Contact

Daria Pašalić
Editor-in-Chief
Department of Medical Chemistry, Biochemistry and Clinical Chemistry
Zagreb University School of Medicine
Šalata ul 2.
10 000 Zagreb, Croatia
Phone +385 (1) 4590 205; +385 (1) 4566 940
E-mail: dariapasalic [at] gmail [dot] com

Useful links

S7-1

Automated microscopy in laboratory medicine

 

Giuseppe Lippi. Automated microscopy in laboratory medicine.Biochemia Medica 2015;25(Suppl 1):S41-S42.

Laboratory of Clinical Chemistry and Hematology, Academic Hospital of Parma, Parma, Italy

 

Laboratory hematology represents an essential part of the clinical decision making in patients with hematological disturbances. Historically, the only reliable approach for identification, enumeration and sizing of blood cells has been represented by optical microscopy of peripheral blood smears. Nevertheless, this practice was inherently time consuming and required peculiar skills, also being plagued by a remarkable degree of inter- and intra-observer inaccuracy. In the past decades a newer generation of hematological analyzers has become available, which now allow a rapid, accurate and precise identification and enumeration of blood cells, thus overcoming the inherent shortages of microscopic analysis. Recent technological advances have also allowed to develop and introduce automated image analysis systems that can be physically connected to the hematological analyzers. In brief, this specific instrumentation is designed to automatically prepare blood films (i.e., wedging on a glass slide staining) according to customized criteria obtained from the complete blood count, scan the slides (usually corresponding at a picture of ×100 objective), and finally prepare digital images of blood smears at higher magnification. These images are then analysed by artificial neural networks according to a manufacturer’s provided database of blood elements, which can be customized and updated by the local users. The images can also be transmitted and displayed on computer screens, which can be placed at various distances from the scanner, for analysis and possible reclassification of blood elements. In particular, the operator can magnify the image and enlarge single sections of the scan to obtain a more accurate view, accept and maintain the actual categorization, or shift individual elements to another category, for a more precise reclassification. The feature originally provided by automated image analysis systems enable the classification of white blood cells (WBCs) in normal elements (i.e., WBC differential, band or segmented neutrophils), as well as in atypical leukocytes (i.e., immature cells, blasts, variant form lymphocytes and plasma cells, smudge cells). Additional information about erythrocyte and platelet morphology can be generated, including polychromasia, hypochromia, anysocytosis, microcytosis or macrocytosis, poikilocytosis, sickle cells, helmet cells, teardrop cells, schizocytosis, spherocytosis, elliptocytosis, ovalocytosis, stomatocytosis, acantocytosis, echinocytosis, large platelets, platelet aggregation, etc.

Recent evidence attests that this novel generation of automated image analysis systems provides data that are highly correlated with those of optical microscopy, and is hence applicable to the vast majority of blood smears analysis in daily practice. Interestingly, this approach not only reduces technical staff time and improves laboratory workout, but is also effective to optimize the identification of pathological and spurious hematological abnormalities that may be present at low frequency, since the alterations can be more efficiently displayed on the screen.

e-mail: glippi [at] ao [dot] pr [dot] it

 

S7-2

Automatization of the preanalytical phase

 

Marijana Miler. Automatization of the preanalytical phase. Biochemia Medica 2015;25(Suppl 1):S42-S43.

University Department of Chemistry, Clinical Hospital Center Sestre milosrdnice, Zagreb, Croatia

 

Even 60-70% of laboratory errors in total testing process (TTP) belong to the preanalytical phase. The frequency of errors in preanalytical phase could be reduced with measures of improvement. All laboratory processes and procedures should be precisely defined and the continuing education of laboratory personnel should be available. It is also important to define quality indicators for monitoring the quality of samples and processes in the laboratory.

The automatization of preanalytical phase is one of the key methods for increasing the efficiency and reducing the number of errors in preanalytical phase.

There are two types of automation systems available in preanalytical phase. Modular laboratory automation (MLA) includes only a few analyzers and laboratory processes and procedures are partly automated. In total laboratory automation (TLA) entire preanalytical, analytical and postanalytical phase in laboratory is automated. TLA has various analyzers and units that may consist of barcode readers, unit for identifying types of test tubes, centrifuges, decapper, track system for samples transport from the input unit to the analyzer, aliquoter and storage unit.

The main goal of automatization of the preanalytical phase is to ensure reliable and accurate results for physicians and patients reduce time consumption and simplify processes in the laboratory. Laboratory automatization system enables optimization and standardization of procedures in laboratory.

The implementation of automated system reduces the workplace area for different analyzers and manual detection of sample quality. Total laboratory automation increases the productivity and efficiency of laboratory and personnel. TLA reduces number of errors that can occur during the transport or manual handling of samples. TLA also reduces the turnaround time and direct contact of personnel with the samples which enhances the safety of laboratory personnel.

Each laboratory should select an automated system that will fulfill their predefined criteria.

Many manufacturers have different solutions for automatization of preanalytical phase and each of them may have different analyzers and units and sample workload.

This lecture will provide the advantages and disadvantages of the modular automation systems and total laboratory automation. Also, it will review all benefits for the laboratory and personnel regarding to the automatization of the preanalytical phase.

e-mail: marijana [dot] miler [at] gmail [dot] com

 

S7-3

Autovalidation in clinical laboratory

 

Željka Vogrinc, Vladimira Rimac, Dunja Rogić. Autovalidation in clinical laboratory. Biochemia Medica 2015;25(Suppl 1):S44-S45.

Department of Laboratory Diagnostics, Clinical Hospital Centre Zagreb, Zagreb, Croatia

 

Modern clinical laboratories must respond to daily challenges such as increasing the quality of services, streamlining processes, reducing labor requirements and shortening the time from receipt of samples to reporting the results of analyses (turnaround time; TAT). At the same time, the number of requests required by clinical staff is steadily increasing. One way of raising the efficiency of laboratories, besides automatisation and the development of a laboratory information system (LIS), is the application of autovalidation. Autovalidation is a procedure of semi-automatic selection, validation and reporting of test results using the LIS under previously strictly defined rules. This means that the test results that meet the set autovalidation rules are issued automatically, without manual human intervention. Autovalidation can greatly reduce manual review time and effort by laboratory staff, limiting burden and fatigue of personnel caused by examination of a large number of results, and allows them to focus attention on a small number of potentially problematic samples and results. Before application of autovalidation in routine, it is necessary to set qualifying rules for autovalidation, define values ​​for each rule, and finally to validate the selected rules.
In the Department of Laboratory Diagnostics, University Hospital Centre Zagreb, autovalidation in clinical biochemistry has been routinely carried out since July 2014, and included 30 tests in serum and plasma. All tests included in autovalidation are performed on biochemical analyzers from Roche Diagnostics and the analyzers were interfaced to LIS via Roche Middleware Instrument Manager. The LIS used for processing and reporting of test results in our laboratory is Bionet from IN2 company.
One of the most important steps in the process of implementation of autovalidation in routine work was the establishment of validation rules. The rules were set by a team of specialists from our laboratory with the approval of the department head, and tested on 9805 patients. Autovalidation rules were divided into two groups. The first group comprised the rules that were applied to all test results, and included warning messages from the device (e.g., presence of a clot, available sample, serum index values), the linearity limits of the test, critical values
​​for a particular search, and the comparison of results for specific analysis with previous results (delta check). The second group of rules included additional criteria that are applied only in some specific tests (e.g., measurement of LDL in high concentrations of triglycerides). All autovalidation rules were entered through LIS Bionet Data Entry, and tested individually and in combination. Validation of autovalidation rules showed that 78.3% of samples met the established rules and the compatibility of autovalidation and manual validation was 99.5%. According to these results, it was possible to start autovalidation in routine practice. Our experience with autovalidation showed that autovalidation shortens the TAT, reduces the number of incorrect findings and increases the consistency of results.

e-mail: zvogrinc [at] kbc-zagreb [dot] hr