# Definitions

## Sensitivity

• The probability that a test will return positive, if the patient has the disease

## Specificity

• The probability that a test will return negative, if the patient does not have the disease

## Positive Predictive Value

• The probability that patient has the disease, if the test returns positive

## Negative Predictive Value

• The probability that the patient does not have the disease, if the test returns negative

## Incidence

• The rate of new cases of a disease  or diagnosis
• Generally reported as the number of new cases occurring within a period of time (eg incidence of x per year)
• Often expressed as a fraction of the population (eg incidence of x per 100,000 population per year)
• May be reported using a different format of rate, eg the rate of difficult intubation is 1 per 2,000 elective general anaesthetics

## Prevalence

• The actual number of cases alive, with the disease either during a period of time (period prevalence) or at a particular date in time (point prevalence)
• Often expressed as a proportion of the study population

# Statistical Analysis

## Incidence vs Prevalence

• Prevalence tells you how many people have a condition right now
• Incidence tells you how many people will newly acquire a condition, over a set study period

## 2 x 2 Contingency Tables

 Test Patients With Disease Patients Without Disease Positive Result a (true positive) b (false positive) Negative Result c (false negative) d (true negative)

Sensitivity = a / (a + c)
Specificity = d / (d + b)
Positive Predictive Value = a / (a + b)
Negative Predictive Value = d / (d + c)

How "reliable" is a test?

• How much you can "trust" the results of a test to be correct depends on the PPV (if it is a positive test) and the NPV (if it is a negative test)
• Looking at the sensitivity or specificity alone can be misleading because of the influence of the incidence of the condition being tested

Example - High Incidence

• Incidence of disease: 30%
• Sensitivity: 90%
• Specificity: 90%

Lets look at a sample of 1,000 patients

 Test Patients With Disease (300) Patients Without Disease (700) Positive Result 270 (true positive) 70 (false positive) Negative Result 30 (false negative) 630 (true negative)

Positive Predictive Value = 79.4%
Negative Predictive Value = 95.5%

Example - Low Incidence

• Incidence of disease: 1%
• Sensitivity: 90%
• Specificity: 90%

Lets look at a sample of 1,000 patients

 Test Patients With Disease (10) Patients Without Disease (990) Positive Result 9 (true positive) 99 (false positive) Negative Result 1 (false negative) 891 (true negative)

Positive Predictive Value = 8.3%
Negative Predictive Value = 99.9%

Conclusion

• If the incidence of a condition is low, eg difficult intubation, regardless of how sensitive a test is, it will never have a high positive predictive value, and thus the clinical utlility of the test will be limited

## Predicting Difficult Airway

Read this editorial, which gives insight into pretty much the only statistics SAQ that can be asked, after the 2013 curriculum revision - and which is also clinically relevant!

Predicting difficult intubation - worthwhile exercise or pointless ritual?
Yentis. Anaesthesia, 2002, 57, pages 105-109

e-journal available via the ANZCA Library