Transformation of Sequential Hospital and Outpatient Laboratory Data into Between-Day Reference Change Values.

BACKGROUND: Serial differences between intrapatient consecutive measurements can be transformed into Taylor series of variation vs time with the intersection at time = 0 (y0) equal to the total variation (analytical + biological + preanalytical). With small preanalytical variation, y0, expressed as a percentage of the mean, is equal to the variable component of the reference change value (RCV) calculation: (CVA2 + CVI2)1/2. METHODS: We determined the between-day RCV of patient data for 17 analytes and compared them to healthy participants' RCVs. We analyzed 653 consecutive days of Dartmouth-Hitchcock Roche Modular general chemistry data (4.2 million results: 60% inpatient, 40% outpatient). The serial patient values of 17 analytes were transformed into 95% 2-sided RCV (RCVAlternate), and 3 sets of RCVhealthy were calculated from 3 Roche Modular analyzers' quality control summaries and CVI derived from biological variation (BV) studies using healthy participants. RESULTS: The RCVAlternate values are similar to RCVhealthy derived from known components of variation. For sodium, chloride, bicarbonate calcium, magnesium, phosphate, alanine aminotransferase, albumin, and total protein, the RCVs are equivalent. As expected, increased variation was found for glucose, aspartate aminotransferase, creatinine, and potassium. Direct bilirubin and urea demonstrated lower variation. CONCLUSIONS: Our RCVAlternate values integrate known and unknown components of analytic, biologic, and preanalytic variation, and depict the variations observed by clinical teams that make medical decisions based on the test values. The RCVAlternate values are similar to the RCVhealthy values derived from known components of variation and suggest further studies to better understand the results being generated on actual patients tested in typical laboratory environments.

Average of Patient Deltas: Patient-Based Quality Control Utilizing the Mean Within-Patient Analyte Variation.

BACKGROUND: Because traditional QC is discontinuous, laboratories use additional strategies to detect systematic error. One strategy, the delta check, is best suited to detect large systematic error. The moving average (MA) monitors the mean patient analyte value but cannot equitably detect systematic error in skewed distributions. Our study combines delta check and MA to develop an average of deltas (AoD) strategy that monitors the mean delta of consecutive, intrapatient results. METHODS: Arrays of the differences (delta) between paired patient results collected within 20-28 h of each other were generated from historical data. AoD protocols were developed using a simulated annealing algorithm in MatLab (Mathworks) to select the number of patient delta values to average and truncation limits to eliminate large deltas. We simulated systematic error by adding bias to arrays for plasma albumin, alanine aminotransferase, alkaline phosphatase, amylase, aspartate aminotransferase, bicarbonate, bilirubin (total and direct), calcium, chloride, creatinine, lipase, sodium, phosphorus, potassium, total protein, and magnesium. The average number of deltas to detection (ANDED) was then calculated in response to induced systematic error. RESULTS: ANDED varied by combination of assay and AoD protocol. Errors in albumin, lipase, and total protein were detected with a mean of 6 delta pairs. The highest ANDED was calcium, with a positive 0.6-mg/dL shift detected with an ANDED of 75. However, a negative 0.6-mg/dL calcium shift was detected with an ANDED of 25. CONCLUSIONS: AoD detects systematic error with relatively few paired patient samples and is a patient-based QC technique that will enhance error detection.

Evaluation of Thyroid Function in Pregnant Women Using Automated Immunoassays.

INTRODUCTION: Automated free thyroxine (FT4) immunoassays are widely available, but professional guidelines discourage their use in pregnant women due to theoretical under-recoveries attributed to increased thyroid hormone binding capacity and instead advocate the use of total T4 (TT4) or free thyroxine index (FTI). The impact of this recommendation on the classification of thyroid status in apparently euthyroid pregnant patients was evaluated. METHODS: After excluding specimens with thyroid autoantibody concentrations above reference limits, thyroid-stimulating hormone (TSH), FT4, TT4, and T-uptake were measured on the Roche Cobas® platform in remnant clinical specimens from at least 147 nonpregnant women of childbearing age and pregnant women at each trimester. Split-sample comparisons of FT4 as measured by the Cobas and equilibrium dialysis were performed. RESULTS: FT4 decreased with advancing gestational age by both immunoassay and equilibrium dialysis. TSH declined during the first trimester, remained constant in the second, and increased throughout the third, peaking just before delivery. Interpretation of TT4 concentrations using 1.5-times the nonpregnant reference interval classified 13.6% of first trimester specimens below the lower reference limit despite TSH concentrations within trimester-specific reference intervals. Five FTI results from 480 pregnant individuals (about 1.0%) fell outside the manufacturer's reference interval. CONCLUSIONS: Indirect FT4 immunoassay results interpreted in the context of trimester-specific reference intervals provide a practical and viable alternative to TT4 or FTI. Declining FT4 and increasing TSH concentrations near term suggest that declining FT4 is not an analytical artifact but represents a true physiological change in preparation for labor and delivery.

Benefits, limitations, and controversies on patient-based real-time quality control (PBRTQC) and the evidence behind the practice.

BACKGROUND: In recent years, there has been renewed interest in the "old" average of normals concept, now generally referred to as moving average quality control (MA QC) or patient-based real-time quality control (PBRTQC). However, there are some controversies regarding PBRTQC which this review aims to address while also indicating the current status of PBRTQC. CONTENT: This review gives the background of certain newly described optimization and validation methods. It also indicates how QC plans incorporating PBRTQC can be designed for greater effectiveness and/or (cost) efficiency. Furthermore, it discusses controversies regarding the complexity of obtaining PBRTQC settings, the replacement of iQC, and software functionality requirements. Finally, it presents evidence of the added value and practicability of PBRTQC. OUTLOOK: Recent developments in, and availability of, simulation methods to optimize and validate laboratory-specific PBRTQC procedures have enabled medical laboratories to implement PBRTQC in their daily practice. Furthermore, these methods have made it possible to demonstrate the practicability and added value of PBRTQC by means of two prospective "clinical" studies and other investigations. Although internal QC will remain an essential part of any QC plan, applying PBRTQC can now significantly improve its performance and (cost) efficiency.

Implementation of patient-based real-time quality control.

The quest to use patient results as quality control for routine clinical chemistry testing has long been driven by issues of the unavailability and cost of suitable quality control material and the matrix effects of synthetic material. Hematology laboratories were early adopters of average of normals techniques, primarily because of the difficulty in acquiring appropriate, stable quality control material, while in the chemistry laboratories, the perceived advantages and availability of synthetic material outweighed the disadvantages. However, the increasing volume of testing in clinical chemistry plus the capability of computer systems to deal with large and complex calculations has now made the use of patient-based quality control algorithms feasible. The desire to use patient-based quality control is also driven by increasing awareness that common quality control rules and frequency of analysis may fail to detect clinically significant assay biases. The non-commutability of quality control material has also become a problem as laboratories seek to harmonize results across regions and indeed globally. This review describes the history of patient-based quality control in clinical chemistry, summarizes the various approaches that can be implemented by laboratory professionals, and discusses how patient-based quality control can be integrated with traditional quality control techniques.

Understanding Patient-Based Real-Time Quality Control Using Simulation Modeling.

BACKGROUND: Patient-based real-time quality control (PBRTQC) avoids limitations of traditional quality control methods based on the measurement of stabilized control samples. However, PBRTQC needs to be adapted to the individual laboratories with parameters such as algorithm, truncation, block size, and control limit. METHODS: In a computer simulation, biases were added to real patient results of 10 analytes with diverse properties. Different PBRTQC methods were assessed on their ability to detect these biases early. RESULTS: The simulation based on 460 000 historical patient measurements for each analyte revealed several recommendations for PBRTQC. Control limit calculation with "percentiles of daily extremes" led to effective limits and allowed specification of the percentage of days with false alarms. However, changes in measurement distribution easily increased false alarms. Box-Cox but not logarithmic transformation improved error detection. Winsorization of outlying values often led to a better performance than simple outlier removal. For medians and Harrell-Davis 50 percentile estimators (HD50s), no truncation was necessary. Block size influenced medians substantially and HD50s to a lesser extent. Conversely, a change of truncation limits affected means and exponentially moving averages more than a change of block sizes. A large spread of patient measurements impeded error detection. PBRTQC methods were not always able to detect an allowable bias within the simulated 1000 erroneous measurements. A web application was developed to estimate PBRTQC performance. CONCLUSIONS: Computer simulations can optimize PBRTQC but some parameters are generally superior and can be taken as default.

Recommendation for performance verification of patient-based real-time quality control.

Patient-based real-time quality control (PBRTQC) is a laboratory tool for monitoring the performance of the testing process. It includes well-established procedures like Bull's algorithm, average of nomals, moving median, moving average (MA) and exponentially (weighted) MAs. Following the setup and optimization processes, a key step prior to the routine implementation of PBRTQC is the verification and documentation of the performance of the PBRTQC as part of the laboratory quality system. This verification process should provide a realistic representation of the performance of the PBRTQC in the environment it is being implemented in, to allow proper risk assessment by laboratory practitioners. This document focuses on the recommendation on performance verification of PBRTQC prior to implementation.

An Intact ACTH LC-MS/MS Assay as an Arbiter of Clinically Discordant Immunoassay Results.

BACKGROUND: Measurement of plasma adrenocorticotropic hormone (ACTH) is key in the differential diagnosis of hypothalamic-pituitary-adrenal disorders. Two-site sandwich immunoassays dominate clinical testing of ACTH in North America; however, discordant results between manufacturers have been repeatedly reported. To resolve the discrepancy, we developed a liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay for the intended measurand, biologically active intact ACTH (iACTH). METHODS: The multiple reaction monitoring LC-MS/MS assay was designed to selectively measure full-length iACTH, as well as ACTH analogs and fragments (i.e., ACTH1-24 and ACTH18-39). Epitope assignment of the Roche Elecsys antibodies was performed by MALDI-TOF mass spectrometry. A method comparison between Roche Elecsys and Siemens Immulite ACTH immunoassays was performed and clinically concordant/discordant results identified. In a subset of these samples, the iACTH concentration was determined using the LC-MS/MS method. RESULTS: The lower limit of the measuring interval of the iACTH LC-MS/MS assay was 9 pg/mL (2 pmol/L). The assay was linear from 9 to 1938 pg/mL (2 to 427 pmol/L). Epitope mapping revealed that the Roche capture and detection antibodies bound residues 9-12 and 36-39 of ACTH, respectively. The iACTH LC-MS/MS analysis demonstrated that for discordant results between 2 immunoassays studied, only the Roche results were highly positively correlated with the iACTH concentration. CONCLUSIONS: Immunoprecipitation of biologically active ACTH molecules followed by LC-MS/MS analysis enabled selective detection of iACTH and relevant biologically active fragments in plasma. Applied to the investigation of clinically discrepant results, this method can act as an arbiter of the concentration of iACTH present.

Patient-Based Real-Time Quality Control: Review and Recommendations.

For many years the concept of patient-based quality control (QC) has been discussed and implemented in hematology laboratories; however, the techniques have not been widely implemented in clinical chemistry. This is mainly because of the complexity of this form of QC, as it needs to be optimized for each population and often for each analyte. However, the clear advantages of this form of QC, together with the ongoing realization of the shortcomings of "conventional" QC, have driven a need to provide guidance to laboratories to assist in deploying patient-based QC. This overview describes the components of a patient-based QC system (calculation algorithm, block size, truncation limits, control limits) and the relationship of these to the analyte being controlled. We also discuss the need for patient-based QC system optimization using patient data from the individual testing laboratory to reliably detect systematic errors while ensuring that there are few false alarms. The term patient-based real-time quality control covers many activities that use data from patient samples to detect analytical errors. These activities include the monitoring of patient population parameters such as the mean or median analyte value or using single within-patient changes such as the delta check. In this report, we will restrict the discussion to population-based parameters. This overview is intended to serve as a guide for the implementation of a patient-based QC system. The report does not cover the clinical evaluation of the population.

Optimization of a Moving Averages Program Using a Simulated Annealing Algorithm: The Goal is to Monitor the Process Not the Patients.

BACKGROUND: The patient moving average (MA) is a QC strategy using the mean patient result to continuously monitor assay performance. Developing sensitive MA protocols that rapidly detect systematic error (SE) is challenging. We compare MA protocols established using a previously published report as a guide and demonstrate the use of a simulated annealing (SA) algorithm to optimize MA protocol performance. METHODS: Using 400 days of patient data, we developed MA protocols for 23 assays. MA protocols developed using a previously published report and our SA algorithm were compared using the average number of patient samples affected until error detection (ANPed). RESULTS: Comparison of the strategies demonstrated that protocols developed using the SA algorithm generally proved superior. Some analytes such as total protein showed considerable improvement, with positive SE equal to 0.8 g/dL detected with an ANPed of 135 samples using the previously published method whereas the SA algorithm detected this SE with an ANPed of 18. Not all analytes demonstrated similar improvement with the SA algorithm. Phosphorus, for instance, demonstrated only minor improvements, with a positive SE of 0.9 mg/dL detected with an ANPed of 34 using the previously published method vs an ANPed of 29 using the SA algorithm. We also demonstrate an example of SE detection in a live environment using the SA algorithm derived MA protocols. CONCLUSIONS: The SA algorithm-developed MA protocols are currently in use in our laboratory and they rapidly detect SE, reducing the number of samples requiring correction and improving patient safety.