Brain-Derived Neurotrophic Factor: Concepts and Concerns in Clinical Chemistry: a Short Review

Brain-derived neurotrophic factor (BDNF) is a bioprotein, a member of the neurotrophic family of growth factors. It is associated with the canonical nerve growth factor. The protein has many roles in clinical disorders, including neurological, psychiatric, and other medical disorders. There are many concerns in the laboratory cycle for analyzing BDNF in clinical chemistry. Conclusions: In this review, the authors summarize insight to highlight the important details of the clinical chemistry laboratory diagnosis of BDNF.

SIGMA METRICS ASSESSMENT AS QUALITY IMPROVEMENT METHODOLOGY IN A CLINICAL CHEMISTRY LABORATORY

Background:The concept of sigma metrics & lean six sigma is well known in the field of healthcare. However not many labs utilize the six sigma metrics for maintenance of high quality laboratory performance. A minimum value of 3 σ is desired in any clinical laboratory & values of σ≥6 are regarded as gold standard for obtaining high quality lab reports. Aims &Objectives: To calculate bias, cv & sigma metrics from the IQC & EQC data in order to ascertain extent of quality management in our lab. Materials &Methods:An extensive study of sample processing and quality practices was carried out in the Central Laboratory of Department of Biochemistry; PGIMER &Dr. RML Hospital, New Delhi; from Feb 2020 to July 2020. The IQC used(both level I & level II) were from Biorad Laboratories India (lyphochek assayed chemistry control) & the EQC used was from Randox Laboratories, UK. All the controls were run on Beckman Coulter clinical chemistry analyser AU 680. Total 14 clinical parameters were analysed & subsequently; Mean, S.D., CV, bias & σ were calculated through their respective formulas. Results:Sigma level was more than 6 for both levels of IQC was observed for Amylase. It indicates world class performance. Total bilirubin, AST, Triglyceride & HDL depicted σ values between 3.1 to 6 for both L1 & L2. Iron showed σ value of 5.5 in L1 whereas it was 3.78 in L2. Conclusion:-:- Sigma metrics in clinical laboratory is an essential technique to ascertain poor assay performance, along with assessment of the efficiency of existing laboratory process.

Quality Indicators for Evaluating Errors in the Preanalytical Phase

Objective The aim of this study was to study the incidence of preanalytical errors in the clinical chemistry laboratory attached to a tertiary care hospital. Design and Methods The study was conducted in a clinical chemistry laboratory using the samples and forms received for analysis. Five hundred random samples were analyzed using a predefined set of quality indicators (QIs) over a period of 3 months. The incidence of each preanalytical error was described as a percentage of the total samples analyzed in the study. Statistical Analysis Individual QIs were assigned values as 0 and 1 and were used to assess each sample; 0 if the error was present, and 1 if absent. The incidence of each preanalytical error was described as a percentage of the total samples analyzed in the study. Result Out of the 500 samples observed, 138 samples were error free, while 21 samples had the maximum number of errors, that is, 6. The error committed most often was the omission of provisional diagnosis being mentioned on the requisition form. No preanalytical error was observed for QIs: selecting the appropriate blood collection vial or storage of sample. Conclusion This study confirms that error rate in the preanalytical phase is high and vastly ignored. Errors committed here may be overlooked, given the large number of samples received in the clinical laboratory of a tertiary center. To reduce these errors, the laboratory should provide training to all workers involved in the preanalytical phase. Daily or weekly QI scores should be recorded to assess and rectify shortcomings, thereby improving patient care.

Activity-based cost analysis of laboratory tests in clinical chemistry

Ojectives Since health care budgets are limited and must be allocated efficiently, there is an economic pressure to reduce the costs of health care interventions. This study aims to investigate the cost of testing within a Clinical Chemistry laboratory. Methods This study was conducted in the Clinical Chemistry laboratory of the University Hospital UZ Brussel, Belgium, in which 156 tests were included and an average cost per test was calculated for the year 2018. Activity-based costing (ABC) was applied, using a top-down perspective. Costs were first allocated to different activity centers and subsequently to different tests. Number of tests, parameters, analyzers and time estimates were used as activity cost drivers. Results The blood glucose test on the point-of-care testing (POCT) analyzer Accu Chek Inform II had the lowest unit cost (€0.92). The determination of methanol, ethanol and isopropanol on the GC-FID (7820A) is the test with the highest unit cost (€129.42). In terms of average cost per test per activity center, core laboratory (€3.37) scored lowest, followed consecutively by POCT (€3.49), diabetes (€22.09), toxicology (€31.52), metabolic disorder (€41.53) and cystic fibrosis (€86.02). The cost per test was mainly determined by staff (57%), costs of support services (23%) and reagents (14%). Conclusions High-volume and automated tests have lower unit costs, as is the case with the core laboratory. ABC provides the ability to identify high average cost tests that can benefit from optimizations, such as focusing on automation or outsourcing low-volume tests that can benefit from economies of scale.