Technologies for implementation of quality assurance in the clinical laboratory.

To face the rapid evolution of the clinical laboratory activity from sample analysis towards an in-vitro diagnostic network, a Total Quality Management system must be implemented by laboratory professionals. Technological advances make it possible to introduce new tools and techniques for many issues surrounding the analytical process, as has happened for analysis automation and laboratory management. Preanalytical steps should benefit from extended traceability, using new identification devices such as electronic labels. This may promote the improvement of sample handling in this phase, such as during transportation or centrifugation. Another field is the expansion of metrology. Many factors can now easily be controlled in the clinical laboratory. New reliable automated systems are available to evaluate the performance of pipetting devices. Autonomous miniaturized recorders and probes connected to monitoring softwares allow traceable temperature monitoring. In this paper, some examples are presented to illustrate how technical solutions can support the implementation of Quality Assurance in the clinical laboratory.

Approach to automation in immunohistochemistry.

The introduction of immunochemical techniques into the routine pathology laboratory has significantly expanded the capabilities of the pathologist in diagnostic procedures. Immunostaining represents a powerful diagnostic tool in the identification and localization of cellular antigens, in paraffin sections, frozen tissues and cell preparations. The labeled-streptavidin-biotin method provides excellent sensitivity and performance. This multistep procedure includes: incubation of the slide with primary antibody, reaction with the biotinylated secondary antibody, binding with an enzyme conjugated streptavidin and revelation with chromogen substrate. Evaluation of the finished product is directly dependent on the quality of the technique. The main critical steps of this manual method are reagents application, incubation times and rinsing. These steps could be accessible to automation. Automation in immunohistochemistry could guarantee a continuous quality of labelling in improving standardisation, optimization and traceability of operations. The required qualifications are analytical flexibility, low cost, walkaway operation, user-friendly interface and biosafety.

Comparative evaluation of automated systems in immunohistochemistry.

From October 1995 to March 1997, we evaluated five instruments for immunohistochemistry automation: The Techmate 500 (Dako), the Ventana 320/ES (Ventana), the Optimax Plus (Biogenex, Menarini), the Cadenza (Shandon), and the Immunostainer (Coulter-Immunotech). The aim of the evaluation was to compare the different instruments to the manual method in our laboratory which performs about 17 500 immunohistochemistries per year. PRINCIPLE: Three instruments use flat immunohistolabelling, the others use capillarity immunohistolabelling. ANALYTICAL FLEXIBILITY: we compared the number of protocols per run, the multitask capability, and the ability to adapt manual protocols to the different instruments. To compare the management of the workcell, we used the level of selfchecking, reagent and slides preparation time, and waste management. We measured the duration of the different steps of the process, the throughput in slides/h, and the operator working time per slide. Compared to the manual method, the total cost for reagents and consumables was found to be multiplied by 3 for the Ventana which is a closed system, by 2 for the Techmate, by 1.5 for the Optimax and Cadenza, and identical for the Immunostainer. CONCLUSION: Automation of immunohistochemistry is now possible; the Optimax is still in development, small laboratories will appreciate the Cadenza, laboratories requiring a high flexibility with many protocols will use the Immunostainer open system, laboratories with few technicians will prefer the Ventana closed instrument, now available as the Nexes modular system.

Detection of disseminated tumor cells in peripheral blood of colorectal cancer patients.

All cancer staging systems seek to identify clinical and pathological features that can predict outcome or guide therapy. In particular, a non-invasive method for the early detection of disseminating disease would be of great interest. We investigated the use of cytokeratin genes expression to detect blood metastases from colorectal tumors. Epithelial tumor cells were isolated from whole blood using the monoclonal antibody (MAb) BerEP4 and magnetic beads, and detected by reverse transcription-polymerase chain reaction using oligonucleotides derived from the cDNA sequences of cytokeratins 8, 19 and 20. The sensitivity of this assay was determined by spiking SW620 colon carcinoma cells in normal blood. Using cytokeratin 19 expression we were able to detect 1 epithelial tumor cell in 1 ml of whole blood. The clinical applicability of this technique was explored by evaluating patients with a colorectal carcinoma. Epithelial cells were detected in the blood of 12 out of 23 patients, 2 (20%) of 10 with Astler-Coller stage A or B, and 10 (77%) of 13 with stage C or D cancer. In conclusion, this test is a non-invasive, sensitive, and specific assay for detecting circulating epithelial cells in blood. It may be useful for the early diagnosis of disseminating disease, to determine whether the presence of micrometastatic cells at the time of surgery is correlated with an early relapse and for monitoring adjuvant therapeutic trials.

New tools for laboratory design and management.

The clinical laboratory is changing from a place of activity based on sample analysis to an in vitro diagnostic network. To convince our team, partners, and administrators, we need new comprehensive tools to define a strategy with limited risk of failure or conflicts. Specific quality goals should be established before choosing automated tools for sample handling, analytical systems, laboratory information systems, communication systems, or advanced technologies. A system approach maps and simplifies the process, based more on a functional study than on classical disciplines. A customer-supplier approach establishes the requirements between partners either inside or outside the laboratory. The quality system must be a management tool, linking samples, tasks, information, and documents. Quantitative simulation modeling explores different automation alternatives and their impact on laboratory workflow. Finally, integration of results in interactive semirealistic simulation tools for laboratory design or reengineering can be used as communications tools to involve laboratory professionals in the change of their practice.

Use of artificial intelligence in analytical systems for the clinical laboratory.

OBJECTIVE: To consider the role of software in system operation, control and automation, and attempts to define intelligence. METHODS AND RESULTS: Artificial intelligence (Al) is characterized by its ability to deal with incomplete and imprecise information and to accumulate knowledge. Expert systems, building on standard computing techniques, depend heavily on the domain experts and knowledge engineers that have programmed them to represent the real world. Neural networks are intended to emulate the pattern-recognition and parallel processing capabilities of the human brain and are taught rather than programmed. The future may lie in a combination of the recognition ability of the neural network and the rationalization capability of the expert system. In the second part of this paper, examples are given of applications of Al in stand-alone systems for knowledge engineering and medical diagnosis and in embedded systems for failure detection, image analysis, user interfacing, natural language processing, robotics and machine learning, as related to clinical laboratories. CONCLUSION: Al constitutes a collective form of intellectual property, and that there is a need for better documentation, evaluation and regulation of the systems already being used widely in clinical laboratories.

Use of artificial intelligence in analytical systems for the clinical laboratory.

The incorporation of information-processing technology into analytical systems in the form of standard computing software has recently been advanced by the introduction of artificial intelligence (AI), both as expert systems and as neural networks.This paper considers the role of software in system operation, control and automation, and attempts to define intelligence. AI is characterized by its ability to deal with incomplete and imprecise information and to accumulate knowledge. Expert systems, building on standard computing techniques, depend heavily on the domain experts and knowledge engineers that have programmed them to represent the real world. Neural networks are intended to emulate the pattern-recognition and parallel processing capabilities of the human brain and are taught rather than programmed. The future may lie in a combination of the recognition ability of the neural network and the rationalization capability of the expert system.In the second part of the paper, examples are given of applications of AI in stand-alone systems for knowledge engineering and medical diagnosis and in embedded systems for failure detection, image analysis, user interfacing, natural language processing, robotics and machine learning, as related to clinical laboratories.It is concluded that AI constitutes a collective form of intellectual propery, and that there is a need for better documentation, evaluation and regulation of the systems already being used in clinical laboratories.

International Federation of Clinical Chemistry. Use of artificial intelligence in analytical systems for the clinical laboratory. IFCC Committee on Analytical Systems.

The incorporation of information-processing technology into analytical systems in the form of standard computing software has recently been advanced by the introduction of artificial intelligence (AI) both as expert systems and as neural networks. This paper considers the role of software in system operation, control and automation and attempts to define intelligence. AI is characterized by its ability to deal with incomplete and imprecise information and to accumulate knowledge. Expert systems, building on standard computing techniques, depend heavily on the domain experts and knowledge engineers that have programmed them to represent the real world. Neural networks are intended to emulate the pattern-recognition and parallel-processing capabilities of the human brain and are taught rather than programmed. The future may lie in a combination of the recognition ability of the neural network and the rationalization capability of the expert system. In the second part of this paper, examples are given of applications of AI in stand-alone systems for knowledge engineering and medical diagnosis and in embedded systems for failure detection, image analysis, user interfacing, natural language processing, robotics and machine learning, as related to clinical laboratories. It is concluded that AI constitutes a collective form of intellectual property and that there is a need for better documentation, evaluation and regulation of the systems already being used widely in clinical laboratories.

Incremento de la bioseguridad de sistemas analíticos en el laboratorio clínico / Increment of the biosecurity of analytical system in the clinic laboratory

La bioseguridad es una parte importante del conocimiento práctivo de todos los profesionales de laboratorio clínicos. Debe focalizarse la atención en la reducción del manipuleo de especímenes biológicos, en la reducción de los materiales biológicos peligrosos para el personal de laboratorio y en el mejoramiento del rotulado y envasado de los materiales biopeligrosos. En este artículo, los temas de bioseguridad se discuten en relación al diseño de sistemas analíticos, su utilización y su mantenimiento

Incremento de la bioseguridad de sistemas analíticos en el laboratorio clínico / Increment of the biosecurity of analytical system in the clinic laboratory

La bioseguridad es una parte importante del conocimiento práctivo de todos los profesionales de laboratorio clínicos. Debe focalizarse la atención en la reducción del manipuleo de especímenes biológicos, en la reducción de los materiales biológicos peligrosos para el personal de laboratorio y en el mejoramiento del rotulado y envasado de los materiales biopeligrosos. En este artículo, los temas de bioseguridad se discuten en relación al diseño de sistemas analíticos, su utilización y su mantenimiento (AU)

International Federation of Clinical Chemistry (IFCC): systematic top-down approach to clinical chemistry.

This paper introduces a systematic approach to organizing the discipline of clinical chemistry. The approach is called a top-down, systems approach because it starts at the top with the most general concepts and works down through less general concepts to the most specific details and techniques. The hypothesis is that the discipline can be organized into hierarchical levels of functional processes and operational approaches to those processes. The functional processes represent what clinical scientists do; the operational approaches represent how they do it. Because functional processes change little, if at all, with time, they are use to develop a stable infrastructure or framework for the discipline. That infrastructure is then used to organize and understand operational approaches that tend to change rapidly with time in response to technological advances. This paper begins with the most general functional processes and then uses selected examples of the more general functions to illustrate lower hierarchical levels of functional processes and operational approaches.

Increasing the biosafety of analytical systems in the clinical laboratory.

Biosafety is an important part of the know-how of all clinical laboratory professionals. Biosafely must have high priority in the design and use of analytical systems. Attention should be focused on reducing the handling of biological specimens, reducing biohazards to laboratory personnel, and on improving the labelling and containment of biohazardous materials. In this paper, biosafety issues are discussed in relation to the design of analytical systems, their use and maintenance.

Incremento de la bioseguridad de sistemas análiticos en el laboratorío clínico

La bioseguridad es una parte importante del conocimiento práctico de todos los profesionales de laboratorios clínicos. Debe focalizarse la atención en la reducción del manipuleo de especímenes biológicos, en la reducción de los materiales biológicos peligrosos para el personal de laboratorio y en el mejoramiento del rotulado y envasado de los materiales biopeligrosos. en este artículo, los temas de bioseguridad se discuten en relación al diseño de sistemas analíticos, su utilización y su mantenimiento

La expansión del rol de la robótica en el laboratorio clínico / The spansion of roll of the robotic in the clinic laboratory

Un número creciente de robots serán empleados en laboratorios químicos industriales. Muchos de ellos serán usados para reducir las tareas monótonas de preparación de muestras, para minimizar la exposición humana a entornos riesgosos o para realizar gran número de procedimientos experimentales repetitivos. Por ejemplo, buscar la condición más efectiva o sus combinaciones en síntesis química o el mejor microorganismo en un gran número de cultivos. En el laboratorio clínico la situación es ligeramente diferente y la robótica no es tan ampliamente aplicada, pero hay una tendencia definida para emplear robots o sistemas robóticos, tanto como para reducir el volumen de trabajo y la exposición del personal a posibles biopeligros y para ayudar a obtener resultados más precisos y correctos. Estas necesidades son difíciles de llenar a través delos dispositivos automáticos usuales y especialmente cuando no estan disponibles dispositivos adecuados. Aparatos especialmente diseñados deberán ser producidos para satisfacer estas demandas y la robótica jugará una parte. Finalmente necesitamos evaluar la efectividad de la introducción de la robótica en términos de economía, estrategia, bioseguridad y otros aspectos. ejemplos típicos de implementación de la robótica en el laboratorio clínico son el transporte de especímenes, la automatización de la preparación de muestras, separación, fraccionamiento en al cuotas, así como procesos seleccionados en sistemas automatizados en gran escala. Como se describió previamente, los robots que están comercialmente disponibles actualmente, no son suficientemente inteligentes para ser fácilmente manejados por personal no entrenado en robótica. Hay una necesidad de personal dedicado a robótica que se incorpore al proyecto desde el principio del plan y que pueda mantener el sistema adecuadamente. Nosotros predecimos que esta situación permanecerá de esta forma por un tiempo considerable en el futuro. Los sistemas robots o mecanismos serán gradualmente introducidos en los laboratorios clínicos. La robótica será una de las mejores formas para mantener la bioseguridad en el entorno del laboratorio clínico y en el futuro se necesitarán laboratorios más automatizados con menos personal. Una vez más nosotros queremos enfatizar que se debe establecer una interfase estandarizada y un protocolo para la comunicación entre robots, computadoras e instrumentos, antes de que la robótica sea ampliamente empleada

La expansión del rol de la robótica en el laboratorio clínico / The spansion of roll of the robotic in the clinic laboratory

Un número creciente de robots serán empleados en laboratorios químicos industriales. Muchos de ellos serán usados para reducir las tareas monótonas de preparación de muestras, para minimizar la exposición humana a entornos riesgosos o para realizar gran número de procedimientos experimentales repetitivos. Por ejemplo, buscar la condición más efectiva o sus combinaciones en síntesis química o el mejor microorganismo en un gran número de cultivos. En el laboratorio clínico la situación es ligeramente diferente y la robótica no es tan ampliamente aplicada, pero hay una tendencia definida para emplear robots o sistemas robóticos, tanto como para reducir el volumen de trabajo y la exposición del personal a posibles biopeligros y para ayudar a obtener resultados más precisos y correctos. Estas necesidades son difíciles de llenar a través delos dispositivos automáticos usuales y especialmente cuando no estan disponibles dispositivos adecuados. Aparatos especialmente diseñados deberán ser producidos para satisfacer estas demandas y la robótica jugará una parte. Finalmente necesitamos evaluar la efectividad de la introducción de la robótica en términos de economía, estrategia, bioseguridad y otros aspectos. ejemplos típicos de implementación de la robótica en el laboratorio clínico son el transporte de especímenes, la automatización de la preparación de muestras, separación, fraccionamiento en al cuotas, así como procesos seleccionados en sistemas automatizados en gran escala. Como se describió previamente, los robots que están comercialmente disponibles actualmente, no son suficientemente inteligentes para ser fácilmente manejados por personal no entrenado en robótica. Hay una necesidad de personal dedicado a robótica que se incorpore al proyecto desde el principio del plan y que pueda mantener el sistema adecuadamente. Nosotros predecimos que esta situación permanecerá de esta forma por un tiempo considerable en el futuro. Los sistemas robots o mecanismos serán gradualmente introducidos en los laboratorios clínicos. La robótica será una de las mejores formas para mantener la bioseguridad en el entorno del laboratorio clínico y en el futuro se necesitarán laboratorios más automatizados con menos personal. Una vez más nosotros queremos enfatizar que se debe establecer una interfase estandarizada y un protocolo para la comunicación entre robots, computadoras e instrumentos, antes de que la robótica sea ampliamente empleada(AU)