{"title":"Double-module ratio as a patient-based real-time quality control index for small shift detection and comparative assay.","authors":"Yuanyuan Li, Xiaoling Chen, Ying Zhao","doi":"10.1016/j.cca.2025.120214","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>Irregular fluctuations in patient data affect patient-based real-time quality control (PBRTQC) performance. Moreover, results among multiple instruments can be inconsistent. Here, we explored a simple index, double-module ratio, to improve PBRTQC performance through overcoming irregular fluctuations, and ensure consistency across multiple instruments in real time.</p><p><strong>Methods: </strong>Laboratory results from the same departments tested centrally such that the original patient data fluctuated irregularly during the day were included. Albumin (Alb), calcium (Ca), cholinesterase (ChE), and total protein (TP) indexes derived from a single module were processed through conventional PBRTQC. The ratios calculated from PBRTQC data of two modules were defined as double-module ratios (R<sub>Alb</sub>, R<sub>Ca</sub>, R<sub>ChE,</sub> and R<sub>TP</sub>). The four index pairs were compared using various PBRTQC procedures, including conventional algorithms: moving average (MA), moving median, exponentially weighted MA, and moving standard deviation.</p><p><strong>Results: </strong>All four ratios (R<sub>Alb</sub>, R<sub>Ca</sub>, R<sub>ChE</sub>, and R<sub>TP</sub>) were more powerful in identifying system error (SE) than their counterparts (Alb, Ca, ChE, and TP). These improvements were proportional to the degree of reduction in QC data variation after ratios were taken. In particular, R<sub>Alb</sub>, R<sub>Ca</sub>, and R<sub>TP</sub> could aid in detecting small SEs (about <1.5 folds of the allowable total errors) significantly. Although ChE demonstrated almost no surveillance capacity for negative SEs, R<sub>ChE</sub> demonstrated a significant improvement. The impact of the ratios on random error detection was dependent on the indexes and PBRTQC parameters.</p><p><strong>Conclusions: </strong>As a PBRTQC index, double-module ratios can facilitate small shift detection and ensure result consistency among various modules in real time.</p>","PeriodicalId":10205,"journal":{"name":"Clinica Chimica Acta","volume":" ","pages":"120214"},"PeriodicalIF":3.2000,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinica Chimica Acta","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.cca.2025.120214","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MEDICAL LABORATORY TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Objectives: Irregular fluctuations in patient data affect patient-based real-time quality control (PBRTQC) performance. Moreover, results among multiple instruments can be inconsistent. Here, we explored a simple index, double-module ratio, to improve PBRTQC performance through overcoming irregular fluctuations, and ensure consistency across multiple instruments in real time.
Methods: Laboratory results from the same departments tested centrally such that the original patient data fluctuated irregularly during the day were included. Albumin (Alb), calcium (Ca), cholinesterase (ChE), and total protein (TP) indexes derived from a single module were processed through conventional PBRTQC. The ratios calculated from PBRTQC data of two modules were defined as double-module ratios (RAlb, RCa, RChE, and RTP). The four index pairs were compared using various PBRTQC procedures, including conventional algorithms: moving average (MA), moving median, exponentially weighted MA, and moving standard deviation.
Results: All four ratios (RAlb, RCa, RChE, and RTP) were more powerful in identifying system error (SE) than their counterparts (Alb, Ca, ChE, and TP). These improvements were proportional to the degree of reduction in QC data variation after ratios were taken. In particular, RAlb, RCa, and RTP could aid in detecting small SEs (about <1.5 folds of the allowable total errors) significantly. Although ChE demonstrated almost no surveillance capacity for negative SEs, RChE demonstrated a significant improvement. The impact of the ratios on random error detection was dependent on the indexes and PBRTQC parameters.
Conclusions: As a PBRTQC index, double-module ratios can facilitate small shift detection and ensure result consistency among various modules in real time.
期刊介绍:
The Official Journal of the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC)
Clinica Chimica Acta is a high-quality journal which publishes original Research Communications in the field of clinical chemistry and laboratory medicine, defined as the diagnostic application of chemistry, biochemistry, immunochemistry, biochemical aspects of hematology, toxicology, and molecular biology to the study of human disease in body fluids and cells.
The objective of the journal is to publish novel information leading to a better understanding of biological mechanisms of human diseases, their prevention, diagnosis, and patient management. Reports of an applied clinical character are also welcome. Papers concerned with normal metabolic processes or with constituents of normal cells or body fluids, such as reports of experimental or clinical studies in animals, are only considered when they are clearly and directly relevant to human disease. Evaluation of commercial products have a low priority for publication, unless they are novel or represent a technological breakthrough. Studies dealing with effects of drugs and natural products and studies dealing with the redox status in various diseases are not within the journal''s scope. Development and evaluation of novel analytical methodologies where applicable to diagnostic clinical chemistry and laboratory medicine, including point-of-care testing, and topics on laboratory management and informatics will also be considered. Studies focused on emerging diagnostic technologies and (big) data analysis procedures including digitalization, mobile Health, and artificial Intelligence applied to Laboratory Medicine are also of interest.