Laboratory Testing for Powassan Virus: Past, Present, and Future.

Powassan virus is a tick-borne flavivirus that can cause severe neuroinvasive disease, with areas of endemicity in the Northeast and Midwest United States, Canada, and Russia. Diagnosis is challenging and relies on a high index of suspicion and choosing the right test based on duration of infection and the patient's immune status. This review covers laboratory testing for Powassan virus, including historical considerations, modern options, and methods being developed in the research space.

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.

Analyzing laboratory test utilization trends in belgian primary care: a decade of insights with international perspectives.

BACKGROUND: Clinical laboratory testing, essential for medical diagnostics, represents a significant part of healthcare activity, influencing around 70% of critical clinical decisions. The automation of laboratory equipment has expanded test menus and increased efficiency to meet the growing demands for clinical testing. However, concerns about misutilization remain prevalent. In Belgium, primary care has seen a dramatic increase in lab test usage, but recent utilization data is lacking. METHODS: We conducted a comprehensive retrospective analysis of laboratory test utilization trends within the primary care settings of Belgium over a ten-year period, spanning from 2012 to 2021, incorporating a vast dataset of 189 million test records for almost 1.5 million persons. This was the first study to integrate the metadata from both the INTEGO & THIN databases, which are derived from the two major electronic medical record (EMR) systems used in primary care in Belgium, providing a comprehensive national perspective. This research provides crucial insights into patient-level patterns, test-level utilization, and offers international perspectives through comparative analysis. RESULTS: We found a subtle annual increase in the average number of laboratory tests per patient (ranging from approximately 0.5-1%), indicative of a deceleration in growth in laboratory test ordering when compared to previous decades. We also witnessed stability and consistency of the most frequently ordered laboratory tests across diverse patient populations and healthcare contexts over the years. CONCLUSIONS: These findings emphasize the need for continued efforts to optimize test utilization, focusing not only on tackling overutilization but on enhancing the diagnostic relevance of tests ordered. The frequently ordered tests should be prioritized in these initiatives to ensure their continued effectiveness in patient care. By consolidating extensive datasets, employing rigorous statistical analysis, and incorporating international perspectives, this study provides a solid foundation for evidence-based strategies aimed at refining laboratory test utilization practices. These strategies can potentially improve the quality of healthcare delivery while simultaneously addressing cost-effectiveness concerns in healthcare.

Incorporating informatively collected laboratory data from EHR in clinical prediction models.

BACKGROUND: Electronic Health Records (EHR) are widely used to develop clinical prediction models (CPMs). However, one of the challenges is that there is often a degree of informative missing data. For example, laboratory measures are typically taken when a clinician is concerned that there is a need. When data are the so-called Not Missing at Random (NMAR), analytic strategies based on other missingness mechanisms are inappropriate. In this work, we seek to compare the impact of different strategies for handling missing data on CPMs performance. METHODS: We considered a predictive model for rapid inpatient deterioration as an exemplar implementation. This model incorporated twelve laboratory measures with varying levels of missingness. Five labs had missingness rate levels around 50%, and the other seven had missingness levels around 90%. We included them based on the belief that their missingness status can be highly informational for the prediction. In our study, we explicitly compared the various missing data strategies: mean imputation, normal-value imputation, conditional imputation, categorical encoding, and missingness embeddings. Some of these were also combined with the last observation carried forward (LOCF). We implemented logistic LASSO regression, multilayer perceptron (MLP), and long short-term memory (LSTM) models as the downstream classifiers. We compared the AUROC of testing data and used bootstrapping to construct 95% confidence intervals. RESULTS: We had 105,198 inpatient encounters, with 4.7% having experienced the deterioration outcome of interest. LSTM models generally outperformed other cross-sectional models, where embedding approaches and categorical encoding yielded the best results. For the cross-sectional models, normal-value imputation with LOCF generated the best results. CONCLUSION: Strategies that accounted for the possibility of NMAR missing data yielded better model performance than those did not. The embedding method had an advantage as it did not require prior clinical knowledge. Using LOCF could enhance the performance of cross-sectional models but have countereffects in LSTM models.

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.

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.

Dengue: A review of laboratory diagnostics in the vaccine age.

Background. Dengue is an important arboviral infection of considerable public health significance. It occurs in a wide global belt within a variety of tropical regions. The timely laboratory diagnosis of Dengue infection is critical to inform both clinical management and an appropriate public health response. Vaccination against Dengue virus is being introduced in some areas.Discussion. Appropriate diagnostic strategies will vary between laboratories depending on the available resources and skills. Diagnostic methods available include viral culture, the serological detection of Dengue-specific antibodies in using enzyme immunoassays (EIAs), microsphere immunoassays, haemagglutination inhibition or in lateral flow point of care tests. The results of antibody tests may be influenced by prior vaccination and exposure to other flaviviruses. The detection of non-structural protein 1 in serum (NS1) has improved the early diagnosis of Dengue and is available in point-of-care assays in addition to EIAs. Direct detection of viral RNA from blood by PCR is more sensitive than NS1 antigen detection but requires molecular skills and resources. An increasing variety of isothermal nucleic acid detection methods are in development. Timing of specimen collection and choice of test is critical to optimize diagnostic accuracy. Metagenomics and the direct detection by sequencing of viral RNA from blood offers the ability to rapidly type isolates for epidemiologic purposes.Conclusion. The impact of vaccination on immune response must be recognized as it will impact test interpretation and diagnostic algorithms.

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.

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.

Monkeypox Diagnosis in Clinical Settings: A Comprehensive Review of Best Laboratory Practices.

An outbreak of monkeypox (Mpox) was reported in more than 40 countries in early 2022. Accurate diagnosis of Mpox can be challenging, but history, clinical findings, and laboratory diagnosis can establish the diagnosis. The pre-analytic phase of testing includes collecting, storing, and transporting specimens. It is advised to swab the lesion site with virus transport medium (VTM) containing Dacron or polyester flock swabs from two different sites. Blood, urine, and semen samples may also be used. Timely sampling is necessary to obtain a sufficient amount of virus or antibodies. The analytical phase of infectious disease control involves diagnostic tools to determine the presence of the virus. While polymerase chain reaction (PCR) is the gold standard for detecting Mpox, genome sequencing is for identifying new or modified viruses. As a complement to these methods, isothermal amplification methods have been designed. ELISA assays are also available for the determination of antibodies. Electron microscopy is another effective diagnostic method for tissue identification of the virus. Wastewater fingerprinting provides some of the most effective diagnostic methods for virus identification at the community level. The advantages and disadvantages of these methods are further discussed. Post-analytic phase requires proper interpretation of test results and the preparation of accurate patient reports that include relevant medical history, clinical guidelines, and recommendations for follow-up testing or treatment.

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.

Nefropatía por enfermedad relacionada a IGG4 / Nephropathy due to IGG4-related disease

IgG4-related disease (ER-IgG4) is a group of systemic fibro-inflammatory diseases, whose renal involvement is rare and difficult to diagnose. Diagnosis is usually made by serological and histological studies. Treatment is based on systemic corticosteroids. The renal prognosis is determined by the patient's comorbidities and the degree of fibrosis in the renal biopsy. We present the case of an elderly patient with exacerbated chronic kidney disease, whose study showed nephropathy associated with ER-IgG4.

La enfermedad relacionada a IgG4 (ER-IgG4) es un grupo de enfermedades fibro-inflamatorias sistémicas, cuya afectación renal es poco frecuente y de difícil diagnóstico. Habitualmente el diagnóstico se realiza mediante estudios serológicos e histológicos. El tratamiento se basa en corticoides sistémicos. El pronóstico renal está determinado por las comorbilidades del paciente y el grado de fibrosis en la biopsia renal. Se presenta el caso de un paciente adulto mayor con enfermedad renal crónica reagudizada, cuyo estudio demostró nefropatía asociada a ER-IgG4.

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.

Inappropriate laboratory test utilization in outpatient tertiary care: Implications for value-based healthcare.

OBJECTIVES: To assess the rate of inappropriate repetition of laboratory testing and estimate the cost of such testing for thyroid stimulating hormone (TSH), total cholesterol, vitamin D, and vitamin B12 tests. METHODS: A retrospective cohort study was carried out in the Family Medicine and Polyclinic Department at King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia. Clinical and laboratory data were collected between 2018-2021 for the 4 laboratory tests. The inappropriate repetition of tests was defined according to international guidelines and the costs were calculated using the hospital prices. RESULTS: A total of 109,929 laboratory tests carried out on 23,280 patients were included in this study. The percentage of inappropriate tests, as per the study criteria, was estimated to be 6.1% of all repeated tests. Additionally, the estimated total cost wasted amounted to 2,364,410 Saudi Riyals. Age exhibited a weak positive correlation with the total number of inappropriate tests (r=0.196, p=0.001). Furthermore, significant differences were observed in the medians of the total number of inappropriate tests among genders and nationalities (p<0.001). CONCLUSION: The study identified significantly high rates of inadequate repetitions of frequently requested laboratory tests. Urgent action is therefore crucial to overcoming such an issue.