Clinical chemistry and laboratory medicine最新文献

Objectives: Autoverification increases the efficiency of laboratories. Laboratories accredited according to ISO 15189:2022 need to validate their processes, including autoverification, and assess the associated risks to patient safety. The aim of this study was to propose a systematic verification algorithm for autoverification and to assess its potential risks.

Methods: The study was conducted using retrospective data from the Laboratory Information System (LIS). Seven laboratory medicine specialists participated. Autoverification rules were defined for analytes in serum, stool, urine and whole blood determined on Alinity ci (Abbott), Atellica 1500 (Siemens) and ABL90 FLEX (Radiometer). Criteria included internal quality control results, instrument flags, hemolysis/icteria/lipemia indices, median patient values, critical values, measurement ranges, delta checks, and reference values. Verification was performed step by step. Risk analysis was performed using Failure Modes and Effects Analysis and the Risk Priority Number (RPN) was calculated.

Results: During the study, 23,633 laboratory reports were generated, containing 246,579 test results for 167 biochemical tests. Of these, 198,879 (80.66 %) met the criteria for autoverification. For 2,057 results (0.83 %), the experts disagreed with the autoverification criteria (false negatives). Discrepancies were mainly associated to median and delta check values. Only 45 false positives (0.02 %) were identified, resulting in an RPN of 0 for all cases.

Conclusions: The autoverified and non-autoverified results showed high agreement with the expert opinions, with minimal disagreement (0.02 % and 0.83 %, respectively). The risk analysis showed that autoverification did not pose a significant risk to patient safety. This study, the first of its kind, provides step-by-step recommendations for implementing autoverification in laboratories.

Objectives: This paper further explores the Sigma Metric (SM) and its application in clinical chemistry. It discusses the SM, assay stability, and control failure relationship.

Content: : SM is not a valid measure of assay stability or the likelihood of failure. When an out-of-control event occurs for an assay with a higher SM value, the same QC rule will have greater power to detect error than assays with a lower SM value. Thus, it is easier to prevent errors from happening for higher SM assays. This rationale encourages using more frequent QC events and more QC samples for a QC scheme of a low SM assay or simply more QC cost for low SM assays. A laboratory can have a high-precision instrument that frequently fails and a low-precision instrument that hardly ever fails. Parvin's patient risk model presumes the bracketed continuous mode (BCM) testing workflow. If overlooked when designing QC schemes, this leads to the common misconception of the SM that one can save the cost of QC since assays with high SM require less frequent QC to ensure patient risk. There is no evidence that an assay's precision is correlated with its failure rate. Schmidt et al., in a series of papers, showed that an assay with a higher P_f or shift in probability will have a higher expected number of unacceptable results. Incorporating P_f into the QC design process presents significant challenges despite the proactive quality control (PQC) methodology.

Summary: Unfortunately, TEa Six Sigma, as widely practiced in Clinical Chemistry, is not based on classical Six Sigma mathematical statistics. Classical Six Sigma would facilitate comparing results across activities where the principles of Six Sigma are employed.

Objectives: Preanalytical phase is an elemental part of laboratory diagnostics, but is prone to humane errors. The aim of this study was to evaluate performance in preanalytical phase external quality assessment (EQA) cases. We also suggest preventive actions for risk mitigation.

Methods: We included 12 EQA rounds (Labquality Ltd.) with three patient cases (36 cases, 54-111 participants, 7-15 countries) published in 2018-2023. We graded performance according to percentage of correct responses in each case as ≥900 % excellent, 70-89 % good, 50-69 % satisfactory, 30-49 % fair and <30 % poor. Performance was simultaneously failed with ≥10 % of responses leading to harmful events.

Results: Overall performance was excellent in 7, good in 12, satisfactory in 10, fair in 4 and poor in 3 cases. Additionally, 7 cases showed failed performance. Routine requests with incorrect sample tubes or incorrect sample handling were detected with good performance. Lower performance was seen with sudden abnormal results, with rare requests, with false patient identification (never-events) and with incorrect test requests. Information technology (IT) solutions (preanalytical checklists, autoverification rules and patient specific notifications) could have prevented 33 of 36 preanalytical errors.

Conclusions: While most common errors were detected with good performance, samples with rare requests or those requiring individualised consideration are vulnerable to human misinterpretation. In many instances, samples with preanalytical errors should have been identified and rejected before reaching the laboratory or being directed to analysis. Optimising IT solutions to effectively detect these preanalytical errors allows for focus on infrequent events demanding accessible professional consultation. EQA preanalytical cases may help in education of correct actions in these occasions.

Objectives: Accurate assessment of calcium levels is crucial for optimal management of regular Haemodialysis (HD) patients. Different calcium adjustment equations and albumin methods; including bromocresol purple (BCP) and bromocresol green (BCG) assays are employed by laboratories, which cause considerable discrepancies between reported results. The aim of this study is to assess the influence of albumin assays on calcium status in stable haemodialysis patients against free calcium (fCa) as a gold standard test.

Methods: 103 paired serum and fCa samples were collected from a cohort of stable HD patients. Albumin levels were measured by either the BCP or BCG method, and samples were also analysed for the total calcium (T.Ca), phosphate, bicarbonate, and pH levels. The performance of BCG-based and BCP-based adjusted calcium equations was compared using Z-scores scatter plots, intraclass correlation coefficient and Cohen Kappa statistic, with fCa being the reference standard.

Results: Unadjusted T.Ca achieved a 70 % overall classification agreement with fCa and identified 61 % of the "true" hypocalcaemic samples. Adjusted calcium concentrations, calculated by either BCP- or BCG-based equation, were poor predictors of fCa; with more than 50 % of the hypocalcaemic samples being misclassified as normocalcaemic. Notably, both equations misclassified the calcium status in 5 (4.9 %) patients with severe hypocalcaemia (i.e., potentially requiring calcium infusion) as mild hypocalcaemia.

Conclusions: Our study showed evidence of hidden hypocalcaemia being missed by the current practice of using adjusted calcium in HD patients. Therefore, we recommend abandoning the adjustment procedure in samples from stable HD patients in favour of fCa measurement.

Objectives: Hemoglobin A_1c (HbA_1c) is an established tool in the diagnosis and follow-up of patients with diabetes. However, in some patients the interpretation of HbA_1c results faces challenges due to additional biological variation or non-steady-state conditions. This study aimed to demonstrate the value of the L-HbA_1c/HbA_1c-ratio as a tool to flag HbA_1c results, which do not reflect average glycemia "as expected" in routine clinical practice.

Methods: A total of 450 samples of unique patients were selected based on the L-HbA_1c/HbA_1c-ratio determined on a Tosoh G8 analyzer resulting in a group with a high ratio (≥0.50), a group with a low ratio (≤0.27) and a group with a normal ratio (0.27-0.50). The relationship between HbA_1c and glycemic markers (fructosamine and random glucose) was established for all ratio groups. In a smaller cohort of type 1 diabetes patients, continuous glucose monitoring was used as glycemic marker.

Results: The correlation between HbA_1c and glycemia (random glucose and fructosamine) differs significantly between the ratio groups. For the same HbA_1c level random glucose levels and protein-corrected fructosamine are higher in the high ratio group compared to the normal and low ratio groups, pointing to an underestimation of the glycemic status by HbA_1c in patients with high L-HbA_1c/HbA_1c-ratios. The sensitivity of a high ratio to predict a glycation gap lower than -1.5 NGSP units is 82 % and the specificity is 65 %.

Conclusions: The results of this study reveal the usefulness of the L-HbA_1c/HbA_1c-ratio as an additional check in the interpretation of HbA_1c results in order to detect HbA_1c results not reflecting glycemia as expected.

Poor health literacy and inappropriate test prescribing hamper the value of laboratory medicine. The disintermediation between test producers and interpreters may happen both in Point of Care Tests, where doctor mediation is provided, but laboratory expert supervision is not, and in Direct to Consumer Testing, where no medical mediation is provided at all. In these cases, the respect for patient's autonomy must not preclude the principles of non-maleficence (as an individual concern) and justice (as a societal concern), as wrong test interpretation can generate confusion, anxiety, inappropriate social behavior, useless medical examinations and considerable cost increase. Considering how different is patient ability to understand test results (if any) and handle any physical and psychological consequence, promoting health literacy and professional laboratory mediation become crucial professional priorities. The aims of this review are 1) to describe the importance of health literacy on laboratory test interpretation, medical advice and therapeutic compliance; 2) to discuss doctor-level, patient-level and caregiver-level educational interventions in light of the four principles of the value-based framework (personal value, technical value, allocative value and societal value). Based on these premises, the authors support the need to enhance health literacy in patients, help doctors improve the communication of results and validate commercial tests under the scrutiny of scientific community.