Sigma Metric Evaluation of Drugs in a Clinical Laboratory: Importance of Choosing Appropriate Total Allowable Error and a Troubleshooting Roadmap

Objectives Stringent quality control is an essential requisite of diagnostic laboratories to deliver consistent results. Measures used to assess the performance of a clinical chemistry laboratory are internal quality control and external quality assurance scheme (EQAS). However, the number of errors cannot be measured by the above but can be quantified by sigma metrics. The sigma scale varies from 0 to 6 with “6” being the ideal goal, which is calculated by using total allowable error (TEa), bias, and precision. However, there is no proper consensus for setting a TEa goal, and influence of this limiting factor during routine laboratory practice and sigma calculation has not been adequately determined. The study evaluates the impact of the choice of TEa value on sigma score derivation and also describes a detailed structured approach (followed by the study laboratory) to determine the potential causes of errors causing poor sigma score. Materials and Methods The study was conducted at a clinical biochemistry laboratory of a central government tertiary care hospital. Internal and external quality control data were evaluated for a period of 5 months from October 2019 to February 2020. Three drugs (carbamazepine, phenytoin, and valproate) were evaluated on the sigma scale using two different TEa values to determine significant difference, if any. Statistical Analysis Bias was calculated using the following formula: Bias% = (laboratory EQAS result − peer group mean) × 100 / peer group mean Peer group mean sigma metric was calculated using the standard equation: Sigma value = TEa − bias / coefficient of variation (CV)%. Results Impressive sigma scores (> 3 sigma) for two out of three drugs were obtained with TEa value 25, while with TEa value 15, sigma score was distinctly dissimilar and warranted root cause analysis and corrective action plans to be implemented for both valproate and carbamazepine. Conclusions The current study evidently recognizes that distinctly different sigma values can be obtained, depending on the TEa values selected, and using the same bias and precision values in the sigma equation. The laboratories should thereby choose appropriate TEa goals and make judicious use of sigma metric as a quality improvement tool.

Recentrifugation of Lithium Heparin Gel Separator Tubes up to 8 h after Blood Collection Has No Relevant Influence on the Stability of 30 Routine Biochemical Analytes

Background Venipuncture for the purpose of blood analysis is often performed at remote locations, and samples may be centrifuged locally to preserve the integrity of analytes. At the central laboratory, these tubes may be centrifuged again in the routine process. However, limited research shows that >1 centrifugation cycle of gel separator tubes causes significant changes in analytes, in particular troponin I and potassium. These preanalytical test changes are undesirable and may lead to errors in diagnosis and treatment of patients. Methods Ten volunteers donated blood in 10 lithium heparin gel tubes. Per volunteer, 5 tubes were centrifuged with Becton Dickinson centrifugation settings and 5 tubes with our local centrifugation settings. For each centrifugation setting, 1 tube was centrifuged directly after venipuncture; the second tube, directly after venipuncture and again after 4 h; the third tube, directly after venipuncture and again after 8 h; the fourth tube, 4 h after venipuncture; the last tube, 8 h after venipuncture. Thirty routine chemistry analyses were performed in plasma directly after the last centrifugation cycle. All tubes were kept at room temperature. Analytes were considered unstable when the mean percentage deviation exceeded the total allowable error. Results Except for calcium, which slightly exceeded the predefined total allowable error limit, all the investigated analytes remained stable up to 8 h after a second centrifugation cycle with both centrifugation settings. Conclusion This study shows that recentrifugation up to 8 h after blood collection does not cause relevant deviations in test results and may be applied safely.

Assessment of sigma metrics results of serum glucose and lipid profile tested by automated chemistry analyzer in medical city hospitals in Iraq

Background: A major target of quality assurance is the minimization of error rates in order to enhance patient safety, six sigma or sigma metrics were used to assess the analytical quality of automated clinical chemistry, six sigma metrics is used in combination with total allowable error, method imprecision and bias. The goal is to attain the highest possible sigma scale within the acceptable limits of total allowable error. For assessment of sigma metrics results of serum glucose and lipid profile and verification of reference values for these analytes tested by automated chemistry analyzer in Medical City hospitals.Methods: In the present study, internal quality control (EQA) and external quality assessment (EQA) data were analyzed for the period from May to July 2017 using chemistry autoanalyzer (Siemens Dimension RxL Max) at the Teaching Laboratories of the Medical City. Mean, standard deviation, coefficient of variation, bias, total error and sigma metrics were calculated for glucose, cholesterol, triglycerides and HDL.Results: Excellent sigma values (≥6) were elicited for triglycerides (10.9), Satisfactory sigma values (≥3) were elicited for cholesterol (3.4) and HDL (3.4), while glucose performed poorly (2.3) on the sigma scale.Conclusions: Sigma metrics helps to assess analytical methodologies and augment laboratory performance. It acts as a guide for planning quality control strategy. It can be a self-assessment tool regarding the functioning of clinical laboratory. Triglycerides was the best performer when it was gauzed on the sigma scale, with a sigma metrics value of 10.9 and glucose had the least sigma metrics value of 2.5 so there is need for improvement and the method should be controlled with greater attention to ensure quality.

Effects of time and temperature on 48 routine chemistry, haematology and coagulation analytes in whole blood samples

Background Phlebotomy for the purpose of blood analysis is often performed at remote locations, and samples are usually temporarily stored before transport to a central laboratory for analysis. The circumstances during storage and shipment may not meet the necessary requirements. If analysed anyway, false results may be generated. We therefore examined the influence of precentrifugation time and temperature of the most frequently requested tests in whole blood. Methods Healthy volunteers donated blood in which 48 analytes were tested. Routine chemistry was performed in lithium heparin tubes, haematology in ethylenediaminetetraacetic acid tubes, coagulation in citrate tubes and glucose in sodium fluoride tubes. One tube was measured directly. The others were kept at different temperatures (4, 8, 20 or 30℃) and stored for 4, 6, 8 or 24 h before analysis. Additionally, some analytes were examined at 12, 16, 24 and 28℃. The mean percentage deviation was compared with different decision levels, including the total allowable error. Results When using the total allowable error as an acceptable limit, most of the investigated analytes remained stable. However, bicarbonate is unstable at almost all tested time-points and temperatures. Calcium, lactate dehydrogenase, potassium and sodium are particularly affected at low temperatures, while phosphate is mainly affected at and above room temperature after 8 h. Conclusion We established the influence of time and temperature on a broad range of analytes, which may be applied to set the limits in transportation and storage of whole blood samples.

Glucose Meter Performance Criteria for Tight Glycemic Control Estimated by Simulation Modeling

Background: Glucose meter analytical performance criteria required for safe and effective management of patients on tight glycemic control (TGC) are not currently defined. We used simulation modeling to relate glucose meter performance characteristics to insulin dosing errors during TGC. Methods: We used 29 920 glucose values from patients on TGC at 1 institution to represent the expected distribution of glucose values during TGC, and we used 2 different simulation models to relate glucose meter analytical performance to insulin dosing error using these 29 920 initial glucose values and assuming 10%, 15%, or 20% total allowable error (TEa) criteria. Results: One-category insulin dosing errors were common under all error conditions. Two-category insulin dosing errors occurred more frequently when either 20% or 15% TEa was assumed compared with 10% total error. Dosing errors of 3 or more categories, those most likely to result in hypoglycemia and thus patient harm, occurred infrequently under all error conditions with the exception of 20% TEa. Conclusions: Glucose meter technologies that operate within a 15% total allowable error tolerance are unlikely to produce large (≥3-category) insulin dosing errors during TGC. Increasing performance to 10% TEa should reduce the frequency of 2-category insulin dosing errors, although additional studies are necessary to determine the clinical impact of such errors during TGC. Current criteria that allow 20% total allowable error in glucose meters may not be optimal for patient management during TGC.