Patient-based Quality Control Research Articles

QC Constellation: a cutting-edge solution for risk and patient-based quality control in clinical laboratories.

Clinical laboratories face limitations in implementing advanced quality control (QC) methods with existing systems. This study aimed to develop a web-based application to addresses this gap, and improve QC practices. QC Constellation, a web application built using Python 3.11, integrates various statistical QC modules. These include Levey-Jennings charts with Westgard rules, sigma-metric calculations, exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) charts, and method decision charts. Additionally, it offers a risk-based QC section and a patient-based QC module aligning with modern QC practices. The codes and the web application links for QC Constellation were shared at https://github.com/hikmetc/QC_Constellation, and http://qcconstellation.com, respectively. Using synthetic data, QC Constellation demonstrated effective implementation of Levey-Jennings charts with user-friendly features like checkboxes for Westgard rules and customizable moving averages graphs. Sigma-metric calculations for hypothetical performance values of serum total cholesterol were successfully performed using allowable total error and maximum allowable measurement uncertainty goals, and displayed on method decision charts. The utility of the risk-based QC module was exemplified by assessing QC plans for serum total cholesterol, showcasing the application's capability in calculating risk-based QC parameters including maximum unreliable final patient results, risk management index, and maximum run size andoffering risk-based QC recommendations. Similarly, the patient-based QC and optimization modules were demonstrated using simulated sodium results. In conclusion, QC Constellation emerges as a pivotal tool for laboratory professionals, streamlining the management of quality control and analytical performance monitoring, while enhancing patient safety through optimized QC processes.

B-130 Time-of-day dependence of running averages for some components of metabolic panels at our hospital: relation to strategy for patient-based quality control

Abstract Background Our hospital system recently reorganized its lab services so as to restrict in-house testing almost exclusively to inpatient testing. This disruption in patient mix caused us to reassess properties of running averages for our most common analytes, for potential use of such data in real-time patient-based quality control (PBQC). In particular, we examined whether there were analytes for which there was significant dependence of running averages on 24-h clock time (time-of-day, TOD). Methods We performed a retrospective analysis of 3-months' data (FY24, Q2) for patient results of 13 components of the Basic Metabolic Panel (BMP) and Comprehensive Metabolic Panel: Albumin*, Alkaline phosphatase, Alanine amino transferase, Aspartate aminotransferase*, Calcium*, Chloride*, CO2*, Creatinine, Potassium*, Total Protein*, Sodium, Total Bilirubin, and Urea. Running averages for 20 consecutive results (20-mers) were computed for data restricted to results within reference intervals. This produced an overall mean (X) and standard-deviation (SD) of 20-mers for each analyte. We then computed the average 20-mer result (Y) reported within 1-h bins across 24-hour clock time (t). Y(t) was regarded as having TOD-dependence if: (a) there was an 8-h interval of contiguous results for Y that were strictly either below or above X; and (b) either nadir or apex values for |Y-X| exceeded 0.5 SD. Results Seven analytes (*, above) demonstrated TOD-dependence of running means for 20-mers according to criteria given above. An example of TOD-dependent Y(t) is shown in Figure (Albumin). Conclusions For our hospital, TOD-dependence of running means was identified for 7 of 13 metabolic panel analytes. TOD-dependence of running means is a circumstance in which use of only a fixed target for running means in PBQC would lead to inherent TOD-dependent variation in capability for systematic error detection. Use of TOD-dependent targets for PBQC would be appropriate in these cases.

Enhanced patient-based real-time quality control using the graph-based anomaly detection.

Patient-based real-time quality control (PBRTQC) is an alternative tool for laboratories that has gained increasing attention. Despite the progress made by using various algorithms, the problems of data volume imbalance between in-control and out-of-control results, as well as the issue of variation remain challenges. We propose a novel integrated framework using anomaly detection and graph neural network, combining clinical variables and statistical algorithms, to improve the error detection performance of patient-based quality control. The testing results of three representative analytes (sodium, potassium, and calcium) and eight independent variables of patients (test date, time, gender, age, department, patient type, and reference interval limits) were collected. Graph-based anomaly detection network was modeled and used to generate control limits. Proportional and random errors were simulated for performance evaluation. Five mainstream PBRTQC statistical algorithms were chosen for comparison. The framework of a patient-based graph anomaly detection network for real-time quality control (PGADQC) was established and proven feasible for error detection. Compared with classic PBRTQC, the PGADQC showed a more balanced performance for both positive and negative biases. For different analytes, the average number of patient samples until error detection (ANPed) of PGADQC decreased variably, and reductions could reach up to approximately 95 % at a small bias of 0.02 taking calcium as an example. The PGADQC is an effective framework for patient-based quality control, integrating statistical and artificial intelligence algorithms. It improves error detection in a data-driven fashion and provides a new approach for PBRTQC from the data science perspective.

Time-of-day dependence of running averages for some components of metabolic panels at our hospital: Relation to strategy for patient-based quality control

Background and objectivesWe assessed properties of running averages for our hospital's most common chemistry analytes, for use in real-time patient-based quality control (PBQC). We determined whether there was dependence of any running averages on 24-h clock time (time-of-day, TOD). Materials and methodsWe analyzed 3-months' data for measurements of 13 metabolic panel components. Running averages for 20 consecutive results (20-mers) were computed for data restricted to results within reference intervals. This produced an overall mean (X) and standard-deviation (SD) of 20-mers for each analyte. We then computed the average 20-mer result (Y) reported within 1-h bins across 24-hour clock time (t). Y(t) was regarded as having TOD-dependence if either nadir or apex values for |Y-X| exceeded 0.5 SD, occurring within a contiguous series of at least 4 Y(t) values on one side of the mean. ResultsSeven analytes (albumin, aspartate aminotransferase, calcium, chloride, CO2, potassium, total protein) demonstrated TOD-dependence of running means for 20-mers. ConclusionsAt our hospital, TOD-dependence of running means was identified for 7 of 13 metabolic panel analytes. TOD-dependence is likely to be hospital-specific. Utilization of TOD-dependent targets for PBQC, rather than fixed targets, would be appropriate in these cases.

Integrating patient-based quality control and patient-based quality assurance

Integrating patient-based quality control and patient-based quality assurance

Example of use of clock time-dependent targets for patient-based quality control.

Running means for total calcium (Ca) results at our laboratory exhibit a stable time-of-day (TOD) periodic pattern. We examined use of TOD-dependent targets for running means in patient-based quality control (PBQC) for Ca. Primary data were Ca results over a 3 month interval, restricted to weekday data within the Ca reference interval (8.5-10.3mg/dL; 2.12-2.57mmol/L). Running means were evaluated as sliding averages of 20 samples (20-mers). Data comprised 39,629 consecutive Ca measurements (75.3% inpatient (IP)) for which Ca was 9.29±0.47mg/dL. The all data average for 20-mers was 9.29 ± 0.18mg/dL. When parsed in 1h TOD intervals, however, averages among 20-mers ranged from 9.1 to 9.5mg/dL, with blocs of contiguous results above (0800-2300 h; 53.3% of results; IP = 75.3%) and below (2300-0800 h; 46.7% of results; IP = 99.9%) the all-data mean. There was thus an inherent TOD-dependent pattern of deviation of means from target when using a fixed PBQC target. Using Fourier series analysis as an example approach, characterization of the pattern to produce TOD-dependent PBQC targets eliminated this inherent inaccuracy. In circumstances of periodic variation in running means, simple characterization of that variation can reduce the probability of both false positive and false negative flags in PBQC.

How to Implement Patient-Based Quality Control: Trial and Error

How to Implement Patient-Based Quality Control: Trial and Error

Patient-based quality control for glucometers: using the moving sum of positive patient results and moving average.

IntroductionThe capability of glucometer internal quality control (QC) in detecting varying magnitude of systematic error (bias), and the potential use of moving sum of positive results (MovSum) and moving average (MA) techniques as potential alternatives were evaluated.Materials and methodsThe probability of error detection using routine QC and manufacturer’s control limits were investigated using historical data. Moving sum of positive results and MA algorithms were developed and optimized before being evaluated through numerical simulation for false positive rate and probability of error detection.ResultsWhen the manufacturer’s default control limits (that are multiple times higher than the running standard deviation (SD) of the glucometer) was used, they had 0-75% probability of detecting small errors up to 0.8 mmol/L. However, the error detection capability improved to 20-100% when the running SD of the glucometer was used. At a binarization threshold of 6.2 mmol/L and block sizes of 200 to 400, MovSum has a 100% probability of detecting a bias that is greater than 0.5 mmol/L. Compared to MovSum, the MA technique had lower probability of bias detection, especially for smaller bias magnitudes; MA also had higher false positive rates.ConclusionsThe MovSum technique is suited for detecting small, but clinically significant biases. Point of care QC should follow conventional practice by setting the control limits according to the running mean and SD to allow proper error detection. The glucometer manufacturers have an active role to play in liberalizing QC settings and also enhancing the middleware to facility patient-based QC practices.

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.

A Robust Method for Estimating B0 Inhomogeneity Field in the Liver by Mitigating Fat Signals and Phase-Wrapping.

We developed an optimized and robust method to estimate liver B0 field inhomogeneity for monitoring and correcting susceptibility-induced geometric distortion in magnetic resonance images for precision therapy. A triple-gradient-echo acquisition was optimized for the whole liver B0 field estimation within a single-exhale breath-hold scan on a 3 T scanner. To eliminate chemical-shift artifacts, fat signals were chosen in-phase between 2 echoes with an echo time difference (ΔTE) of 2.3 milliseconds. To avoid phase-wrapping, other 2 echoes provided a large field dynamic range (1/ΔTE) to cover the B0 field inhomogeneity. In addition, using high parallel imaging factor of 4 and a readout-bandwidth of 1955 Hz/pixel, an ∼18-second acquisition time for breath-held scans was achieved. A 2-step, 1-dimensional regularized method for the ΔB0 field map estimation was developed, tested and validated in phantom and patient studies. Our method was validated on a water phantom with fat components and air pockets; it yielded ΔB0-field maps that had no chemical-shift and phase-wrapping artifacts, and it had a <0.5 mm of geometric distortion near the air pockets. The ΔB0-field maps of the patients' abdominal regions were also free from phase-wrapping and chemical-shift artifacts. The maximum field inhomogeneity was found near the lung–liver interface, up to ∼300 Hz, resulting in ∼2 mm of distortions in anatomical images with a readout-bandwidth of 440 Hz/pixel. The field mapping method in the abdominal region is robust; it can be easily integrated in clinical workflow for patient-based quality control of magnetic resonance imaging geometric integrity.

Practice of patient based quality assessment procedure in clinical chemistry unit at diagnostic laboratories in Nepal

Background: The clinical laboratory is the major producer of information used to diagnose, treat, and monitor patients. Errors in laboratory testing may occur at many different points in the total testing process (TTP). Application of quality control plays a vital role in recognizing probable errors. The current dominant technique for error identification uses quality control materials has several inherent drawbacks; otherwise, patient based quality control procedure ensures the detection of pre-analytical errors, analytical, post-analytical errors, clerical errors, and random errors that cannot be detected using commonly used quality control methods, thereby improving the reliability of clinical tests. Objective: Thus the objective of this study was to evaluate the practice of patient based quality control procedure in clinical chemistry unit at diagnostic laboratories in Nepal. Materials and Methods: The questionnaire based study was conducted in clinical chemistry unit of diagnostic laboratories across the country. Questionnaires were personally dropped in 217 clinical biochemistry laboratories and were asked to complete a practice based questionnaire. The responses of 169 laboratories were analyzed using Microsoft Excel 2007 and expressed in terms of percentage. Results: In foremost study undertaken, a total of 169 laboratories responded to the questionnaire. A total 65.9 % of the laboratories monitored errors using patient based quality control procedure but not as a part of quality control. Very few of participant.s laboratories responded accurately regarding utility and practical aspects of patient based quality control included in the checklist. Conclusion: Practice of patient based quality control procedure was not well established to identify possible errors. Hence, the study extent the existing information and explored that the current classical approaches were not adequate to assure accurate patients test results for specific analytes. DOI: http://dx.doi.org/10.3126/stcj.v1i1.7983 Sunsari Technical College Journal Vol.1(1) 2012 9-17

Performance evaluation and planning for patient-based quality control procedures.

In a recent publication, the effect of the length of time between the routine testing of quality-control (QC) samples on QC performance was studied using a newly proposed performance measure: the average number of patient samples that contain unacceptable analytic error due to an out-of-control error condition (ANPTE). We show that ANPTE also perfectly suits the evaluation of patient-based QC procedures. We used ANPTE to study the effect of the number of patient results averaged and the width of the truncation limits on an average of normals QC procedure. Estimates of ANPTE were obtained by using computer simulations that use actual patient result distributions. Based on ANPTE, the conclusions about the effects of truncation limits and number of patient results averaged on the performance of an average of normals QC procedure are substantially different from those described by others using the probability of error detection to characterize QC performance.