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Patient-based real-time QC (PBRTQC) uses patient-derived data to assess assay performance. PBRTQC algorithms have advanced in parallel with developments in computer science and the increased availability of more powerful computers. The uptake of Artificial Intelligence in PBRTQC has been rapid, with many stated advantages over conventional approaches. However, until this review, there has been no critical comparison of these. The PBRTQC algorithms based on moving averages, regression-adjusted real-time QC, neural networks and anomaly detection are described and contrasted. As Artificial Intelligence tools become more available to laboratories, user-friendly and computationally efficient, the major disadvantages, such as complexity and the need for high computing resources, are reduced and become attractive to implement in PBRTQC applications.

Functional reference limits describe key changes in the physiological relationship between a pair of physiologically related components. Statistically, this can be represented by a significant change in the curvature of a mathematical function or curve (e.g., an observed plateau). The point at which the statistical relationship changes significantly is the point of curvature inflection and can be mathematically modeled from the relationship between the interrelated biomarkers. Conceptually, they reside between reference intervals, which describe the statistical boundaries of a single biomarker within the reference population, and clinical decision limits that are often linked to the risk of morbidity or mortality and set as thresholds. Functional reference limits provide important physiological and pathophysiological insights that can aid laboratory result interpretation. Laboratory professionals are in a unique position to harness data from laboratory information systems to derive clinically relevant values. Increasing research on and reporting of functional reference limits in the literature will enhance their contribution to laboratory medicine and widen the evidence base used in clinical decision limits, which are currently almost exclusively contributed to by clinical trials. Their inclusion in laboratory reports will enhance the intellectual value of laboratory professionals in clinical care beyond the statistical boundaries of a healthy reference population and pave the way to them being considered in shaping clinical decision limits. This review provides an overview of the concepts related to functional reference limits, clinical examples of their use, and the impetus to include them in laboratory reports.

Background: Calibration is a critical component for the reliability, accuracy, and precision of mass spectrometry measurements. Optimal practice in the construction, evaluation, and implementation of a new calibration curve is often underappreciated. This systematic review examined how calibration practices are applied to liquid chromatography-tandem mass spectrometry measurement procedures., Methods: The electronic database PubMed was searched from the date of database inception to April 1, 2022. The search terms used were "calibration," "mass spectrometry," and "regression." Twenty-one articles were identified and included in this review, following evaluation of the titles, abstracts, full text, and reference lists of the search results., Results: The use of matrix-matched calibrators and stable isotope-labeled internal standards helps to mitigate the impact of matrix effects. A higher number of calibration standards or replicate measurements improves the mapping of the detector response and hence the accuracy and precision of the regression model. Constructing a calibration curve with each analytical batch recharacterizes the instrument detector but does not reduce the actual variability. The analytical response and measurand concentrations should be considered when constructing a calibration curve, along with subsequent use of quality controls to confirm assay performance. It is important to assess the linearity of the calibration curve by using actual experimental data and appropriate statistics. The heteroscedasticity of the calibration data should be investigated, and appropriate weighting should be applied during regression modeling., Conclusions: This review provides an outline and guidance for optimal calibration practices in clinical mass spectrometry laboratories.