The 2023 Orthopedic Research Society's international consensus meeting on musculoskeletal infection: Summary from the in vitro section.

Antimicrobial strategies for musculoskeletal infections are typically first developed with in vitro models. The In Vitro Section of the 2023 Orthopedic Research Society Musculoskeletal Infection international consensus meeting (ICM) probed our state of knowledge of in vitro systems with respect to bacteria and biofilm phenotype, standards, in vitro activity, and the ability to predict in vivo efficacy. A subset of ICM delegates performed systematic reviews on 15 questions and made recommendations and assessment of the level of evidence that were then voted on by 72 ICM delegates. Here, we report recommendations and rationale from the reviews and the results of the internet vote. Only two questions received a ≥90% consensus vote, emphasizing the disparate approaches and lack of established consensus for in vitro modeling and interpretation of results. Comments on knowledge gaps and the need for further research on these critical MSKI questions are included.

Digoxin immunoassays on the ARCHITECT i2000SR and ARCHITECT c8000 analyzers are free from interferences of Asian, Siberian, and American ginseng.

Asian, Siberian, and American ginseng are known to interfere with serum digoxin measurements using fluorescence polarization technology, Digoxin II and Digoxin III assays (Abbott Laboratories, Green oaks, IL) as well as other digoxin assays. Abbott Laboratories more recently launched two new digoxin assays: iDigoxin, a chemiluminescent microparticle immunoassay for application on the ARCHITECT i1000SR and i2000SR immunoassay analyzers, and cDigoxin, a particle-enhanced turbidimetric inhibition immunoassay for application on the ARCHITECT c4000, c8000, and c1600 clinical chemistry analyzers; and we studied potential interferences of ginsengs with these two assays in vitro. When aliquots of drug-free serum pool treated with activated charcoal were supplemented with extracts of various ginsengs, no significant apparent digoxin values were observed. In addition, when aliquots of the digoxin pool prepared from patients taking digoxin were further supplemented with these ginseng extracts and the digoxin values were re-measured, we observed no statistically significant difference in observed digoxin values compared to the original digoxin value of the pool. These results further establish that relatively new digoxin assays for application on the ARCHITECT analyzers that employ specific monoclonal antibodies against digoxin are free from interferences from Asian, Siberian, and American ginseng.

Clinical Chemistry Laboratory Automation in the 21st Century - Amat Victoria curam (Victory loves careful preparation).

The era of automation arrived with the introduction of the AutoAnalyzer using continuous flow analysis and the Robot Chemist that automated the traditional manual analytical steps. Successive generations of stand-alone analysers increased analytical speed, offered the ability to test high volumes of patient specimens, and provided large assay menus. A dichotomy developed, with a group of analysers devoted to performing routine clinical chemistry tests and another group dedicated to performing immunoassays using a variety of methodologies. Development of integrated systems greatly improved the analytical phase of clinical laboratory testing and further automation was developed for pre-analytical procedures, such as sample identification, sorting, and centrifugation, and post-analytical procedures, such as specimen storage and archiving. All phases of testing were ultimately combined in total laboratory automation (TLA) through which all modules involved are physically linked by some kind of track system, moving samples through the process from beginning-to-end. A newer and very powerful, analytical methodology is liquid chromatography-mass spectrometry/mass spectrometry (LC-MS/MS). LC-MS/MS has been automated but a future automation challenge will be to incorporate LC-MS/MS into TLA configurations. Another important facet of automation is informatics, including middleware, which interfaces the analyser software to a laboratory information systems (LIS) and/or hospital information systems (HIS). This software includes control of the overall operation of a TLA configuration and combines analytical results with patient demographic information to provide additional clinically useful information. This review describes automation relevant to clinical chemistry, but it must be recognised that automation applies to other specialties in the laboratory, e.g. haematology, urinalysis, microbiology. It is a given that automation will continue to evolve in the clinical laboratory, limited only by the imagination and ingenuity of laboratory scientists.

Reference Intervals of Common Clinical Chemistry Analytes for Adults in Hong Kong.

BACKGROUND: Defining reference intervals is a major challenge because of the difficulty in recruiting volunteers to participate and testing samples from a significant number of healthy reference individuals. Historical literature citation intervals are often suboptimal because they're be based on obsolete methods and/or only a small number of poorly defined reference samples. METHODS: Blood donors in Hong Kong gave permission for additional blood to be collected for reference interval testing. The samples were tested for twenty-five routine analytes on the Abbott ARCHITECT clinical chemistry system. Results were analyzed using the Rhoads EP evaluator software program, which is based on the CLSI/IFCC C28-A guideline, and defines the reference interval as the 95% central range. RESULTS: Method specific reference intervals were established for twenty-five common clinical chemistry analytes for a Chinese ethnic population. The intervals were defined for each gender separately and for genders combined. Gender specific or combined gender intervals were adapted as appropriate for each analyte. CONCLUSION: A large number of healthy, apparently normal blood donors from a local ethnic population were tested to provide current reference intervals for a new clinical chemistry system. Intervals were determined following an accepted international guideline. Laboratories using the same or similar methodologies may adapt these intervals if deemed validated and deemed suitable for their patient population. Laboratories using different methodologies may be able to successfully adapt the intervals for their facilities using the reference interval transference technique based on a method comparison study.

The need for standardization of tacrolimus assays.

BACKGROUND: Owing to the lack of an internationally recognized tacrolimus reference material and reference method, current LC-MS and immunoassay test methods used to monitor tacrolimus concentrations in whole blood are not standardized. The aim of this study was to assess the need for tacrolimus assay standardization. METHODS: We sent a blinded 40-member whole-blood tacrolimus proficiency panel (0-30 µg/L) to 22 clinical laboratories in 14 countries to be tested by the following assays: Abbott ARCHITECT (n = 17), LC-MS (n = 9), and Siemens Dade Dimension (n = 5). Selected LC-MS laboratories (n = 4) also received a common calibrator set. We compared test results to a validated LC-MS method. Four samples from the proficiency panel were assigned reference values by using exact-matching isotope-dilution mass spectrometr at LGC. RESULTS: The range of CVs observed with the tacrolimus proficiency panel was as follows: LC-MS 11.4%-18.7%, ARCHITECT 3.9%-9.5%, and Siemens Dade 5.0%-48.1%. The range of historical within-site QC CVs obtained with the use of 3 control concentrations were as follows: LC-MS low 3.8%-10.7%, medium 2.0%-9.3%, high 2.3%-9.0%; ARCHITECT low 2.5%-9.5%, medium 2.5%-8.6%, high 2.9%-18.6%; and Siemens/Dade Dimension low 8.7%-23.0%, medium 7.6%-13.2%, high 4.4%-10.4%. Assay bias observed between the 4 LC-MS sites was not corrected by implementation of a common calibrator set. CONCLUSIONS: Tacrolimus assay standardization will be necessary to compare patient results between clinical laboratories. Improved assay accuracy is required to provide optimized drug dosing and consistent care across transplant centers globally.

Limit of blank, limit of detection and limit of quantitation.

* Limit of Blank (LoB), Limit of Detection (LoD), and Limit of Quantitation (LoQ) are terms used to describe the smallest concentration of a measurand that can be reliably measured by an analytical procedure. * LoB is the highest apparent analyte concentration expected to be found when replicates of a blank sample containing no analyte are tested. LoB = mean(blank) + 1.645(SD(blank)). * LoD is the lowest analyte concentration likely to be reliably distinguished from the LoB and at which detection is feasible. LoD is determined by utilising both the measured LoB and test replicates of a sample known to contain a low concentration of analyte. * LoD = LoB + 1.645(SD (low concentration sample)). * LoQ is the lowest concentration at which the analyte can not only be reliably detected but at which some predefined goals for bias and imprecision are met. The LoQ may be equivalent to the LoD or it could be at a much higher concentration.

Sample to sample carryover: a source of analytical laboratory error and its relevance to integrated clinical chemistry/immunoassay systems.

BACKGROUND: Integrated systems that combine clinical chemistry and immunoassay analyzers are used routinely. Sample to sample carryover is an inherent risk and can cause erroneously high patient test results for immunoassays. IVD manufacturers and laboratories must be aware of this phenomenon and guard against it. METHODS: We used a sample carryover protocol that directs the clinical chemistry module to process samples with very high immunoassay analyte concentrations followed by samples with very low concentrations for the same analyte. Low concentration samples were then tested by the immunoassay module to determine if the clinical chemistry module caused primary sample tube to primary sample tube carryover of the immunoassay analyte. RESULTS: Sample carryover was assessed on the Abbott ci8200 for HBsAg, AFP, beta-hCG, and PSA. Observed HBsAg carryover met the design specification of <0.1 ppm. Carryover for the other analytes was <0.1 ppm or below the assay limit of detection. CONCLUSIONS: IVD manufacturers must design integrated systems to minimize primary specimen tube carryover and avoid analytical laboratory error that can impact patient safety. Carryover testing is difficult for clinical laboratories to perform in order to verify system performance. Laboratories must consider the potential for specimen carryover and its impact on results whether moving primary sample tubes between separate analyzers or using an integrated system.