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Factors influencing the quality of life in children with atopic dermatitis in Korea: A multicenter cross-sectional study
No full textBackground: There is a lack of studies about which factors affect the quality of life (QoL) in children with atopic dermatitis (AD), although it is well known that AD has considerably negative effects on their QoL. Objective: This study aimed to measure the QoL in children with AD and identify the factors that affect their QoL. Methods: A questionnaire derived from the Children’s Dermatology Life Quality Index (CDLQI) was used to measure QoL. Family history, allergic comorbidities, exacerbation-related factors, time of exacerbation, and previous and current treatment were also evaluated. The total immunoglobulin E (IgE) level and specific IgE sensitization were determined by the multiple allergen simultaneous test, allergy test, or skin-prick test. AD severity was categorized into mild, moderate, and severe based on treatments. Results: In total, 254 children (46.4 months, 53% boys) from seven hospitals completed the survey. The mean CDLQI score was 7.2 6 5.5 (total score range of 0–30). The respondents were divided into three groups according to their QoL score distribution, with 0 - 4 points (n = 84), 5 - 9 points (n = 90), and $10 points (n = 80) representing good, fair, and poor QoL, respectively. The more severe AD showed the higher CDLQI score significantly (p = 0.001). Compared with other groups, children with poor QoL were more sensitized to inhalant allergens (odds ratio [OR] 1.29 [95% confidence interval {CI}], 1.03 - 1.62) and had more exacerbating factors (OR 1.26 [95% CI, 1.04 - 1.54]), which included inhalation allergen–related exacerbating factors (OR 2.54 [95% CI, 1.23 - 5.23), even after adjusting for age, total IgE, body mass index, severity, and use of moisturizer. The concordance between animal sensitization and an exacerbating factor, including dog and cat, was fair, with 0.39 k and 0.85 accuracy. Conclusion: This study showed that impaired QoL in children with AD is associated with inhalant allergen sensitization and inhalant allergen–related exacerbation factors. Especially, dog and cat sensitization was a significant exacerbating factor. The inhalation-related exacerbation factors, including animal allergens, might be addressed to improve AD management in children
Characteristics of fall-from-height patients: a retrospective comparison of jumpers and fallers using a multi-institutional registry
Get PDFObjective Fall from height (FFH) is a major public health problem that can result in severe injury, disability, and death. This study investigated how the characteristics of jumpers and fallers differ. Methods This was a retrospective study of FFH patients enrolled in an Emergency Department-based Injury In-depth Surveillance (EDIIS) registry between 2011 and 2018. Depending on whether the injury was intentional, FFH patients who had fallen from a height of at least 1 m were divided into two groups: jumpers and fallers. Patient characteristics, organ damage, and death were compared between the two groups, and factors that significantly affected death were identified using multivariable logistic analysis. Results Among 39,419 patients, 1,982 (5.0%) were jumpers. Of the jumpers, 977 (49.3%) were male, while 30,643 (81.9%) of fallers were male. The jumper group had the highest number of individuals in their 20s, with the number decreasing as age increased. In contrast, the number of individuals in the faller group rose until reaching their 50s, after which it declined. More thoracoabdominal, spinal, and brain injuries were found in jumpers. The in-hospital mortality of jumpers and fallers was 832 (42.0%) and 1,268 (3.4%), respectively. Intentionality was a predictor of in-hospital mortality, along with sex, age, and fall height, with an odds ratio of 7.895 (95% confidence interval, 6.746–9.240). Conclusion Jumpers and fallers have different epidemiological characteristics, and jumpers experienced a higher degree of injury and mortality than fallers. Differentiated prevention and treatment strategies are needed for jumpers and fallers to reduce mortality in FFH patients
Towards global model generalizability: independent cross-site feature evaluation for patient-level risk prediction models using the OHDSI network
No full textBackground: Predictive models show promise in healthcare, but their successful deployment is challenging due to limited generalizability. Current external validation often focuses on model performance with restricted feature use from the original training data, lacking insights into their suitability at external sites. Our study introduces an innovative methodology for evaluating features during both the development phase and the validation, focusing on creating and validating predictive models for post-surgery patient outcomes with improved generalizability. Methods: Electronic health records (EHRs) from 4 countries (United States, United Kingdom, Finland, and Korea) were mapped to the OMOP Common Data Model (CDM), 2008-2019. Machine learning (ML) models were developed to predict post-surgery prolonged opioid use (POU) risks using data collected 6 months before surgery. Both local and cross-site feature selection methods were applied in the development and external validation datasets. Models were developed using Observational Health Data Sciences and Informatics (OHDSI) tools and validated on separate patient cohorts. Results: Model development included 41 929 patients, 14.6% with POU. The external validation included 31 932 (UK), 23 100 (US), 7295 (Korea), and 3934 (Finland) patients with POU of 44.2%, 22.0%, 15.8%, and 21.8%, respectively. The top-performing model, Lasso logistic regression, achieved an area under the receiver operating characteristic curve (AUROC) of 0.75 during local validation and 0.69 (SD ¼ 0.02) (averaged) in external validation. Models trained with cross-site feature selection significantly outperformed those using only features from the development site through external validation (P < .05). Conclusions: Using EHRs across four countries mapped to the OMOP CDM, we developed generalizable predictive models for POU. Our approach demonstrates the significant impact of cross-site feature selection in improving model performance, underscoring the importance of incorporating diverse feature sets from various clinical settings to enhance the generalizability and utility of predictive healthcare models
Evaluation of the Diagnostic Performance and Efficacy of Wearable Electrocardiogram Monitoring for Arrhythmia Detection after Cardiac Surgery
Get PDFBackground: Postoperative atrial fibrillation (A-fib) is a serious complication of cardiac surgery that is associated with increased mortality and morbidity. Traditional 24-hour Holter monitors have limitations, which have prompted the development of innovative wearable electrocardiogram (ECG) monitoring devices. This study assessed a patch-type wearable ECG device (MobiCARE-MC100) for monitoring A-fib in patients undergoing cardiac surgery and compared it with 24-hour Holter ECG monitoring. Methods: This was a single-center, prospective, investigator-initiated cohort study that included 39 patients who underwent cardiac surgery between July 2021 and June 2022. Patients underwent simultaneous monitoring with both conventional Holter and patchtype ECG devices for 24 hours. The Holter device was then removed, and patch-type monitoring continued for an additional 48 hours, to determine whether extended monitoring provided benefits in the detection of A-fib. Results: This 72-hour ECG monitoring study included 39 patients, with an average age of 62.2 years, comprising 29 men (74.4%) and 10 women (25.6%). In the initial 24 hours, both monitoring techniques identified the same number of paroxysmal A-fib in 7 out of 39 patients. After 24 hours of monitoring, during the additional 48-hour assessment using the patch-type ECG device, an increase in A-fib burden (9%→38%) was observed in 1 patient. Most patients reported no significant discomfort while using the MobiCARE device. Conclusion: In patients who underwent cardiac surgery, the mobiCARE device demonstrated diagnostic accuracy comparable to that of the conventional Holter monitoring system
Prediction of immunochemotherapy response for diffuse large B-cell lymphoma using artificial intelligence digital pathology
Get PDFDiffuse large B-cell lymphoma (DLBCL) is a heterogeneous and prevalent subtype of aggressive non-Hodgkin lymphoma that poses diagnostic and prognostic challenges, particularly in predicting drug responsiveness. In this study, we used digital pathology and deep learning to predict responses to immunochemotherapy in patients with DLBCL. We retrospectively collected 251 slide images from 216 DLBCL patients treated with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP), with their immunochemotherapy response labels. The digital pathology images were processed using contrastive learning for feature extraction. A multi-modal prediction model was developed by integrating clinical data and pathology image features. Knowledge distillation was employed to mitigate overfitting on gigapixel histopathology images to create a model that predicts responses based solely on pathology images. Based on the importance derived from the attention mechanism of the model, we extracted histological features that were considered key textures associated with drug responsiveness. The multi-modal prediction model achieved an impressive area under the ROC curve of 0.856, demonstrating significant associations with clinical variables such as Ann Arbor stage, International Prognostic Index, and bulky disease. Survival analyses indicated their effectiveness in predicting relapse-free survival. External validation using TCGA datasets supported the model's ability to predict survival differences. Additionally, pathology-based predictions show promise as independent prognostic indicators. Histopathological analysis identified centroblastic and immunoblastic features to be associated with treatment response, aligning with previous morphological classifications and highlighting the objectivity and reproducibility of artificial intelligence-based diagnosis. This study introduces a novel approach that combines digital pathology and clinical data to predict the response to immunochemotherapy in patients with DLBCL. This model shows great promise as a diagnostic and prognostic tool for clinical management of DLBCL. Further research and genomic data integration hold the potential to enhance its impact on clinical practice, ultimately improving patient outcomes
Global, regional, and national burden of disorders affecting the nervous system, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021
Get PDFBackground: Disorders affecting the nervous system are diverse and include neurodevelopmental disorders, late-life neurodegeneration, and newly emergent conditions, such as cognitive impairment following COVID-19. Previous publications from the Global Burden of Disease, Injuries, and Risk Factor Study estimated the burden of 15 neurological conditions in 2015 and 2016, but these analyses did not include neurodevelopmental disorders, as defined by the International Classification of Diseases (ICD)-11, or a subset of cases of congenital, neonatal, and infectious conditions that cause neurological damage. Here, we estimate nervous system health loss caused by 37 unique conditions and their associated risk factors globally, regionally, and nationally from 1990 to 2021. Methods: We estimated mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs), with corresponding 95% uncertainty intervals (UIs), by age and sex in 204 countries and territories, from 1990 to 2021. We included morbidity and deaths due to neurological conditions, for which health loss is directly due to damage to the CNS or peripheral nervous system. We also isolated neurological health loss from conditions for which nervous system morbidity is a consequence, but not the primary feature, including a subset of congenital conditions (ie, chromosomal anomalies and congenital birth defects), neonatal conditions (ie, jaundice, preterm birth, and sepsis), infectious diseases (ie, COVID-19, cystic echinococcosis, malaria, syphilis, and Zika virus disease), and diabetic neuropathy. By conducting a sequela-level analysis of the health outcomes for these conditions, only cases where nervous system damage occurred were included, and YLDs were recalculated to isolate the non-fatal burden directly attributable to nervous system health loss. A comorbidity correction was used to calculate total prevalence of all conditions that affect the nervous system combined. Findings: Globally, the 37 conditions affecting the nervous system were collectively ranked as the leading group cause of DALYs in 2021 (443 million, 95% UI 378–521), affecting 3·40 billion (3·20–3·62) individuals (43·1%, 40·5–45·9 of the global population); global DALY counts attributed to these conditions increased by 18·2% (8·7–26·7) between 1990 and 2021. Age-standardised rates of deaths per 100 000 people attributed to these conditions decreased from 1990 to 2021 by 33·6% (27·6–38·8), and age-standardised rates of DALYs attributed to these conditions decreased by 27·0% (21·5–32·4). Age-standardised prevalence was almost stable, with a change of 1·5% (0·7–2·4). The ten conditions with the highest age-standardised DALYs in 2021 were stroke, neonatal encephalopathy, migraine, Alzheimer's disease and other dementias, diabetic neuropathy, meningitis, epilepsy, neurological complications due to preterm birth, autism spectrum disorder, and nervous system cancer. Interpretation: As the leading cause of overall disease burden in the world, with increasing global DALY counts, effective prevention, treatment, and rehabilitation strategies for disorders affecting the nervous system are needed. Funding: Bill & Melinda Gates Foundation
Imaging of the vascular distribution of the outer ear using optical coherence tomography angiography for highly accurate positioning of a hearable sensor
Get PDFNovel hearable technology is securely and comfortably positioned within the ear canal minimizing inaccuracies caused by accessory movements during activities. Despite extensive research on hearable technologies within the outer ear, there is a lack of research in the field of vascular imaging and quantitative analysis in the outer ear in vivo, which is one of the crucial factors to select the appropriate sensor position. Therefore, in this paper, we introduced optical coherence tomography angiography (OCTA)-based qualitative and quantitative analyses to visualize the inner vasculature of the outer ear to acquire vascular maps for microvascular assessments in vivo. By generating maximum amplitude projection images from three-dimensional blood vascular volume, we identified variations of blood vessel signal caused by the different biological characteristics and curvature of the ear among individuals. The performance of micro-vascular mapping using the proposed method was validated through the comparison and analysis of individual vascular parameters using extracted 20 vascular-related variables. In addition, we extracted pulsatile blood flow signals, demonstrating its potential to provide photoplethysmographic signals and ear blood maps simultaneously. Therefore, our proposed OCTA-based method for ear vascular mapping successfully provides quantitative information about ear vasculature, which is potentially used for determining the position of system-on-chip sensors for health monitoring in hearable devices
Digital Templating of Hip Arthroplasty Using Microsoft PowerPoint: A Pilot Study with Technical Details
Get PDFTemplating is essential in hip arthroplasty preparation, facilitating implant size prediction and surgical rehearsal. It ensures the selection of suitable implants according to patient anatomy and disease, aiming to minimize post-operative complications. Various templating methods exist, including traditional acetate templating on both analog and digital images, alongside digital templating on digital images, which is categorized into 2D and 3D approaches. Despite the popularity of acetate templating on digital images, challenges such as the requirement for physical templates and result preservation persist. To address these limitations, digital templating with software like OrthoSize and Orthoview has been suggested, although not universally accessible. This technical note advocates for Microsoft PowerPoint as an effective alternative for 2D digital templating, highlighting its user-friendly features for image manipulation without needing specialized software. The described method involves scanning acetate templates, adjusting the images in PowerPoint 365 for size, position, and calibration on patient radiographs, and demonstrating reliability through preliminary assessments, with intraclass correlation coefficient (ICC) values indicating a high level of agreement for cup and stem size (ICC = 0.860, 0.841, respectively) but moderate for neck length (ICC = 0.592). We have introduced a method for performing 2D digital templating in the clinical field without the need for specialized software dedicated to digital templating. We believe this method significantly improves the accessibility to 2D digital templating, which was previously limited by the need for digital templating software. Additionally, it enables surgeons to easily establish arthroplasty plans and share them, overcoming the limitations of acetate templates
Multi-System-Level Analysis with RNA-Seq on Pterygium Inflammation Discovers Association between Inflammatory Responses, Oxidative Stress, and Oxidative Phosphorylation
Get PDFA pterygium is a common conjunctival degeneration and inflammatory condition. It grows onto the corneal surface or limbus, causing blurred vision and cosmetic issues. Ultraviolet is a well-known risk factor for the development of a pterygium, although its pathogenesis remains unclear, with only limited understanding of its hereditary basis. In this study, we collected RNA-seq from both pterygial tissues and conjunctival tissues (as controls) from six patients (a total of twelve biological samples) and retrieved publicly available data, including eight pterygium samples and eight controls. We investigated the intrinsic gene regulatory mechanisms closely linked to the inflammatory reactions of pterygiums and compared Asian (Korea) and the European (Germany) pterygiums using multiple analysis approaches from different perspectives. The increased expression of antioxidant genes in response to oxidative stress and DNA damage implies an association between these factors and pterygium development. Also, our comparative analysis revealed both similarities and differences between Asian and European pterygiums. The decrease in gene expressions involved in the three primary inflammatory signaling pathways—JAK/STAT, MAPK, and NF-kappa B signaling—suggests a connection between pathway dysfunction and pterygium development. We also observed relatively higher activity of autophagy and antioxidants in the Asian group, while the European group exhibited more pronounced stress responses against oxidative stress. These differences could potentially be necessitated by energy-associated pathways, specifically oxidative phosphorylation
