Improving Compliance With Evidence-Based Laboratory Testing Recommendations and Monitoring Associated Patient Outcomes.

A team comprising nursing, medical staff, and administrative leaders at an urban academic orthopedic hospital in the northeastern United States sought to revise a preoperative laboratory testing protocol based on evidence and practice guidelines. The goal was to decrease unnecessary tests by 20% without negatively affecting patient outcomes. After adding the revised protocol to the electronic health record, audits revealed that the target goal was not met and additional strategies were implemented, including educational webinars for surgeon office personnel who ordered tests, additional webinars for advanced practice professionals, and the creation of scorecards to track surgeons' progress. Overall, a downward trend in the ordering of unnecessary laboratory tests for patients without identified risks was observed, but a 20% reduction was not achieved. Surgical complications during the project were not associated with laboratory tests. Clinicians continue to use the revised preoperative laboratory testing protocol at the facility.

Quantitative Evaluation of the Real-World Harmonization Status of Laboratory Test Items Using External Quality Assessment Data.

Background: In recent decades, the analytical quality of clinical laboratory results has substantially increased because of collaborative efforts. To effectively utilize laboratory results in applications, such as machine learning through big data, understanding the level of harmonization for each test would be beneficial. We aimed to develop a quantitative harmonization index that reflects the harmonization status of real-world laboratory tests. Methods: We collected 2021-2022 external quality assessment (EQA) results for eight tests (HbA1c, creatinine, total cholesterol, HDL-cholesterol, triglyceride, alpha-fetoprotein [AFP], carcinoembryonic antigen [CEA], and prostate-specific antigen [PSA]). This EQA was conducted by the Korean Association of External Quality Assessment Service, using commutable materials. The total analytical error of each test was determined according to the bias% and CV% within peer groups. The values were divided by the total allowable error from biological variation (minimum, desirable, and optimal) to establish a real-world harmonization index (RWHI) at each level (minimum, desirable, and optimal). Good harmonization was arbitrarily defined as an RWHI value ≤ 1 for the three levels. Results: Total cholesterol, triglyceride, and CEA had an optimal RWHI of ≤ 1, indicating an optimal harmonization level. Tests with a desirable harmonization level included HDL-cholesterol, AFP, and PSA. Creatinine had a minimum harmonization level, and HbA1c did not reach the minimum harmonization level. Conclusions: We developed a quantitative RWHI using regional EQA data. This index may help reflect the actual harmonization level of laboratory tests in the field.

Outcome-based analytical performance specifications: current status and future challenges.

Analytical performance specifications (APS) based on outcomes refer to how 'good' the analytical performance of a test needs to be to do more good than harm to the patient. Analytical performance of a measurand affects its clinical performance. Without first setting clinical performance requirements, it is difficult to define how good analytically the test needs to be to meet medical needs. As testing is indirectly linked to health outcomes through clinical decisions on patient management, often simulation-based studies are used to assess the impact of analytical performance on the probability of clinical outcomes which is then translated to Model 1b APS according to the Milan consensus. This paper discusses the related key definitions, concepts and considerations that should assist in finding the most appropriate methods for deriving Model 1b APS. We review the advantages and limitations of published methods and discuss the criteria for transferability of Model 1b APS to different settings. We consider that the definition of the clinically acceptable misclassification rate is central to Model 1b APS. We provide some examples and guidance on a more systematic approach for first defining the clinical performance requirements for tests and we also highlight a few ideas to tackle the future challenges associated with providing outcome-based APS for laboratory testing.

Use of hospital big data to optimize and personalize laboratory test interpretation with an application.

BACKGROUND AND AIMS: In laboratory medicine, test results are generally interpreted with 95% reference intervals but correlations between laboratory tests are usually ignored. We aimed to use hospital big data to optimize and personalize laboratory data interpretation, focusing on platelet count. MATERIAL AND METHODS: Laboratory tests were extracted from the hospital database and exploited by an algorithmic stepwise procedure. For any given laboratory test Y, an "optimized and personalized reference population" was defined by keeping only patients whose laboratory values for all Y-correlated tests fell within their own usual reference intervals, and by partitioning groups by individual-specific variables like sex and age category. The method was applied to platelet count. RESULTS: Laboratory data were recorded for 28,082 individuals. At the end of the algorithmic process, seven correlated laboratory tests were chosen, resulting in a reference sample of 159 platelet counts. A new 95 % reference interval was constructed [152-334 × 109/L], notably reduced (27.2 %) compared to conventional reference values [150-400 × 109/L]. The reference interval was validated on a sample of 2,129 patients from another downtown laboratory, emphasizing the potential transference of the hospital-derived reference limits. CONCLUSION: This method offers new perspectives in laboratory data interpretation, especially in patient screening and longitudinal follow-up.

ADLM Guidance Document on Laboratory Diagnosis of Respiratory Viruses.

Respiratory viral infections are among the most frequent infections experienced worldwide. The COVID-19 pandemic has highlighted the need for testing and currently several tests are available for the detection of a wide range of viruses. These tests vary widely in terms of the number of viral pathogens included, viral markers targeted, regulatory status, and turnaround time to results, as well as their analytical and clinical performance. Given these many variables, selection and interpretation of testing requires thoughtful consideration. The current guidance document is the authors' expert opinion based on the preponderance of available evidence to address key questions related to best practices for laboratory diagnosis of respiratory viral infections including who to test, when to test, and what tests to use. An algorithm is proposed to help laboratories decide on the most appropriate tests to use for the diagnosis of respiratory viral infections.

Applying the Milan models to setting analytical performance specifications - considering all the information.

Analytical performance specifications (APS) are used for decisions about the required analytical quality of pathology tests to meet clinical needs. The Milan models, based on clinical outcome, biological variation, or state of the art, were developed to provide a framework for setting APS. An approach has been proposed to assign each measurand to one of the models based on a defined clinical use, physiological control, or an absence of quality information about these factors. In this paper we propose that in addition to such assignment, available information from all models should be considered using a risk-based approach that considers the purpose and role of the actual test in a clinical pathway and its impact on medical decisions and clinical outcomes in addition to biological variation and the state-of-the-art. Consideration of APS already in use and the use of results in calculations may also need to be considered to determine the most appropriate APS for use in a specific setting.

Driving consistency: CEPI-Centralized Laboratory Network's conversion factor initiative for SARS-CoV-2 clinical assays used for efficacy assessment of COVID vaccines.

To date, thousands of SARS-CoV-2 samples from many vaccine developers have been tested within the CEPI-Centralized Laboratory Network. To convert data from each clinical assay to international standard units, the WHO international standard and the CEPI standard generated by the Medicines and Healthcare products Regulatory Agency were run in multiple facilities to determine the conversion factor for each assay. Reporting results in international units advances global understanding of SARS-CoV-2 immunity and vaccine efficacy, enhancing the quality, reliability, and utility of clinical assay data.

Analytical and Clinical Performance of the NeuMoDx™ Platform for Cytomegalovirus and Epstein-Barr Virus Viral Load Testing.

DNA assays for viral load (VL) monitoring are key tools in the management of immunocompromised patients with cytomegalovirus (CMV) or Epstein-Barr virus (EBV) infection. In this study, the analytical and clinical performances of the NeuMoDx™ CMV and EBV Quant Assays were compared with artus CMV and EBV QS-RGQ Kits in a primary hospital testing laboratory. Patient plasma samples previously tested using artus kits were randomly selected for testing by NeuMoDx assays. The NeuMoDx CMV Quant Assay and artus CMV QS-RGQ Kit limits of detection (LoDs) are 20.0 IU/mL and 69.7 IU/mL, respectively; 33/75 (44.0%) samples had CMV DNA levels above the LoD of both assays. The Pearson correlation coefficient was 0.9503; 20 samples (60.6%) had lower NeuMoDx CMV quantification values versus the artus kit. The LoD of the NeuMoDx EBV Quant Assay and artus EBV QS-RGQ Kit are 200 IU/mL and 22.29 IU/mL, respectively; 16/75 (21.3%) samples had EBV DNA levels above the LoD of both assays. The Pearson correlation coefficient was 0.8990. EBV quantification values with the NeuMoDx assay were higher versus the artus kit in 15 samples (93.8%). In conclusion, NeuMoDx CMV and EBV Quant Assays are sensitive and accurate tools for CMV and EBV DNA VL quantification.

Assessing post-analytical phase harmonization in European laboratories: a survey promoted by the EFLM Working Group on Harmonization.

OBJECTIVES: Harmonization of the laboratory total testing process (TTP) is critical to improving patient outcome. In 2016, an EFLM survey on the harmonization of TTP underlined the serious shortcomings pertaining to the post-analytical phase. In 2023, the WG-H conducted a new survey aiming to update information in the 2016 harmonization report in order to ascertain whether countries that had declared they were keen to adopt SI units had continued with this program, the aim being to verify the state-of art in harmonization units in areas of laboratory medicine not included in the previous survey. METHODS: Questionnaires were distributed to the Presidents and National Representatives of EFLM Full Member Societies and EFLM affiliate Members. The survey questions were grouped into three categories: measurement units, reference intervals, and nomenclature/terminology, and results were evaluated using Survey Monkey software and Excel. RESULTS: A total of 123 questionnaires from 31 countries were analyzed. A trend (+19.3 %) was observed toward a wider use of SI units for general clinical biochemistry parameters. The results for tests not included in the 2016 survey (i.e., endocrinology diagnostics and coagulation panels), demonstrated that for reports on hormones, responses were satisfactory, 70-90 % of the responders adopting the recommended units, whereas for coagulation test panels, a serious lack of harmonization was found, "seconds", which are inaccurate and not recommended, being widely used units (91 %). CONCLUSIONS: The findings made in the 2023 survey demonstrated a progressive, albeit slow, improvement in harmonization reports. However, further efforts at improvement are mandatory.

Establishing the Reportable Interval for Routine Clinical Laboratory Tests: A Data-Driven Strategy Leveraging Retrospective Electronic Medical Record Data.

BACKGROUND: This paper presents a data-driven strategy for establishing the reportable interval in clinical laboratory testing. The reportable interval defines the range of laboratory result values beyond which reporting should be withheld. The lack of clear guidelines and methodology for determining the reportable interval has led to potential errors in reporting and patient risk. METHODS: To address this gap, the study developed an integrated strategy that combines statistical analysis, expert review, and hypothetical outlier calculations. A large data set from an accredited clinical laboratory was utilized, analyzing over 124 million laboratory test records from 916 distinct tests. The Dixon test was applied to identify outliers and establish the highest and lowest non-outlier result values for each test, which were validated by clinical pathology experts. The methodology also included matching the reportable intervals with relevant Logical Observation Identifiers Names and Codes (LOINC) and Unified Code for Units of Measure (UCUM)-valid units for broader applicability. RESULTS: Upon establishing the reportable interval for 135 routine laboratory tests (493 LOINC codes), we applied these to a primary care laboratory data set of 23 million records, demonstrating their efficacy with over 1% of result records identified as implausible. CONCLUSIONS: We developed and tested a data-driven strategy for establishing reportable intervals utilizing large electronic medical record (EMR) data sets. Implementing the established interval in clinical laboratory settings can improve autoverification systems, enhance data reliability, and reduce errors in patient care. Ongoing refinement and reporting of cases exceeding the reportable limits will contribute to continuous improvement in laboratory result management and patient safety.

The role of analytical performance specifications in international guidelines and standards dealing with metrological traceability in laboratory medicine.

The goal of metrological traceability is to have equivalent results for a measurand in clinical samples (CSs) irrespective of the in-vitro diagnostic medical device (IVD-MD) used for measurements. The International Standards Organization standard 17511 defines requirements for establishing metrological traceability of values assigned to calibrators, trueness control materials and human samples used with IVD-MDs. Each step in metrological traceability has an uncertainty associated with the value assigned to a material. The uncertainty at each step adds to the uncertainty from preceding steps such that the combined uncertainty gets larger at each step. The combined uncertainty for a CS result must fulfil an analytical performance specification (APS) for the maximum allowable uncertainty (umax CS). The umax CS can be partitioned among the steps in a metrological traceability calibration hierarachy to derive the APS for maximum allowable uncertainty at each step. Similarly, the criterion for maximum acceptable noncommutability bias can be derived from the umax CS. One of the challenges in determining if umax CS is fulfilled is determining the repeatability uncertainty (u Rw) from operating an IVD-MD within a clinical laboratory. Most of the current recommendations for estimating u Rw from internal quality control data do not use a sufficiently representative time interval to capture all relevant sources of variability in measurement results. Consequently, underestimation of u Rw is common and may compromise assessment of how well current IVD-MDs and their supporting calibration hierarchies meet the needs of clinical care providers.

Using analytical performance specifications in a medical laboratory.

Analytical performance specifications (APS) are used for the quantitative assessment of assay analytical performance, with the aim of providing information appropriate for clinical care of patients. One of the major locations where APS are used is in the routine clinical laboratory. These may be used to assess and monitor assays in a range of settings including method selection, method verification or validation, external quality assurance, internal quality control and assessment of measurement uncertainty. The aspects of assays that may be assessed include imprecision, bias, selectivity, sample type, analyte stability and interferences. This paper reviews the practical use of APS in a routine clinical laboratory, using the laboratory I supervise as an example.

Analytical performance specifications for combined uncertainty budget in the implementation of metrological traceability.

In addition to the correct implementation of calibration traceability, the definition and fulfillment of maximum allowable measurement uncertainty (MAU) are essential in assuring that laboratory measurements are clinically usable. Across the entire calibration hierarchy, three major contributors to the measurement uncertainty (MU) budget are identified, starting with the higher-order reference providers, extending through the in vitro diagnostic (IVD) manufacturers and their processes for assigning calibrator values, and ending with medical laboratories generating the random variability of results reported to clinicians. To understand if it is possible to achieve MAU and, consequently, to fix the possible drawbacks, the definition of combined MU budget limits across the entire calibration hierarchy has a central role. In particular, quality specifications for MU of reference and commercial calibrator materials should be defined according to the MAU on clinical samples. All involved stakeholders (i.e., higher-order reference providers, IVD manufacturers, medical laboratories) should be prepared to improve their performance whenever the clinical application of the test is made questionable by the failure to achieve MAU.