Setting analytical performance specifications based on outcome studies - is it possible?

The 1st Strategic Conference of the European Federation of Clinical Chemistry and Laboratory Medicine proposed a simplified hierarchy for setting analytical performance specifications (APS). The top two levels of the 1999 Stockholm hierarchy, i.e., evaluation of the effect of analytical performance on clinical outcomes and clinical decisions have been proposed to be replaced by one outcome-based model. This model can be supported by: (1a) direct outcome studies; and (1b) indirect outcome studies investigating the impact of analytical performance of the test on clinical classifications or decisions and thereby on the probability of patient relevant clinical outcomes. This paper reviews the need for outcome-based specifications, the most relevant types of outcomes to be considered, and the challenges and limitations faced when setting outcome-based APS. The methods of Model 1a and b are discussed and examples are provided for how outcome data can be translated to APS using the linked evidence and simulation or decision analytic techniques. Outcome-based APS should primarily reflect the clinical needs of patients; should be tailored to the purpose, role and significance of the test in a well defined clinical pathway; and should be defined at a level that achieves net health benefit for patients at reasonable costs. Whilst it is acknowledged that direct evaluations are difficult and may not be possible for all measurands, all other forms of setting APS should be weighed against that standard, and regarded as approximations. Better definition of the relationship between the analytical performance of tests and health outcomes can be used to set analytical performance criteria that aim to improve the clinical and cost-effectiveness of laboratory tests.

[Analysis of effects of pharmacotherapy on certain parameters of clinical laboratory diagnostics].

AIM: To study effects of pharmaceutical products on the results of clinical and laboratory diagnostics. MATERIALS AND METHODS: The list of vitally important pharmaceuticals, clinico-pharmacological articles of the National registry of medicinal products, formulary articles from the Federal guidelines on the use of pharmaceutical products. Formulary system, instructions for use of individual pharmaceutical products. Systemic and information-based approaches, logical and comparative analysis were used throughout the study. RESULTS: Results of analysis enabled the authors to draw up separate lists of pharmaceuticals based on their influence on parameters of clinical laboratory diagnostics, such as the list of pharmaceuticals exerting marked effect on diagnostic characteristics (blood properties, primary and coagulative hemostasis, serum enzymes), the list of pharmaceuticals exerting marked effect on systematized laboratory characteristics; the list of pharmaceuticals exerting no effect on clinical and laboratory diagnostics; the list of pharmaceuticals whose effect on diagnostic characteristics awaits clarification. CONCLUSION: The results of the study can be used for the development of recommendations on pharmaceutical counseling and rational choice of pharmaceutical products being prescribed to concrete patients.

Review and classification of variability analysis techniques with clinical applications.

Analysis of patterns of variation of time-series, termed variability analysis, represents a rapidly evolving discipline with increasing applications in different fields of science. In medicine and in particular critical care, efforts have focussed on evaluating the clinical utility of variability. However, the growth and complexity of techniques applicable to this field have made interpretation and understanding of variability more challenging. Our objective is to provide an updated review of variability analysis techniques suitable for clinical applications. We review more than 70 variability techniques, providing for each technique a brief description of the underlying theory and assumptions, together with a summary of clinical applications. We propose a revised classification for the domains of variability techniques, which include statistical, geometric, energetic, informational, and invariant. We discuss the process of calculation, often necessitating a mathematical transform of the time-series. Our aims are to summarize a broad literature, promote a shared vocabulary that would improve the exchange of ideas, and the analyses of the results between different studies. We conclude with challenges for the evolving science of variability analysis.

COVID-19 TestNorm: A tool to normalize COVID-19 testing names to LOINC codes.

Large observational data networks that leverage routine clinical practice data in electronic health records (EHRs) are critical resources for research on coronavirus disease 2019 (COVID-19). Data normalization is a key challenge for the secondary use of EHRs for COVID-19 research across institutions. In this study, we addressed the challenge of automating the normalization of COVID-19 diagnostic tests, which are critical data elements, but for which controlled terminology terms were published after clinical implementation. We developed a simple but effective rule-based tool called COVID-19 TestNorm to automatically normalize local COVID-19 testing names to standard LOINC (Logical Observation Identifiers Names and Codes) codes. COVID-19 TestNorm was developed and evaluated using 568 test names collected from 8 healthcare systems. Our results show that it could achieve an accuracy of 97.4% on an independent test set. COVID-19 TestNorm is available as an open-source package for developers and as an online Web application for end users (https://clamp.uth.edu/covid/loinc.php). We believe that it will be a useful tool to support secondary use of EHRs for research on COVID-19.

COVID-19 paraclinical diagnostic tools: Updates and future trends.

MOTIVATION: COVID-19 is one of the most widely affecting pandemics. As for many respiratory viruses-caused diseases, diagnosis of COVID-19 relies on two main compartments: clinical and paraclinical diagnostic criteria. Rapid and accurate diagnosis is vital in such a pandemic. On one side, rapidity may enhance management effectiveness, while on the other, coupling efficiency and less costly procedures may permit more effective community-scale management. METHODOLOGY AND MAIN STRUCTURE: In this review, we shed light on the most used and the most validated diagnostic tools. Furthermore, we intend to include few under-development techniques that may be potentially useful in this context. The practical intent of our work is to provide clinicians with a realistic summarized review of the essential elements in the applied paraclinical diagnosis of COVID-19.

Translating biological parameters into clinically useful diagnostic tests.

Psychiatry has lagged behind other specialties in developing diagnostic laboratory tests for the purpose of confirming or ruling out a diagnosis. Biological research into the pathophysiology of psychiatric disorders has, however, yielded some highly replicable abnormalities that have the potential for development into clinically useful diagnostic tests. To achieve this goal, a process for systematic translation must be developed and implemented. Building on our previous work, we review a proposed process using four clearly defined steps. We conclude that biological parameters currently face challenges in their pathways to becoming diagnostic tests because of both the premature release and premature abandonment of tests. Attention to a systematic translation process aided by these principles may help to avoid these problems.

Assessment of laboratory capacity of public secondary health facilities in performing assay of selected epidemic-prone diseases in Oyo State, Nigeria.

This study assessed the capacity of public secondary facility-based laboratories in conducting diagnostic tests for selected epidemic-prone diseases in Oyo State, Nigeria. A descriptive cross-sectional study was conducted in 17 secondary facility-based laboratories in Oyo State. Capacity was assessed on a 100-point scale in which scores were rated low (≤49%), fair (50-79%) and good (≥80%). Diagnostic testing capacity for bacterial meningitis, cholera, and measles was "low" in all the laboratories. The reasons reported for laboratories not conducting diagnostic tests for the selected diseases included inadequate instruments, unavailable reagents, and clinicians' failure to request those diagnostic tests. Laboratory capacity to perform diagnostic tests for the selected diseases was low in Oyo State secondary hospitals. There is a need for the provision of modern instruments and reagents, as well as clinician laboratorian quality assurance programs, to improve diagnostic services relating to the selected diseases.

Standardizing laboratory data by mapping to LOINC.

The authors describe a pilot project to standardize local laboratory data at five Indian Health Service (IHS) medical facilities by mapping laboratory test names to Logical Observation Identifier Names and Codes (LOINC). An automated mapping tool was developed to assign LOINC codes. At these sites, they were able to map from 63% to 76% of the local active laboratory tests to LOINC using the mapping tool. Eleven percent to 27% of the tests were mapped manually. They could not assign LOINC codes to 6% to 19% of the laboratory tests due to incomplete or incorrect information about these tests. The results achieved approximate other similar efforts. Mapping of laboratory test names to LOINC codes will allow IHS to aggregate laboratory data more easily for disease surveillance and clinical and administrative reporting efforts. This project may provide a model for standardization efforts in other health systems.

A modified method of activity-based costing for objectively reducing cost drivers in hospitals.

OBJECTIVES: Activity-based costing (ABC) is widely used to precisely allocate indirect costs to medical services. In the ABC method, the indirect cost is divided among the medical services in proportion to the volume of "cost drivers", for example, labor hours and the number of hours of surgery. However, the workload of data collection of cost drivers can be time-consuming and a considerable burden if there are many cost drivers. The authors aim to develop a method for objectively reducing the cost drivers used in the ABC method. METHODS: In the ABC method, the cost driver is assigned for each activity. We assume that these activities and cost drivers are the best combination. Our method, that is cost driver reduction (CDR), can objectively select surrogates of the cost drivers for each activity in ABC from candidate cost drivers. Concretely, the total indirect cost of an activity is temporarily allocated to the medical services using each candidate of cost drivers. The difference between the costs calculated by each candidate and the proper cost driver used in ABC is calculated to evaluate the similarity by the evaluation function. RESULTS: We estimated the cost of laboratory tests using our method and revealed that the number of cost drivers could be reduced from seven in the ABC to four. Similarly, the results of cost estimation obtained by our method were as accurate as those calculated using the ABC. CONCLUSIONS: Our method provides two advantages compared to the ABC method: 1) it provides results that are as accurate as those of the ABC method, and 2) it is simpler to perform complicated estimation of hospital costs.

Implementation of a Decision Support System for Interpretation of Laboratory Tests for Patients.

The paper presents the results of the development and implementation of an expert system that automatically generates doctors' letters based on the results of laboratory tests. Medical knowledge is expressed using a first order predictate logic based language. The system was implemented and evaluated in the Helix laboratory service.

Frequency of laboratory test utilization in the intensive care unit and its implications for large-scale data collection efforts.

OBJECTIVE: Mapping local use names to standardized nomenclatures such as LOINC (Logical Observation Identifiers Names and Codes) is a time-consuming task when done retrospectively or during the configuration of new information systems. The author sought to identify a subset of intensive care unit (ICU) laboratory tests, which, because of their frequency of use, should be the focus of efforts to standardize test names in ICU information systems. DESIGN: The author reviewed the ordering practices in medical, surgical, and pediatric ICUs within a large university teaching hospital to identify the subset of laboratory tests that represented the majority of tests performed in these settings. The author compared the results of his findings with the laboratory tests required to complete several of the most frequently used ICU acuity scoring systems. RESULTS: It was found that between 104 and 202 tests and profiles represented 99% of all testing in the three ICUs. All the laboratory studies needed for six commonly used ICU scoring systems fell into the top 21 laboratory studies and profiles performed in each ICU. CONCLUSION: The author identified a small subset of the LOINC database that should be the focus of efforts to standardize test names in ICU information systems. Mapping this subset of laboratory tests and profiles to LOINC vocabulary will simplify the process of collecting data for large-scale databases such as ICU scoring systems and the configuration of new ICU information systems.

Mapping Russian Laboratory Terms to LOINC.

The paper presents the results of semiautomatic mapping of Russian laboratory terms to LOINC. Two clinics (A and B) and a laboratory service participated in the project. We were able to map 86% (Clinic A) and 87% (Clinic B) of laboratory terms. The required effort was reasonable and the price of mapping and maintenance was considered as relatively low. We established LOINC as a canonical coding method for a laboratory data exchange. This provided semantic interoperability for the data exchange process.

A system dynamics approach to analyze laboratory test errors.

Although many researches have been carried out to analyze laboratory test errors during the last decade, it still lacks a systemic view of study, especially to trace errors during test process and evaluate potential interventions. This study implements system dynamics modeling into laboratory errors to trace the laboratory error flows and to simulate the system behaviors while changing internal variable values. The change of the variables may reflect a change in demand or a proposed intervention. A review of literature on laboratory test errors was given and provided as the main data source for the system dynamics model. Three "what if" scenarios were selected for testing the model. System behaviors were observed and compared under different scenarios over a period of time. The results suggest system dynamics modeling has potential effectiveness of helping to understand laboratory errors, observe model behaviours, and provide a risk-free simulation experiments for possible strategies.

[Monoclonal gammopathies: classification of laboratory tests]. / Le gammopatie monoclonali: inquadramento delle indagini di laboratorio.

Monoclonal gammopathies have been examined and classified. In the field of chemico-clinical and immunological investigations, the various methods of analysing dysproteinaemia, of which the monoclonal component is a typical expression (electrophoresis, radial immunodiffusion, immunoelectrophoresis, immunofixation), and the main haematological and immunohaematological changes (lymphocyte biological markers) involved are reviewed. After describing the main histological and histo-cytochemical investigations, particularly amyloidosis, electronic microscope aspects and the most important chromosome abnormalities revealed by karyotype study are reported for their diagnostic interest.