would produce a high percentage of false-positive results (33% in the preceding example). Therefore deciding who to test for a given condition based on the probability of the condition being present is as important as the sensitivity and specificity of the test. A good working knowledge of pathophysiology is necessary to generate the hypotheses that guide collection of appropriate data and facilitate the diagnostic process.

Individual Factors Influencing Normality Variations in physiologic processes may be a result of factors other than disease or illness. Age, gender, genetic and ethnic background, geographic area, and time of day may influence various physiologic parameters. Care must be taken to interpret “abnormal” findings with consideration of these possible confounding factors. In addition, the potential for spurious findings always exists. Thus trends and changes in a particular individual are more reliable than single observations. Single measure- ments, observations, or laboratory results that seem to indicate abnormality must always be judged in the context of the entire health picture of the individual. One slightly elevated blood glucose level does not mean clinical diabetes, a single high blood pressure reading does not denote hypertension, and a temporary feeling of hopelessness does not indicate clinical depression.

Cultural Considerations Each culture defines health and illness in a manner that reflects its experience. Cultural factors determine which signs, symptoms, or behaviors are perceived as abnormal. An infant from an impoverished culture with endemic chronic diarrhea and a degree of malnutrition would be viewed as abnormal in a progressive culture, such as a well-baby

must be carefully selected to represent the individual to be tested for disease, because many variables are influenced by age and gender.

For example, bone density can be measured in the population by radiologic imaging, and then a mean and standard deviation can be calculated. Women typically have lower bone density than men, and older women have lower bone density than younger women. If an elderly woman’s bone density is compared with women of her own age group, it may fall within the normal range, but compared with a group of younger women, it is more than 2 standard deviations below the mean. Which is the right comparison group to use to determine whether she has osteoporosis? There is controversy on this point because, in this situation, it is difficult to determine the difference between disease and the effects of normal aging.

Often, when assessing a person’s health status, a change in some value or factor is more significant than the actual value of the factor. A blood pressure of 90/70 mm Hg may not be significant if that is the usual value. However, if a person usually has a blood pressure of 120/80 mm Hg, a reading of 90/70 mm Hg could indicate a significant change. Individuals are typically evaluated more than once—generally two or three times—to establish deviation from their usual value.

Reliability, Validity, and Predictive Value The accurate determination of whether a specific condition is present or absent depends on the quality and adequacy of the data collected, as well as the skill of interpretation. Decisions about the data needed are based on the initial clinical presentation and a working knowledge of pathophysiology, which guide hypothesis generation about probable etiologies. During the clinical examination, data are analyzed, and a number of likely explanations for the clinical presentation may emerge. These possible explanations are “probabilities” based on knowledge and past experience with similar cases. The purpose of further data collection, particularly laboratory and diagnostic testing, is to refine the initial probability estimates and identify the most likely diagnosis. The success of this approach depends on the selection of appropriate tests based on the pretest probabilities, as well as on the validity, reliability, and predictive value of the tests.

Validity, or accuracy, is the degree to which a measurement reflects the true value of the object it is intended to measure. For example, a pulse oximeter is designed to measure arterial oxygen saturation, and the closeness of the reading to a direct measurement of oxygen saturation in an arterial blood sample reflects its accuracy. Reliability, or precision, is the ability of a test to give the same result in repeated measurements. An instrument or laboratory test can be reliable, yet inaccurate. Repeated measurements with the pulse oximeter could give the same result each time, but if those values are significantly different from the “gold standard” of an arterial blood sample, the oximeter data would have poor validity.

Some measurements vary according to the reagents and laboratory methods used. For example, prothrombin time (PT) is sensitive to the reagent used. In one method of determining PT, the reagent—a substance composed of thromboplastin and calcium—is added to decalcified plasma to create a reaction resulting in clot formation. The PT is then determined by measuring the length of time it takes for clotting to occur after this reagent is added and compared with the normative average. Portions of the same blood sample sent to several different laboratories could return significantly different PT results. In fact, this is such a problem that laboratories now use a correction procedure to normalize the PT values across labs. The corrected PT value is reported as the international normalized ratio (INR), which has higher reliability than the PT.

The predictive value of a test is the extent to which the test can differentiate between the presence and absence of a condition in an