Search

Atypical hemolytic uremic syndrome (aHUS) is a type of HUS. We herein report a case of aHUS triggered by pancreatitis in a patient with a heterozygous variant of membrane cofactor protein (MCP; P165S), a complement-related gene. Plasma exchange therapy and hemodialysis improved thrombocytopenia and anemia without leading to end-stage kidney disease. This MCP heterozygous variant was insufficient to cause aHUS on its own. Pancreatitis, in addition to a genetic background with a MCP heterozygous variant, led to the manifestation of aHUS. This case supports the "multiple hit theory" that several factors are required for the manifestation of aHUS.

Machine- and patient-specific quality assurance (QA) is essential to ensure the safety and accuracy of radiotherapy. QA methods have become complex, especially in high-precision radiotherapy such as intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT), and various recommendations have been reported by AAPM Task Groups. With the widespread use of IMRT and VMAT, there is an emerging demand for increased operational efficiency. Artificial intelligence (AI) technology is quickly growing in various fields owing to advancements in computers and technology. In the radiotherapy treatment process, AI has led to the development of various techniques for automated segmentation and planning, thereby significantly enhancing treatment efficiency. Many new applications using AI have been reported for machine- and patient-specific QA, such as predicting machine beam data or gamma passing rates for IMRT or VMAT plans. Additionally, these applied technologies are being developed for multicenter studies. In the current review article, AI application techniques in machine- and patient-specific QA have been organized and future directions are discussed. This review presents the learning process and the latest knowledge on machine- and patient-specific QA. Moreover, it contributes to the understanding of the current status and discusses the future directions of machine- and patient-specific QA., (© The Author(s) 2024. Published by Oxford University Press on behalf of The Japanese Radiation Research Society and Japanese Society for Radiation Oncology.)

Background: For patient-specific quality assurance (PSQA) for small targets, the dose resolution can change depending on the characteristics of the dose calculation algorithms., Purpose: This study aimed to evaluate the influence of the dose calculation algorithms Acuros XB (AXB), anisotropic analytical algorithm (AAA), photon Monte Carlo (pMC), and collapsed cone (CC) on a helical diode array using volumetric-modulated arc therapy (VMAT) for small targets., Materials and Methods: ArcCHECK detectors were inserted with a physical depth of 2.9 cm from the surface. To evaluate the influence of the dose calculation algorithms for small targets, rectangular fields of 2×100, 5×100, 10×100, 20×100, 50×100, and 100×100 mm 2 were irradiated and measured using ArcCHECK with TrueBeam STx. A total of 20 VMAT plans for small targets, including the clinical sites of 19 brain metastases and one spine, were also evaluated. The gamma passing rates (GPRs) were evaluated for the rectangular fields and the 20 VMAT plans using AXB, AAA, pMC, and CC., Results: For rectangular fields of 2×100 and 5×100 mm 2 , the GPR at 3%/2 mm of AXB was < 50% because AXB resulted in a coarser dose resolution with narrow beams. For field sizes > 10×100 mm2, the GPR at 3%/2 mm was > 88.1% and comparable for all dose calculation algorithms. For the 20 VMAT plans, the GPRs at 3%/2 mm were 79.1 ± 15.7%, 93.2 ± 5.8%, 94.9 ± 4.1%, and 94.5 ± 4.1% for AXB, AAA, pMC, and CC, respectively., Conclusion: The behavior of the dose distribution on the helical diode array differed depending on the dose calculation algorithm for small targets. Measurements using ArcCHECK for VMAT with small targets can have lower GPRs owing to the coarse dose resolution of AXB around the detector area., (© 2024 The Authors. Journal of Applied Clinical Medical Physics is published by Wiley Periodicals, Inc. on behalf of The American Association of Physicists in Medicine.)

This study aimed to identify systematic errors in measurement-, calculation-, and prediction-based patient-specific quality assurance (PSQA) methods for volumetric modulated arc therapy (VMAT) on lung cancer and to standardize the gamma passing rate (GPR) by considering systematic errors during data assimilation. This study included 150 patients with lung cancer who underwent VMAT. VMAT plans were generated using a collapsed-cone algorithm. For measurement-based PSQA, ArcCHECK was employed. For calculation-based PSQA, Acuros XB was used to recalculate the plans. In prediction-based PSQA, GPR was forecasted using a previously developed GPR prediction model. The representative GPR value was estimated using the least-squares method from the three PSQA methods for each original plan. The unified GPR was computed by adjusting the original GPR to account for systematic errors. The range of limits of agreement (LoA) were assessed for the original and unified GPRs based on the representative GPR using Bland-Altman plots. For GPR (3%/2 mm), original GPRs were 94.4 ± 3.5%, 98.6 ± 2.2% and 93.3 ± 3.4% for measurement-, calculation-, and prediction-based PSQA methods and the representative GPR was 95.5 ± 2.0%. Unified GPRs were 95.3 ± 2.8%, 95.4 ± 3.5% and 95.4 ± 3.1% for measurement-, calculation-, and prediction-based PSQA methods, respectively. The range of LoA decreased from 12.8% for the original GPR to 9.5% for the unified GPR across all three PSQA methods. The study evaluated unified GPRs that corrected for systematic errors. Proposing unified criteria for PSQA can enhance safety regardless of the methods used., (© 2024. Australasian College of Physical Scientists and Engineers in Medicine.)

Intensive care units (ICUs) are designed for critically ill patients who often experience high mortality rates owing to the severity of their conditions. Although the primary goal is patient recovery, it is crucial to understand the quality of death in the ICU setting. Nevertheless, there is a notable lack of systematic reviews on measured death quality and its associated factors. This study aims to conduct a quantitative synthesis of evidence regarding the quality of death in the ICU and offers a comprehensive overview of the factors influencing this quality, including its relationship with the post-intensive care syndrome-family (PICS-F). A thorough search without any language restrictions across MEDLINE, CINAHL, PsycINFO, and Igaku Chuo Zasshi databases identified relevant studies published until September 2023. We aggregated the results regarding the quality of death care for patients who died in the ICU across each measurement tool and calculated the point estimates and 95% confidence intervals. The quantitative synthesis encompassed 19 studies, wherein the Quality of Dying and Death-single item (QODD-1) was reported in 13 instances (Point estimate: 7.0, 95% CI: 6.93-7.06). Patient demographic data, including age and gender, as well as the presence or absence of invasive procedures, such as life support devices and cardiopulmonary resuscitation, along with the management of pain and physical symptoms, were found to be associated with a high quality of death. Only one study reported an association between quality of death and PICS-F scores; however, no significant association was identified. The QODD-1 scale emerged as a frequently referenced and valuable metric for evaluating the quality of death in the ICU, and factors associated with the quality of ICU death were identified. However, research gaps persist, particularly regarding the variations in the quality of ICU deaths based on cultural backgrounds and healthcare systems. This review contributes to a better understanding of the quality of death in the ICU and emphasises the need for comprehensive research in this critical healthcare domain., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2024, Naya et al.)