Diagnostic Trends Research Articles

Status of inpatients with pediatric epilepsy in children's hospitals in China (2016-2021).

Pediatric epilepsy is the most common disease of pediatric neurological diseases in China. Current demographic characteristics and economic burdens are needed to guide public health policy. In this study, we provide the demographic characteristics and economic burden of pediatric epilepsy patients in China from 2016 to 2021, and describe the change trend and correlation of demographic characteristics and economic burden by province. Using data from the Futang Research Center of Pediatric Development, we conducted a demographic-based cross-sectional study of pediatric epilepsy patients in 29 children's hospitals in China to assess changes in the distribution and economic burden over 6 years and related factors. We obtained per capita disposable income information from the Economic Statistical Bulletin of the National Bureau of Statistics of China and the statistical bureaus of provincial administrative regions. Demographic, diagnostic classification proportion and distribution trends, and economic burden from 2016 to 2021 were verified for pediatric patients with epilepsy. From 2016 to 2021, there were 149,375 patients diagnosed with epilepsy. The proportion of patients younger than 3 years decreased significantly from 51.13% to 29.43% (P=0.00853, R2=0.9803). The root-mean-square-error between the Inter-Provincial Visits Indexes decreased from 1.70 to 0.90, decreasing significantly (R2=0.8306). The disease burden decreased from 17.26% to 12.11% with a significant upward trend (P=0.02417, R2=0.8817). A negative correlation between disease burden and the Inter-Provincial Visits Index (P=0.0383) was observed, along with a positive correlation with the proportion of patients younger than 3 years old (P<0.0001). From 2016 to 2021, the proportion of young patients, population mobility and economic burden on patients decreased, and healthcare level of pediatric epilepsy improved. In addition, based on the current observations, the healthcare level of pediatric epilepsy in Western and Central China is lower than in other regions, and the economic burden on patients is higher than in other regions. In the future, these areas require higher priority concerning policy and financial support.

Salivary Immunoglobulin a Alterations in Health and Disease: A Bibliometric Analysis of Diagnostic Trends from 2009 to 2024

Background/Objectives: Salivary immunoglobulin A (IgA) is a mediator of local immunity and host defence. Altered IgA levels may predispose to bacterial invasion of the mucosa in the gastrointestinal tract, including the oral cavity. Our study aimed to present the diagnostic trends related to salivary IgA in health and disease based on a bibliometric analysis of published papers between 2009 and 2024. Methods: By 14 September 2024, 1247 English original articles were found in the database Web of Science. We selected 838 records considering the diagnostic usefulness of IgA in human subjects. Based on bibliographic data, we created citation and keyword co-occurrence maps using VOSviewer 1.6.20. Results: Most articles belonged to the “Sport Sciences” category (n = 169), followed by the “Immunology” category (n = 93). The Brazilian researcher Alexandre Moreira from the University of Sao Paulo had the most published and most frequently cited papers. Most of the included articles came from the USA (n = 158), England (n = 105), Brazil (n = 95), and Japan (n = 95). The most cited article described research on IgA in response to SARS-CoV-2 infection (n = 690), but the subsequent two papers considered the role of salivary IgA in the dysbiosis of the intestinal microbiota in inflammatory bowel diseases (n = 272) and the formation of systemic immune responses from the gastrointestinal tract (n = 245). Conclusions: Salivary IgA is a widely evaluated diagnostic marker in both patients and healthy individuals. Numerous reports have identified its changes as a result of physical exertion in various groups of athletes, during infections (including SARS-CoV-2) and in the course of local diseases (e.g., periodontal disease) or systemic diseases (e.g., inflammatory bowel disease).

Conventional and Emerging Diagnostic Approaches for Differentiated Thyroid Carcinoma

Differentiated thyroid carcinoma (DTC) is among the most prevalent endocrine cancers. The diagnosis of DTC has witnessed tremendous progress in terms of technological advancement and clinical operational guidelines. DTC diagnostics have evolved significantly over centuries, from early clinical examinations to modern molecular testing and imaging modalities. The diagnosis and management of DTC are currently dependent on the international histological classification and identification of specific genetic abnormalities in tumor tissue, as well as the prognostic implications that can inform treatment decisions. This study goes down the memory lanes of various diagnostic methods for DTCs, highlighting recent advancements in molecular testing and point-of-care (POC) technology. Beginning with conventional methods like fine needle aspiration biopsy (FNAB), fine needle aspiration cytology (FNAC), and ultrasound (US) and moving to contemporary innovative approaches such as POC-thyroglobulin (POC-Tg) and liquid biopsy, this review showcases the current trends in DTC diagnostics. Although considerable progress has been achieved in early malignancy detection, patient stratification, prognosis, and personalized treatment, there is a need to refine the mainstay diagnostic procedures. Finally, future perspectives were provided, and emerging roles of artificial intelligence in DTC diagnostics were explored.

An epidemiological update on infective endocarditis: a two-decade retrospective longitudinal analysis

Abstract Background Infective endocarditis (IE) is a rare yet serious condition affecting the heart valves and the endocardium. Despite advances in management, IE's incidence is on the rise, maintaining a mortality rate similar to that of the late 20th century. Notably, there has been a notable shift in the risk-factor profile, transitioning from traditional factors such as rheumatic heart disease and poor dental health to more iatrogenic causes such as prosthetic heart valves, cardiac devices, foreign body implants, haemodialysis, and immunosuppression. This shift impacts microbiological and imaging diagnosis. Purpose While IE has been extensively studied in the past, the evolving landscape prompts an epidemiological re-evaluation of vulnerable patient populations and the challenges in current diagnostic parameters. Our primary objective is to examine the trends in microbiological and imaging diagnostics over two decades and to compare them across native valve endocarditis (NVE), prosthetic valve endocarditis (PVE), and cardiac implantable electronic device-related IE (CIED-IE). Methods We conducted a retrospective analysis of longitudinal data encompassing 912 patients admitted with either a possible or definite IE diagnosis between 2001 and 2023. Results The incidence of IE increased over the study duration (p&amp;lt;0.01) with octogenarians most affected (21%). Iatrogenic risk factors were associated with nearly two-thirds (63%) of patients diagnosed with IE, while traditional risk factors were evident in almost one-eighth (13%). Blood culture-negative endocarditis increased over the study duration (81% versus73%, p&amp;lt;0.01) and Staphylococcus aureus (29%, p&amp;lt;0.01) became the dominant pathogen over Viridans Streptococcus (14%, p=0.001). Imaging with transthoracic (56% in 2004 versus 75% in 2023) and transoesophageal echocardiography (57% in 2004 versus 79% in 2023) had an increasing contribution in the diagnosis of IE over the two decades. The subgroup analysis found PVE and CIED-IE were more likely to have negative blood cultures (OR=3.7, CI [1.2-6.8] &amp; OR=4.9, CI [1.3-8]) compared to NVE (OR=0.04, CI [0.02-0.8]). PVE and CIED-IE were more likely to have inconclusive imaging (OR=3.7, CI [1.2-6.6] &amp; OR=2.2, CI [0.07-7.8]) compared to NVE (OR=0.63, CI [0.3-3.2]). Complications of stroke were higher in PVE (OR=3.5, CI [0.89-10]) compared to NVE which also had a lower stroke rate (OR=.16, CI [.04-.73]). PVE had more valvular insufficiency (OR=2.1, CI [0.6-4]) compared to NVE (OR=1.2, CI [0.9-6]). The 12-month mortality was no different across the entities (OR=0.3, CI [0.5-7.7] &amp; OR=.49, CI [0.14-1.8] &amp; OR=0.85, CI [0.3-2.4]). Conclusion Our study underscores the evolving nature of IE, now predominantly a healthcare-related disease. Diagnostic challenges persist due to the heterogeneity of the disease, with the emergence of distinct entities such as PVE and CIED-IE.Graphs: Blood culture &amp; echocardiographyForest plot: NVE vs PVE vs CIED-IE

Using Atrial Fibrillation Burden Trends and Machine Learning to Predict Near-Term Risk of Cardiovascular Hospitalization.

Atrial fibrillation is associated with an increased risk of cardiovascular hospitalization (CVH), which may be triggered by changes in daily burden. Machine learning of dynamic trends in atrial fibrillation burden, as measured by insertable cardiac monitors (ICMs), may be useful in predicting near-term CVH. Using Optum's deidentified Clinformatics Data Mart Database (2007-2019), linked with the Medtronic CareLink ICM database, we identified patients with >1 days of ICM-detected atrial fibrillation. ICM-detected diagnostic parameters were transformed into simple moving averages over different periods for daily follow-up. A diagnostic trend was defined as the comparison of 2 simple moving averages of different periods for each diagnostic parameter. CVH was defined as any hospital, emergency department, or ambulatory surgical center encounter with a cardiovascular diagnosis-related group or diagnosis code. Machine learning was used to determine which diagnostic trends could best predict patient risk 5 days before CVH. A total of 2616 patients with ICMs met the inclusion criteria (71±11 years; 55% male). Among them, 1998 (76%) had a planned or unplanned CVH over 605 363 days. Machine learning revealed distinct groups: (A) sinus rhythm (reference), (B) below-average burden, (C) above-average burden, and (D) above-average burden with decreasing patient activity. The relative risk was increased in all groups versus the reference (B, 4.49 [95% CI, 3.74-5.40]; C, 8.41 [95% CI, 7.00-10.11]; D, 11.15 [95% CI, 9.10-13.65]), including a 21% increase in CVH detection over prespecified burden thresholds of duration (≥1 hour) and quantity (≥5%). The area under the receiver operating characteristic curve increased from 0.55 when using hourly burden amounts to 0.66 when using burden trends and decreasing patient activity (P<0.001), a 20% increase in predictive power. Trends in atrial fibrillation were strongly associated with near-term CVH, especially above-average burden coupled with low patient activity. This approach could provide actionable information to guide treatment and reduce CVH.

B-122 Improving the Diagnostic Utility of Multiplexed Assays in Laboratory Medicine with Machine Learning

Abstract Background The application of machine learning (ML) and artificial intelligence (AI) in precision medicine is of great interest and continues to grow. ML approaches offer a flexible and data-centric methodology for modeling diagnostic and prognostic trends, capable of identifying complex non-linear patterns and relationships between predictor variables and outcomes within large data sets. Although ML holds great promise, there are several common pitfalls that must be avoided for ML models to provide clinical utility. In the current study, we provide a rigorous and systematic evaluation of the utility of ML algorithms to predict disease in several representative data sets generated via multiplexed biomarker assays. We compare five major classes of ML algorithms compared to more traditional combinatorial analysis (logistic regression) to offer perspective against the current gold standard. Methods We obtained five data sets from published literature that include measurements of multiplexed biomarkers in patients with obstructive sleep apnea, sepsis, and breast and colon cancers in comparison to healthy controls. These datasets were generated by bead-based and planar multiplexed immunoassays. We chose representative ML algorithms from the classes of support vector machines (SVM), random forests (RF), neural networks (NNet), and extreme gradient boosting (xgBoost) in comparison to logistic regression (LR). Datasets were randomly split into internal validation and external validation sets via 10 iterations of permutation analysis. We tuned and validated the models using 5-fold cross validation, then tested those on external subsets. We report the average of several model classification metrics and associated variances across all resampled data sets. Results For the classification of sepsis using inflammatory cytokines measured on the Luminex bead-based immunoassay, RF outperformed LR in external validation sets by a 9.8% increase in AUROC. For classification of breast cancer patients, xgBoost and RF performed best with 20.0% and 16.9% increases in AUROC, respectively, whereas for classification of colon cancer patients, NNet outperformed LR by 11.5%. Across all datasets, on average, all ML algorithms outperformed LR in the internal validation sets by a 7.9% (6.3% - 9.9%) increase in AUROC. However, in external validation sets, only xgBoost and SVM outperformed LR, where RF and NNet showed signs of overfitting. Conclusions Overall, certain ML algorithms show improvement over LR for diagnostic applications using multiplexed assays, by up to 20% increase in AUROC. Interpretation of ML results must be performed rigorously to prevent overly optimistic and unrealistic conclusions. In the context of laboratory medicine, our results showcase the utility of ML in clinical applications, while highlighting potential disadvantages of ML. This analysis underscores the need for establishing rigorous guidelines to support data analytics during the development of novel multiplexed assays, which is needed to advance precision medicine.

Disparities in age and gender-specific SARS-CoV-2 diagnostic testing trends: a retrospective study from Pakistan

BackgroundPakistan reported 1.57 million COVID-19 cases between 2020 and 2022, based on approximately 30.6 million SARS-CoV-2 RT-PCR (reverse-transcription polymerase chain reaction) tests conducted. This study utilized data from one of the largest in-country testing facilities, Aga Khan University Hospital (AKUH) in Karachi, Pakistan, to explore gender and age-related in RT-PCR testing patterns.MethodsWe conducted a retrospective review of SARS-CoV-2 RT-PCR test data extracted from AKUH clinical laboratory records between February 2020 and February 2022. Gender and age distributions were examined in the context of testing patterns across the period. Multivariate regression models assessed independent associations between COVID-19 positivity and key variables.ResultsWe reviewed 470,249 RT-PCR tests, finding that most tests were in those aged 21–40 years (48.1%). Overall, COVID-19 test positivity was 20.6%. In all, 57.7% were performed for males, predominant amongst those tested across all age groups and waves. Females had significantly lower odds of testing positive for COVID-19 (OR: 0.9; 95% CI: 0.9-1.0). However, when adjusted for gender, age and pandemic phases, the positivity rates between males and females were the same. The odds of a positive result increased significantly with age; individuals aged > 80 years had 2.5 times higher odds of testing positive than those aged 0–10 years (aOR 2.5, 95% CI 2.3–2.7).ConclusionsThe analysis indicates a consistent male dominance in COVID-19 testing, with higher positivity rates in older age groups. Our study highlight the importance of examining demographic characteristics in disease associated data especially, representation of females amongst cohorts.

SIALOLITHIASIS: REVIEW

Objectives. Sialolithiasis is the most common obstructive salivary gland disease, responsible for approximately 50% of salivary glands pathology cases. The aim of this study is to provide the literature review to present the incident rate, etiology, diagnostic and treatment trends of sialolithiasis Materials and methods: Data from the Medline database, PubMed.gov and supplementary sources were used to conduct a systematic literature search. Results: The 116 studies were analyzed. The relevant data were exported, summarized and presented. Conclusion: The most of available studies regarding the incidence rate of sialolithiasis have been based on selected hospital data and there are a few multicenter analyses. The etiology of salivary stones formation remains not completely clear and various hypotheses have been put forward, thus research into etiologic factors is still continues. Despite the introduction of new technologies in the diagnostics of salivary gland, the routine methods like radiographs and ultrasound examination are still relevant and widely used. Gland preserving techniques in the treatment of sialolithiasis have continuously replaced radical surgery

Caught in the Crossfire: Ecuador’s Safety Crisis Impact on Teen and Young Adult Mental Health

Introduction: During the past decade, Ecuador has become one of the most dangerous countries in Latin America, with a significant rise in violence impacting mental health. This study investigates hospital admissions for mood disorders among adolescents and young adults in Ecuador from 2021 to 2022. Methods: We used public access data from the National Institute of Census and Statistics (INEC) to analyze hospital admissions for mood disorders in individuals aged 12 to 21, categorized using the International Classification of Diseases (ICD-10) codes F30 to F39. Results: Results show a female predominance in admissions, with a female-to-male ratio of 3:1 by 2022. Admissions for depressive episodes accounted for 66.5% of cases, peaking in 2022. The regions with the highest admission rates were the Galapagos Islands and Pichincha. A notable reduction in admissions was observed in 2020, likely due to the COVID-19 pandemic´s impact on healthcare access. Discussion: The study's findings highlight significant gender disparities, diagnostic trends and regional differences in mood disorder admissions. The increasing trend, particularly for depressive episodes, underscores the need for enhanced mental health resources. Conclusion: The study concludes that the escalation of violence in Ecuador significantly impacts mental health, emphasizing the urgency of addressing social determinants of health and focussing efforts on regions most affected by conflict.

Evolution and applications of CRISPR-Cas9 technology in last decade

CRISPR-Cas9 technology has rapidly evolved and expanded its applications since its inception in 2015. This research paper provides a comprehensive overview of the advancements and applications of CRISPR-Cas9 technology from 2015 to 2023. We review key milestones in CRISPR-Cas9 research, including improvements in genome editing efficiency, novel applications in gene therapy and biotechnology, and emerging trends in CRISPR-based diagnostics and therapeutics. Additionally, we discuss the ethical considerations, regulatory challenges, and future prospects of CRISPR-Cas9 technology. Through a thorough analysis of the literature spanning nearly a decade, this paper aims to highlight the transformative impact of CRISPR-Cas9 technology on biomedical research and its potential for addressing pressing societal challenges.

Dizziness and Imbalance Across the Lifespan: Findings of a Pediatric and Adult Vestibular Clinic.

To evaluate diagnostic trends in pediatric and adult patients presenting for multidisciplinary subspecialty evaluation of dizziness and imbalance across the lifespan. Retrospective chart review. Single pediatric and single adult academic tertiary care hospital. Retrospective review of electronic health record for patients presenting to an adult or pediatric multidisciplinary vestibular clinic from 2017 to 2020, including clinical data, physical therapy evaluation, and audiovestibular testing. A total of 1934 patients aged 1 to 95 were evaluated. Most patients were female (n = 1188, 61%); the largest cohort was in the fifth decade of life (n = 321, 17%). Seventy-six percent of patients (n = 1470) were assigned a pathologic diagnosis. Central causes of dizziness were most common in children and young adults, comprising 38% to 54% of all diagnoses in ages 1 to 30. The proportion of peripheral vestibular disorders increased with age, peaking at 32% in ages 61 to70. Vestibular migraine was the most common pathologic diagnosis in ages 6 to 20 (n = 110, 39%) and 31 and 50 (n = 69, 17%) regardless of gender, but was more prevalent in females (21% vs 14%; P < .0001). The prevalence of benign paroxysmal positional vertigo (BPPV) increased throughout the lifespan, peaking at age 71 to 80. Meniere's disease (MD) did not occur within the first decade of life, but increased thereafter, peaking at ages 51 to 60. Multidisciplinary vestibular evaluation resulted in a diagnosis for the majority of patients. Vestibular diagnoses vary across the lifespan however among most age groups, central disorders, including migraine disorders, outnumber peripheral vestibulopathies. The prevalence of peripheral vestibular disorders such as BPPV and MD increased with age. Level IV.

Early-Stage Lung Cancer in the Era of Lung Cancer Screening: Veterans Health Administration Outperforms Other Insurance Models

BackgroundLung cancer screening guidelines were introduced in the United States in 2013, with variable implementation. This study evaluated temporal diagnostic trends in non-small cell lung cancer (NSCLC) diagnosis since the introduction of these guidelines. MethodsThis retrospective cohort analysis used data from the Veterans Administration Corporate Data Warehouse and the National Cancer Database. We evaluated temporal trends in the distribution of NSCLC stage at the time of diagnosis along with differences based on insurance coverage type, including uninsured, privately insured, Medicare, Medicaid, and Veterans Affairs (VA) coverage, with adjustment for clinically relevant variables. ResultsAmong 1,450,965 patients diagnosed from 2006 to 2020, the proportion of NSCLC cases diagnosed at stage I increased in all insurance groups by 12.74%, 2%, 0.25%, and 2.57% for the VA, Medicare, private insurance, and Medicaid, respectively. If all insurance systems achieved the unadjusted stage distribution seen in the Veterans Health Administration, an additional 45,684 patients would be diagnosed with stage I NSCLC and 65,933 fewer patients would be diagnosed with stage IV disease. ConclusionsFor patients with any form of insurance, there has been an increase in the proportion of early-stage NSCLC (stage I and II) and a corresponding decrease in the proportion of stage III and IV since the introduction of national lung cancer screening guidelines. As the largest integrated single-payer health care system in the United States, the VA dramatically outperforms other insurance types, perhaps attributable to universal coverage and robust lung cancer screening programs.

IDMap: Leveraging AI and Data Technologies for Early Cancer Detection

Cancer screening is vital in cutting mortality rates, and containing the impact of cancer in a worldwide basis. The current conventional detection techniques including imaging and biopsy though efficient are also characterized with drawbacks like; invasive, expensive, and inaccurate. This abstract will describe the new AI and data solution in the fight against early cancer detection, which presents a massive opportunity to improve accuracy, cut down the time that it takes to deliver a diagnosis, and bring quality health care to possibly millions of patients. ML and, in particular, DL are prospective in terms of decision making upon medical data including imaging, genomic sequences, and electronic health records to detect biomarkers of cancer in early stages. The statistics show that the AI-driven systems are capable to provide better diagnostic outcomes than conventional methods in some fields including mammography for breast cancer and CT for lung. Moreover, AI’s integration in genomic studies helps in determining Cancer related genes and biomarkers hence supporting precision medicine that adapts treatment to the specific genetic information of the patient. Apart from having outlets in AI, big data analytics, cloud computing, and IoT are equally important in early cancer detection as well. Big data analysis enables the analysis of large and complicated data sets with the aid of which one may identify inklings that may point towards possible early development of cancer. The use of cloud computing in health care mainly provides meaningful platforms for the storage and management of the large volumes of medical data in a way that allows improved efficiency and high levels of security. Wearable sensors collect data on different biomarkers throughout a patient’s body, and convey real-time information regarding whether the biomarkers’ levels are approaching cancerous state. Despite this great promise, there are various issues that have to be solved: data protection, privacy, and security, problems with the algorithms’ biases, and integration into practice. Ethical questions are generally important to tackle the uncertainty surrounding data and decision-making in clinical care using A I systems. The future trends in early cancer diagnostics will involve deeper integration of the approaches as AI and big data technology, which will enable more precise prevention and treatment. The applicability of this approach can also extend to the early identification of cancer, but also the prevention of its occurrence through proper intervention. In conclusion, conversing AI and data technologies will be useful for enhancing the efficiency of the early cancer diagnosis, and that is why the perspectives for the patients’ recovery and the further decrease in the mortality rates connected with cancer are rather promising. The approaches in this area remain informative developing technologies that are likely to be integrated into clinical work as the leading organizational models for the future of oncology and preventive health.

Cutaneous T-cell Lymphoma Diagnostic and Therapeutic Trends amidst the COVID-19 Pandemic.

is missing (Short communication).

Trends in Diagnostics and Monitoring of High-Voltage Insulation

Trends in Diagnostics and Monitoring of High-Voltage Insulation

Hodgkin’s lymphoma, lymphocyte-depleted, reticular: An NCDB analysis of demographic and socioeconomic factors of diagnosed patients.

e19033 Background: Lymphocyte-depleted Hodgkin's lymphoma (LDHL) is an aggressive, rare form of Hodgkin’s lymphoma (HL). The LDHL reticular subtype (LDHL-R) is characterized by a scant invasion of lymphocytes without an increase in collagen fibers by fibroblasts. Patient demographics and clinical features of LDHL-R are poorly understood, causing it to frequently be misdiagnosed as other variants of HL. At diagnosis, patients have often already progressed to late disease staging. The demographic and socioeconomic factors of patients diagnosed with LDHL-R within the National Cancer Database (NCDB) were analyzed to better understand its epidemiology. Methods: A retrospective cohort analysis was performed using data from the 2004-2020 NCDB of the 51 cases of LDHL-R (ICD-O-3 9655). Demographic and socioeconomic factors such as age, sex, race, Hispanic status, treatment facility type, Charles-Deyo score, and 90-day mortality were examined using descriptive statistics. Diagnostic trends were quantified through regression analysis. Results: A total of 51 patients were diagnosed with Hodgkin lymphoma, lymphocyte depletion, reticular subtype within the NCDB. The most common primary site of diagnosis was within lymph nodes of multiple regions (41%). The majority of patients were males (65%), White (72%), and non-Hispanic (74%). Patients tended to have residence in metropolitan counties with populations greater than 1 million (71%) and were enrolled in academic/research programs (36%). Most of the patients were privately insured (41%) followed by those insured by Medicare (29%). A higher percentage of patients were in the top quartile of income earners (31%) than those in the second, third, or fourth quartiles. The incidence rate of diagnosis remained constant from 2004 to 2020 (R² = 0.02). The mean age of diagnosis was 51 years (SD=21, Range=14–90). Using the AJCC System, patients tended to be classified as Stage IV (41%) at diagnosis and had a Charlson-Deyo comorbidity score of 0 (71%). Primary treatment methods involved chemotherapy (84%) and radiation therapy (20%). Surgical intervention was given to 14 patients, and post-surgery survival rates were 93% after 90 days. Conclusions: To the best of our knowledge, this is the first NCDB analysis of the demographic factors of patients diagnosed with LDHL-R. Consistent with previous studies about the aggressiveness of this subtype of Hodgkin’s Lymphoma, most patients received a Stage IV diagnosis. Understanding LDHL-R’s epidemiology could result in significantly earlier diagnoses and improved prognosis. Patients tended to be male, White, and non-Hispanic with a residence in metropolitan counties. To improve understanding of the impact of demographic and socioeconomic factors of the diagnosis, treatment, and survival of patients with LDHL-R, more research is recommended.

Analysis of demographic and socioeconomic factors in mixed type rhabdomyosarcoma using NCDB.

e23538 Background: Mixed type rhabdomyosarcoma is a rare soft tissue tumor characterized by the presence of two histologically different subtypes of rhabdomyosarcoma, commonly found in the testis. Despite a majority of the patients undergoing aggressive treatment, the prognosis is poor. Analysis of its diagnostic trends could prove valuable in understanding mixed type rhabdomyosarcoma’s epidemiology. The 2004-2020 National Cancer Database (NCDB) was analyzed to determine the demographic factors of patients with mixed type rhabdomyosarcoma. Methods: A retrospective cohort analysis of the 2004–2020 NCDB, using ICD-O-3 code 8902/3, included patients diagnosed with mixed type rhabdomyosarcoma (N = 94). Descriptive statistics were used to analyze various demographic factors such as sex, age, race, Hispanic status, educational background, insurance status, facility type and location, distance from facility, and Charlson-Deyo score. Incidence trends were examined through regression analysis. Results: The NCDB identified a total of 94 patients with a confirmed diagnosis of mixed type rhabdomyosarcoma from 2004-2020, with a slight decline in incidences diagnosed per year (R² = 0.0903). Females had a slightly higher likelihood of being diagnosed (55%) compared to males (45%), with average age at diagnosis being 40.06 years (SD = 2.8, Range = 1-90). The majority of patients were White (72%) and of non-Hispanic descent (82%). The top primary site was the testis (23%), with endometrium being another major site (15%). Most (83%) patients had a Charles/Deyo comorbidity score of 0. All patients received immunotherapy as primary treatment. Most (46%) patients were privately insured. A majority (26%) of patients received treatment at an academic/research program. A greater number (53%) of patients resided in metropolitan counties with a population larger than 1 million. Most (40%) patients belonged to the highest quartile of income earners of $74,063 or greater. Conclusions: Our research on mixed type rhabdomyosarcoma, the first NCDB analysis of its kind, aims to address a notable knowledge gap on the topic. Similar to information presented in earlier studies, our analysis revealed mixed type rhabdomyosarcoma has a relatively poor prognosis and is mostly observed in White, Non-Hispanic individuals. Analysis on the demographics of this patient population revealed most patients were top quartile income earners, resided in a metropolitan location, and were more likely to be treated at an academic/research program. Further research should aim to better understand the impact of such demographic and socioeconomic features on the diagnosis, treatment plans, and overall mortality rates of patients with mixed type rhabdomyosarcoma.

Dedifferentiated chordoma: Socioeconomic factors and treatment outcomes.

e23507 Background: Dedifferentiated chordoma (DC) is a rare and aggressive chordoma that arises in the spine and skull base characterized by frequent metastasis. Comparatively common in children, these rare tumors arise from the remnants of the fetal notochord. Though surgery and radiation therapy are common treatments, definitive therapeutic guidelines are poorly characterized due to DC’s rarity. Examining the diagnostic trends of DC may yield valuable information about its epidemiology. Thus, the National Cancer Database (NCDB) was analyzed to explore the demographics of patients diagnosed with this rare condition. Methods: The 2004–2020 NCDB recorded 53 patients histologically diagnosed with DC (ICD-O-3 code 9372). Diagnostic trends were analyzed with linear regression and demographic factors such as age, sex, race, and Charles/Deyo score were quantified descriptively. Results: Of the 53 NCDB cases, the most frequently diagnosed group were white (94%), non-Hispanic (85%) women (57%) in the top income quartile (36%) with no comorbidities (87%) living in metropolitan areas with a population of 1 million or more (51%). They often received treatment at academic/research programs (36%) or integrated network cancer programs (21%). In the years 2004-2020, rates of diagnosis remained constant (R2 = 0.0235). Nearly all (98%) of patients were insured, most commonly with Medicare or privately (42% each). The average age at diagnosis was 51 ± 26 years. Over half (55%) of DC cases originated in the pelvic bones, sacrum, or coccyx. Most (74%) patients received surgery, with the most common procedure being wide excision of the primary site (32%), eliminating the tumor (30%). Radiation therapy (55%) was another common treatment. Ninety days post-surgery, 94% of patients were still alive. However, the survivorship (58%) plummets after two years, with an overall survival time of just 5.8 years. Conclusions: This review is the first to explore the demographic and diagnostic factors of DC on the NCDB. Though rare, DC is an aggressive cancer that half of patients succumb to in around 2 years. It commonly originates in the pelvis, sacrum, or coccyx, contrary to prior literature that suggests it affects the skull base or spine. White, non-Hispanic women in the top income bracket were the most frequently diagnosed group. With patient ages ranging from 1 to 86, it affects children and adults alike. Despite this, prior literature mainly explores childhood cases, highlighting a gap in current scientific understanding of DC, a significant barrier to effective treatment. Intriguingly, 51% of DC cases occurred in cities, but 83% of all Americans live in metropolises. This discrepancy suggests that lower-income urban populations may face greater challenges obtaining diagnosis or treatment. Thus, further research into the effects of sociodemographic factors on the prognosis of adult-onset DC is critical to treatment equity for this poorly understood disease.

THU-167 Diagnostic and therapeutic trends in primary biliary cholangitis: insights from the european reference network registry (R-LIVER)

THU-167 Diagnostic and therapeutic trends in primary biliary cholangitis: insights from the european reference network registry (R-LIVER)

High-grade surface osteosarcoma: A National Cancer Database study of demographic characteristics.

e23508 Background: High-grade surface osteosarcoma (HGSO) is an extremely rare malignant subtype of osteosarcoma. HGSO often affects long bones, particularly the femur, and is the rarest subtype of surface osteosarcomas. Though data is limited, current information suggests this cancer presents with a poor prognosis, generally affecting adults aged 20 to 40. Current treatment standards involve wide resection and chemotherapy. The National Cancer Database (NCDB) was examined to establish the demographic factors among HGSO patients and identify diagnostic and treatment trends. Methods: A retrospective cohort study from 2005-2020 was conducted using the NCDB. A total of 99 patients were found with a histologically confirmed diagnosis of high-grade surface osteosarcoma (ICD-9194-3). Factors such as gender, age, race, insurance status, rural/urban residence, treatments, mortality rates, and Charles/Deyo scores were analyzed using descriptive statistics, and trends in incidence were interpreted via regression analysis. Results: Yearly HGSO incidence rates increased from 2005 to 2020 (R2 = 0.19). The average age of diagnosis was 41 years (SD = 24.9, range = 10-90) with men diagnosed (54%) slightly more than women. The majority of patients were non-Hispanic white (73%), had private insurance (55%), and lived in a metropolitan area with over 250,000 in population (89%). The average patient was diagnosed at a hospital 40.4 miles away from their home (SD = 84.9, range 0.4-648.4 miles). Most patients (55%) were in stage II at diagnosis, and 86% of patients had a Charlson-Deyo comorbidity score of 0. The most common primary site of this malignancy was the long bones of the lower limb and associated joints (67%), while the upper extremities (8%) and pelvic bone (8%) served as less common primary sites. Surgery was the most common primary treatment (62%). Data from 69 patients revealed the 90-day mortality post-surgery was 3%. The mean survival rate was 70% at two years and 52% at five years. Conclusions: To the best of our knowledge, this study represents the first NCDB analysis of HGSO. Our analysis demonstrated that HGSO occurs most often, but not exclusively, within the lower extremity long bones, which may be an important diagnostic distinction. It was previously indicated that the average age at HGSO diagnosis is 20-40 years, but our analysis suggested it is closer to 40. Our analysis revealed a five-year survival rate of 52% which is worse than prior studies have indicated. Demographic data has revealed the majority of patients were non-Hispanic white, privately insured, and living within a metropolitan area. This trend may suggest access to healthcare resources is influencing this cancer’s diagnosis and treatment. Further research is warranted to explore whether the demographic patterns seen in HGSO are indicative of biological causes or socioeconomic factors that are significantly influencing diagnosis.