Chronic cases only,a e.g., prevalence of diabetes in United States
24.2.1 Sensitivity, Specificity, Positive Predictive Value (PPV), and Negative Predictive Value (NPV)
Patients with true diseasea
Patients without diseasea
TP/(TP + FP) = TP/(total number of patients with positive result) = PPVb
TN/(TN + FN) = TN/ (total number of patients with negative result) = NPVc
TP/(TP + FN) = TP/(total number of patients with disease) = sensitivityd
TN/(TN + FP) = TN/(total number of patients without disease) = specificitye
A test developed for diagnosing lung cancer has a sensitivity of 80%. There are 100 patients who actually have lung cancer in the study and 300 who do not. Calculate the total number of false negatives?
A 60-year-old man requests fecal occult blood (FOB) testing for colon cancer screening as he does not want to do colonoscopy. He reports that he has been eating barbecued meat, processed food, and bacon at least two times per day all his life. A detailed artificial intelligence software analysis reveals that patients with similar baseline characteristics have a 10% prevalence of colon cancer. FOB comes back positive. Studies have reported that FOB has a sensitivity of 80% and a specificity of 70%. What is the likelihood that this patient really has colon cancer?
Let’s say that we have created a test for diagnosis and that the test result has a numerical value. When we plot the frequency distribution of results obtained from general population, this will generally produce a bell-shaped (symmetric) distribution curve. Examples of such tests include fasting blood glucose, HbA1C, serum cholesterol, etc.
SEN = Sensitivity moves with the Negative predictive value. When you are lowering the cut-off point, you are including more patients with disease, thus false negative rate decreases. So, when the test is negative, it is more likely to be truly negative. But as we try to increase the sensitivity of the test, we lose the specificity.
SPE = Specificity moves with the Positive predictive value. When you increase the cutoff point, you are making sure that you only get patients with the disease, thus the false positive rate decreases. So, when the test is positive, it is more likely to be truly positive.