Predictive Value and Efficiency of Hematology Data

Abstract

Laboratory test results and procedures can be evaluated at four levels:

• 1. Analytic analysis of laboratory test: precision, technical sensitivity, technical specificity;

• 2. Diagnostic analysis of laboratory test: diagnostic sensitivity, diagnostic specificity, Youden index, likelihood ratio, etc.;

• 3. Operational analysis of laboratory test: predictive value of positive result, predictive value of negative result, efficiency, discriminant function, etc.;

• 4. Medical decision-making analysis of laboratory test: threshold probability, cost-benefit analysis, solving the decision tree.

Analysis of results or selection of tests can occur at any level, without knowledge of the test’s evaluation or performance at the remaining levels. Alternately, the development of new laboratory tests can proceed from level 1 to level 4, or vice versa. Unfortunately, the former is usually the case and most of the tests in use today have never been evaluated at the medical decision-making level (level 4). Recent efforts at developing automated WBC differential counters represent a disproportionate amount of time and energy expended at level 1, and typify our backward approach to laboratory medicine. In thinking about the development of new diagnostic tests, we should begin at level 4 to characterize the properties and specifications that the test must meet. As an example, an in vitro test for the diagnosis of pulmonary embolism could be characterized in this fashion with criteria specified at each of the lower levels.

Returning to the question of “How good should a laboratory test be?”, we can see that the answer must come from an analysis of the benefit-cost equation (level 4). Figure 2 is a plot of the net benefit and cost of treatment versus the threshold probability. Since the threshold probability defines how certain one must be of the diagnosis before proceeding with treatment, it serves as a minimum probability which should be exceeded by the predictive value of the test.

When the benefit-cost ratio is low, a test with a very high predictive value is required to exceed the threshold probability. On the other hand, when the benefit-cost ratio is high, even a test with a low predictive value would be of use to the physician in making the decision to treat the patient. Within this framework, a number of clinical situations could be evaluated and problems requiring the development of highly predictive laboratory tests (low benefit-cost ratios) could be identified. Too much emphasis in laboratory medicine has been placed on the “laboratory” and not enough on the “medicine”. How important is the coefficient of variation when the benefit-cost ratio is high? Tests can not be developed or selected appropriately in a therapeutic vacuum.