Emergency (****); * (*): *-* Copyright © 2015 Shahid Beheshti University of Medical Sciences. All rights reserved. Downloaded from: www.jemerg.com 87 Emergency (2015); 3 (3): 87-88 EDUCATIONAL Evidence Based Emergency Medicine Part 2: Positive and negative predictive values of diagnostic tests Saeed Safari1, Alireza Baratloo1, Mohamed Elfil2, Ahmed Negida3* 1. Emergency Department, Shohadaye Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran. 2. Faculty of medicine, Alexandria University, Alexandria, Egypt. 3. Faculty of medicine, Zagazig University, Zagazig, Egypt. *Corresponding Author: Ahmed Negida; Faculty of Medicine, Zagazig University, El-Kanayat, El-Sharkia, Zagazig, Egypt. Tel: +201125549087; Email: ahmed01251@medicine.zu.edu.eg Received: June 2015; Accepted: June 2015 Introduction: n volume 3, number 2, pages 48-49, we explained some screening characteristics of a diagnostic test in an educational manuscript entitled “Simple defini- tion and calculation of accuracy, sensitivity and specific- ity" (1). The present article was aimed to review other screening performance characteristics including posi- tive 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 sub- ject receives a certain diagnosis by a test, predictive val- ues describe how likely it is for the diagnosis to be cor- rect Definitions: Patient: positive for disease Healthy: negative for disease True positive (TP)= the number of cases correctly iden- tified 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 giv- ing 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 some- one to truly be patient, in case of a positive test result. Positive predictive value= FPTP TP 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 pa- tients 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= FNTN TN 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 dis- ease and can differ from one setting to another for the same diagnostic test. The lower the prevalence of the dis- ease, 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 pop- ulation, 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). I Figure 1: A schematic presentation of an example test with 66.6% PPV, and 100% NPV. Copyright © 2015 Shahid Beheshti University of Medical Sciences. All rights reserved. Downloaded from: www.jemerg.com Safari et al 88 Example 2: In a study by Aminiahidashti et al. (4), out of a total population of 80 cirrhotic patients, 21 (26%) had opaque ascites fluid appearance (Figure 2). 15 people out of the 21 had spontaneous bacterial peritonitis (SBP). Question: Please calculate sensitivity, specificity, accu- racy, PPV, and NPV of opaque ascites fluid in prediction of SBP if the total number of SBP patients was 40 cases (50%). Answer: Considering the total number of 40 patients and 15 TP cases, there were 25 cases of FN. In addition, total number of negative test was equal to 59. Therefore, number of TN cases: 59 – 25 = 34. Based on the above-mentioned calculations, screening performance characteristics of ascites fluid appearance in prediction of SBP are as follows: Sensitivity: 15/40 = 37.5% Specificity: 34/40 = 85% Accuracy: (15 + 34) / 80 = 61.2% PPV: 15/21= 71.4% NPV: 34/59 = 57.6% Example 3: In the Haghighi et al. study (5), out of the 130 patients, 13 already had traumatic lens dislocation and 117 were healthy. However, ultrasonography was posi- tive for lens dislocation in 13 cases, while 2 cases were FP (Figure 3). Question: Please calculate sensitivity, specificity, accu- racy, PPV, and NPV of ultrasonography in detection of traumatic lens dislocation. Answer: Considering the total number of 13 patients and 2 FP cases, there were 11 TP cases. Screening performance characteristics of ultrasonogra- phy in prediction of traumatic lens dislocation are as fol- lows: Sensitivity: 11/13 = 84.6% Specificity: 115/117 = 98.3% Accuracy: (11 + 115) / 130 = 96.9% PPV: 11/13 = 84.6% NPV: 115/117 = 98.3% References: 1. Baratloo A, Hosseini M, Negida A, El Ashal G. Part 1: Simple Definition and Calculation of Accuracy, Sensitivity and Specificity. Emergency. 2015;3(2): 48-9. 2. Fletcher RH, Fletcher SW, Fletcher GS. Clinical epidemiology: the essentials: Lippincott Williams & Wilkins; 2012. p: 127. 3. Altman DG, Bland JM. Statistics Notes: Diagnostic tests 2: predictive values. BMJ. 1994;309(6947):102. 4. Aminiahidashti H, Hosseininejad SM, Montazer H, Bozorgi F, Jahanian F, Raee B. Diagnostic Accuracy of Ascites Fluid Gross Appearance in Detection of Spontaneous Bacterial Peritonitis. Emergency. 2014;2(3): 138-40. 5. Haghighi SHO, Begi HRM, Sorkhabi R, et al. Diagnostic Accuracy of Ultrasound in Detection of Traumatic Lens Dislocation. Emergency. 2014;2(3): 121-4. Figure 3: A schematic presentation of the example 3. Lens dislocation Total Positive Negative Ultrasonography Positive TP = 11 FP = 2 13 Negative FN = 2 TN = 115 117 Total 13 117 130 Figure 2: A schematic presentation of the example 2. SBP Total Positive Negative Ascites fluid appearance Positive TP = 15 FP = 6 21 Negative FN = 25 TN = 34 59 Total 40 40 80