Abstract
Highlights • Prediction interval has a great potential to be used in laboratory medicine • It is a powerful tool for computing personalized reference interval and reference change value • It can be used to assess the stability of analytical systems • It can be used in monitoring the accuracy and reproducibility of analytical systems Monitoring is indispensable for assessing disease prognosis and evaluating the effectiveness of treatment strategies, both of which rely on serial measurements of patients’ data. It also plays a critical role in maintaining the stability of analytical systems, which is achieved through serial measurements of quality control samples. Accurate monitoring can be achieved through data collection, following a strict preanalytical and analytical protocol, and the application of a suitable statistical method. In a stable process, future observations can be predicted based on historical data collected during periods when the process was deemed reliable. This can be evaluated using the statistical prediction interval. Statistically, prediction interval gives an “interval” based on historical data where future measurement results can be located with a specified probability such as 95%. Prediction interval consists of two primary components: (i) the set point and (ii) the total variation around the set point which determines the upper and lower limits of the interval. Both can be calculated using the repeated measurement results obtained from the process during its steady-state. In this paper, (i) the theoretical bases of prediction intervals were outlined, and (ii) its practical application was explained through examples, aiming to facilitate the implementation of prediction intervals in laboratory medicine routine practice, as a robust tool for monitoring patients’ data and analytical systems.
Key Questions
1. What is the primary objective of the study?
The study aims to explore the theoretical foundations and practical applications of prediction intervals in laboratory medicine, emphasizing their role in monitoring patient data and analytical systems.
2. What methodology was employed in the research?
The research outlines the theoretical bases of prediction intervals and demonstrates their practical application through examples, aiming to facilitate their implementation in routine laboratory practice.
3. What were the main findings of the study?
The study highlights the potential of prediction intervals as a robust tool for computing personalized reference intervals and reference change values. It also emphasizes their utility in assessing the stability and performance of analytical systems.
Summary
Abdurrahman Coskun (2024) discusses the application of prediction intervals in laboratory medicine, focusing on their role in monitoring patient data and analytical systems. The study underscores the importance of prediction intervals in computing personalized reference intervals, assessing analytical system stability, and enhancing the accuracy and reproducibility of laboratory measurements.
