Repeat patient testing-based quality control shows promise for use in veterinary biochemistry testing.

BACKGROUND: Repeat patient testing-based quality control (RPT-QC) is a form of statistical QC and an alternative to commercial quality control materials (QCM). OBJECTIVE: This study investigated the suitability of canine heparinized plasma for use in RPT-QC and assessed the predicted performance of RPT-QC for the detection of analytical error in chemistry testing. METHODS: The stability of canine plasma for RPT-QC was investigated via storage at two temperatures for three or six time points. Storage data were analyzed using repeated measures ANOVA and by comparing results for stored specimens to baseline data using predetermined criteria. To generate RPT-QC limit-setting and -validation data, leftover plasma was prospectively measured. Once control limits were established, these were challenged by measuring specimens for which the repeat aliquot had been manipulated to mimic analytical error. Finally, the predicted performance of RPT-QC and QCM-QC with four control rules was investigated using Westgard's EZ Rules 3. RESULTS: Refrigerated storage of canine plasma for 7 days allowed mild changes facilitating RPT-QC. RPT-QC limits for 12 of 17 common measurands were validated. Validated limits successfully flagged differences from manipulated specimen pairs as "error." The predicted performance of RPT-QC for analytical error detection (represented by smallest achievable allowable total error, given a probability of error detection ≥ 85% and a probability of false rejection ≤ 5%) for four common control rules is comparable to that of QCM-QC. CONCLUSIONS: This study provides evidence that RPT-QC using canine heparinized plasma refrigerated for 7 days can be used with simple control rules and low numbers of control materials, suggesting RPT-QC is applicable to both reference and in-clinic laboratory settings.

A Bayesian control chart based on the beta distribution for monitoring the two-dimensional gamma index pass rate in the context of patient-specific quality assurance.

PURPOSE: In the context of quality assurance in intensity modulated radiation therapy (IMRT), the aim of this work was two-fold: (a) to show that the beta distribution characterizes the two-dimensional gamma index pass rate (GIPR), and that the quantiles of the distribution should be used in order to compute the control limit (CL) for the detection of abnormally low GIPR, and (b) to introduce a Bayesian control chart that allows calculation of CLs from the first measurement. METHODS: In order to enable monitoring of the GIPR from the first measurement, we developed a Bayesian control chart based on the beta distribution, elaborated according to the following two steps: (a) an iterative bayesian inference approach without any prior information on the GIPR distribution was used at the start of monitoring and the CL was progressively updated; and (b) when sufficient in-control arcs had been recorded and the estimators of the parameters of the beta distribution were sufficiently accurate, the CL of the chart was fixed to a constant value corresponding to the quantile of the beta distribution. The clinical utility of this approach is illustrated through a real data case study: monitoring the GIPR of patients treated with a moving gantry IMRT technique RapidArcTM on a Novalis TrueBeam STx (Varian Medical Systems) linear accelerator equipped with an aS1200 electronic portal imager device. RESULTS: We showed that some commonly used distributions for monitoring GIPR in the literature, such as normal or logarithm transformation, are not appropriate. We compared the CLs of those solutions with the CL of our chart based on the BD (CL = 95.14%). The comparison revealed that the CL for the normal law (CL = 97.62%) generated too many false positives, and that the CL of the Logarithm transformation (CL = 83.74%) could fail to efficiently detect (i.e., sufficiently early on or faster) changes in the process. CONCLUSIONS: Successful GIPR monitoring requires careful and rigorous application of well-established statistical concepts in the field of statistical process control. In this paper, we stress the importance of carefully analyzing the distribution of the monitored characteristic that is plotted on the control chart. We propose a Bayesian control chart that can be viewed as a practical solution for early implementation of GIPR monitoring, starting from the first arc. We demonstrate that beta distribution is a better method for characterizing the GIPR, and thus, the use of this approach is expected to improve patient-specific quality assurance plans in radiotherapy.

A general evidence-based sequence variant control limit for recombinant therapeutic protein development.

Sequence variants (SVs) resulting from unintended amino acid substitutions in recombinant therapeutic proteins have increasingly gained attention from both regulatory agencies and the biopharmaceutical industry given their potential impact on efficacy and safety. With well-optimized production systems, such sequence variants usually exist at very low levels in the final protein products due to the high fidelity of DNA replication and protein biosynthesis process in mammalian expression systems such as Chinese hamster ovary cell lines. However, their levels can be significantly elevated in cases where the selected production cell line has unexpected DNA mutations or the manufacturing process is not fully optimized, for example, if depletion of certain amino acids occurs in the cell culture media in bioreactors. Therefore, it is important to design and implement an effective monitoring and control strategy to prevent or minimize the possible risks of SVs during the early stage of product and process development. However, there is no well-established guidance from the regulatory agencies or consensus across the industry to assess and manage SV risks. A question frequently asked is: What levels of SVs can be considered acceptable during product and process development, but also have no negative effects on drug safety and efficacy in patients? To address this critical question, we have taken a holistic approach and conducted a comprehensive sequence variant analysis. To guide biologic development, a general SV control limit of 0.1% at individual amino acid sites was proposed and properly justified based on extensive literature review, SV benchmark survey of approved therapeutic proteins, and accumulated experience on SV control practice at Regeneron.

Augmented reality-assisted pedicle screw insertion: a cadaveric proof-of-concept study.

OBJECTIVE Augmented reality (AR) is a novel technology that has the potential to increase the technical feasibility, accuracy, and safety of conventional manual and robotic computer-navigated pedicle insertion methods. Visual data are directly projected to the operator's retina and overlaid onto the surgical field, thereby removing the requirement to shift attention to a remote display. The objective of this study was to assess the comparative accuracy of AR-assisted pedicle screw insertion in comparison to conventional pedicle screw insertion methods. METHODS Five cadaveric male torsos were instrumented bilaterally from T6 to L5 for a total of 120 inserted pedicle screws. Postprocedural CT scans were obtained, and screw insertion accuracy was graded by 2 independent neuroradiologists using both the Gertzbein scale (GS) and a combination of that scale and the Heary classification, referred to in this paper as the Heary-Gertzbein scale (HGS). Non-inferiority analysis was performed, comparing the accuracy to freehand, manual computer-navigated, and robotics-assisted computer-navigated insertion accuracy rates reported in the literature. User experience analysis was conducted via a user experience questionnaire filled out by operators after the procedures. RESULTS The overall screw placement accuracy achieved with the AR system was 96.7% based on the HGS and 94.6% based on the GS. Insertion accuracy was non-inferior to accuracy reported for manual computer-navigated pedicle insertion based on both the GS and the HGS scores. When compared to accuracy reported for robotics-assisted computer-navigated insertion, accuracy achieved with the AR system was found to be non-inferior when assessed with the GS, but superior when assessed with the HGS. Last, accuracy results achieved with the AR system were found to be superior to results obtained with freehand insertion based on both the HGS and the GS scores. Accuracy results were not found to be inferior in any comparison. User experience analysis yielded "excellent" usability classification. CONCLUSIONS AR-assisted pedicle screw insertion is a technically feasible and accurate insertion method.

Optimal Policies for Reducing Unnecessary Follow-up Mammography Exams in Breast Cancer Diagnosis.

Mammography is the most effective screening tool for early diagnosis of breast cancer. Based on the mammography findings, radiologists need to choose from one of the following three alternatives: 1) take immediate diagnostic actions including prompt biopsy to confirm breast cancer; 2) recommend a follow-up mammogram; 3) recommend routine annual mammography. There are no validated structured guidelines based on a decision-analytical framework to aid radiologists in making such patient management decisions. Surprisingly, only 15-45% of the breast biopsies and less than 1% of short-interval follow-up recommendations are found to be malignant, resulting in unnecessary tests and patient-anxiety. We develop a finite-horizon discrete-time Markov decision process (MDP) model that may help radiologists make patient-management decisions to maximize a patient's total expected quality-adjusted life years. We use clinical data to find the policies recommended by the MDP model and also compare them to decisions made by radiologists at a large mammography practice. We also derive the structural properties of the MDP model, including sufficiency conditions that ensure the existence of a double control-limit type policy.