Guideline to radiopharmaceutical procedures for in vitro labeling of leukocytes.

A comprehensive field data set from foam stimulation treatments is presented that illustrates the use of improved instrumentation, quality control (QC) and flowback practices. The integration of the field instrumentation with computer monitoring and real-time instrument cross-checks is also presented. The field tests of the instrumentation illustrates the application of magnetic flow meters to reliably measure actual foam flow rate. Foam flowback procedures are presented that provide controlled, monitored and clean return of foamed fluid.

Q-Pro is an application for Quality Control and Inspection of Medical Devices. General system requirements include friendly and comprehensive graphical environment and proper, quick, easy and intuitive user interface. Functions such as, a tool library for protocol design widely used multimedia, as well as, a support of a local database for protocol and inventory data archiving are provided by the system. In order to serve the different categories of users, involved in Quality Control procedures, the system has been split into three modules of different functionality and complexity, each of which can work as a stand-alone application. The implementation of protocols and use of the software functions, as well as, the user interface itself have been proved by the evaluators to be clear and intuitive. The software seems to adapt easily to different kinds of Quality Control procedures and objectives. Q-Pro effectively supports and enhances the processes to attain a highly tuned, professional, responsive and effective quality control and preventive maintenance procedures for biomedical equipment management.

An essential feature of the production of all microbial control agents is an effective quality control system. Well-defined product specifications with accompanying quality control procedures help to maximize product performance, ensure product safety, standardize manufacturing costs and reduce the risks of supply failure, thus building user confidence. A production system that does not have a quality control system is one whose output is uncontrolled and a lack of thorough quality feedback can result in batches of product with variable concentrations of active agent. This results in products with variable performance leading to control failures by users and serious loss of user confidence. Strict quality control procedures are not only essential for product consistency, but also for safety. Where quality control is inadequate, microbial contamination of the final product is inevitable. In most of such cases this will merely lead to a loss of efficacy due to dilution of the active ingredient by competing microorganisms, but also the potential of producing human pathogens must be ruled out. Recognition of contaminants and quantification of the degree of contamination are therefore important in determining any possible risk to human health. Many low technology production systems in use around the world have minimal or no quality control procedures. This is unacceptable and can damage the reputation of microbial control in addition to possibly posing health risks to those that produce or are exposed to the product. Two case studies from developing countries, are used to illustrate how the lack of quality control procedures can lead to the production of low viability, highly contaminated products with low or negligible concentrations of the active ingredient. However, it is also demonstrated that low technology production systems in developing countries can produce high quality products, provided appropriate quality control procedures are firmly implemented. It must be recognized that quality control procedures can be more complex and technologically demanding than the production procedures themselves, but it is largely on the effectiveness of these control procedures that the long-term acceptability of fungal and viral products depends. This paper details the quality control procedures considered necessary in the mass production of fungi and viruses for use as biocontrol agents, and attempts to suggest reasonable standards that can be achieved by all producers.

A quality control (QC) procedure is applied to the Challenging Minisatellite Payload (CHAMP) level‐2 Global Position System (GPS) radio occultation (RO) data provided by the University Corporation for Atmospheric Research (UCAR) Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) Data Analysis and Archival Center (CDAAC). It consists of a range check removing negative values, a biweight check removing data which deviate from the biweight mean by more than four times the biweight standard deviation, another biweight check removing data with large deviations to the National Center for Environmental Prediction (NCEP) analysis, and finally a symmetric check removing the negative bias of GPS observations in the low troposphere below 4 km. These four QC checks are applied sequentially to identify outliers in GPS bending angle and refractivity data at each vertical level using data primarily in the month of March 2004. Having removed 5.5% outlier data, the GPS RO observations compared much more favorably with the NCEP analyses than the original data without QC, resulting in an improved spatial consistency, a more symmetric probability distribution, significantly reduced error variances, and a nearly diagonal vertical error correlation matrix. The effectiveness of the proposed QC procedure is further confirmed by showing that most RO profiles removed by the QC procedure proposed in this study have those characteristic physical parameter values indicating poor quality of RO data. Similar results are obtained when the same QC procedure is applied to CHAMP data in July 2002, which shows the robustness of the proposed GPS QC procedure.

Computed radiography and digital radiography systems now comprise a large segment of radiography work. It is important that appropriate performance testing and quality control (QC) tools and procedures be made available for these systems. The vendors and/or manufacturers of digital imaging systems may provide‐or make available‐test tools, software and procedures for performance evaluation and routine quality controltesting of their digital systems. Currently, such tools and programs vary significantly from manufacturer to manufacturer. This two‐part workshop will include both formal presentations and hands‐on demonstrations. During the first half of the workshop, each participating vendor or manufacturer will present a brief (12–15 minute) overview of tools and procedures for performance testing and QC of their systems. The second half of the workshop is dedicated to hands‐on demonstrations at table‐top exhibits hosted by each participating vendor. Exhibits will include test tools, workstations and samples of performance testing and QC test procedures. Companies participating in this workshop will include Agfa, Fuji, General Electric, Kodak, Konica, IDC, LoDox, Philips and potentially others. Educational Objectives: 1. To understand performance test procedures used by various DR/CR systems. 2. To see types QC test tools and procedures made available with DR/CR systems. 3. To obtain hands‐on demonstration of DR/CR ttesting and QC procedures.

Introduction: MammoSite High Dose Rate (HDR) brachytherapy is becoming an increasingly widespread procedure delivering radiotherapy to post surgery breast cancer patients. This paper examined the application of risk assessment and its relation to Quality Assurance (QA) and Quality control (QC) procedures. Materials and Methods: IDEF⊘ diagrams were used to look at the steps in the process. These diagrams were used to identify potential treatment delivery errors and to develop a protocol for QA based on this assessment. The risk assessment identified the risks from this procedure, taking into account the levels of probability of an incident occurring, and the levels of severity of the incident. This study examined how QA and QC protocols can be implemented using a prospective study of risk though IDEF⊘ diagrams and applying identified hazards to a risk matrix. The probability of occurrence and the levels of severity were applied to hazards and errors identified, and a resulting risk number was calculated from the risk matrix. A number of different QA and QC procedures were then applied to the system to analyze how they affect the calculated risk number. This paper provides an example of good practice for applying systems engineering tools that can be used to strengthen the QA and QC program. These different QA and QC procedures were then applied to the potential errors and hazards to analyze how they affect the risk number. Results and discussion: The use of IDEF⊘ diagrams showed the steps at a detailed level making them easier to identify hazards. The risk matrix can then be used to improve and analyze on the QA and QC protocol. This methodology for risk assessment is useful for a prospective study of risk.

Computed radiography and digital radiography systems now comprise a large segment of radiography work. It is important that appropriate performance testing and quality control (QC) tools and procedures be made available for these systems. The vendors and/or manufacturers of digital imaging systems may provide‐or make available‐test tools, software and procedures for performance evaluation and routine quality control testing of their digital systems. Currently, such tools and programs vary significantly from manufacturer to manufacturer.This two‐part workshop will include both formal presentations and hands‐on demonstrations. During the first half of the workshop, each participating vendor or manufacturer will present a brief (12–15 minute) overview of tools and procedures for performance testing and QC of their systems. The second half of the workshop is dedicated to hands‐on demonstrations at table‐top exhibits hosted by each participating vendor. Exhibits will include test tools, workstations and samples of performance testing and QC test procedures.Companies participating in this workshop will include Agfa, Fuji, General Electric, Kodak, Konica, IDC, LoDox, Philips and potentially others.Educational Objectives:1. To understand performance test procedures used by various DR/CR systems.2. To see types QC test tools and procedures made available with DR/CR systems.3. To obtain hands‐on demonstration of DR/CR ttesting and QC procedures.

Introduction The quality control (QC) procedures for positron emission tomography (PET) scanners are covered by National Electrical Manufacturers Association and International Electrotechnical Commission. QC must be carried out at regular intervals according to the specifications of the scanner manufacturer. Daily and weekly QC plays a valuable role in monitoring positron emission tomography (PET) scanner performance changes. This study shares operational and performance experience of QC procedures that do not require a radioactive Ge-68 source to perform daily QC and experience with fluorodeoxyglucose F18 (18F-FDG) as a substitute for germanium-68/sodium-22 (Ge-68/Na-22) source for weekly QC.Method This study was performed on an uMI550 digital positron emission tomography-computed tomography (PET-CT) scanner. In this scanner daily QC checks system temperature and humidity, system count rate, data link status, and voltage. QC was performed at the console control, the position of the scanner table was in the home position pulled out from the gantry, and the room was closed during the quick QC. Weekly full QC check items include look-up table drift, energy drift, time-of-flight status, C-map status, temperature and humidity, and voltage. Weekly full QC was performed with a18F-FDG source in a rod phantom source.Results Over 200 daily QC tests without a radioactive source Ge-68 phantom and 50 full weekly QC tests using a18F-FDG rod phantom were performed with this scanner according to the manufacturer's instructions and a test report was generated. No daily QC errors or warnings were observed during this period.Conclusion The new approach for the daily PET QC does not expose operators to radiation. This translates into commercial and operational merits with consistent performance and results.Implications for Practice Reduction in radiation exposure to operating staff during QC procedure in PET-CT scanner.

Appropriate quality assurance (QA) and quality control (QC) procedures have been well developed and validated for dual X-ray absorptiometry (DXA), and are widely applied in multicenter clinical trials to monitor device stability used to check the treatment effects on bone mineral density. This is not yet the case for quantitative ultrasound (QUS) technology, for which no QC approaches have yet been fully tested. The first Achilles (GE-Lunar Corporation, Madison, WI, USA) has been on the market for 10 years (1991). The goal of this study was to develop the QC methodology for the QUS Achilles+ device using its past/current experience (log and maintenance files.) as well as by integrating the progress made over the last years in the ultrasound domain so as to better understand the influence of temperature on ultrasound parameters. Because of the lack of confidence in the external black rubber phantom used in daily QC with the Achilles+, to monitor the device stability, we selected several QC parameters known to be influenced by potential malfunctions as experienced by the maintenance department of GE-Lunar company as well as the physical approach. These are phantom temperature-adjusted speed of sound (PSOS-TC) and broadband ultrasound attenuation (PBUA-TC), water speed of sound error (WSE), water spectrum slope (WSS) and water gain (WG). We used four Achilles+ devices perfectly stable during their entire QC range, to calculate the optimum thresholds (based mostly on 95% confidence interval) for each of these parameters as well as the precision for the in vitro SOS and BUA. An additional not fully stable Achilles device has been used to run a QC procedure example. The precision expressed as the CV was 0.22% and 0.65% for the PSOS-TC and PBUA-TC, respectively. The alarm thresholds used for QC process are +/- 0.6%, +/- 1.9%, +/- 6.8 m/s, +/- 5.3% and +/- 7.3% for the PSOS-TC, PBUA-TC, WSE, WSS and WG, respectively. Applying a logical approach on the impact of each parameter on each other as well as their respective reactivity to malfunctions, we build a QC process flowchart meant to detect real malfunction in the daily QC. We found that in case of real malfunctions, the in vivo SOS should be decreased by 1.33 m/s for each 1 m/s increase in WSE. Unfortunately, in vivo BUA can not be adjusted when real malfunction occurs. Nevertheless, the BUA can be qualified as bad quality data and excluded from the medical interpretation. Using the currently available phantom and parameters, the best possible QC procedures to detect long-term drift in the daily QC of the Achilles+ was developed. To fully validate our approach and gain confidence in the defined limits it is our plan to apply this QC processing to a higher number of QUS devices.

Randomized controlled trials are considered the “gold standard” for evaluating the effectiveness of an intervention. However, large-scale, cluster-randomized trials are complex and costly to implement. The generation of accurate, reliable, and high-quality data is essential to ensure the validity and generalizability of findings. Robust quality assurance and quality control procedures are important to optimize and validate the quality, accuracy, and reliability of trial data. To date, few studies have reported on study procedures to assess and optimize data integrity during the implementation of large cluster-randomized trials. The dearth of literature on these methods of trial implementation may contribute to questions about the quality of data collected in clinical trials. Trial protocols should consider the inclusion of quality assurance indicators and targets for implementation. Publishing quality assurance and control measures implemented in clinical trials should increase public trust in the findings from such studies. In this manuscript, we describe the development and implementation of internal and external quality assurance and control procedures and metrics in the Pneumococcal Vaccine Schedules trial currently ongoing in rural Gambia. This manuscript focuses on procedures and metrics to optimize trial implementation and validate clinical, laboratory, and field data. We used a mixture of procedure repetition, supervisory visits, checklists, data cleaning and verification methods and used the metrics to drive process improvement in all domains.

Randomized controlled trials are considered the “gold standard” for evaluating the effectiveness of an intervention. However, large-scale, cluster-randomized trials are complex and costly to implement. The generation of accurate, reliable, and high-quality data is essential to ensure the validity and generalizability of findings. Robust quality assurance and quality control procedures are important to optimize and validate the quality, accuracy, and reliability of trial data. To date, few studies have reported on study procedures to assess and optimize data integrity during the implementation of large cluster-randomized trials. The dearth of literature on these methods of trial implementation may contribute to questions about the quality of data collected in clinical trials. Trial protocols should consider the inclusion of quality assurance indicators and targets for implementation. Publishing quality assurance and control measures implemented in clinical trials should increase public trust in the findings from such studies. In this manuscript, we describe the development and implementation of internal and external quality assurance and control procedures and metrics in the Pneumococcal Vaccine Schedules trial currently ongoing in rural Gambia. This manuscript focuses on procedures and metrics to optimize trial implementation and validate clinical, laboratory, and field data. We used a mixture of procedure repetition, supervisory visits, checklists, data cleaning and verification methods and used the metrics to drive process improvement in all domains.

BACKGROUND Remote, interdisciplinary, observational clinical studies and clinical trials are increasingly emerging in the scientific literature. Remote videoconference-based neuropsychological assessment offers numerous advantages, including improved access to care, reduced travel burden, and the ability to monitor patients over time. However, such methodologies present challenges, particularly regarding the fidelity of collected data. Data fidelity, defined as the accuracy, completeness, and consistency of data, can be compromised by technical issues, variability in testing environments, and risks of data loss. These challenges necessitate the development of robust, standardized protocols to ensure high-quality data collection and analysis. OBJECTIVE This study aimed (1) to evaluate the data fidelity of a remote videoconference-administered neuropsychology protocol in the context of the Health in Aging, Neurodegenerative Diseases, and Dementias in Ontario (HANDDS-ONT) study, guided by the Ontario Neurodegenerative Disease Research Initiative (ONDRI); (2) to generate three roadmaps to support data fidelity procedures for future remote neuropsychological research; and (3) to characterize the sample and their neuropsychological assessment performance. Quality assurance and quality control procedures were implemented, and missing data, virtual environment-related errors, and outcomes of quality assurance and quality control measures were evaluated to assess data fidelity. METHODS 148 participants (62% female; median age=67, mean=15.1 education years) completed the neuropsychology protocol. Data were analyzed as descriptive statistics. RESULTS Implementing our quality assurance and control procedures, the average number of queries per participant was 5.59 during the data monitoring phase and 0.30 during the cleaning and curation pipeline phase, with only 0.34% of data missing. Virtual environment factors, such as internet connectivity and distractions, had minimal impact on data quality as only 8 participants (5.4%) had 1-2 tasks impacted by the virtual environment resulting in missing data. The fidelity of the remote videoconference-administered neuropsychology protocol was comparable to that of in-person assessments, and our procedures effectively minimized missing data and reduced the need for data corrections. The study highlighted the feasibility of collecting reliable neuropsychology data remotely while identifying practical adaptations to mitigate potential challenges. CONCLUSIONS The current study highlights the potential of remote videoconference-administered neuropsychological assessment to deliver high-fidelity data in clinical and research settings. The protocol performed similarly to an in-person neuropsychology protocol as it pertained to quality control indices (i.e., missingness and number of data corrections needed), and few virtual environment-related factors impacted data missingness. Quality assurance and quality control measures were crucial for ensuring the data were collected robustly remotely. The proposed roadmaps offer a template for future studies, addressing common challenges in remote neuropsychological testing and enabling wider adoption of telehealth methodologies. By advancing standardized protocols, this research supports the ongoing evolution of remote, interdisciplinary approaches to studying and managing neurodegenerative diseases. CLINICALTRIAL CTO Project ID #3589

Aim This study was carried out to estimate sigma values of two HbA1c analysers and compare laboratory’s quality control (QC) procedure with a patient risk based statistical quality control (SQC) strategy. Materials and methods Internal quality control (IQC) and External quality control (EQC) results are downloaded from the laboratory data for different six-months’ period when each analyser was used in the laboratory. Mean percent coefficient of variation (CV) and bias values of study period were calculated. Sigma values for each system were calculated. QC constellation program was used for risk-based QC parameters calculation and severity of harm level for HbA1c was taken as ‘serious’. Results Sigma values for Capillarys 3 Tera analyser (Captera) were 3.83 and for Premier Hb9210 analyser (Premier) were 2.95. Using ±2 standard deviation (SD) as control limits; on Captera with a run size of 110 samples, probability of false rejection rate (Pfr) was 8.89%, expected maximum unreliable patient results (Max E(Nuf)) was 0.78 and on Premier analyser with a run size of 800 samples Pfr was 8.89% and Max E(Nuf) was 40.73. Effective statistical quality control strategies for both systems with acceptable Pfr and affordable Max E(Nuf) are provided in the study. Conclusion HbA1c is a key parameter in diabetes management and accurate methods are required for an efficient clinical use.

Enhanced quality control procedure for in-situ timeseries of marine-meteorological data

BackgroundA small shift in high-sensitivity cardiac troponin T (hs-cTnT) assays can lead to different result interpretation and consequent patient management. We explored whether a small bias could be detected using conventional internal quality control (QC) procedures, evaluated the performance of moving average (MA)-based QC procedures, and proposed a new QC procedure based on the moving rate (MR) of positive patient results of hs-cTnT assays.MethodsThe ability of conventional QC to detect a 5 ng/L bias was examined using the13s/ 22s/R4s multi-rule procedure as deviation rules.We developed MA and MR procedures for the hs-cTnT assay using eight months of patient data. The performance of different MA or MR procedures was investigated by calculating the median number of patient samples affected until a bias introduced into the dataset was detected (MNPed). After comparing the MNPed across different procedures, we selected an optimal MA or MR procedure for validation. Validation graphs were plotted using the minimum, median, and maximum number of results affected until bias detection.ResultsOur conventional QC procedures could not detect a positive bias of 5 ng/L. When a positive bias was introduced, MNPed was much higher using MA than using MR, with cut-off values of 5 ng/L and 14 ng/L, respectively. MR validation charts for optimal procedures provided insight into the MR performance.ConclusionsThe MR procedure could detect different errors with few false alarms. In the hs-cTnT assay, the MR procedure with a smaller cut-off value outperformed MA and conventional QC procedures for small bias detection.