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The selection of new instrumentation is a complex process. Each step must be carefully evaluated, since mistakes will remain in the laboratory for years to come. Infatuation with technology often leads to expensive mistakes.
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The performance of the SE-9000 automated haematology analyser in a laboratory receiving a high number of abnormal specimens from haematological oncology patients was assessed according to formal protocols for the evaluation of blood cell counters. Linearity over a useful working range, precision in clinically important ranges and negligible carry-over were demonstrated in this group of patient samples confirming the results of previous investigators. The comparability of instrument derived differential leucocyte counts from both normal and distributionally abnormal samples with those obtained by visual microscopy using the NCCLS H-20 A protocol was very good. The sensitivity of flags for the detection of immature granulocytes and myeloid blast cells was high and this can be attributed to the incorporation of a new measuring channel (Immature Myeloid Information or IMI channel). The number of unrecognized abnormalities was low and when compared with the poor sensitivity of the routine 100-cell visual differential leucocyte count, the analyser was judged suitable for monitoring patients with haematological malignancies. The performance of flags such as 'left shift' and 'atypical lymphocytes' can be improved by taking into consideration distributional abnormalities such as neutrophilia and lymphocytosis. The trigger level for these flags should be adapted to the clinical need particularly in cases of neutropenia following chemotherapy, and in lymphoproliferative disorders and infection.
A five-step systematic procedure for comparison and selection of automated instruments for the clinical laboratory is presented. Thirteen criteria commonly used for selection of instruments in the clinical laboratory are discussed, and from these an opinion survey form is developed for use in decision making. A procedure for using this opinion survey form, tabulating the survey data, and making the final recommendation is also discussed.
The diagnostic accuracy of the Roche Amplicor human immunodeficiency virus type 1 DNA PCR assay (version 1.5) on DNA extracted from pediatric heel prick dried blood spots using Roche MagNA Pure nucleic acid purification technology was evaluated. The methodologies transfer successfully from the labor-intensive research laboratory to the high-throughput automated routine laboratory.
CONTEXT: One of the major attributes of laboratory testing is cost. Although fully automated central laboratory glucose testing and semiautomated bedside glucose testing (BGT) are performed at most institutions, rigorous determinations of interinstitutional comparative costs have not been performed. OBJECTIVES: To compare interinstitutional analytical costs of central laboratory glucose testing and BGT and to provide suggestions for improvement. DESIGN: Participants completed a demographic form about their institutional glucose monitoring practices. They also collected information about the costs of central laboratory glucose testing, BGT at a high-volume testing site, and BGT at a low-volume testing site, including specified cost variables for labor, reagents, and instruments. PARTICIPANTS: A total of 445 institutions enrolled in the College of American Pathologists Q-Probes program. MAIN OUTCOME MEASURE: Median cost per glucose test at 3 testing sites. RESULTS: The median (10th-90th percentile range) costs per glucose test were 1.18 dollars (5.59 dollars-0.36 dollars), 1.96 dollars (9.51 dollars-0.77 dollars), and 4.66 dollars (27.54 dollars-1.02 dollars) for central laboratory, high-volume BGT sites, and low-volume BGT sites, respectively. The largest percentages of the cost per test were for labor (59.3%, 72.7%, and 85.8%), followed by supplies (27.2%, 27.3%, and 13.4%) and equipment (2.1%, 0.0%, and 0.0%) for the 3 sites, respectively. The median number of patient specimens per month at the high-volume BGT sites was 625 compared to 30 at the low-volume BGT sites. Most participants did not include labor, instrument maintenance, competency assessment, or oversight in their BGT estimated costs until required to do so for the study. CONCLUSIONS: Analytical costs per glucose test were lower for central laboratory glucose testing than for BGT, which, in turn, was highly variable and dependent on volume. Data that would be used for financial justification for BGT were widely aberrant and in need of improvement.
We report on the organisation of our coagulation laboratory which had to meet the requirements of both, the large routine operations and those of a special laboratory for specific blood coagulation problems. A valuable support for the routine activities of our coagulation laboratory was the installation of 2 automated coagulation analysers, type ACL 300. Due to the selective (diagnosis/therapy) patient-related allotment of samples and the establishment of a final diagnosis according to a step-wise method it was possible to integrate the 2 ACL 300 analysers into our existing system and consequently to take full advantage of the savings in staff time. Moreover we report on the evaluation of the 1st ACL 300 (810) analyser and the specific characteristics of the ACL series.
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The Phoenix Automated Microbiology System (BD Biosciences, USA) is a new, fully automated system for the rapid identification and antimicrobial susceptibility testing of gram-positive and gram-negative bacteria. The objective of this study was to evaluate the quality of performance of the Phoenix system in the identification and antimicrobial susceptibility testing of 260 gram-negative ( n=174) and gram-positive ( n=86) isolates collected from Polish hospitals in recent years. Two Phoenix panel types for identification/antimicrobial susceptibility testing, NMIC/ID-5 for gram-negative rods and PMIC/ID-4 for gram-positive cocci, were used in the analysis according to the manufacturer's recommendations. The results produced by the system were compared with data obtained by reference or conventional microbiological methods. A high rate of agreement between the Phoenix and the conventional methods was observed for identification, ranging from 100% for gram-positive cocci to 96.0% for gram-negative nonfermenters and 92.5% for members of the family Enterobacteriaceae. Similarly, a high level of agreement characterized the antimicrobial susceptibility data obtained with the Phoenix and by the agar dilution method (2,361 test results). For staphylococci, enterococci and Enterobacteriaceae, the methods were 100% concordant in determining the category of susceptibility of isolates to the majority of the antimicrobial agents tested. A category agreement value of below 90% was found for the susceptibility of enterococci and gram-negative nonfermenters to ciprofloxacin (84.6% and 88.5%, respectively) and for susceptibility of Stenotrophomonas maltophilia to trimethoprim-sulfamethoxazole (80.0%).
An automated cell analysis system (Autoplan-MIAC) for the early detection of precancerous lesions of the cervix was tested under semi-routine conditions in a clinical cytology laboratory. A set of 1500 specimens, highly enriched with abnormal cases, was analysed. Cervical scrapings were collected in suspension and processed by cytocentrifugation for microscopy. Two slides were prepared from each sample: one for staining according to Papanicolaou for the visual reference diagnosis and one for Feulgen staining for automated analysis. The specimens were evaluated in two ways: the first one, which is referred to as the automated machine classification system (AMC), classifies the specimens according to the number and ratio of selected objects (alarms) and is a fully automated system. The second system classifies the specimens after visual evaluation of the stored alarms as they are displayed on a TV monitor, and is designated the interactive machine classification system (IMC). The AMC results showed a false positive rate of 16.5% when the cut-off threshold was selected so that all 117 positively diagnosed specimens were classified 'positive' by the system. In that case 87.4% of the CIN I and 96.9% of the CIN II cases were AMC-positive. The IMC results showed a false positive rate of 2.5%, when 86.3% of the CIN I cases, 96.9% of the CIN II cases and all CIN III and invasive carcinoma cases were positively classified.
BACKGROUND: Many public medical laboratories use automated techniques for analysis of samples due to the few number of laboratory staff required and the speed at which it operates. This study was carried out to compare haematological values obtained from automated techniques and manual techniques. METHOD: A total of 86 apparently healthy adult Nigerians, aged 18-50 years comprising 50 males and 36 females who came for medical examination between June, 2004 and May, 2005 at Aminu Kano Teaching Hospital, Kano were used for the study. Standard manual methods and cell-dyn 3700cs system techniques were used to determine haematocrit, total and differential leucocyte counts. RESULTS: Statistically significant differences were found between automated and manual techniques in males with regard to haematocrit levels, total white cell counts, differential monocyte and basophil counts (0.45 +/- 0.031/1, 5.1 +/- 1.9 x 10(9)/l, 0.54 +/- 0.19 x 10(9)/l and 0.11 +/- 0.08 x 10(9)/l respectively) for automation and 0.42 +/- 0.041/l, 4.4 +/- 1.4 x 10(9)/1, 0.05 +/- 0.06 x 10(9)/l and 0.004 +/- 0.013 x 10(9)/l respectively for manual methods at P < 0.001, P < 0.05, P < 0.001 and P < 0.001 respectively while there were no significant differences in differential neutrophil, lymphocyte and eosinophil counts (2.1 +/- 0.7 x 10(9)/l, 2.2 +/- 0.7 x 10(9)/l and 0.14 +/- 0.16 x 10(9)/l respectively) of the automation when compared to 2.1 +/- 0.5 x 10(9)/l, 2.1 +/- 0.5 x 10(9)/l and 0.09 +/- 0.09 x 10(9)/l respectively using manual methods (P > 0.05). There were no statistically significant differences between automated and manual methods in females with regard to total white cell count, differential neutrophil, lymphocyte and eosinophil counts (5.8 +/- 2.3 x 10(9)/l, 2.6 +/- 0.9 x 10(9)/l, 2.4 +/- 0.8 x 10(9)/l and 0.1 +/- 0.1 x 10(9)/l respectively) of the automation when compared to 4.9 +/- 1.6 x 10(9)/l, 2.5 +/- 0.7 x 10(9)/l, 2.3 +/- 0.6 x 10(9)/l and 0.06 +/- 0.09 x 10(9)/l respectively of manual techniques (P > 0.05) while significant differences were observed in haematocrit, differential monocyte and basophil counts (0.39 +/- 0.03 l/l, 0.55 +/- 0.3 x 10(9)/l and 0.1 +/- 0.09 x 10(9)/l respectively) of automation when compared to 0.37 +/- 0.03 l/l, 0.03 +/- 0.04 x 10(9)/l and 0.002 +/- 0.01 x 10(9)/l respectively of the manual methods at P < 0.01, P < 0.001 and P < 0.001 respectively. CONCLUSION: The results have proven that differential monocyte and basophil counts from automation are unreliable and efforts should be made to carry out manual differential counts for confirmation.
An automated and computerized system (Automicrobic System [AMS]) for the detection of frequently encountered bacteria in clinical urine specimens was tested in a collaborative study among six laboratories. The sensitivity, specificity, reliability, and reproducibility of the AMS were determined, and the system was compared with conventional detection and identification systems. In this study, pure cultures and mixtures of pure cultures were used to simulate clinical urine specimens. With pure cultures, the sensitivity of the AMS in identifying the nine groups of organisms most commonly found in urine averaged 92.8%. The specificity averaged 99.4%, and the reliability of a positive result averaged 92.1%. The latter value was strongly influenced by a relatively high occurrence of false positive Escherichia coli results. The AMS was capable of detecting growth of most organisms, including those which it was not designed to identify. However, it identified some of these incorrectly as common urinary tract flora. Reproducibility of results, both within laboratories and among different laboratories, was high. Fast-growing organisms, such as E. coli and Klebsiella/Enterobacter species, were detected often at cell populations well below the AMS enumeration threshold of 70,000/ml. In mixed culture studies, high levels of sensitivity and specificity were maintained but when Serratia species were present in mixtures with other organisms, there was often a false positive report of E. coli. The overall performance of the AMS was considered satisfactory under the test conditions used.
Research studies have shown that apolipoprotein A-I (apo A-I) is a better marker for coronary artery disease (CAD) than high-density lipoprotein cholesterol (HDLC). Yet, it is unclear whether assays for apolipoprotein A-I, which is part of macromolecular lipoprotein complex, can perform as well when measured by routine automated clinical laboratory techniques. The purpose of this study was to compare automated apolipoprotein A-I assays with HDLC as a marker for CAD. The authors studied two groups of angiographically documented men, aged 44-70 years, 42 with CAD and 123 without CAD in an unmatched, controlled study. Standard clinical laboratory techniques for assaying HDLC, and automated kit rate immunonephelometric, end point immunonephelometric, and immunoturbidimetric assays for apolipoprotein A-I were used. High-density lipoprotein cholesterol was a better marker than apolipoprotein A-I, according to the Mann Whitney test; HDLC also showed better diagnostic sensitivity, specificity, and predictive value. Using a precipitation method, HDL3 was a better marker than HDL2, but not as good as total HDLC. The authors concluded that HDLC remains the best routine single CAD marker commonly available for evaluation of HDL status in a high-risk population.
Automation offers laboratories the ability to improve patient care, enhance client and employee satisfaction, and increase workload capacity while maintaining a cost-effective department. "The overall objective of any organization's automation project is simple--to do more with less, better," states Davis. "We know our future is on the information systems side," Clarke states. "Vendors' investment in the development and creativity of automation is what is going to drive the future of the laboratory." "Implementing the automation solution was absolutely the right thing to do for Sacred Heart Health," says Wright. "With the transition complete, we are pleased with the results. All we want now is more automation that will enable us to do even more with what we have."
Nifedipine, a calcium-channel blocking drug was analysed in dog plasma after oral dosing with two different formulations. Sample preparation was automated with a laboratory robot. Quantitative determination of the drug was performed on a reversed-phase HPLC system with electrochemical detection (ED) using an internal standard. Validation of the analytical method showed that the system is well suited for pharmacokinetic studies on dogs. The assay was linear in the range 1-50 ng/ml. Inter-day and intra-day variability were between 6.43-18.15% C.V. and 1.57-5.53% C.V., respectively.
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On-line connection of automated analyzers to laboratory information system (LIS) reduces mistakes in inputing data for each samples. It also makes reporting faster in clinical laboratory. Moreover, connection of these instruments with sample transporting system enables analyses without touching samples directly. It, however, costs extremely high to construct such a system. It is because every automated analyzer uses different connecting protocol, so that we have to make a different program for each machine. For solving this problem, we have to make a standard for connecting protocol. It is very difficult to make a standard protocol fitting on all of the analyzers, considering its cost and other things. Furthermore, it must take a few decades to spreading the standard through the end users. ICCLS and NCCLS has been taking a central role for this problem since 1996, with a five-year plan to make an international standard. Until the standard will be laid, each clinical laboratories have to pay high costs to construct their systems, connecting different manufacturers' analyzers each other. Thus, we have developed a novel system which enables us to construct a laboratory automation system in a shorter time. For realizing this new system, we have reduced the number of connecting protocols for analyzers. Moreover, we have corrected the flow of laboratory works in order. Each programs are put together into a system as parts, or modules. This software system is now in operation in the clinical laboratory of Kochi Medical School. In this report, we describe the construction of this novel software system, and the effect obtained by using this system. Furthermore, we would show problems and things to be improved for making international standards for communication protocols between host and analyzing instruments.