Large Databases Research Articles

Using MRI-derived observed-to-expected total fetal lung volume to predict lethality in fetal skeletal dysplasia.

Pulmonary hypoplasia is the primary cause of perinatal death in lethal skeletal dysplasias. The antenatal ultrasound correlates for lethality are indirect, measuring the thorax (thoracic circumference, TC) or femur compared to the abdomen (TC/AC, FL/AC). A single study has correlated lethality with the observed-to-expected total lung volume (O/E-TFLV) on fetal MRI in 23 patients. Our aim was to define a cutoff value to predict lethality more specifically using MRI-derived O/E-TFLV. Two large fetal center databases were searched for fetuses with skeletal dysplasia and MRI; O/E-TFLV was calculated. Ultrasound measures were included when available. Each was evaluated as a continuous variable against lethality (stillbirth or death in the first month of life). Logistic regression and receiver operating characteristic (ROC) curve analyses evaluated the prediction ability. AUC, sensitivity, and specificity were calculated. P < 0.05 was considered statistically significant. A total of 80 fetuses met inclusion criteria. O/E-TFLV < 0.49 was a significant risk factor in predicting lethality, with sensitivity and specificity of 0.63 and 0.93, respectively, and an AUC of 0.81 (P < 0.001). FL/AC < 0.129 was also a strong variable with sensitivity, specificity, and AUC of 0.73, 0.88, and 0.78, respectively (P < 0.001). TC/AC and TC percentile were not significant risk factors for lethality. An O/E-TFLV of < 0.38 defines a specificity for lethality at 1.00. MRI-derived O/E-TFLV and US-derived FL/AC are significant predictors of lethality in fetuses with skeletal dysplasia. When prognosis is uncertain after ultrasound, calculation of MRI-derived O/E-TFLV may provide additional useful information for prognosis and delivery planning.

Simple way to model the mechanical properties of concretes with recycled concrete aggregates

Researchers have made efforts to model the mechanical properties of concretes with recycled aggregates joining large research databases. The replacement ratio criterion of natural aggregates by recycled ones has been widely used in the models, however, itself leads to a great dispersion of results and is inaccurate. In the manuscript, the weighted water absorption of both aggregates (coarse natural and recycled concrete one, Ww) and the effective water/cement ratio were used to easily predict accurately the porosity, compressive strength, and elastic modulus of the recycled concretes. For the modelling, a systematic review of the Brazilian studies from 1999 to 2020 was conducted compiling 169 concrete compositions and joined with 212 concrete mixes from international studies. A supplementary Excel file with the database and all existing correlations was provided. The 20%-replacement ratio limit of many standard codes for structural recycled concretes can be exceeded by using Ww < 5 % for structural concrete [40–60 MPa], or Ww < 8 % [25–40 MPa]. Aggregates with Ww > 8 % can be used for non-structural concretes, with little reduction in compressive strength in absolute terms (<5 MPa). The best %-replacement can be defined for each case, practical for concrete formulations in ready-mix companies.

Comparison of machine learning approaches for the identification of top-performing materials for hydrogen storage

Unique properties of Metal-Organic Frameworks (MOFs), such as their extremely high surface areas and porosity, render them as one of, if not the most promising adsorbents for gas storage applications. However, their extremely flexible and tunable nature has resulted in an enormous expansion of the available material pool which in turn begs the question as to whether an efficient identification of the best materials is feasible. In the last years, Machine Learning (ML) techniques have been extensively applied for the exploration of large material databases, since they can significantly accelerate this process. In this work, “traditional” ML models and models based on our recently developed iterative self-consistent approach are compared with respect to their ability to efficiently identify the best materials of a database. As a case study, we have used hydrogen adsorption in MOFs at different thermodynamic conditions. Despite their high accuracy, traditional models struggle to pinpoint the best materials, regarding usable gravimetric uptake, without compromising computational resources. On the other hand, self-consistent models can even reduce by two orders of magnitude the amount of reference data required for the identification of the best gravimetric materials compared to traditional ones. Notably, 300 training samples are enough for the SC models to correctly identify the top-100 gravimetric materials of a database. Nevertheless, both type of models underperform when they are queried for the top-performing materials with regards to usable volumetric uptake.

Evaluation of Empirical Ground-Motion Models for the 2022 New Zealand National Seismic Hazard Model Revision

ABSTRACT This article presents an evaluation of empirical ground-motion models (GMMs) for active shallow crustal, subduction interface, and subduction slab earthquakes using a recently developed New Zealand (NZ) ground-motion database for the 2022 New Zealand National Seismic Hazard Model revision. This study considers both NZ-specific and global models, which require evaluation to inform of their applicability in an NZ context. A quantitative comparison between the models is conducted based on intensity measure residuals and a mixed-effects regression framework. The results are subsequently investigated to assess how the models are performing in terms of overall accuracy and precision, as well as to identify the presence of any biases in the model predictions when applied to NZ data. Many models showed reasonable performance and could be considered appropriate for inclusion within suites of models to properly represent ground-motion predictions and epistemic uncertainty. In general, the recent models that are NZ-specific or developed on large international databases performed the best. This evaluation of models helped inform suitable GMMs for the ground-motion characterization model logic tree. In addition, spatial trends in systematic site-to-site residuals to the west of the Taupō Volcanic Zone demonstrated the need for backarc attenuation modifications for slab earthquakes.

Polysocial Risk Scores: Implications for Cardiovascular Disease Risk Assessment and Management.

To review current evidence, discuss key knowledge gaps and identify opportunities for development, validation and application of polysocial risk scores (pSRS) for cardiovascular disease (CVD) risk prediction and population cardiovascular health management. Limited existing evidence suggests that pSRS are promising tools to capture cumulative social determinants of health (SDOH) burden and improve CVD risk prediction beyond traditional risk factors. However, available tools lack generalizability, are cross-sectional in nature or do not assess social risk holistically across SDOH domains. Available SDOH and clinical risk factor data in large population-based databases are under-utilized for pSRS development. Recent advances in machine learning and artificial intelligence present unprecedented opportunities for SDOH integration and assessment in real-world data, with implications for pSRS development and validation for both clinical and healthcare utilization outcomes. pSRS presents unique opportunities to potentially improve traditional "clinical" models of CVD risk prediction. Future efforts should focus on fully utilizing available SDOH data in large epidemiological databases, testing pSRS efficacy in diverse population subgroups, and integrating pSRS into real-world clinical decision support systems to inform clinical care and advance cardiovascular health equity.

Efficient weighted sequential pattern mining

In real-life applications, data mining task involves extracting valuable but hidden information from massive data. How to effectively find out interesting patterns from large databases is a current topic. Sequential pattern mining is the most popular approach in data mining domain. Traditional sequential pattern mining research generally focuses on discovering frequent sequential patterns. However, the account of occurrence times of patterns does not adequately indicate their importance. For instance, frequent patterns (e.g., pencil and eraser) are not profitable, whereas infrequent patterns (e.g., extreme weather) are high-risk. To extract more useful information, researchers study a weighted sequential pattern mining task. In this paper, an efficient algorithm for weighted sequential pattern mining task, called EWSPM, is proposed. Two new strict upper bounds, namely MWEbound and MSRIWbound, are designed based on the concepts of maximum weight estimation (simplified as MWE) and maximum sumation of remaining item weights (simplified as MSRIW), respectively. These upper bounds achieve better pruning effects and reduce the size of search space during the mining process, which significantly shortens execution time. In addition, a database-projection method is employed to optimize memory usage. It addresses potential memory explosion issues in a certain degree. Finally, we also conducted extensive experiments on nine datasets (including real and synthetic). The experimental results demonstrate that the EWSPM algorithm is capable of mining all interesting patterns efficiently, with the smallest size of search space. Additionally, the novel algorithm also exhibits superior performance in terms of execution time and memory consumption.

Strategies to develop, design, implement and support digital health equity in key underserved groups: protocol for a systematic review

AbstractBackgroundDigital health technologies (DHT) can be used to detect and remotely monitor digital biomarkers associated with prodromal dementia. However, there are growing concerns that DHT are not leading to demonstrable health benefits in all populations, particularly the underserved who are typically neglected in research resulting in worse health outcomes. One possible cause is digital exclusion, which refers to the unfair differences in access, use, motivation and reliability in DHT across different demographic groups. The UK regulatory bodies acknowledge the need to mitigate digital exclusion, leading to the development of inclusive digital strategies such as tailoring DHT to ensure it is appropriate to individuals in terms of affordability, culturally applicable, and accessible. This systematic review will synthesize the literature regarding strategies to improve digital inclusivity for underserved groups at risk of digital exclusion.MethodThis review will follow PRISMA guidelines and involve systematically searching four large databases (Medline, Embase, PsycINFO, Scopus). It has been registered on PROSPERO: CRD42022378199. Search terms will be used to identify relevant studies focusing on factors associated with digital exclusion, including: Culture (ethnicity, language, religion), Limiting conditions (visual and hearing impairments), Education, Age, Residence (rural or deprived areas, homelessness), and Socioeconomic status (low to middle‐income and unemployed individuals), summarised by the acronym CLEARS (Figure 1). Primary research English‐language studies using qualitative or mixed methods to explore facilitators and barriers to digital inclusion will be included. Studies will be independently screened by two additional reviewers using a computer software, Rayyan. The methodological quality of studies will be assessed using a checklist designed to assess qualitative and mixed methods. A narrative synthesis of data from included studies will be conducted.ResultThis review will integrate the evidence available on the facilitators and barriers associated with the design and implementation of digitally inclusive strategies for DHT in CLEARS groups. Key recommendations for digitally inclusive strategies to support health equity will also be produced.ConclusionThe results of this review will be used to support digital health equity in research by providing recommendations for the development, design, and implementation of strategies to promote digital health equity in key underserved groups.

Deep learning, data ramping, and uncertainty estimation for detecting artifacts in large, imbalanced databases of MRI images.

Magnetic resonance imaging (MRI) is increasingly being used to delineate morphological changes underlying neurological disorders. Successfully detecting these changes depends on the MRI data quality. Unfortunately, image artifacts frequently compromise the MRI utility, making it critical to screen the data. Currently, quality assessment requires visual inspection, a time-consuming process that suffers from inter-rater variability. Automated methods to detect MRI artifacts could improve the efficiency of the process. Such automated methods have achieved high accuracy using small datasets, with balanced proportions of MRI data with and without artifacts. With the current trend towards big data in neuroimaging, there is a need for automated methods that achieve accurate detection in large and imbalanced datasets. Deep learning (DL) is the ideal MRI artifact detection algorithm for large neuroimaging databases. However, the inference generated by DL does not commonly include a measure of uncertainty. Here, we present the first stochastic DL algorithm to generate automated, high-performing MRI artifact detection implemented on a large and imbalanced neuroimaging database. We implemented Monte Carlo dropout in a 3D AlexNet to generate probabilities and epistemic uncertainties. We then developed a method to handle class imbalance, namely data-ramping to transfer the learning by extending the dataset size and the proportion of the artifact-free data instances. We used a 34,800 scans (98% clean) dataset. At baseline, we obtained 89.3% testing accuracy (F1 = 0.230). Following the transfer learning (with data-ramping), we obtained 94.9% testing accuracy (F1 = 0.357) outperforming focal cross-entropy (92.9% testing accuracy, F1 = 0.304) incorporated for comparison at handling class imbalance. By implementing epistemic uncertainties, we improved the testing accuracy to 99.5% (F1 = 0.834), outperforming the results obtained in previous comparable studies. In addition, we estimated aleatoric uncertainties by incorporating random flips to the MRI volumes, and demonstrated that aleatoric uncertainty can be implemented as part of the pipeline. The methods we introduce enhance the efficiency of managing large databases and the exclusion of artifact images from big data analyses.

Strategies to develop, design, implement and support digital health equity in key underserved groups: protocol for a systematic review

AbstractBackgroundDigital health technologies (DHT) can be used to detect and remotely monitor digital biomarkers associated with prodromal dementia. However, there are growing concerns that DHT are not leading to demonstrable health benefits in all populations, particularly the underserved who are typically neglected in research resulting in worse health outcomes. One possible cause is digital exclusion, which refers to the unfair differences in access, use, motivation and reliability in DHT across different demographic groups. The UK regulatory bodies acknowledge the need to mitigate digital exclusion, leading to the development of inclusive digital strategies such as tailoring DHT to ensure it is appropriate to individuals in terms of affordability, culturally applicable, and accessible. This systematic review will synthesize the literature regarding strategies to improve digital inclusivity for underserved groups at risk of digital exclusion.MethodThis review will follow PRISMA guidelines and involve systematically searching four large databases (Medline, Embase, PsycINFO, Scopus). It has been registered on PROSPERO: CRD42022378199. Search terms will be used to identify relevant studies focusing on factors associated with digital exclusion, including: Culture (ethnicity, language, religion), Limiting conditions (visual and hearing impairments), Education, Age, Residence (rural or deprived areas, homelessness), and Socioeconomic status (low to middle‐income and unemployed individuals), summarised by the acronym CLEARS (Figure 1). Primary research English‐language studies using qualitative or mixed methods to explore facilitators and barriers to digital inclusion will be included. Studies will be independently screened by two additional reviewers using a computer software, Rayyan. The methodological quality of studies will be assessed using a checklist designed to assess qualitative and mixed methods. A narrative synthesis of data from included studies will be conducted.ResultThis review will integrate the evidence available on the facilitators and barriers associated with the design and implementation of digitally inclusive strategies for DHT in CLEARS groups. Key recommendations for digitally inclusive strategies to support health equity will also be produced.ConclusionThe results of this review will be used to support digital health equity in research by providing recommendations for the development, design, and implementation of strategies to promote digital health equity in key underserved groups.

The effects of art therapy on quality of life and psychosomatic symptoms in adults with cancer: a systematic review and meta-analysis

BackgroundCancer-related psychological and physical disorders can mean stressful and painful experiences for patients. Art therapy, a form of complementary and alternative medicine, is an increasingly popular way to decrease emotional stress, alleviate somatic symptoms, and improve quality of life in patients with cancer. However, current systematic reviews have not explored the beneficial effects of art therapy. Moreover, there have been inconsistent findings on the effect of this therapy, and there is insufficient evidence to confirm the effects in adults with cancer. The objective of this study was to determine the efficacy of art therapy in improving quality of life and psychosomatic symptoms in adults with cancer.MethodsThis systematic review and meta-analysis included adults with all kinds of cancer. Six English-language and three large Chinese-language databases were comprehensively searched for relevant studies. Gray literature and references were also checked. The quality of the included studies was evaluated using the Cochrane risk-of-bias assessment tool.ResultsEight eligible randomized controlled trials conducted in four countries were included. Art therapy improved overall quality of life, but had no significant effect on psychological health or physical health sub-dimensions in women with cancer. Moreover, art therapy alleviated anxiety and depression, but had only a tendency toward an effect on somatic symptoms.ConclusionsModerate-quality evidence shows that art therapy is beneficial for women with cancer in terms of improving the overall quality of life and alleviating emotional symptoms (anxiety and depression). However, more high-quality randomized controlled trials are needed to determine the efficacy of this therapy on somatic symptoms.

Award for Distinguished Scientific Early Career Contributions to Psychology: Anu Asnaani.

This award recognizes excellent psychologists who are at early stages of their research careers. "For her groundbreaking work in the area of cross-cultural psychology and anxiety disorders. Anu Asnaani has been tackling clinically important and scientifically challenging questions using sophisticated statistical procedures and experimental designs. Her studies range from analyses of large epidemiological databases to carefully designed experimental investigations on the physiological effects of different emotion regulation strategies on anxiety in different cultural contexts. She is a trailblazer; the Energizer Bunny of psychology." (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Abstract A134: Predicting activity of IMM-1-104 as single agent and in combination for patients with RAS or RAF mutant tumors

Abstract Introduction: Many tumors are addicted to MAPK pathway activation, including the &amp;gt;20% of human tumors with mutations in RAS or RAF1. IMM-1-104 is an oral once-daily treatment currently in Phase 1 in patients with RAS-mutant solid tumors [NCT05585320]. To date, drugs disrupting the MAPK pathway have done so chronically, leading to dose-limiting toxicities (DLTs) and poor response durability. In contrast, IMM-1-104 was designed to provide deep cyclic inhibition (DCI) of the MAPK pathway via a unique pharmacokinetic (PK) profile with high peak plasma drug levels and a near zero drug trough between doses. This promotes pulsatile inhibition of MEK, depriving tumors of sustained signaling of a critical oncogenic pathway while limiting toxicity and durability issues associated with chronic MEK inhibition. In Phase 1a dose escalation, no DLTs were observed, the plasma drug half-life was ~2-hours, and pharmacodynamic (PD) data were consistent with DCI. Phase 1b dose expansion is underway. Translational efforts are focused on identifying MAPK pathway addiction and sensitivity to IMM-1-104. Tumor models displaying patient-aligned genomic profiles against large patient databases such as AACR Project GENIE1 were tested in humanized 3D tumor growth assays (3D-TGA). Computational modeling based on response and in-house genomic data was used to inform identification of patient populations for IMM-1-104 monotherapy and potential combination opportunities. Experimental Procedures: Using cancer-specific, patient-aligned cell lines, IMM-1-104 activity was characterized in the 3D-TGA. Whole exome sequencing was performed to confirm alteration status, and a further subset subjected to RNA sequencing. Pharmacogenomic data were used to generate a model predictive of response to IMM-1-104 and identify biomarker-aligned patient subpopulations. Selected model predictions were then tested in subcutaneous tumor xenograft models in female BALB/c nude mice. Summary of New Data: Assessment of IMM-1-104 across &amp;gt;190 patient-aligned models demonstrated diverse responses across a wide range of MAPK-driven tumor types, including those with RAS or RAF mutations. In addition to RAS, these data suggested additional potential for IMM-1-104 in BRAF-mutant disease. Therefore, IMM-1-104 was tested alone and with encorafenib in the HT-29 colorectal BRAFV600E mutant xenograft model. Monotherapy with either encorafenib or IMM-1-104 displayed superior tumor growth inhibition to binimetinib. IMM-1-104 in combination with encorafenib drove deeper regressions and superior durability of response in a head-to-head in vivo comparison versus binimetinib plus encorafenib. Conclusions: We used an integrated platform of translational experiments and informatics to identify patient-aligned model systems, prioritize factors relevant for response to IMM-1-104’s unique DCI profile, and elucidate combination opportunities to potentially inform clinical development strategies. Citation Format: Praveen Nair, Sarah Kolitz, Jason Funt, Jan de Jong, Peter King, Amy Yamamura, Mai Johnson, Jenny Zhang, Kevin D Fowler, Anna Travesa, Amy Axel, Chris Walker, Scott Barrett, Benjamin J Zeskind, Brett Hall. Predicting activity of IMM-1-104 as single agent and in combination for patients with RAS or RAF mutant tumors [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr A134.

The Medical and Cognitive Research Unit in Austin Health, Australia

AbstractThe Medical and Cognitive Research Unit (MRCU), Austin Health and University of Melbourne, is sited in Heidelberg, Australia at Austin Health, a large University Teaching Hospital. Part of the Aged and Continuing Care Department, it is headed by geriatrician Associate Professor Michael Woodward.MCRU employs 25 staff‐ nurse coordinators, psychologists, medical doctors, scientists and administrative. Some 10 research affiliates each year complete projects in collaboration with the department, but the main focus is sponsored clinical trials. Research areas include Investigational Products for AD, with over 200 Phase 1‐4 trials completed over the last 30 years. Other research foci include wound healing, immunization and frailty.AD research is accelerated when the skills to recruit into and conduct trials are developed and maintained in this centre of excellence. Close affiliation with clinical cognitive practice, including the Austin Health Memory Clinic and private practices of doctors working in MCRU attracts patients to such services, providing a pipeline of patients suitable for studies. These patients also contribute to large data bases, including the Alzheimer’s Disease Network (ADNet) and the Australian Imaging, Biomarkers and Lifestyle study (AIBL), and inturn these studies identify patients suitable for clinical trials in MCRU. The Head is one of 3 Medical Advisors to Dementia Australia, and as a member of the Dementia Australia Research Foundation funds dementia research throughout Australia. Numerous media exposures involve MCRU staff, raising national awareness of AD and related disorders‐ eg a recent National TV Documentary on mAbs for AD.International AD research looks to large sites to conduct trials, and inturn these site develop research protocols and contribute to national and international meetings. MCRU has recently contributed to protocols involving several small molecules to treat AD.International collaboration is a hallmark of a successful AD Research Centre‐ MCRU through AIBL and ADNet, and the affiliated Florey, have collaborated with ADNI and the World Wide FINGERS initiatives to better understand and prevent AD and related disorders.

Design of a system for bioinformatics processing of metagenomic data

Advancements in acquiring, storing and processing information have led to a significant accumulation of large databases in the field of metagenomics—a branch of biology that deals with the study of genetic material directly recovered from environmental or clinical samples. The data collected from metagenomic experiments are massive and noisy, often containing fragmented information representing over 10,000 species. Processing and analyzing this data require software installed on supercomputers. In this paper, the authors present the design, development, and creation of a computational server system aimed at processing and analyzing bioinformatics data in metagenomics and 16S rRNA gene studies. They utilized the specialized software product Galaxy Europe for this purpose. The analysis was performed using the metagenomics-based method Targeted Amplicon Sequencing. The data was divided into three test collections in a special fasta file format. These collections were processed using the web-based Galaxy platform and two configuration variants on a high-performance cluster equipped with NVIDIA Tesla V100 32 GB GPU cards. The analysis process was optimized to reduce information processing time, and the parallel performance of the three different system configurations was investigated for the data used.

Data‐driven Bayesian network learning analysis on the regulatory mechanism between carcinogenic genes and immune cells

AbstractBackgroundIn recent years, it has become a research focus to accurately extract key genes influencing the occurrence and development of diseases from massive genomic data and study their regulatory mechanisms. Further exploration of these large databases is beneficial for us to identify key regulatory mechanisms in the occurrence and development of diseases, providing direction and theoretical basis for subsequent experimental design and research.MethodsBy using data from The Cancer Genome Atlas (TCGA) for lung adenocarcinoma (LUAD), the immune cell content of patients was obtained through deconvolution calculations in CIBERSORTx. Combined with Bayesian network inference methods, the impact of key gene expression on the heterocellular network influencing lung cancer was analyzed.ResultsWe found CD36 and ADRA1A genes were identified as two key mRNA genes influencing lung adenocarcinoma. The sensitivity analysis shows that the model performs well on both the testing set (error rate = 4%, AUC = 0.9804), the training set (error rate = 5.637%, AUC = 0.9746) and the verification set (error rate = 28.85%, AUC = 0.8689). The model has excellent predictive capabilities.ConclusionWe found that CD36 and ADRA1A genes may influence the development of lung cancer by affecting regulatory T cell and follicular helper T cell populations. Additionally, plasma cells may affect the expression of the CD36 gene. And the mutual information calculated by the model for these two genes is also the highest, indicating their potential as tumour biomarkers. Combining the network model, it can be inferred that CD36 and ADRA1A are likely to influence the occurrence and development of the disease through follicular helper T cells and regulatory T cells.

Patient Adherence to Oral Anticancer Agents: A Mapping Review of Supportive Interventions.

The development and use of oral anticancer agents (OAAs) continue to grow, and supporting individuals on OAAs is now a priority as they find themselves taking these drugs at home with little professional guidance. This mapping review provides an overview of the current evidence concerning OAA-supportive adherence interventions, identifying potential gaps, and making recommendations to guide future work. Four large databases and the grey literature were searched for publications from 2010 to 2022. Quantitative, qualitative, mixed-method, theses/dissertations, reports, and abstracts were included, whereas protocols and reviews were excluded. Duplicates were removed, and the remaining publications were screened by title and abstract. Full-text publications were assessed and those meeting the inclusion criteria were retained. Data extracted included the year of publication, theoretical underpinnings, study design, targeted patients, sample size, intervention type, and primary outcome(s). 3175 publications were screened, with 435 fully read. Of these, 314 were excluded with 120 retained. Of the 120 publications, 39.2% (n = 47) were observational studies, 38.3% (n = 46) were quasi-experimental, and 16.7% (n = 20) were experimental. Only 17.5% (n = 21) were theory-based. Despite the known efficacy of multi-modal interventions, 63.7% (n = 76) contained one or two modalities, 33.3% (n = 40) included 3, and 3.3% (n = 4) contained four types of modalities. Medication adherence was measured primarily through self-report (n = 31) or chart review/pharmacy refills (n = 28). Given the importance of patient tailored interventions, future work should test whether having four intervention modalities (behavioral, educational, medical, and technological) guided by theory can optimize OAA-related outcomes.

The common VWF variant p.Y1584C: detailed pathogenic examination of an enigmatic sequence change

BackgroundAs knowledge of the human genome has advanced, so too has the recognition that interpretation of the pathogenic nature of sequence variants can be challenging. The von Willebrand factor (VWF) gene exhibits a significant degree of sequence variability, and the first VWF variant associated with type 1 von Willebrand disease (VWD), c.4751 A>G, p.Y1584C, was described in 2003. However, since that time, the pathogenic nature of this variant has remained unclear, being assigned properties ranging from a risk factor to a pathogenic variant. ObjectivesTo provide additional evaluation on the interpretation of pathogenicity for this common VWF variant. MethodsFifty-eight subjects with only the p.Y1584C variant were recruited from 2 cohort studies (the Zimmerman Program and the Canadian type 1 VWD study). Clinical and laboratory phenotypes were assessed. ResultsThe prevalence of the p.Y1584C variant in our cohorts was 23- to 27-fold higher than that in large normal population databases. Significantly more p.Y1584C subjects had an abnormal bleeding score when compared to Y1584 individuals. In comparison with a group of 35 subjects without the p.Y1584C variant, subjects with the variant had lower mean VWF:antigen and VWF:ristocetin cofactor values and significantly higher VWF propeptide/VWF:antigen ratios suggestive of enhanced clearance. ConclusionCollectively, the results of this analysis suggest that p.Y1584C is likely pathogenic, however, due to influences such as incomplete penetrance, variable expressivity, and other genetic modifiers like ABO blood group, the straightforward assignment of pathogenicity to this variant is inevitably challenging.

Is sentinel lymph node biopsy needed for lentigo maligna melanoma?

Sentinel lymph node biopsy (SLNB) is an area of debate in the management of lentigo maligna melanoma (LMM). The utility of SLNB and its prognostic value in LMM have not yet been studied with large databases. We performed a retrospective review of the National Cancer Database (2012-2020) and the Surveillance, Epidemiology, and End Results (2010-2019) database for patients with cutaneous nonmetastatic LMM with Breslow thickness >1.0 mm. Multivariable logistic regression identified factors associated with SLNB performance and sentinel lymph node (SLN) positivity. Univariable and multivariable analyses assessed overall survival (OS) and melanoma-specific survival (MSS) based on SLNB performance and SLN status. Compared to other melanoma subtypes, LMM had lower rates of SLNB (66.6% vs. 80.0%-84.0%) and SLN positivity (11.3% vs. 18.6%-34.2%). Compared to patients who did not undergo SLNB, SLN status was significantly associated with improved OS in patients with SLN positive (HR = 0.64 [0.55-0.76]) and SLN negative (HR = 0.68 [0.49-0.94]), and worse MSS only in patients with positive SLN (HR = 3.93, p < 0.05). The improved OS associated with SLNB likely implies surgical selection bias. Analysis of MSS confirms appropriate patient selection and suggests important prognostic value associated with SLN status. These results support continued SLNB for LMM patients according to standard guidelines.

Machine Learning Model for Predicting Mortality Risk in Pediatric Cerebral Venous Sinus Thrombosis

Introduction: Pediatric Cerebral Venous Sinus Thrombosis (CVST) is a rare but serious disorder with risk for neurologic sequelae and mortality. Prediction of health outcomes for risk stratification and tailored therapy is difficult based on available literature with single center studies and limited sample sizes. We sought to analyze the CVST data in Pediatric Health Information System (PHIS) database, a large, multicenter administrative database of pediatric inpatient data, to assess if supervised machine learning can assist in identifying pediatric CVST patients with increased risk of adverse outcomes. Methods: Patients 0 to 17 years of age with a diagnosis of CVST were identified in the PHIS database from 2011 to 2022. Demographic information, clinical characteristics, evaluation and management, and outcome data was extracted from diagnostic, imaging, procedure, and pharmaceutical billing codes. An XGBOOST model was fitted using 10-fold cross-validation with Bayesian optimization for hyperparameter tuning. The clinical parameters with values optimal for analysis from the “early era” (2011-2015) dataset were used to train for machine learning, and the test was applied to the “late era” (2016-2022) dataset. The final model was chosen based on the highest test set AUC score. The clinical parameters with significant SHAP values from the model were compared to published risk factors for mortality from an International Pediatric Stroke Study (IPSS) (RN Ichord, et al. Arch Dis Child 2015). Results: 6080 hospitalizations with CVST diagnosis (0.1% of all hospitalizations) were identified. The clinical parameters applied for machine learning included the following: age, gender, race, major complications (intracranial bleeding, hydrocephalus, coma), child opportunity index and ICU admission (Table 1.). The overall mortality rate at discharge was 4.2%. 2029 hospitalizations occurred in the early era and 4051 hospital admissions occurred in the late era. With the analyzable clinical parameters, the XGBOOST model could predict mortality with a probability of 0.7906. Of the clinical parameters used for machine learning (Figure 1), ICU admission, intracranial bleeding, hydrocephalus and coma had positive SHAP values indicating higher mortality risk prediction, whereas older age and being non-Hispanic white had negative SHAP values indicating lower mortality risk prediction. Similar risk associations for mortality were reported in the IPSS study, with patients with intracranial bleeding, younger age, and coma having higher mortality risk, consistent with our study finding. Conclusion: Our study in pediatric CVST, using machine learning, shows higher risk of mortality with ICU admission, and complications including intracranial bleeding, hydrocephalus, and coma; and lower risk of mortality with older age and non-Hispanic White status, which are consistent with IPSS study observations. Our study also illustrates how machine learning can be utilized by investigators to predict adverse clinical outcomes in pediatric CVST. In rare diseases which are difficult to study due to limited sample sizes like pediatric CVST, our study shows that data from large databases such as PHIS can be utilized for machine learning to predict health care outcomes, which can in turn inform future clinical practice to improve patient outcome.

Post-Transfusion Purpura: A Literature Review

Introduction: Post-transfusion purpura (PTP) is a rare and occasionally life-threatening transfusion reaction characterized by severe thrombocytopenia usually within two weeks of blood transfusion. It is associated with the development of alloantibodies to human platelet antigens (HPAs). Due to its rarity, our knowledge of PTP is mostly based on reported cases or small cohorts. Methods: This is a systematic literature review of English language articles published in 2 large medical databases (Embase and Pubmed) using the search terms “post-transfusion purpura”, “posttransfusion purpura”, and “post transfusion purpura” between the years 1985 and 2023. Only articles reporting on patients older than 18 years were included. 684 articles were identified, 590 of them were excluded since they represented a combination of case reports of other conditions, laboratory, and epidemiological reports. The 94 articles left, represented case reports and case series of PTP encompassing 149 patients. 21 patients were removed since they represented duplicate cases. 17 patients were excluded due to missing information. 11 patients were excluded due to the presence of confounding clinical conditions. Results: A total of 100 cases were included in the study. The mean age was 56.7 years. 85% were female, 14% were male, and 1 was missing gender data. 62% of cases reported a baseline platelet count, ranging between 78,000 - 600,000 cells/µL. 65% of cases presented after a procedure- or surgery-related transfusion, of these 22 were related to cardiovascular procedures, while 16, 10, and 9 cases were related to gynecological, abdominal, and orthopedic surgeries respectively. A history of prior transfusions was only documented in 24% of cases. A prior pregnancy was reported in 63% of the cases. Red blood cell, fresh frozen plasma, and platelet transfusions were associated to 64%, 11%, and 9% of cases, respectively. The most common HPA antibody detected was HPA-1a (81%). HPA-1b, HPA-5b, and HPA-3a were the next most common antibodies, identified in 7%, 5%, and 5% cases, respectively. The median time from the first transfusion to the development of thrombocytopenia was 8 days, reaching a platelet nadir in 9 days. The platelet nadir ranged between 0 - 50,000 cells/ µL. Clinical presentation included post-transfusion fever (15%), petechial rash, epistaxis, and oral bleeding (65%, 17%, and 12, respectively), and bleeding from the genitourinary system, gastrointestinal tract, surgical incisions, lower respiratory tract, and central nervous system (38%, 28%, 6%, 6%, and 2% of cases, respectively). Death was reported in 9% of cases. The median time from PTP diagnosis to initiation of first treatment was 1 day. 83% of cases achieved an adequate response, defined as a platelet count &amp;gt; 100,000 cells/µL, after a mean/median time of 14.2/11.5 days (available in 68% of cases). Corticosteroids, intravenous immunoglobulin, and plasmapheresis were administered in 68%, 63%, and 27%, of cases, respectively. 6 patients received HPA 1a negative platelet transfusions. Conclusions: The majority of reported PTP cases were females with a history of pregnancy or blood transfusions consistent with existing literature. Over 60% of cases were linked to a surgical procedure, with cardiovascular cases constituting the largest portion. Anti-HPA-1a represented the most commonly detected antibody, followed by HPA-1b, HPA-5b, and HPA-3a. The most common sites of bleeding were the gastrointestinal and genitourinary tracts. 9% died from PTP. The majority of patients (83%) achieved an adequate response, with the most common treatment modalities being corticosteroids, IV immunoglobulins, and plasmapheresis.