Lecture´s Description

**General Considerations **

This **Sqadia** **video** is the demonstration of **Interpretation of Diagnostic Tests: Laboratory Statistics. **Laboratory results can be interpreted as positive or negative. Often by comparison to reference ranges. For example, the normal blood glucose level (tested while fasting) for **non-diabetics**, should be between **3.9 and 5.5 mmol/L** (70 to 100 mg/dL). **Positive Results**
are of two types: True Positive (TP) result and False positive (FP) result. A **True Positive** **result** is positive test result for a person with the appropriate disease. A **False positive result** is a positive test result for a healthy person. Similarly, **negative results** are also of two types i.e. True Negative (TN) result and False Negative (FN) result. A **True Negative result** is a negative test result in a healthy person. A **False Negative result** is a Negative test result in a person with appropriate disease.

**Prevalence and Incidence**

**Prevalence** in epidemiology is the proportion of a particular population found to be affected by a medical condition. It is arrived at by comparing the number of people found to have the condition with the total number of people studied, and is usually expressed as a fraction, as a percentage, or as the number of cases per 10,000 or 100,000 people. If you were to measure prevalence you would simply take the total number of cases and and divide by your sample population. **Point Prevalence** is the proportion of a population that has the condition at a specific point in time. **Period Prevalence** is the proportion of a population that has the condition at some time during a given period. **Life time Prevalence** is the proportion of a population that at some point in their life (up to the time of assessment) have experienced the condition. **Incidence** in epidemiology is a measure of the probability of occurrence of a given medical condition in a population within a specified period of time. **Incidence proportion** (also known as **cumulative incidence**) is the number of new cases within a specified time period divided by the size of the population initially at risk. The difference between prevalence and incidence can be summarized thus: prevalence answers "**How many people have this disease right now?”** and incidence answers **"How many people per year newly acquire this disease?"**

**Positive and Negative Predictive Values and Standard Deviation**

Positive & Negative Predictive Values describe quantitatively the likelihood that a positive or negative result in an individual is correctly predictive of the presence or absence of disease. And these Are dependent on the prevalence of disease in contrast to sensitivity or specificity. **Positive predictive value** can be calculated by taking the number of true positives and dividing them with the sum of number of true positives and number of false positives. Similarly, **Negative predictive value** can be calculated by taking the number of true negatives and divivding them with the sum of number of true negatives and number of false negatives. **Mean** is the sum of the sampled values divided by the number of item. **Standard Deviation** **(SD)**
is a measure that is used to quantify the amount of variation or dispersion of a set of data values.

**Sensitivity and Specificity**

Sensitivity and Specificity describe quantitatively the ability of a test to correctly identify population of persons with and without a disease. Sensitivity and Specificity are **independent** of the prevalence and incidence of the disease. **Sensitivity**
measures the extent to which a laboratory test is positive in patients i.e. correctly identifies persons with appropriate disease. The point of maximum sensitivity is the lowest test value that detects subjects with a disease. **Specificity** measures the extent to which a laboratory test is negative in patients i.e. correctly identifies persons who do not have the appropriate disease. The **point of maximum specificity** is the highest test value that correctly identifies all the subjects without a disease.

**Variation**

**Physiologic Variation** describes variable laboratory results unrelated to disease processes. **Diurnal Variation** when laboratory results vary systematically according to the time of day. **Day to Day Variation** when results vary from day to day. In Physiologic Variation, laboratory results can be influenced from many other **factors **such as: diet, exercise, effects of smoking and alcohol. **Analytic Variation,** also known as **precision** or **reproducibility**, describes the variability of repeated laboratory measurements on identical specimens over a specified time span. Analytic Variation is quantitatively described by Co-efficient of Variation (CV). **Coefficient of Variation and Precision** is an inverse measurement of precision. Tests with **low CV** are very **precise**. Tests with **high CV** are **less precise**. To be analytically useful, the CV ideally should not exceed **25%** of physiologic variation.