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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.

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BACKGROUND: Patient-based real-time quality control (PBRTQC) models must be optimized for use in different clinical laboratories, but the grid search (GS) algorithm explored in recent studies for this purpose is inefficient. Thus, finding an efficient optimization algorithm is critical for future research and implementation of the PBRTQC. METHODS: We compared the efficiency and performance of five commonly used optimization algorithms, including GS, simulated annealing (SA), genetic algorithms (GA), differential evolution (DE), and particle swarm optimization (PSO), to optimize conventional PBRTQC and regression-adjusted real-time quality control (RARTQC) models for serum alanine aminotransferase and sodium. RESULTS: The GS, GA, DE, and PSO provided models with similar performances. However, GA and DE required significantly less computation time than GS. The results also demonstrate a general tradeoff between the optimization method's chance of discovering the optimum and the computation time required. CONCLUSION: More efficient optimization methods should be adopted when establishing PBRTQC or RARTQC models to save time and computing power that will enable the development of more complex models and increase the scalability of extensive PBRTQC applications.

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Background and objectives: Overutilization of phlebotomy tubes at healthcare facilities leads to iatrogenic anemia, patient dissatisfaction, and increase in operational costs. In this study, we analyzed the phlebotomy tube usage data at the Zhongshan Hospital, Fudan University, to show potential inefficiencies with phlebotomy tube usage. Methods: Data of 984,078 patients with 1,408,175 orders and 4,622,349 total phlebotomy tubes were collected during years 2018-2021. Data of different patient types were compared. Furthermore, we assessed the data from subspecialty and test levels to explore the factors influencing the increase in phlebotomy tube usage. Results: We observed an overall 8% increase in both the mean number of tubes used and blood loss per order over the past 4 years. The mean blood loss per day for intensive care unit (ICU) patients was 18.7 ml (maximum 121.6 ml), which was well under the 200 ml/day threshold. However, the maximum number of tubes used reached more than 30 tubes/day. Conclusions: The 8% increase of phlebotomy tubes over 4 years should alarm laboratory managements, as tests offered are expected to increase in the future. Importantly, the whole healthcare community needs to work together to solve this problem with more creative solutions.

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BACKGROUND: Early diagnosis of myocardial infarction is crucial in chest pain management and cardiac troponin (cTn) test is an important step in it. Process improvement to shorten the test turnaround time (TAT) may improve patients' outcomes. The cTn test at chest pain center (CPC) of Zhongshan Hospital had the shortest TAT ever reported, but its process flow was not fully evaluated. METHODS: We performed a stepwise evaluation of CPC cTn TAT and explored the potential factor that might cause delay. The performance of CPC cTn test was also compared with cTn test and human chorionic gonadotropin (HCG) test ordered from emergency department (ED). RESULTS: At least 95% of CPC cTn tests were completed in 60 min, while 62% in 30 min. The medians of monthly order-to-collect time, collect-to-received time, and received-to-result time were ~7 min, ~3 min, and ~13 min, respectively. The samples collected at the bedside had longer collect-to-received time than the ones collected at the blood draw site next to the laboratory. Compared to ED cTn test and ED HCG test, CPC cTn test took less time in each step. A combination of the sample type switch and the centrifugation time reduction contributed the most to the shortening of TAT, which was reflected in the received-to-result time. CONCLUSIONS: The current process flow of CPC cTn test satisfied the requirements of chest pain management, giving an example of how to implement process improvement for emergency medicine to shorten TAT of laboratory tests.

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BACKGROUND: Patient-based real-time quality control (PBRTQC) has gained increasing attention in the field of clinical laboratory management in recent years. Despite the many upsides that PBRTQC brings to the laboratory management system, it has been questioned for its performance and practical applicability for some analytes. This study introduces an extended method, regression-adjusted real-time quality control (RARTQC), to improve the performance of real-time quality control protocols. METHODS: In contrast to the PBRTQC, RARTQC has an additional regression adjustment step before using a common statistical process control algorithm, such as the moving average, to decide whether an analytical error exists. We used all patient test results of 4 analytes in 2019 from Zhongshan Hospital, Fudan University, to compare the performance of the 2 frameworks. Three types of analytical error were added in the study to compare the performance of PBRTQC and RARTQC protocols: constant, random, and proportional errors. The false alarm rate and error detection charts were used to assess the protocols. RESULTS: The study showed that RARTQC outperformed PBRTQC. RARTQC, compared with the PBRTQC, improved the trimmed average number of patients affected before detection (tANPed) at total allowable error by about 50% for both constant and proportional errors. CONCLUSIONS: The regression step in the RARTQC framework removes autocorrelation in the test results, allows researchers to add additional variables, and improves data transformation. RARTQC is a powerful framework for real-time quality control research.

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BACKGROUND: Patient-based real-time quality control (PBRTQC) has gained attention because of its potential to detect analytical errors in situations wherein internal quality control is less effective. Multiple PBRTQC algorithms have been proposed. However, there is a lack of comprehensive comparison of the performance of PBRTQC algorithms on different types of analytical errors. Thus, a comparative study was conducted. METHODS: The performance of six different PBRTQC algorithms was evaluated on three types of analytical errors using 906,552 test results for outpatient serum sodium, chloride, alanine aminotransferase, and creatinine at the Department of Laboratory Medicine at Zhongshan Hospital, Fudan University in 2019. The performance results were compared and assessed. RESULTS: The moving average, moving median, exponentially weighted moving average, and moving quartiles performed similarly for effectively detecting constant errors (CE) and proportional errors (PE) but not random errors (RE). The moving sum of positive patients and moving standard deviation could detect RE for serum sodium and chlorides but performed poorly on detecting the CE and PE. CONCLUSIONS: This study demonstrated the importance of assessing the potential source of error of a particular analyte and the corresponding type of analytical error before choosing a quality control algorithm for implementation.

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OBJECTIVES: Lung cancer in Xuanwei (LCXW), China, is known throughout the world for its distinctive characteristics, but little is known about its pathogenesis. The purpose of this study was to screen potential novel "driver genes" in LCXW. METHODS: Genome-wide DNA copy number alterations (CNAs) were detected by array-based comparative genomic hybridization and differentially expressed genes (DEGs) by gene expression microarrays in 8 paired LCXW and non-cancerous lung tissues. Candidate driver genes were screened by integrated analysis of CNAs and DEGs. The candidate genes were further validated by real-time quantitative polymerase chain reaction. RESULTS: Large numbers of CNAs and DEGs were detected, respectively. Some of the most frequently occurring CNAs included gains at 5p15.33-p15.32, 5p15.1-p14.3, and 5p14.3-p14.2 and losses at 11q24.3, 21q21.1, 21q22.12-q22.13, and 21q22.2. Integrated analysis of CNAs and DEGs identified 24 candidate genes with frequent copy number gains and concordant upregulation, which were considered potential oncogenes, including CREB3L4, TRIP13, and CCNE2. In addition, the analysis identified 19 candidate genes with a negative association between copy number change and expression change, considered potential tumor suppressor genes, including AHRR, NKD2, and KLF10. One of the most studied oncogenes, MYC, may not play a carcinogenic role in LCXW. CONCLUSIONS: This integrated analysis of CNAs and DEGs identified several potential novel LCXW-related genes, laying an important foundation for further research on the pathogenesis of LCXW and identification of novel biomarkers or therapeutic targets.

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