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The predictive value of a positive test indicates the proportion of those with a positive test who actually have the disease. Often, the predictive value of tests is expressed as the probability, or odds, that a condition is present.

## What is the positive predictive value of this diagnostic test?

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).

## What is a good sensitivity for a diagnostic test?

Generally speaking, “a test with a sensitivity and specificity of around 90% would be considered to have good diagnostic performance—nuclear cardiac stress tests can perform at this level,” Hoffman said. But just as important as the numbers, it’s crucial to consider what kind of patients the test is being applied to.

## What additional information would you need to determine the predictive value of the test?

Sensitivity is the proportion of people with the disease who will have a positive test result. The two pieces of information you need to calculate the positive predictive value are circled: the true positive rate (cell a) and the false positive rate (cell b).

## How do you calculate diagnostic value?

DOR of a test is the ratio of the odds of positivity in subjects with disease relative to the odds in subjects without disease (5). It is calculated according to the formula: DOR = (TP/FN)/(FP/TN). DOR depends significantly on the sensitivity and specificity of a test.

## How do I get a PPV?

For a mathematical explanation of this phenomenon, we can calculate the positive predictive value (PPV) as follows: PPV = (sensitivity x prevalence) / [ (sensitivity x prevalence) + ((1 – specificity) x (1 – prevalence)) ]

## Is positive predictive value a percentage?

The predictive value of a test is a measure (%) of the times that the value (positive or negative) is the true value, i.e. the percent of all positive tests that are true positives is the Positive Predictive Value.

## What is a high negative predictive value?

The more sensitive a test, the less likely an individual with a negative test will have the disease and thus the greater the negative predictive value. The more specific the test, the less likely an individual with a positive test will be free from disease and the greater the positive predictive value.

## How do you calculate positive predictive value and negative predictive value?

Sensitivity is the probability that a test will indicate ‘disease’ among those with the disease:

- Sensitivity: A/(A+C) × 100.
- Specificity: D/(D+B) × 100.
- Positive Predictive Value: A/(A+B) × 100.
- Negative Predictive Value: D/(D+C) × 100.

## What is a good value for sensitivity and specificity?

For a test to be useful, sensitivity+specificity should be at least 1.5 (halfway between 1, which is useless, and 2, which is perfect). Prevalence critically affects predictive values. The lower the pretest probability of a condition, the lower the predictive values.

## How does the predictive value of a screening test vary according to the prevalence of disease?

In contrast, the positive predictive value of a test, or the yield, is very dependent on the prevalence of the disease in the population being tested. The higher the prevalence of disease is in the population being screened, the higher the positive predictive values (and the yield).

## What is the difference between specificity and positive predictive value?

The significant difference is that PPV and NPV use the prevalence of a condition to determine the likelihood of a test diagnosing that specific disease. Whereas sensitivity and specificity are independent of prevalence.