Diagnosis

A diagnosis is the designation as to the nature or cause of a health problem (e.g., bacterial pneumonia or hemorrhagic stroke). The diagnostic process usually requires a careful history and physical examination. The history is used to obtain a person's account of his or her symptoms, their progression, and the factors that contribute to a diagnosis. The physical examination is done to observe for signs of altered body structure or function.

The development of a diagnosis involves weighing competing possibilities and selecting the most likely one from among the conditions that might be responsible for the person's clinical presentation. The clinical probability of a given disease in a person of a given age, sex, race, life­style, and locality often is influential in arriving at a pre­sumptive diagnosis. Laboratory tests, radiologic studies, CT scans, and other tests often are used to confirm a diagnosis.

Normality. An important factor when interpreting diag­nostic test results is the determination of whether they are normal or abnormal. Is a blood count above normal, within the normal range, or below normal? Normality usually determines whether further tests are needed or if interven­tions are necessary. What is termed a normal value for a laboratory test is established statistically from test results obtained from a selected sample of people. The normal val­ues refer to the 95% distribution (mean plus or minus two standard deviations [mean ± 2 SD]) of test results for the ref­erence population.¹⁴ Thus, the normal levels for serum sodium (135 to 145 mEq/L) represent the mean serum level for the reference population ± 2 SD. The normal values for some laboratory tests are adjusted for sex or age. For ex­ample, the normal hemoglobin range for women is 12.0 to 16.0 g/dL and for men, 14.0 to 17.4 g/dL.¹⁵ Serum creati-nine level often is adjusted for age in the elderly (see Chap­ter 36), and normal values for serum phosphate differ between adults and children.

Reliability, Validity, Sensitivity, Specificity, and Predictive Value. The quality of data on which a diagnosis is based may be judged for its reliability, validity, sensitivity, speci­ficity, and predictive value.¹⁶,¹⁷ Reliability refers to the extent to which an observation, if repeated, gives the same result. A poorly calibrated blood pressure machine may give in­consistent measurements of blood pressure, particularly of pressures in either the high or low range. Reliability also de­pends on the persons making the measurements. For ex­ample, blood pressure measurements may vary from one observer to another because of the technique that is used (e.g., different observers may deflate the cuff at a different rate, thus obtaining different values), the way the numbers on the manometer are read, or differences in hearing acu­ity. Validity refers to the extent to which a measurement tool measures what it is intended to measure. This often is assessed by comparing a measurement method with the best possible method of measure that is available. For ex­ample, the validity of blood pressure measurements ob-

tained by a sphygmomanometer might be compared with those obtained by intraarterial measurements.

Measures of sensitivity and specificity are concerned with determining how well the test or observation identi­fies people with the disease and people without the dis­ease. Sensitivity refers to the proportion of people with a disease who are positive for that disease on a given test or observation (called a true-positive result). Specificity refers to the proportion of people without the disease who are neg­ative on a given test or observation (called a true-negative result). A test that is 95% specific correctly identifies 95 of 100 normal people. The other 5% are false-positive results. A false-positive test result, particularly for conditions such as human immunodeficiency virus (HIV) infection, can be unduly stressful for the person being tested (see Chap­ter 22). In the case of HIV testing, a positive result on the initial antibody test is followed up with a more sensitive test. On the other hand, false-negative test results in con­ditions such as cancer can delay diagnosis and jeopardize the outcome of treatment.

Predictive value is the extent to which an observation or test result is able to predict the presence of a given disease or condition. A positive predictive value refers to the propor­tion of true-positive results that occurs in a given popula­tion. In a group of women found to have "suspect breast nodules" in a cancer-screening program, the proportion later determined to have breast cancer would constitute the positive predictive value. A negative predictive value refers to the true-negative observations in a population. In a screen­ing test for breast cancer, the negative predictive value rep­resents the proportion of women without suspect nodules who do not have breast cancer. Although predictive values rely in part on sensitivity and specificity, they depend more heavily on the prevalence of the condition in the popula­tion. Despite unchanging sensitivity and specificity, the positive predictive value of an observation rises with prev­alence, whereas the negative predictive value falls.