Analytical Sensitivity:

Analytical sensitivity is the ability of an analytical method to assess small variations of the concentration of analyte and they are often expressed as the slope of calibration curve. The analytical sensitivity depends on the ratio between the SD of calibration function (random error) and the slope.

Analytical Specificity and Interference:

The analytical specificity is the ability of an assay procedure to determine specifically the concentration of the target analyte in the presence of potentially interfering substances or factors in the sample matrix. The interference from hyperlipidemia, hemolysis, and bilirubin is generally concentration dependent and can be quantified as a function of the concentration of interfering compound.

Diagnostic sensitivity

The diagnostic/clinical sensitivity of an assay is the fraction of those with a specified disease that the assay correctly predicts. The sensitivity is also called the true positive rate. Simplifying the term, sensitivity is the ability of an assay to show positive result when the disease is present in these patients.

Diagnostic Specificity

The diagnostic/clinical specificity is the fraction of those without the disease that the assay correctly predicts. The specificity is also called true negative rate. Simplifying the term, specificity is the ability of an assay to show negative result when the disease is absent in the subject taken into consideration.

The sensitivity and specificity can be calculated and represented as in the table

Sensitivity = TP/ (TP+ FN)* 100%

Specificity = TN/ (FP+ TN) * 100%

Theoretically, we may want to develop test that have both high (100%) sensitivity high (100%) specificity. In practical, we have to expense one over other depending upon the need of the assay. Establishing cutoff value to attain maximum specificity (detect false negative) may loses its ability to detect positive rates or sensitivity. Similarly, establishing the cutoff limit to attain maximum sensitivity (true positive) may compromise its ability to detect false positive. In practice, for screening any disease, generally high sensitive test are used such that any diseased individual are not missed. And for diagnostic test, the high specificity is preferred to exclude true negative individuals from true positives obtained from screening. Generally sensitivity and specificity of assay are analyzed using Receiver Operating Characteristics (ROC) Curve for clinical decision making.

Receiver Operating Characteristics curve

ROC curve is a graphical plot that illustrate the performance of a binary classifier system ( positive or negative) as its discrimination threshold varies. The curve is created by plotting the true positive rate (sensitivity) against false positive rate (1- specificity). ROC analysis provides tools to select possibly optimal models and to discard suboptimal ones.

Figure 1: ROC curve that is plotted for comparing two tests. Test A performs better than Test B which can be determined by the area under the curve (Left). The curve shows the distribution of healthy and diseased individuals. The decision threshold can be shifted based on the rate of true negative, true positive and utility of an assay(Right).

Receiver Operating Characteristics curve

ROC curve is a graphical plot that illustrate the performance of a binary classifier system ( positive or negative) as its discrimination threshold varies. The curve is created by plotting the true positive rate (sensitivity) against false positive rate (1- specificity). ROC analysis provides tools to select possibly optimal models and to discard suboptimal ones.

Figure 1: ROC curve that is plotted for comparing two tests. Test A performs better than Test B which can be determined by the area under the curve (Left). The curve shows the distribution of healthy and diseased individuals. The decision threshold can be shifted based on the rate of true negative, true positive and utility of an assay(Right).

## Comments

## Post a Comment