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Abstract：
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.
摘要：
监测对于评估疾病预后和评估治疗策略的有效性是必不可少的。两者都依赖于患者数据的串行测量。它在保持分析系统的稳定性方面也起着至关重要的作用。这是通过质量控制样品的串行测量来实现的。通过数据采集可以实现准确的监测,遵循严格的分析前和分析方案，并应用合适的统计方法。在一个稳定的过程中，可以根据在该过程被认为是可靠的期间收集的历史数据来预测未来的观测结果。这可以使用统计预测间隔来评估。统计上,预测间隔根据历史数据给出“间隔”，未来测量结果可以以指定的概率（如95%）定位。预测区间由两个主要组成部分组成：（i）设定点和（ii）设定点周围的总变化，确定区间的上限和下限。两者都可以使用从该过程在其稳态期间获得的重复测量结果来计算。在本文中,(I)概述了预测区间的理论基础，(二)通过实例说明了其实际应用，旨在促进实验室医学常规实践中预测间隔的实施，作为监测患者数据和分析系统的强大工具。
