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Abstract Introduction Placental histopathologic evaluation of pregnancies affected by congenital heart defects (CHDs) can provide insight into the mechanism connecting placental and fetal vascular systems. The objective of this study was to evaluate the prevalence of maternal vascular malperfusion (MVM) and fetal vascular malperfusion (FVM) lesions of the placenta in pregnancies with and without fetal CHD on blinded pathologic examination. Methods This is a case‐control study of pregnancies complicated by CHD at our institution from January 1, 2011 to June 25, 2021. Controls were matched to cases in a 2:1 ratio based on maternal age and gestational age at delivery. Placental and umbilical cord pathology slides were deidentified prior to standardized pathologic examination. There were two primary composite outcomes: MVM and FVM. The MVM composite included infarction and villous and vascular lesions. The FVM composite included thrombosis, villous lesions, and cord abnormalities. Primary composite outcomes were evaluated based on the presence or absence of a fetal CHD using t‐tests and chi‐squared or Fisher's exact tests, and a multivariable, conditional logistic regression model controlling for relevant confounding variables. Subgroup analysis was performed for those with and without maternal cardiovascular risk factors in the CHD group and for primary outcomes by cardiac lesion type. Results A total of 351 patients were included: 117 cases and 234 controls. The CHD cohort had higher rates of neonatal intensive care unit (NICU) admission (91.5% vs. 22.2%, p < 0.001), neonatal death (12.8% vs. 0%, p < 0.001), and placental chorangiosis (12.0% vs. 4.7%, p = 0.01). Pregnancies with CHD were also more likely to have the FVM composite outcome (odds ratio [OR], 1.88; confidence interval [CI], 1.20–2.97), specifically villous lesions (26.5% vs. 10.7%, p = 0.0001), avascular villi (22.2% vs. 9.8%, p = 0.002), and a two‐vessel umbilical cord (6.8% vs. 0.9%, p = 0.003). The MVM composite did not differ between groups. In the multivariable regression, the CHD group had higher odds of experiencing the FVM composite (OR, 2.01; CI, 1.14–3.56). Within the CHD cohort, those with hypertensive disorders of pregnancy were associated with the MVM composite outcome (53.9% vs. 36.2%, p = 0.05). Conclusions On blinded pathology review, those with fetal CHD were more likely to have FVM lesions. MVM lesions did not show an association with CHD in our cohort.
Accurate diagnosis of gastrointestinal (GI) diseases typically requires invasive procedures or imaging studies that pose the risk of various post-procedural complications or involve radiation exposure. Bowel sounds (BSs), though typically described during a GI-focused physical exam, are highly inaccurate and variable, with low clinical value in diagnosis. Interpretation of the acoustic characteristics of BSs, i.e., using a phonoenterogram (PEG), may aid in diagnosing various GI conditions non-invasively. Use of artificial intelligence (AI) and improvements in computational analysis can enhance the use of PEGs in different GI diseases and lead to a non-invasive, cost-effective diagnostic modality that has not been explored before. The purpose of this work was to develop an automated AI model, You Only Listen Once (YOLO), to detect prominent bowel sounds that can enable real-time analysis for future GI disease detection and diagnosis. A total of 110 2-minute PEGs sampled at 44.1 kHz were recorded using the Eko DUO® stethoscope from eight healthy volunteers at two locations, namely, left upper quadrant (LUQ) and right lower quadrant (RLQ) after IRB approval. The datasets were annotated by trained physicians, categorizing BSs as prominent or obscure using version 1.7 of Label Studio Software®. Each BS recording was split up into 375 ms segments with 200 ms overlap for real-time BS detection. Each segment was binned based on whether it contained a prominent BS, resulting in a dataset of 36,149 non-prominent segments and 6435 prominent segments. Our dataset was divided into training, validation, and test sets (60/20/20% split). A 1D-CNN augmented transformer was trained to classify these segments via the input of Mel-frequency cepstral coefficients. The developed AI model achieved area under the receiver operating curve (ROC) of 0.92, accuracy of 86.6%, precision of 86.85%, and recall of 86.08%. This shows that the 1D-CNN augmented transformer with Mel-frequency cepstral coefficients achieved creditable performance metrics, signifying the YOLO model’s capability to classify prominent bowel sounds that can be further analyzed for various GI diseases. This proof-of-concept study in healthy volunteers demonstrates that automated BS detection can pave the way for developing more intuitive and efficient AI-PEG devices that can be trained and utilized to diagnose various GI conditions. To ensure the robustness and generalizability of these findings, further investigations encompassing a broader cohort, inclusive of both healthy and disease states are needed.
PurposeLimited data are available on the impact of socioeconomic disparities on the survival of patients with non-muscle invasive urothelial carcinoma (NMIBC).MethodsWe analyzed the Surveillance, Epidemiology, and End Results database to review the effects of sex, race, location, and socioeconomic factors on the survival of patients with NMIBC. We calculated 5-year overall survival (OS) and cancer-specific survival (CSS) using the log-rank test. The impact of socioeconomic factors on OS and CSS was analyzed using the Cox proportional hazards model adjusted for clinical characteristics. Hazard ratios (HR) and survival rates were reported with 95% confidence intervals (CI).ResultsAnalysis of 3831 patients showed that older age was associated with worse OS (HR 1.08 [1.08-1.09]) and CSS (HR 1.05 [1.04-1.06]). Women and men had similar OS (HR 0.91 [0.82-1.01]) and CSS (HR 1.12 [0.95-1.32]). Black patients had worse OS (HR 1.33 [1.08-1.62] and CSS [HR 1.54 [1.13-2.05]) than their White counterparts. Patients with an annual household income below $40,000 had worse outcomes compared to those with income above $70,000 for both OS (HR 1.79 [1.37-2.33]) and CSS (HR 1.924 [1.26-2.89]).ConclusionsThere were no gender differences in survival outcomes of NMIBC. Older age, Black, American Indian/Alaskan Native, and patients with a household income below $40,000 appear to have worse survival. However, the area of residence did not seem to affect patient survival.
805 Background: There is a significant gap in oncologic research, which arises from the exclusion of LGBTQ+ individuals from cancer clinical trials, compromising the development of personalized cancer therapies and limiting the generalizability of findings. Despite known health disparities in this population, including higher cancer risk factors and unique psychosocial stressors, data on clinical trial participation remains sparse. This study aims to elucidate the extent of underrepresentation of LGBTQ+ individuals in cancer clinical trials and examine its impact on treatment efficacy, safety, and overall management. Methods: We conducted a systematic review of literature and clinical trial databases, including PubMed, ClinicalTrials.gov, and other registries, focusing on cancer trials from 2010 to 2024. We extracted data on LGBTQ+ enrollment, analyzed demographic inclusivity criteria, and identified barriers to participation. Subgroup analyses evaluated the correlation between LGBTQ+ representation and treatment outcomes, considering variables such as trial design, recruitment strategies, and data collection on sexual orientation and gender identity. The review also examined policy changes and inclusivity initiatives aimed at improving trial access for this community. Results: The review identified a significant underrepresentation of LGBTQ+ individuals in cancer trials, with sexual orientation and gender identity data reported in only 6.2% of trials. Structural and sociocultural barriers, including heteronormative trial designs, lack of inclusive recruitment strategies, and pervasive discrimination, were frequently cited as impediments. Trials that included LGBTQ+ participants rarely conducted stratified analyses to assess differential treatment responses, leading to a paucity of data on drug safety, efficacy, and toxicity profiles specific to this population. The absence of these data may contribute to suboptimal therapeutic decision-making and potential adverse outcomes in clinical practice. Conclusions: The substantial underrepresentation of LGBTQ+ individuals in cancer clinical trials impairs the ability to develop inclusive, evidence-based oncology care. Addressing this gap requires systematic changes, including the integration of sexual and gender minority data collection, implementation of LGBTQ+-specific recruitment frameworks, and stratified analyses of treatment outcomes. Enhancing trial inclusivity will not only improve the external validity of oncologic research but also support the development of tailored therapeutic approaches that address the unique needs of LGBTQ+ cancer patients, ultimately advancing health equity in cancer care.
