Biochemia medica最新文献

{"title":"Pseudonormokalemia case report - What does it mean to have normal blood potassium?","authors":"Tomáš Šálek, David Stejskal","doi":"10.11613/BM.2024.021002","DOIUrl":"10.11613/BM.2024.021002","url":null,"abstract":"<p><p>This case report describes a case of pseudonormokalemia, true hypokalemia. Often, only laboratory values outside the normal range gain attention and false normal results are at risk of not being noticed. However, a disease state may be masked by another pathological process. Here, a 50-year old male was admitted to the Department of Internal Medicine due to sepsis from a dental infection. Initially, serum potassium measurement revealed a normal value of 4 mmol/L (reference interval 3.8-5.1 mmol/L). Thrombocyte number was above 500x10⁹/L. Due to our policy to recommend a repeated measurement of potassium in whole blood or heparin plasma if a patient has thrombocytosis, pseudonormokalemia was identified because the heparin plasma potassium value was only 2.9 mmol/L (reference interval 3.5-4.8 mmol/L). The physiological difference between serum and plasma concentration is no more than 0.3 mmol/L. In this case, potassium concentration were falsely elevated in the serum sample, probably caused by the high number of platelets releasing potassium during clotting. Interpretative comments in patients with thrombocytosis over 500x10⁹/L recommending plasma potassium measurement are helpful. The best way to eliminate pseudohyperkalemia and pseudonormokalemia phenomena caused by thrombocytosis is to completely change towards heparin plasma as the standard material.</p>","PeriodicalId":94370,"journal":{"name":"Biochemia medica","volume":"34 2","pages":"021002"},"PeriodicalIF":0.0,"publicationDate":"2024-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11177651/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141332875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative study on the quality control effectiveness of AI-PBRTQC and traditional PBRTQC model in identifying quality risks.","authors":"Xucai Dong, Xi Meng, Bin Li, Dongmei Wen, Xianfei Zeng","doi":"10.11613/BM.2024.020707","DOIUrl":"10.11613/BM.2024.020707","url":null,"abstract":"<p><strong>Introduction: </strong>We compared the quality control efficiency of artificial intelligence-patient-based real-time quality control (AI-PBRTQC) and traditional PBRTQC in laboratories to create favorable conditions for the broader application of PBRTQC in clinical laboratories.</p><p><strong>Materials and methods: </strong>In the present study, the data of patients with total thyroxine (TT4), anti-Müllerian hormone (AMH), alanine aminotransferase (ALT), total cholesterol (TC), urea, and albumin (ALB) over five months were categorized into two groups: AI-PBRTQC group and traditional PBRTQC group. The Box-Cox transformation method estimated truncation ranges in the conventional PBRTQC group. In contrast, in the AI-PBRTQC group, the PBRTQC software platform intelligently selected the truncation ranges. We developed various validation models by incorporating different weighting factors, denoted as λ. Error detection, false positive rate, false negative rate, average number of the patient sample until error detection, and area under the curve were employed to evaluate the optimal PBRTQC model in this study. This study provides evidence of the effectiveness of AI-PBRTQC in identifying quality risks by analyzing quality risk cases.</p><p><strong>Results: </strong>The optimal parameter setting scheme for PBRTQC is TT4 (78-186), λ = 0.03; AMH (0.02-2.96), λ = 0.02; ALT (10-25), λ = 0.02; TC (2.84-5.87), λ = 0.02; urea (3.5-6.6), λ = 0.02; ALB (43-52), λ = 0.05.</p><p><strong>Conclusions: </strong>The AI-PBRTQC group was more efficient in identifying quality risks than the conventional PBRTQC. AI-PBRTQC can also effectively identify quality risks in a small number of samples. AI-PBRTQC can be used to determine quality risks in both biochemistry and immunology analytes. AI-PBRTQC identifies quality risks such as reagent calibration, onboard time, and brand changes.</p>","PeriodicalId":94370,"journal":{"name":"Biochemia medica","volume":"34 2","pages":"020707"},"PeriodicalIF":0.0,"publicationDate":"2024-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11177656/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141332870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Direct and indirect reference intervals of 25-hydroxyvitamin D: it is not a real vitamin D deficiency pandemic.","authors":"Juan José Perales-Afán, Diego Aparicio-Pelaz, Sheila López-Triguero, Elena Llorente, Juan José Puente-Lanzarote, Marta Fabre","doi":"10.11613/BM.2024.020706","DOIUrl":"10.11613/BM.2024.020706","url":null,"abstract":"<p><strong>Introduction: </strong>Many studies report vitamin D (25-OH-D) deficiency, although there is no consensus among scientific societies on cut-offs and reference intervals (RI). The aim of this study is to establish and compare RI for serum 25-OH-D by direct and indirect methods.</p><p><strong>Materials and methods: </strong>Two studies were performed in Zaragoza (Spain). A retrospective study (N = 7222) between January 2017 and April 2019 was used for RI calculation by indirect method and a prospective study (N = 312) with healthy volunteers recruited in August 2019 and February 2020 for direct method. Seasonal differences were investigated. Measurements were performed on Cobas C8000 (Roche-Diagnostics, Basel, Switzerland) using electrochemiluminescence immunoassay technology.</p><p><strong>Results: </strong>Reference intervals (2.5-97.5 percentile and corresponding 95% confidence intervals, CIs) were as follows: by indirect method 5.6 ng/mL (5.4 to 5.8) - 57.2 ng/mL (55.2 to 59.8), in winter 5.4 ng/mL (5.2 to 5.7) - 55.7 ng/mL (53.6 to 58.4), while in summer 5.9 ng/mL (5.4 to 6.2) - 59.9 ng/mL (56.3 to 62.9). By direct method 9.0 ng/mL (5.7 to 9.5) - 41.4 ng/mL (37.6 to 48.0), in winter 7.4 ng/mL (3.9 to 8.6) - 34.6 ng/mL (30.6 to 51.5), while in summer 13.3 ng/mL (10.1 to 14.1) - 44.1 ng/mL (38.9 to 66.0). In both methods, RIs were higher in summer. A significant difference was observed in 25-OH-D median values between the two methods (P < 0.001).</p><p><strong>Conclusions: </strong>Reference interval calculation according to the studied area may be a useful tool to adapt the deficiency cut-offs for 25-OH-D. Our data support 25-OH-D values over 12.0 ng/mL for healthy population as sufficient, therefore current recommendations should be updated. In addition, differences in seasonality should be taken into account.</p>","PeriodicalId":94370,"journal":{"name":"Biochemia medica","volume":"34 2","pages":"020706"},"PeriodicalIF":0.0,"publicationDate":"2024-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11177660/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141332871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Is the urinary neutrophil gelatinase-associated lipocalin concentration in children and adolescents with type 1 diabetes mellitus different from that in healthy children?","authors":"Bernardica Valent Morić, Ivan Šamija, Lavinia La Grasta Sabolić, Adriana Unić, Marijana Miler","doi":"10.11613/BM.2024.020709","DOIUrl":"10.11613/BM.2024.020709","url":null,"abstract":"<p><strong>Introduction: </strong>Diabetic kidney disease (DKD) is one of the major microvascular complications of type 1 diabetes mellitus (T1DM). Some studies suggest that changes of renal tubular components emerge before the glomerular lesions thus introducing the concept of diabetic tubulopathy with urinary neutrophil gelatinase-associated lipocalin (uNGAL) as a potential marker of DKD. This concept was not confirmed in all studies.</p><p><strong>Materials and methods: </strong>In 198 T1DM patients with median age 15 years and diabetes duration over one year, an albumin/creatinine ratio (ACR) was determined and uNGAL measured in spot urine sample. Urine samples for ACR and uNGAL were also collected in the control group of 100 healthy children of similar age.</p><p><strong>Results: </strong>There was no significant difference in uNGAL concentration or uNGAL/creatinine between T1DM children and healthy subjects (6.9 (2.8-20.1) ng/mL <i>vs</i> 7.9 (2.9-21.0) ng/mL, P = 0.969 and 6.8 (2.2-18.4) ng/mg <i>vs</i> 6.5 (1.9-13.4) ng/mg, P = 0.448, respectively) or between T1DM subjects with albuminuria A2 and albuminuria A1 (P = 0.573 and 0.595, respectively). Among T1DM patients 168 (85%) had normal uNGAL concentrations, while in 30 (15%) patients uNGAL was above the defined cut-off value of 30.9 ng/mL. There was no difference in BMI, HbA1c and diabetes duration between patients with elevated uNGAL compared to those with normal uNGAL.</p><p><strong>Conclusions: </strong>We found no significant difference in uNGAL concentration or uNGAL/creatinine between T1DM children and healthy subjects or between albuminuria A2 and albuminuria A1 T1DM subjects. Therefore, uNGAL should not be recommended as a single marker for detecting diabetic kidney disease in children and adolescents.</p>","PeriodicalId":94370,"journal":{"name":"Biochemia medica","volume":"34 2","pages":"020709"},"PeriodicalIF":0.0,"publicationDate":"2024-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11177655/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141332938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hairy cell leukemia - etiopathogenesis, diagnosis and modern therapeutic approach.","authors":"Katarzyna Maćkowiak, Magdalena Jankowiak, Karolina Szewczyk-Golec, Iga Hołyńska-Iwan","doi":"10.11613/BM.2024.020502","DOIUrl":"10.11613/BM.2024.020502","url":null,"abstract":"<p><p>Hairy cell leukemia (HCL) represents 2% of all leukemia cases, with men aged above 55 years being the most affected. The most common symptoms of this type of leukemia include splenomegaly, monocytopenia, and neutropenia. In the basic blood count examination, leukopenia with monocytopenia and granulocytopenia, as well as aplastic anemia and/or thrombocytopenia occur. The mutation of β-rapidly accelerated fibrosarcoma (<i>BRAF</i>) proto-oncogene, which can be found in nearly 100% of patients, is an important feature of HCL. Immunophenotypic analysis of the HCL cells reveals high expression of B-lineage antigens, including CD19, CD20, and CD22. Additionally, CD11c, CD25, CD103, and CD123 belong to specific markers of HCL. Lactate dehydrogenase activity and β-2-microglobulin concentration are also important in the patient's assessment. The differential diagnosis between HCL, hairy cell leukemia variant (HCL-V) and splenic marginal zone lymphoma (SMZL) is of first importance. Currently, the main treatment for HCL involves the use of purine analogues, excluding pregnant women, individuals with severe infections, and those with relapsing HCL.</p>","PeriodicalId":94370,"journal":{"name":"Biochemia medica","volume":"34 2","pages":"020502"},"PeriodicalIF":0.0,"publicationDate":"2024-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11177658/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141332873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of reverse osmosis membrane replacement of pure water system on clinical chemistry and immunoassay in clinical laboratory.","authors":"Shaocong Liang, Huaxian Wu, Jiayi Zhao, Xuanjie Guo, Yongjie Qiang, Xin Zhao, Meng Lan, Chongquan Zhao, Dongxin Zhang","doi":"10.11613/BM.2024.010705","DOIUrl":"10.11613/BM.2024.010705","url":null,"abstract":"<p><strong>Introduction: </strong>Reverse osmosis (RO) membrane, key component of water-purifying equipment, is often stored in protection fluid containing substances such as glycerol, which may contaminate the water at replacement. This study aims to explore the effects of RO membrane replacement on clinical chemistry and immunoassay, particularly triglyceride (TG), providing reference for managing test interference caused by RO membrane replacement.</p><p><strong>Materials and methods: </strong>The RO membrane of water-purifying equipment A, which provided water to C16000 biochemistry analyzer (Abbott Laboratories, Abbott Park, USA) and E801 electrochemiluminescence analyzer (Roche, Basel, Switzerland), was replaced. Water resistivity was recorded, and quality control (QC) tests were performed on C16000 and E801. Moreover, TG was measured in 29 of selected serum samples on C16000 at 0.5h and 10.5h after RO membrane replacement and on reference biochemistry analyzer BS2000M (Mindray Biomedical Electronics Co., Shenzhen, China), which was connected to water-purifying equipment B without RO membrane replacement. Finally, blank, calibrator 1 and calibrator 2 of TG reagent were measured on C16000 before and at 0.5h, 2.5h and 10.5h after RO membrane replacement. All statistical analyses of data were done using GraphPad Prism (GraphPad Software Inc., San Diego, USA), and a value of P < 0.05 was considered statistically significant.</p><p><strong>Results: </strong>After RO membrane replacement, all QC results of clinical chemistry and immune tests passed except TG that showed positive bias of 536% and 371% at two levels, respectively. Moreover, TG results of the same serum samples were significantly higher at 0.5h than 10.5h after RO membrane replacement. Meanwhile, there was worse agreement and correlation of TG results between C16000 and BS2000M at 0.5h than 10.5h after replacement. Furthermore, the absorbance of TG blank, calibrator 1 and calibrator 2 was significantly higher at 0.5h and 2.5h after replacement than before replacement, and the absorbance gradually returned to normal value at 10.5h after replacement.</p><p><strong>Conclusions: </strong>Replacement of RO membrane could cause significant interference to TG test while have no effects on other laboratory tests performed in the study, which may be due to glycerol contamination. Our data provides important reference for management of test interference caused by RO membrane replacement. Clinical laboratory should observe the effects of RO membrane replacement on laboratory tests through both water quality monitoring and QC detection.</p>","PeriodicalId":94370,"journal":{"name":"Biochemia medica","volume":"34 1","pages":"010705"},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10864026/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139743052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adapting power calculations to include a superiority margin: what are the implications?","authors":"Samuel Bishara","doi":"10.11613/BM.2024.010101","DOIUrl":"10.11613/BM.2024.010101","url":null,"abstract":"<p><p>This paper examines the application of super-superiority margins in study power calculations. Unlike traditional power calculations, which primarily aim to reject the null hypothesis by any margin, a super-superiority margin establishes a clinically significant threshold. Despite potential benefits, this approach, akin to a non-inferiority calculation but in an opposing direction, is rarely used. Implementing a super-superiority margin separates the notion of the likely difference between two groups (the effect size) from the minimum clinically significant difference, without which inconsistent positions could be held. However, these are often used interchangeably. In an audit of 30 recent randomized controlled trial power calculations, four studies utilized the minimal acceptable difference, and nine utilized the expected difference. In the other studies, this was unclarified. In the <i>post hoc</i> scenario, this approach can shed light on the value of undertaking further studies, which is not apparent from the standard power calculation. The acceptance and rejection of the alternate hypothesis for super-superiority, non-inferiority, equivalence, and standard superiority studies have been compared. When a fixed minimal acceptable difference is applied, a study result will be in one of seven logical positions with regards to the simultaneous application of these hypotheses. The trend for increased trial size and the mirror approach of non-inferiority studies implies that newer interventions may be becoming less effective. Powering for superiority could counter this and ensure that a pre-trial evaluation of clinical significance has taken place, which is necessary to confirm that interventions are beneficial.</p>","PeriodicalId":94370,"journal":{"name":"Biochemia medica","volume":"34 1","pages":"010101"},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10864028/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139743050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Elevated plasma pyruvate kinase M2 concentrations are associated with the clinical severity and prognosis of coronary artery disease.","authors":"Zi-Wen Zhao, Yi-Wei Xu, Xin-Tao Zhang, Hang-Hao Ma, Jing-Kun Zhang, Xue Wu, Yu Huang","doi":"10.11613/BM.2024.010704","DOIUrl":"10.11613/BM.2024.010704","url":null,"abstract":"<p><strong>Introduction: </strong>Pyruvate kinase M2 (PKM2) was involved in the pathophysiology of atherosclerosis and coronary artery disease (CAD). We tested whether plasma PKM2 concentrations were correlated with clinical severity and major adverse cardiovascular events (MACEs) in CAD patients.</p><p><strong>Materials and methods: </strong>A total of 2443 CAD patients and 238 controls were enrolled. The follow-up time was two years. Plasma PKM2 concentrations were detected by enzyme-linked immunosorbent assay (ELISA) kits (Cloud-Clone, Wuhan, China) using SpectraMax i3x Multi-Mode Microplate Reader (Molecular Devices, San Jose, USA). The predictors of acute coronary syndrome (ACS) were assessed by logistic regression analysis. The association between PKM2 concentration in different quartiles and MACEs was evaluated by Kaplan-Meier (KM) curves with log-rank test and Cox proportional hazard models. The predictive value of PKM2 and a cluster of conventional risk factors was determined by Receiver operating characteristic (ROC) curves. The net reclassification improvement (NRI) and the integrated discrimination improvement (IDI) were utilized to evaluate the enhancement in risk prediction when PKM2 was added to a predictive model containing a cluster of conventional risk factors.</p><p><strong>Results: </strong>In CAD patients, PKM2 concentration was the independent predictor of ACS (P < 0.001). Kaplan-Meier cumulative survival curves and Cox proportional hazards analyses revealed that patients with a higher PKM2 concentration had higher incidence of MACEs compared to those with a lower PKM2 concentration (P < 0.001). The addition of PKM2 to a cluster of conventional risk factors significantly increased its prognostic value of MACEs.</p><p><strong>Conclusion: </strong>Baseline plasma PKM2 concentrations predict the clinical severity and prognosis of CAD.</p>","PeriodicalId":94370,"journal":{"name":"Biochemia medica","volume":"34 1","pages":"010704"},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10731730/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138833885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Changing the tide in vitamin D testing: An 8-year review of a demand management approach.","authors":"Janne Cadamuro, Ursula Huber-Schönauer, Cornelia Mrazek, Lukas Hehenwarter, Ulrike Kipman, Thomas K Felder, Christian Pirich","doi":"10.11613/BM.2024.010401","DOIUrl":"10.11613/BM.2024.010401","url":null,"abstract":"<p><p></p>","PeriodicalId":94370,"journal":{"name":"Biochemia medica","volume":"34 1","pages":"010401"},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10864025/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139743051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Practical application of the patient data-based quality control method: the potassium example.","authors":"Yan Zhang, Hua-Li Wang, Ye-Hong Xie, Da-Hai He, Chao-Qiong Zhou, Li-Rui Kong","doi":"10.11613/BM.2024.010901","DOIUrl":"10.11613/BM.2024.010901","url":null,"abstract":"<p><strong>Introduction: </strong>Internal quality control (IQC) is a core pillar of laboratory quality control strategies. Internal quality control commercial materials lack the same characteristics as patient samples and IQC contributes to the costs of laboratory testing. Patient data-based quality control (PDB-QC) may be a valuable supplement to IQC; the smaller the biological variation, the stronger the ability to detect errors. Using the potassium concentration in serum as an example study compared error detection effectiveness between PDB-QC and IQC.</p><p><strong>Materials and methods: </strong>Serum potassium concentrations were measured by using an indirect ion-selective electrode method. For the training database, 23,772 patient-generated data and 366 IQC data from April 2022 to September 2022 were used; 15,351 patient-generated data and 246 IQC data from October 2022 to January 2023 were used as the testing database. For both PDB-QC and IQC, average values and standard deviations were calculated, and z-score charts were plotted for comparison purposes.</p><p><strong>Results: </strong>Five systematic and three random errors were detected using IQC. Nine systematic errors but no random errors were detected in PDB-QC. The PDB-QC showed systematic error warnings earlier than the IQC.</p><p><strong>Conclusions: </strong>The daily average value of patient-generated data was superior to IQC in terms of the efficiency and timeliness of detecting systematic errors but inferior to IQC in detecting random errors.</p>","PeriodicalId":94370,"journal":{"name":"Biochemia medica","volume":"34 1","pages":"010901"},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10864027/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139743053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}