Annals of Laboratory Medicine最新文献

Physicians increasingly use laboratory-produced information for disease diagnosis, patient monitoring, treatment planning, and evaluations of treatment effectiveness. Bias is the systematic deviation of laboratory test results from the actual value, which can cause misdiagnosis or misestimation of disease prognosis and increase healthcare costs. Properly estimating and treating bias can help to reduce laboratory errors, improve patient safety, and considerably reduce healthcare costs. A bias that is statistically and medically significant should be eliminated or corrected. In this review, the theoretical aspects of bias based on metrological, statistical, laboratory, and biological variation principles are discussed. These principles are then applied to laboratory and diagnostic medicine for practical use from clinical perspectives.

Background: Risk stratification of patients for incidence of stroke and its outcomes can aid in decision-making regarding treatment options and rehabilitative care. We systematically reviewed the literature to provide comprehensive evidence for the value of serum soluble suppression of tumorigenicity-2 (sST-2) in the prediction of stroke incidence and the evaluation of post-stroke outcomes.

Methods: The Medline, Scopus, Web of Science, and Embase databases were searched until the end of August 2022 for studies investigating the value of serum sST-2 in the prediction of stroke incidence and post-stroke outcomes.

Results: Nineteen articles were included. The articles reported conflicting results on the predictive value of sST-2 measurement in the incidence of stroke. Studies investigating the value of sST-2 measurement for the prognosis of post-stroke outcomes have reported positive associations between sST-2 levels and post-stroke mortality, composite adverse events, major disability, cerebral-cardiac syndrome, and cognitive impairment.

Conclusions: Although some studies have reported a predictive value of serum sST-2 measurement in the incidence of stroke, a clear consensus has yet to be reached because of discrepancies in the results. As for the prognosis of post-stroke outcomes, sST-2 may be a predictor of mortality, composite adverse events, and major disability after stroke. Overall, more well-designed prospective cohort studies are needed to reach a more decisive conclusion on the value of sST-2 measurement for the prediction of stroke and its outcomes and to determine optimal cutoffs.

Real-time reverse transcription (rRT)-PCR, which is the reference standard for the diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, generally involves a time-consuming and costly RNA extraction step prior to amplification. We evaluated the performance of the AdvanSure One-Stop COVID-19 Plus Kit (LG Chem, Seoul, Korea), a novel rRT-PCR assay that can detect SARS-CoV-2 within 90 minutes using a streamlined RNA extraction method. In total, 509 nasopharyngeal swab (NPS) specimens (SARS-CoV-2 positive: N=205; SARS-CoV-2 negative: N=304) previously tested using the PowerChek SARS-CoV-2 Real-time PCR Kit (Kogene Biotech, Seoul, Korea) were tested using the AdvanSure assay. The limit of detection (LOD) of the AdvanSure assay was determined using serially diluted inactivated SARS-CoV-2. The positive and negative percent agreements between the AdvanSure and PowerChek assays were 99.5% (204/205) and 99.3% (302/304), respectively. The LODs of the AdvanSure assay for SARS-CoV-2 nucleocapsid and spike/RNA-dependent RNA polymerase genes were 672 and 846 copies/mL, respectively. The results show that the performance of the AdvanSure assay is comparable to that of the PowerChek assay used for routine SARS-CoV-2 testing, suggesting that the AdvanSure assay is a useful diagnostic tool for rapid and accurate detection of SARS-CoV-2 infection.

Background: The currently recommended pre-transfusion testing techniques for patients with autoantibodies are complex, time-consuming, and labor-intensive. Therefore, although the red blood cell (RBC) selection method using crossmatched RBC agglutination reaction grades (i.e., the "least incompatible" transfusion) is discouraged, many institutions still use it. We aimed to evaluate the effectiveness of this method combined with Rh subgroup phenotyping.

Methods: We retrospectively investigated RBC transfusions from January 2019 to December 2021 in patients presenting as auto-control-positive via antibody identification (auto-control (+) group), where Rh subgroup phenotype-matched RBCs were selected based on the agglutination reaction grades of crossmatched units. For each study patient, an auto-control-negative patient was matched based on age, sex, department, and pre-transfusion Hb levels (auto-control (-) group). The mean Hb change per unit, transfusion-associated symptom/sign reports, and agglutination reaction grades upon crossmatching were analyzed.

Results: In the auto-control (+) group, the Hb change per unit among different agglutination reaction grades of transfused RBCs and among different relative grades of transfused RBCs and crossmatching auto-controls was not significantly different (P=0.392 and P= 0.132, respectively). No significant difference was observed in Hb changes and transfusion-associated symptom/sign occurrence between the auto-control (+) and auto-control (-) groups (P=0.121 and P=0.822, respectively). In addition, no definite evidence of hemolysis in the auto-control (+) group was observed in the medical record review.

Conclusions: Together with Rh subgroup phenotyping, selecting the RBC unit with the lowest agglutination reaction grade upon crossmatching does not adversely affect transfusion efficiency.

With the projected increase in the global population, current healthcare delivery models will face severe challenges. Rural and remote areas, whether in developed or developing countries, are characterized by the same challenges: the unavailability of hospitals, lack of trained and skilled staff performing tests, and poor compliance with quality assurance protocols. Point-of-care testing using artificial intelligence (AI) is poised to be able to address these challenges. In this review, we highlight some key areas of application of AI in point-of-care testing, including lateral flow immunoassays, bright-field microscopy, and hematology, demonstrating this rapidly expanding field of laboratory medicine.

Background: There is no standard cut-off value of serum IgG4 concentration and serum IgG4/total IgG ratio for the diagnosis of IgG4-related disease (IgG4-RD) or as a marker of treatment responses. We aimed to explore this issue through a retrospective cohort analysis of adults in southwest China.

Methods: The diagnostic performance of serum IgG4 concentration and IgG4/IgG ratio for IgG4-RD was evaluated in a retrospective analysis of 177 adults newly diagnosed as having IgG4-RD and 877 adults without IgG4-RD. Dynamic analysis was performed to evaluate the significance of serum IgG4 concentration on IgG4-RD treatment responses.

Results: The serum IgG4 concentration differed according to sex. The optimal cut-off values of serum IgG4 concentration and IgG4/IgG ratio for IgG4-RD diagnosis were 1.92 g/L and 0.12 in males and 1.83 g/L and 0.11 in females, respectively. For patients with serum IgG4 concentration >2.01 g/L, the cut-off values in the total population were >3.00 g/L and 0.19, respectively. The median serum IgG4 concentration decreased over time, and the decrease rate increased over time. The serum IgG4 concentration significantly decreased at >1 week post-treatment (P=0.004), and the median decrease rate was close to 50% at >4 weeks post-treatment.

Conclusions: Serum IgG4 can be a good indicator for IgG4-RD diagnosis; however, different diagnostic cut-off values should be determined according to sex. The decreasing rate is more conducive than the serum IgG4 concentration to monitor treatment efficacy. The IgG4/IgG ratio did not improve the diagnostic efficacy for IgG4-RD.

Background: To ensure valid results of big data research in the medical field, the input laboratory results need to be of high quality. We aimed to establish a strategy for evaluating the quality of laboratory results suitable for big data research.

Methods: We used Korean Association of External Quality Assessment Service (KEQAS) data to retrospectively review multicenter data. Seven measurands were analyzed using commutable materials: HbA1c, creatinine (Cr), total cholesterol (TC), triglyceride (TG), alpha-fetoprotein (AFP), prostate-specific antigen (PSA), and cardiac troponin I (cTnI). These were classified into three groups based on their standardization or harmonization status. HbA1c, Cr, TC, TG, and AFP were analyzed with respect to peer group values. PSA and cTnI were analyzed in separate peer groups according to the calibrator type and manufacturer, respectively. The acceptance rate and absolute percentage bias at the medical decision level were calculated based on biological variation criteria.

Results: The acceptance rate (22.5%-100%) varied greatly among the test items, and the mean percentage biases were 0.6%-5.6%, 1.0%-9.6%, and 1.6%-11.3% for all items that satisfied optimum, desirable, and minimum criteria, respectively.

Conclusions: The acceptance rate of participants and their external quality assessment (EQA) results exhibited statistically significant differences according to the quality grade for each criterion. Even when they passed the EQA standards, the test results did not guarantee the quality requirements for big data. We suggest that the KEQAS classification can serve as a guide for building big data.