Advances in laboratory medicine最新文献

Objectives: Early prediction of critical COVID-19 disease is crucial for an optimal clinical management. The objective of this study was to optimize predictive models for critical COVID-19 disease. Clinical data, laboratory data and genetic polymorphisms were integrated into AI models to compare the performance of different machine learning algorithms.

Methods: Data from 155 inpatients were analyzed, 23 of whom developed critical disease. A univariate analysis was performed to assess potential correlations between seven SNPs, nine clinical variables and 10 laboratory parameters at admission.

Results: Of the 7 SNPs, only three SNPs demonstrated a significant association with critical disase, namely: rs77534576, rs10774671 and rs10490770. The ensemble models exhibited the best performance: Random Forest (AUC=0.989), XGBoost (AUC=0.954) and AdaBoost (AUC=0.927). Variable importance varied across models, with age, C-reactive protein, heart diseases and the three SNPs being the most influential features. The predictive power of models improved with the integration of the three SNPs, as compared to previous studies where genetic data were not included. Internal validation confirmed the superiority and stability of the ensemble models.

Conclusions: Machine learning models may help predict progression into critical COVID-19-disease. The predictive power of models improves when SNPs associated with COVID-19 severity are integrated with laboratory and clinical data. Prior to implementation in clinical practice, larger studies in different populations are needed to validate and support the generalization of these results.

Objectives: Disease monitoring in acute leukemia management is crucial for risk stratification and chemotherapy response assessment. Minimal residual disease (MRD) testing is the most reliable tool, measuring antigenic expression differences between leukemic cells, hematogones, and mature benign B-cells. Modulation of antigen expression during treatment under chemotherapeutic drugs may complicate MRD analysis and detection. The present study investigates immunophenotypic modulation (IM) during chemotherapy phases in children with acute lymphoblastic leukemia (B-ALL), using a modified BFM protocol, and its potential implications for MRD detection.

Methods: The study was conducted at the Hematology Department, Indus Hospital and Health Network, Karachi. All MRD positive cases of pediatric B-ALL (1 month -16 years of age) were included, from April 2019- March 2022. MRD was done on bone marrow aspirate using 8-color flow cytometry. The data from 203 patients, who were MRD positive in bone marrow throughout the evaluation period was considered. The IM was assessed by comparative analysis of the changes in mean fluorescence intensity (MFI) of nine highly relevant antigens expressed by the leukemic cells.

Results: Statistically significant changes in the MFI levels of antigens were observed in leukemic blasts and mature benign B-cells. All the analyzed samples revealed IM to different extents. Our results confirm the presence of immunophenotypic changes for CD10, CD19, CD34, CD45, TdT, and CD66 during chemotherapy.

Conclusions: Measuring the MRD assists in monitoring the disease, however, IM of the different antigens expressed by the leukemic blasts should be considered and cautiously analyzed to prevent erroneous results.

Objectives: The determination of urinary albumin, crucial for chronic kidney disease (CKD) diagnosis and monitoring, typically employs quantitative techniques as the gold standard. However, semi-quantitative methods such as urine reagent strips show promise as cost-effective and rapid screening tools. This study aims to evaluate the feasibility of utilizing Meditape UC-11A strips (Sysmex, Kobe, Japan) for albuminuria screening compared to quantitative assays measuring urine albumin-to-creatinine ratio (ACR).

Methods: We systematically analyzed 1,627 strip samples using Meditape UC-11A strips whenever quantitative urinary albumin measurement was required. We compared the ACR measured by strip test with the gold standard (quantitative method determined by Atellica, Siemens, Marburg, Germany) and calculated diagnostic indicators. Additionally, we assessed the economic implications based on test prices (€2.88 for quantitative albumin plus creatinine, and €0.93 for a Meditape UC-11A strip).

Results: Receiver operator characteristic (ROC) curve analysis was conducted to determine the optimal cut-off point. The concordance rate achieved using the calculated cut-off point was 90.78 %. Using the quantitative test as the gold standard, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of ACR, analyzed using Meditape UC-11A, were 84.2 , 91.9, 62.7, and 97.3 %, respectively. Our findings suggest a potential saving of 81.2 % on quantitative tests during the study period, amounting to €2,291.65.

Conclusions: This study supports the use of Meditape UC-11A strips for detecting abnormal levels of albuminuria, thus offering a viable alternative to quantitative measurement methods. Substantial cost savings can be achieved through this approach.

Epidermal growth factor receptor gene (EGFR) molecular testing is essential for guiding targeted therapies in patients with advanced non-small cell lung cancer (NSCLC). Between 15 and 40 % of patients with NSCLC carry mutations in EGFR that are sensitive to tyrosine kinase inhibitors (TKIs). Due to the significant clinical benefits, identifying patients eligible for TKI therapy is crucial for optimizing treatment. While tumor tissue has been considered the gold standard for this testing, adequate material for EGFR molecular study cannot be obtained in up to 30 % of patients. In this context, circulating tumor DNA (ctDNA) analysis offers a guideline-recommended non-invasive method to detect EGFR mutations. Despite its promise, the widespread adoption of ctDNA analysis faces challenges for integration into clinical practice. This review provides a comprehensive synthesis of current knowledge on the clinical utility of EGFR molecular analysis in ctDNA, alongside its relationship with other circulating biomarkers widely implemented in clinical laboratories, such as serum tumor markers (STMs). It delves into the technical considerations, interpretation of results, and other challenges associated with ctDNA analysis, offering valuable insights into its integration into laboratory workflows.

Objectives: Quality indicators (QIs) are a valuable tool for monitoring and improving extra-analytical phase performance in the clinical laboratory (CL). For that reason, the Spanish Society of Laboratory Medicine (SEQC^ML) launched its Pre-analytical Phase EQA Program in 2001.

Methods: In 2023, a survey was conducted to assess the level of participation in the EQA Program, identify its potential improvements, and enhance the understanding of the collection and estimation processes of the extra-analytical QI.

Results: In total, 51 % of respondents (124 responses in total) were enrolled in the program. The primary reason for participating was the need of monitoring of the extra-analytical phases (38 %). In contrast, the main reason for not taking part in the EQA Program was the difficulty in collecting data (32 %). As to potential points for improvement, 56 % considered that QI should be analyzed disaggregated by type of extraction (ambulatory vs. urgent). As many as 86 % of respondents would be interested in participating in a post-analytical phase EQA Program. Most of the suggestions about indicators to be included were related to events regarding temperature and transport times from peripheral extraction sites.

Conclusions: Although interest in QIs for pre- and post-analytical phases is growing, the percentage of laboratories that collect data regularly is still limited. The findings of this survey have led the future directions for the EQA Pre-analytical Program, updating and adapting it to the current needs of CLs.