11/13/24, 7\:33 PM Guide | Sensitivity, specificity, PPV and NPV

Sensitivity, speci

Table of contents

Introduction

The aim of this article is to help provide an understanding of sensitivity, speci
negative predictive value (NPV) in an intuitive and comprehensible format.

Background

Sensitivity and speci
Positive predictive value (PPV) and negative predictive value (NPV) are best thought of as the clinical relevance of a test.
The signi
diagnosing that speci
Prevalence is the number of cases in a de
percentage.
Sensitivity is the percentage of true positives (e.g. 90% sensitivity = 90% of people who have the target disease will test
positive).
Speci
negative).
These allow you to rule conditions in or out but not de
A classic table that allows sensitivity and speci
Table 1
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Sensitivity

The sensitivity of a test is the proportion of people who test positive among all those who actually have the disease.
A sensitive test helps rule out a disease when the test is negative (e.g. negative amylase in pancreatitis). Highly SeNsitive =
SNOUT = rule out.
Sensitivity can be thought of as ‘how delicate/sensitive the test is to picking up little changes’
. The test for amylase is highly
sensitive because it is capable of picking up very small amounts of amylase in the blood. As a result, the chance of amylase
being present that is “below the threshold for detection” is small. Therefore, a negative result would mean one of two things.
Firstly, that amylase is present but in such small quantities that it is undetectable by the test (unlikely because this test picks up
small changes). Secondly, that amylase is not present at all (more likely).
This example works because the disease (pancreatitis) has a trait (amylase) that is almost always present and the test looks
for that trait. If the trait is not present, the disease is unlikely to be present and can be ruled out.
Sensitivity calculation

Speci

The speci
disease.
A speci
If a disease (UTI) has a trait (nitrites in urine) that is rare in other diseases, a test for that trait can be thought of as being highly
speci
because a highly speci
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Speci

PPV and NPV

Positive predictive value (PPV) and negative predictive value (NPV) are directly related to prevalence and allow you to
clinically say how likely it is a patient has a speci

Positive predictive value (PPV)

The positive predictive value is the probability that following a positive test result, that individual will truly have that speci
disease.
Positive predictive value (PPV)
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Negative predictive value (NPV)

The negative predictive value is the probability that following a negative test result, that individual will truly not have that
speci
Negative predictive value (NPV)
For any given test (i.e. sensitivity and speci
will be more false positives for every true positive. This is because you’re hunting for a “needle in a haystack” and likely to
lots of other things that look similar along the way - the bigger the haystack, the more frequently you mistake things for a
needle.
Therefore, as prevalence decreases, the NPV increases because there will be more true negatives for every false negative.
This is because a false negative would mean that a person actually has the disease, which is unlikely because the disease is
rare (low prevalence).
Examples of how PPV and NPV could vary with prevalence for a speci
Prevalence PPV NPV
1% 8% >99%
10% 50% 99%
20% 69% 97%
50% 90% 90%
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