Identification and comparison of sex-specific serious adverse drug reactions in spontaneous reports and systematically collected reports (ADRED).

AIMS: Adverse drug reactions (ADRs) are known to show sex-specific differences in occurrence and phenotype. The aim of this study was to analyse sex-specific differences in ADR-drug combinations that required hospitalization based on two different datasets. METHODS: We performed a complementary analysis of (i) spontaneously reported (n = 12 564, female = 51.7%) and (ii) systematically collected ADR reports from a prospective multicentre observational study (ADRED, n = 2355, female = 48.2%) from Germany in the ADR database EudraVigilance (EV). Both datasets were analysed separately concerning the suspected drugs, ADRs and ADR-drug combinations more frequently reported for females or males by calculating reporting odds ratios (ROR) with 95% confidence intervals. ADR-drug combinations more frequently reported for either females or males in EV reports were related to prescription data. Finally, the results from both datasets were discussed with regard to their (dis-)concordance. RESULTS: In both datasets, some antineoplastic agents and nervous system drugs were found to be reported more often for females than males (RORs ranging from 1.5 [1.1-2.1] for quetiapine in spontaneous reports to 41.3 [13.1-130.0] for trastuzumab in spontaneous reports). ADRs of the respiratory system, and haemorrhages were described predominantly for males in both datasets. In spontaneous reports the ADR-drug combination self-injurious behaviour-quetiapine was more often reported for females without and with consideration of drug prescriptions (ROR: 3.8 [1.3-11.0]). Quetiapine and psychiatric disorders (superordinate level) was exclusively reported for females in ADRED reports. CONCLUSIONS: Our results can contribute to raise awareness and further knowledge regarding sex-specific ADRs. The findings require further in-depth investigation.

Comparative Analysis of Information Provided in German Adverse Drug Reaction Reports Sent by Physicians, Pharmacists and Consumers.

INTRODUCTION: Adverse drug reactions (ADRs) can be reported by Health Care Professionals (HCPs; e.g., physicians, pharmacists) and non-Health Care Professionals (non-HCPs; e.g., consumers). Previous studies investigating differences between reports from HCPs and non-HCPs rarely considered the completeness of information provided. In addition, they mostly did not distinguish between physicians and pharmacists or were performed years ago. The aim of our study was to analyse and compare the completeness of information provided in reports from physicians, pharmacists and consumers from Germany in a more recent dataset. MATERIALS AND METHODS: We analysed all spontaneous reports from Germany received between 2018 and 2021 in the ADR database EudraVigilance exclusively reported by physicians (n = 69,976), pharmacists (n = 42,396) or consumers (n = 121,144). Demographical parameters of the patients were analysed descriptively. Completeness of reports was evaluated applying an established score (vigiGrade). Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated using logistic regression analysis in order to identify report, patient, drug or ADR-specific information provided more often in reports from physicians, pharmacists or consumers. RESULTS: Within the study period the number of reports per year by physicians and pharmacists decreased steadily, while an opposite trend was observed for consumer reports. The proportion of female patients was higher in reports from pharmacists (64.4%) and consumers (64.8%) compared to those from physicians (55.3%). On average, patients in reports from pharmacists (58.7) were older compared to those from physicians (53.5) and consumers (52.6). As an example for the presence of specific information, the time to onset of the ADR could be calculated more often in consumer compared to physician (OR 1.9 [1.8-1.9]) and pharmacist reports (OR 1.7 [1.6-1.7]). In contrast, pharmacist (OR 0.5 [0.4-0.5]) and consumer (OR 0.5 [0.5-0.5]) reports included the indication of the suspected drug less often than physician reports. Physician reports on average (mean = 0.5) were slightly more complete according to the vigiGrade score compared to reports from consumers (mean = 0.4) and pharmacists (mean = 0.4). CONCLUSION: The ADR reports from consumers were comparable with regard to the completeness score with those from physicians and pharmacists underlining their value. Differences in completeness of specific information between the reporter types were found, suggesting that a common reporting of interactions between the three reporters may further improve the completeness of ADR reports. Furthermore, stratified analysis of ADR reports per reporter type may be helpful for certain objectives in scientific research.

The Liver Tumor Segmentation Benchmark (LiTS).

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://medicaldecathlon.com/. In addition, both data and online evaluation are accessible via https://competitions.codalab.org/competitions/17094.

Adverse Drug Reactions in Children: Comparison of Reports Collected in a Pharmacovigilance Project Versus Spontaneously Collected ADR Reports.

BACKGROUND: Adverse drug reactions (ADRs) and medication errors in children may result from a lack of appropriate drugs, dosages, and pharmaceutical forms. In addition, children may respond differently to drugs than adults. Reporting of ADRs in the pediatric population is therefore of importance in order to increase the amount of safety data. However, different methodological approaches are used to collect ADRs. OBJECTIVE: The aim of the present study was to analyze whether there were differences in the ADRs collected in the KiDSafe project (845 ADR reports) compared with the spontaneous ADR reports sent to EudraVigilance (697 reports) in the same time period. The strengths and limitations of these two different approaches should be discussed. METHODS: The same inclusion criteria were applied for the systematically collected ADRs in the KiDSafe project and the spontaneous reports from EudraVigilance, and only reports of ADRs coded with hospitalization were considered. In both datasets, the number of reports (related to number of hospitals), their documentation quality (VigiGrade), causal relationship (World Health Organization-Uppsala Monitoring Centre [WHO-UMC] criteria), most frequently reported drugs and ADRs, demographical parameters of the patients, reported medical histories, and the seriousness of ADR reports were analyzed descriptively. The results of the two analyses were compared. RESULTS: There was considerable underreporting of ADRs via the spontaneous reports (0.4 reports per hospital; 697/1902) compared with 70.4 reports per hospital (845/12) in the systematically collected KiDSafe reports. Documentation quality assessment yielded similar results in both datasets. Among the 10 most frequently reported drugs, anticonvulsants such as levetiracetam (6.6%), valproic acid (5.6%), oxcarbazepine (3.6%), and lamotrigine (3.4%) were mainly reported in the KiDSafe reports, while in the EudraVigilance reports, mite allergen extract (4.4%) and allergens (3.6%) were preferentially reported. Seizures were the most frequently reported clinically specific ADRs in the KiDSafe reports, whereas anaphylactic reactions and urticaria were prominent in the spontaneous reports from EudraVigilance. Notably, the proportion of reports referring to medication errors and other medication safety related issues were more prominent in KiDSafe than in the spontaneous reports (27.8% vs. 12.6% and 46.0% vs. 29.0%, respectively). CONCLUSION: In general, reports from both data sources contributed to the identification of ADRs and dedicated issues related to drug therapy. However, these differed by nature and strength of the signal, likely due to the characteristics of the individual method. A combined approach could likely compensate for limitations inherent to the single approaches, but will most likely only be applied to dedicated pharmacovigilance topics or research objectives.

The Medical Segmentation Decathlon.

International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Although segmentation is the most widely investigated medical image processing task, the various challenges have been organized to focus only on specific clinical tasks. We organized the Medical Segmentation Decathlon (MSD)-a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities to investigate the hypothesis that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. MSD results confirmed this hypothesis, moreover, MSD winner continued generalizing well to a wide range of other clinical problems for the next two years. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to scientists that are not versed in AI model training.

Automated Whole-Body Bone Lesion Detection for Multiple Myeloma on 68Ga-Pentixafor PET/CT Imaging Using Deep Learning Methods.

The identification of bone lesions is crucial in the diagnostic assessment of multiple myeloma (MM). 68Ga-Pentixafor PET/CT can capture the abnormal molecular expression of CXCR-4 in addition to anatomical changes. However, whole-body detection of dozens of lesions on hybrid imaging is tedious and error prone. It is even more difficult to identify lesions with a large heterogeneity. This study employed deep learning methods to automatically combine characteristics of PET and CT for whole-body MM bone lesion detection in a 3D manner. Two convolutional neural networks (CNNs), V-Net and W-Net, were adopted to segment and detect the lesions. The feasibility of deep learning for lesion detection on 68Ga-Pentixafor PET/CT was first verified on digital phantoms generated using realistic PET simulation methods. Then the proposed methods were evaluated on real 68Ga-Pentixafor PET/CT scans of MM patients. The preliminary results showed that deep learning method can leverage multimodal information for spatial feature representation, and W-Net obtained the best result for segmentation and lesion detection. It also outperformed traditional machine learning methods such as random forest classifier (RF), k-Nearest Neighbors (k-NN), and support vector machine (SVM). The proof-of-concept study encourages further development of deep learning approach for MM lesion detection in population study.