## Medical Decision Limits

One important aspect of setting a medical decision limit is the diagnostic specificity of a laboratory test. This is the ability of the laboratory test to predict the absence of disease when the test result is outside of that decision limit. For example, with a tumor marker such as CA 125, the diagnostic specificity would be close to 100% if the medical decision limit was set to a point at which, below that value, there was a high likelihood that the patient did not have ovarian cancer. Similarly, the negative predictive value of a test is defined by the number of patients with a negative result, or one below the medical decision limit, who don't have the disease or tumor. These is also sometimes referred to as the true-negative (TN) test result, while a false-negative (FN) test result is one that is negative based on the decision limit despite the presence of the disease in the patient. The predictive value of a negative result is the number of true negatives out of the total negative test results, including true-negative and false-negative results. The true-negative rate is another way to define diagnostic specificity, which is the ratio of the true-negative test results to the total number of negative test results, including those falsely positive that should have tested as negative.

The diagnostic sensitivity predicts the presence of a disease such as a tumor using a tumor marker test. Thus the diagnostic sensitivity of CA 125 predicts the likelihood of ovarian cancer given a certain medical decision limit. Diagnostic sensitivity relates to the positive predictive value of a test: the percentage of patients with a positive result above the medical decision limit who actually have the tumor. This is sometimes referred to as a true-positive (TP) result. Diagnostic sensitivity also relates to the false-positive (FP) rate, which is equal to the percent specificity subtracted from 100%. Refer to the following formulas for calculating these values:

Predictive value of negative result Predictive value of positive result False-positive rate True-negative rate = diagnostic specificity True-positive rate = diagnostic sensitivity

TN/(TN + FN) X 100% TP/(TP + FP) X 100% 100% - % specificity TN/(TN + FP) X 100% TP/(TP + FN) X 100%

Let us practice using these formulas with the specific example of CA 125 and its ability to predict ovarian cancer in women. In this example, the medical decision limit of 20.0 U/mL was chosen and 113 women were studied. Of these 113 women, 70 women were found to have ovarian cancer as determined by imaging techniques and biopsy, while 43 women had no evidence of ovarian cancer. A positive test result means the CA 125 is greater than or equal to 20.0 U/L and a negative result is below 20.0 U/L. Of the 113 patients, 33 had ovarian cancer and a positive CA 125 test. Therefore, these 33 patients had a true-positive CA 125. There were also 37 patients with ovarian cancer but a CA 125 of less than 20.0 U/L, a negative test result. These 37 patients had false-negative results. The 33 true-positive and 37 false-negative test results account for the total of 70 women who did have ovarian cancer. Likewise, there were 4 patients without ovarian cancer but a CA 125 greater than or equal to 20.0 U/L. These positive test results were falsely positive. The remaining 39 patients had true-negative test results since they did not have ovarian cancer and also had negative results (CA 125 less than or equal to 20.0 U/L). These data are presented in Table 13-3.

Using the equations presented earlier, the calculation for the predictive value of a negative result is 39/(39 + 37) X 100% = 51%. The calculation of the sensi-

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