Introduction

Diagnostic tests are a fundamental tool in medical practice, as they allow the timely detection, diagnosis, and monitoring of diseases. A diagnostic test may be a simple blood test, an X-ray, or a more specialized examination such as ultrasound imaging. Their aim is to provide a quick, reliable, and, if possible, cost-effective indication of whether a specific disease is present or not. To evaluate the usefulness of a new test, it is compared with an established, often more expensive or invasive examination considered as the reference test. The effectiveness of the new method is assessed through specific quality indicators, which are based on its ability to accurately distinguish patients from healthy individuals.

The Fourfold Table of Results

The evaluation of every diagnostic method is summarized in a fourfold table, where actual health data are compared with the test result. From this comparison, four possible outcomes emerge. When the test is positive and the person is indeed ill, the outcome is a true positive. When it is negative and the individual is truly healthy, it is a true negative. However, false positives also exist, when the test indicates disease although the individual is healthy, as well as false negatives, when the test indicates health although the individual has the disease. A reliable diagnostic test should minimize false results and maximize the accuracy of true outcomes.

Main Quality Indicators

Sensitivity reflects the ability of a test to correctly identify patients with the disease. It expresses the probability of obtaining a positive result when the individual is truly ill. The higher the sensitivity, the fewer false negatives appear. Specificity, on the other hand, captures the ability of the test to correctly identify healthy individuals. It expresses the probability of obtaining a negative result when the person does not have the disease. A method with high specificity minimizes the number of false positives. Effectiveness refers to the overall proportion of examined individuals who were correctly classified as either ill or healthy, and thus it serves as a combined indicator of reliability.

Prevalence shows the frequency of a disease in a population at a given moment in time. It also functions as a measure of how likely it is for someone to have the disease before being tested. The positive predictive value expresses the probability that disease is truly present among those with a positive result, while the negative predictive value shows the probability that disease is absent among those with a negative result. It is important to note that both of these values are strongly influenced by the prevalence of the disease in the studied population.

Likelihood Ratios

Beyond the basic indicators, likelihood ratios are used for a more complete assessment. The positive likelihood ratio expresses how much more likely a positive result is in a diseased person compared to a healthy one, while the negative likelihood ratio expresses how much more likely a negative result is in a diseased person compared to a healthy one. These measures allow sensitivity and specificity to be combined, offering a stronger estimate of how much the probability of disease presence or absence changes after the test is performed.

Example: A Pregnancy Test

In a study conducted with 670 women who experienced delayed menstruation, their urine was examined with a pregnancy test. Among them, 530 results were true positives, ten were false positives, sixty-two were false negatives, and sixty-eight were true negatives. The prevalence of pregnancy in this specific group was 88%, the sensitivity of the test was 90%, and its specificity 87%. The positive predictive value demonstrated that when the test was positive, the probability of pregnancy rose to 98%, whereas the negative predictive value showed that when the test was negative, the probability of not being pregnant was only 53%. This example highlights the importance of prevalence as well as the need for careful interpretation of all diagnostic indicators.

Conclusion

The quality indicators of diagnostic tests form the cornerstone for assessing their reliability. Understanding sensitivity, specificity, predictive values, and likelihood ratios is crucial for making sound clinical decisions. With their proper interpretation, healthcare professionals can select the most appropriate diagnostic tools, accurately assess the probability of disease presence or absence, and provide higher-quality patient care. Therefore, the evaluation of diagnostic indicators is not simply a theoretical process but a practical necessity that directly affects daily medical practice and patient safety.