Evidence Based Emergency Medicine Part 2: Positive and negative predictive values of diagnostic tests.

Abstract

In volume 3, number 2, pages 48-49, we explained some screening characteristics of a diagnostic test in an educational manuscript entitled “Simple definition and calculation of accuracy, sensitivity and specificity (1). The present article was aimed to review other screening performance characteristics including positive and negative predictive values (PPV and NPV). PPV and NPV are true positive and true negative results of a diagnostic test, respectively (2). In other words, if a subject receives a certain diagnosis by a test, predictive values describe how likely it is for the diagnosis to be correct Definitions: Patient: positive for disease Healthy: negative for disease True positive (TP)= the number of cases correctly identified as patient False positive (FP) = the number of cases incorrectly identified as patient True negative (TN) = the number of cases correctly identified as healthy False negative (FN) = the number of cases incorrectly identified as healthy Positive predictive value: Positive predictive value is the proportion of cases giving positive test results who are already patients (3). It is the ratio of patients truly diagnosed as positive to all those who had positive test results (including healthy subjects who were incorrectly diagnosed as patient). This characteristic can predict how likely it is for someone to truly be patient, in case of a positive test result. Positive predictive value=TPTP+FP Negative predictive value: Negative predictive value is the proportion of the cases giving negative test results who are already healthy (3). It is the ratio of subjects truly diagnosed as negative to all those who had negative test results (including patients who were incorrectly diagnosed as healthy). This characteristic can predict how likely it is for someone to truly be healthy, in case of a negative test result. Negative predictive value=TNTN+FN SBP Total Positive Negative Ascites fluid appearance Positive TP = 15 FP = 6 21 Negative FN = 25 TN = 34 59 Total 40 40 80 Open in a separate window Lens dislocation Total Positive Negative Ultrasonography Positive TP = 11 FP = 2 13 Negative FN = 2 TN = 115 117 Total 13 117 130 Open in a separate window Predictive values and the prevalence of the disease: Since the ratio includes both healthy and patient subjects, predictive values are affected by the prevalence of the disease and can differ from one setting to another for the same diagnostic test. The lower the prevalence of the disease, the higher its negative predictive value. On the other hand, the higher the prevalence of the disease, the higher the positive predictive value. For solving these problems, positive and negative likelihood ratios were developed, which will be introduced and discussed in part three of EBM series articles of Emergency. Examples: Example 1: Imagine we have a sample population of 100 people, 50 healthy and the others patients. If the test was positive for 75 people of this population, the PPV and NPV of test are as follows: PPV: 50/75 = 0.66 or 66.6%. This means that in this population, 66.6% of people whose test result is positive, have the disease. NPV: 25/25 = 100%. This means that in this population, 100% of the people whose test result is negative, are healthy (Figure 1). Open in a separate window Figure 1 A schematic presentation of an example test with 66.6% PPV, and 100% NPV