Fundamentally, any medical practitioner or health institution will never treat a disease of condition appropriately without correctly diagnosing the disease or condition. Many clinical tests rely on the sensitivity and specific value to confirm or refute the reliability of the presence of a disease/condition or diagnostic process. In an ideal condition, good clinical test will correctly identify all patients with the disease (positives) in question and negatively verify patients without the disease (negatives). However, these tests are not error proof or ideal tests. Hence, clinicians often deals with false positive and false negative results.

Some of the fundamental criteria that deals with sensitivity and specificity of a test include but are not limited to the following, true positive, false positive, true negative, false negative, positive predictive value (PPV) and negative predictive (NPV) (Lalkhen, & McCluskey, 2008). Therefore, to address sensitivity and specificity appropriately, understanding the fundamental concepts of these terms is an invaluable asset to the reliability of any given test and the proceeding medical intervention. The sensitivity and specificity in a clinical test is independent of the population of interest subjected to the tests while PPV and NPV are dependent on the prevalence of the disease in question within the population of interest (Lalkhen, & McCluskey, 2008). Hence, defining the sensitivity rate is subject to a clinical test’s ability in identifying correctly patients with a condition or disease (True Positive). Thus, it is very important to emphasize that a “test” sensitivity rate defines only true positives or patients with the disease/condition and false negative patients or patients with the disease/condition but had a negative test result. For instance, a test with a 95% sensitivity rate means that the test correctly identifies 95% of patients as true positive, but 5% of patients with the disease/condition go undetected (false negative). Hence, the formula associated with sensitivity calculation is defined as the sum of all true positives (TP) divided by the sum of true positives (TP) plus the sum of false negatives (FN) (Lalkhen, & McCluskey, 2008). Therefore, a high sensitivity test is very crucial in clinical tests and studies.

On the other hand, specificity is a test’s ability to correctly identify patients without the disease or condition in question (Lalkhen, & McCluskey, 2008). Hence, specificity rate do not diagnose the disease, but it is only used to define the true negative patients or patients without the disease who had a negative test result and false positive patients or patients without the disease but had a positive test result (Lalkhen, & McCluskey, 2008). Therefore, the clinical formula for specificity is defined as the sum of the true negative (TN) divided by the sum of the true negatives (TN) and the sum of false positives (FP). For instance, a test that had 95% specificity simply means that 95% of patients without the disease had a negative (true negative) test result, but 5% of patients without the disease in question are incorrectly identified with a positive test result (false positive).

To further illustrate clinical test validity consider the following scenario; prior to a clinical study for “condition Q”, if “test A” had a sensitivity of 95% and specificity of 75%, “test A” is a better candidate test for identifying potential positive subjects or patients for “condition Q” than identifying true negative patients because it will identify 95% of true positive for the condition Q, and only 5% of subjects will have a false negative result for condition Q. Furthermore, for a test with specificity of 75%, it means that 75% of patients without “condition Q” will be correctly identified (true negatives) with “test A”, and 25% of patients without the disease will be incorrectly diagnosed as false positive. Possibly, if no further test is conducted to clear out the false positives, 25% of patients will be treated for a condition that they did not have. Therefore, to solve the false positive diagnostic issue in the study for condition Q, it is necessary to employ another “test” with higher specificity value among all patients tested positive for condition Q in order to eliminate all false positive result and correctly identify the true negatives. Once this is done, the researcher will be confident that nearly if not all subjects participating in the trial who tested positive are true positives and those tested negative are the true negatives.

In addition, the PPV predicts the positive value of any given clinical “test”. It is defined as the sum of true positives divided by the sum of true positive and false positives. The NPV predicts the negative value of any given clinical “test”. The NPV is defined as the sum of true negative divided by the sum of true negatives and false negatives (Lalkhen, & McCluskey, 2008). For instance, the PPV expectation for a “test” with sensitivity of 95% and specificity of 75% among 100 subjects/patient will be (95/95+25)*(100), which gives a PPV of 79%, and the NPV will be (75/75+5), which gives an NPV of 93.8%. On the other hand, the PPV expectation for a “test” with sensitivity of 75% and 95% specificity among 100 subjects/patients will be 93.8%, and the NPV will be 79%.

Reference

Lalkhen, A., & McCluskey, A. (2008). Clinical tests: Sensitivity and specificity. Contin Educ Anaesth Crit Care Pain, 8 (6)2 21-223. doi: 10.1093/bjaceaccp/mkn041 . Retrieve from http://ceaccp.oxfordjournals.org/content/8/6/221.full.

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