This review examines the bidirectional relationship between the gut microbiota and cardiovascular diseases (CVDs), aiming to understand how microbial dysbiosis contributes to CVDs, including atherosclerosis, hypertension, and heart failure. Recent research emphasizes the gut microbiota's role in modulating immunity via SCFAs and tryptophan metabolites, maintaining intestinal barrier integrity, and producing metabolites such as SCFAs (acetate, propionate, butyrate) and pro-atherogenic TMAO. Dietary patterns, particularly the Mediterranean versus Western diet, significantly influence gut microbiota composition and CVD risk. Polyphenols and exercise have shown positive effects on gut microbiota and cardiovascular outcomes. A significant interplay exists between gut microbiota and cardiovascular health. Dysbiosis and metabolites like TMAO and LPS are implicated in CVD, while SCFAs and a balanced microbiota offer protection. Future research should focus on precision medicine, next-gen probiotics, optimized FMT, and multiomics approaches to identify personalized CVD therapies.

Microplastics (MPs) are a new kind of pollution that may be found in food, water, and the atmosphere, raising substantial alarms about their impact on human health. This study inspects the most recent findings on the impact of MPs on gastrointestinal (GI) health, with a focus on their role in causing gut inflammation and dysbiosis. Animal studies and in vitro tests have indicated that MPs may disrupt the intestinal barrier, increase proinflammatory cytokines, and alter the composition of gut bacteria. These changes include a less diverse microbiota, the extinction of helpful bacteria that produce short-chain fatty acids, and the emergence of detrimental species, which cause chronic low-grade inflammation. Although there is currently limited human data, preliminary studies recommend a possible relation between MP exposure, GI problems, and inflammatory indications. The study covers the biological processes that cause these effects, how the consequences change depending on the size and composition of the particles, and the current gaps in research that may be applied to individuals. Concerning this, standardized methods to assess MP exposure in people, as well as long-term research to better understand the long-term effects of continuous MP use, are needed. Plastic pollution is worsening globally; therefore, public health must understand its impact on the gut.

e14101 Background: Pediatric and adolescent/young adult (AYA) malignant brain tumors remain a leading cause of cancer-related death. Population-level geographic and rural–urban disparities may reflect differences in access to specialized care and supportive services. Methods: Using CDC WONDER mortality data (1999–2023), we identified deaths from malignant brain tumors using ICD-10 codes. Pediatric (0–14 years) and AYA (15–39 years) populations were analyzed separately. Age-adjusted mortality rates (AAMR) per 100,000 were calculated by state, region, and urban–rural classification. Joinpoint regression assessed temporal trends, and interactions evaluated differential trends by rurality and geography. Results: A total of 14,532 deaths were analyzed. National AAMR declined modestly in pediatric patients (AAPC −1.1%, p < 0.01) but remained relatively stable in AYA patients (AAPC −0.3%, p = 0.12). Rural areas consistently showed higher mortality than urban areas in both age groups (p < 0.01). Regional disparities were evident, with highest pediatric and AYA mortality in the Southeast and Midwest. Inflection points indicated modest improvements following 2007 and 2015, likely reflecting advances in treatment and supportive care, but rural–urban gaps persisted. Rolling averages confirmed stability of these trends despite small-area data suppression. Conclusions: Geographic and rural–urban disparities in pediatric and AYA malignant brain tumor mortality persist despite modest national improvements, highlighting populations at greatest risk. Clinical takeaway: Targeted resource allocation and improved access to specialized neuro-oncology care in high-burden and rural regions are needed to reduce mortality and close disparities.

e24087 Background: Radiotherapy-induced nausea and vomiting (RINV) negatively impacts adherence and quality of life. Multiple antiemetic strategies exist, but comparative effectiveness and ranking across regimens remain unclear, particularly regarding olanzapine-containing combinations. Methods: We conducted a systematic review and network meta-analysis of randomized and comparative studies evaluating antiemetic prophylaxis for RINV. Databases included MEDLINE, Embase, CENTRAL, and clinical trial registries. Interventions were categorized into clinically interpretable nodes (e.g., 5-HT3 antagonist ± dexamethasone, NK1-based combinations, olanzapine-containing regimens). The primary outcome was complete response (no emesis, no rescue therapy) during acute and delayed phases. Secondary outcomes included no nausea, severity, sedation, and discontinuation. Random-effects network meta-analysis was performed with treatment ranking using SUCRA probabilities. Results: Twenty-two studies encompassing 3,145 patients were included. Olanzapine-containing regimens ranked highest for complete response during both acute (SUCRA 92%) and delayed phases (SUCRA 89%), outperforming standard 5-HT3 + dexamethasone (SUCRA 64%). NK1-based triple therapy showed intermediate efficacy (SUCRA 78%). Olanzapine regimens had modest increases in sedation (RR 1.14, 95% CI 1.01–1.29) but no significant differences in discontinuation rates. Subgroup analyses indicated consistent benefits across radiation sites, fractionation schedules, and emetogenic risk. Conclusions: Olanzapine-based antiemetic regimens provide superior prevention of RINV compared with standard therapies, with acceptable tolerability across radiotherapy settings. Clinical Takeaway: Incorporating olanzapine into prophylactic antiemetic strategies can optimize symptom control, improve patient adherence, and enhance quality of life during radiotherapy.

Background: Hemorrhagic transformation (HT) is a common outcome of acute ischemic stroke (AIS), especially following thrombolytic or endovascular reperfusion therapy. Early detection of HT may guide therapeutic decisions and reduce risk. With the advancement of artificial intelligence in neuroimaging, several studies have investigated machine learning (ML) and radiomics models for predicting HT using imaging and clinical data. Objective: The purpose of this meta-analysis was to analyze the diagnostic efficacy of ML-based radiomic models for predicting HT after AIS and their generalizability via external validation. Methods: We conducted a systematic review and meta-analysis of works published until May 2025 using PubMed, EMBASE, Scopus, and IEEE Xplore. The inclusion criteria were studies that used ML-based radiomic or deep learning models with CT, MRI, or multimodal imaging to predict HT in AIS patients. A bivariate random-effects model was used to examine the pooled sensitivity, specificity, diagnostic odds ratio (DOR), and area under the summary receiver operating characteristic curve (AUC-SROC). The risk of bias was assessed using the QUADAS-2 method. Subgroup analyses were performed based on the imaging modality and algorithm type. An external validation cohort (n=1,150) was used to assess the generalizability of top-performing models. Results: 18 studies (n = 3,945 patients; ~455 with HT) met the inclusion criteria. The pooled sensitivity and specificity for machine learning-based radiomics models were 0.81 (95% CI: 0.78-0.84) and 0.84 (95% CI: 0.80-0.88), respectively, with a diagnostic odds ratio of 22.5 (95% CI: 15.0-33.8). The overall AUC-SROC value was 0.88 (95% CI: 0.85–0.91). MRI-based models outperformed CT-only models (AUC: 0.85; p = 0.01). yielded an AUC of 0.87, sensitivity of 0.83, and specificity of 0.82, indicating generalizability. There was no significant publication bias, and heterogeneity was large (I square = 46%). Conclusion: ML-based radiomics models that use multimodal neuroimaging show high prediction for hemorrhagic transition after AIS, with consistent results across modalities and external datasets. MRI-based models provide marginally higher diagnosis accuracy.

Background: Acute leukemia (AL), which includes both acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL), often requires extensive inpatient treatment. Long hospital stays may indicate how severe the condition is, how difficult it is to treat, or how poor the healthcare system is. Nonetheless, there is a dearth of national data on the relationship between extended hospitalization and in-hospital outcomes in AL patients. Objective: To assess the impact of prolonged hospitalization on mortality, complications, and healthcare resource utilization among patients admitted with acute leukemia in the United States. Methods: We conducted a retrospective cohort study utilizing the National Inpatient Sample (NIS) from 2019 to 2021. We selected adult patients (≥18 years) with a primary diagnosis of AML or ALL using ICD-10-CM codes. Prolonged hospitalization was defined as a length of stay (LOS) exceeding 14 days (upper quartile criterion). Multivariable logistic regression was used to assess the correlations between longer LOS and in-hospital mortality, sepsis, ICU admission, and discharge disposition, after controlling for age, gender, race, insurance, comorbidities (Elixhauser Index), and hospital factors. Survey weights were used to obtain national estimates. Results: Among 47,283 weighted hospitalizations for acute leukemia, 24.3% (n=11,472) required protracted hospitalization. Patients with longer stays had higher in-hospital mortality (10.2% vs. 6.1%; adjusted odds ratio [aOR] 1.79, 95% confidence interval [CI] 1.61-1.98, p<0.001), higher incidence of sepsis (31.5% vs. 14.8%; aOR 2.52, 95% CI 2.35-2.70, p<0.001), and were more likely to require ICU admission (18.6% vs. 9.2%; aOR 2.14, 95% CI 1.95-2.35, p<0.001). Furthermore, they had higher rates of non-home release (37.1% vs. 20.5%; aOR 2.09, 95% CI 1.95-2.23, p<0.001) and spent significantly more on hospitalization ($112,400 vs. $68,900; p<0.001). Conclusions: Prolonged hospitalization in people with acute leukemia is independently associated with worse clinical outcomes and higher healthcare costs. Early identification of at risk for lengthy stays may allow for targeted efforts to decrease difficulties and improve care delivery. These results the need for hospital-based strategies to better manage high-risk leukemia admissions.

Background: Referrals for neutropenia are common in hematology clinics serving the Department of Corrections (DOC) population. Many patients present with asymptomatic mild to moderate neutropenia (absolute neutrophil count [ANC] 500–1500), often undergoing costly and unnecessary evaluations. The Duffy-null phenotype, prevalent in individuals of African and Middle Eastern ancestry, is associated with physiologically lower ANCs and no increased infection risk. Despite evidence supporting its diagnostic utility, Duffy typing is underutilized in clinical practice. Objective: To implement Duffy null phenotyping as a triage tool for asymptomatic neutropenia in a DOC population and assess its clinical and economic impact on diagnostic workup. Methods: An academic medical center's DOC Hematology Clinic has launched a quality improvement initiative. Adult patients hospitalized with asymptomatic mild/moderate neutropenia (ANC 500-1500) received Duffy-null phenotyping and a peripheral smear as a first-line evaluation. Additional testing (e.g., nutritional panels, viral serologies, autoimmune markers, imaging) was reserved for Duffy non-null or symptomatic patients. A Plan-Do-Study-Act (PDSA) paradigm was implemented. De-identified clinical data and cost analysis were collected for three groups: the Duffy-null phenotype with no further workup, the complete conventional workup, and Duffy typing followed by traditional workup for negative patients. IRB approval was obtained. Results: Among the 31 people tested, 41.9% (n=13) were found to have the Duffy-null phenotype and were not subjected to additional testing. The Duffy-first procedure cost $9,300, compared to $77,748 for regular testing, representing an 88% savings. Even with negative occurrences demanding a thorough workup, the total treatment cost $84,413 (only 8.6% more than normal testing). Duffy-null patients were discharged after a single telemedicine follow-up, which reduced clinic load and follow-up visits by more than 40%. Internal and published data reveal that up to 66.7% of Black people have the Duffy-null phenotype, emphasizing the significance of ancestry-specific diagnostic techniques in this setting. Conclusions: Routine use of Duffy null phenotyping in the examination of asymptomatic neutropenia in high-prevalence groups, such as incarcerated Black people, might significantly reduce healthcare costs, diagnostic burden, and needless patient follow-up. This study demonstrates the potential of precision hematology in reducing structural inequities and improving treatment in underserved regions.

Background: Hypertensive crises are life-threatening episodes of elevated blood pressure that coincide with acute target organ damage. Early detection of those at risk of poor outcomes is critical for tailoring treatments and lowering mortality. Artificial intelligence (AI) provides unique capabilities in real-time risk assessment by harnessing enormous amounts of clinical data. This research applies machine learning to national inpatient data to predict important outcomes in hypertensive crisis patients. Methods: We extracted adult inpatient data from the National Inpatient Sample (NIS, 2016-2021) for admissions with a primary diagnosis of hypertensive emergency, identified using validated ICD-10-CM codes such as I16.0 (hypertensive urgency), I16.1 (hypertensive emergency), and I16.9 (unspecified hypertensive crisis). Demographics, comorbidities (Charlson Comorbidity Index), hospital characteristics, and socioeconomic indicators were also considered predictive variables. Key outcomes were in-hospital mortality, increased length of stay (LOS > 4 days), and 30-day readmission (via the Nationwide Readmissions Database). Machine learning models, such as random forest, XGBoost, and logistic regression, were trained on 80% of the dataset and graded on 20%. Performance was tested using the area under the ROC curve (AUC), F1-score, and calibration plots. Results: Among 82,614 weighted hospitalizations for hypertensive episodes, 3.4% died in the hospital, 38.2% had a longer length of stay, and 15.1% were readmitted within 30 days. The XGBoost model had the best predictive performance, with an AUC of 0.91 and an F1-score of 0.79 for death prediction, an AUC of 0.87 for extended LOS, and an AUC of 0.84 for readmission. The SHAP research revealed acute kidney damage, age over 65, low-income quartile, and congestive heart failure as major contributions to model predictions. Calibration plots revealed significant agreement between anticipated probability and observed occurrences. Figure 1 displays the ROC curves for all three outcome models, which demonstrate model differences across clinical endpoints. Conclusion: Using real-world inpatient data, AI-based models can reliably predict important outcomes in patients admitted with hypertensive crises. These techniques allow risk-based triage, discharge planning, and focused follow-up, especially in high-risk, resource-constrained populations.

Background: Heart failure with preserved ejection fraction (HFpEF) is a syndrome that may manifest differently in different people, and doctors lack clear tools for predicting how it will progress or how well medications will work. Traditional clinical risk assessments fail to capture the molecular complexity of HFpEF. Using machine learning (ML) to aggregate multi-omic data is a potential strategy for improving risk classification and discovering new biological subtypes. Objective: To develop and validate a multi-omic ML model that integrates clinical variables, genomic, proteomic, and metabolomic data to predict 1-year mortality and heart failure hospitalization in HFpEF patients. Methods: We analyzed data from 1,802 UK Biobank participants with validated HFpEF (LVEF > 50%). The patients' healthcare records, echocardiograms, genotyping arrays, proteomic profiles (Olink platform), and metabolomic profiles (Nightingale Health) were all connected. The main outcome was either death from any cause or hospitalization for heart failure within one year after the baseline evaluation. We built a machine learning pipeline with 10-fold cross-validation that used the XGBoost and random forest algorithms. We used recursive feature elimination and SHAP (Shapley Additive Explanations) to find features and make the model easier to understand. Results: The integrated multi-omic ML model had an AUC of 0.91 (95% CI: 0.88-0.93), significantly outperforming models based only on clinical factors (AUC 0.74) or individual omic layers (genomic: 0.79; proteomic: 0.82; metabolomic: 0.84). Key predictors were NT-proBNP, IL-6, GDF-15, branched-chain amino acids, and SNPs in myocardial fibrosis genes (TITIN, COL1A1). SHAP analysis revealed different high-risk molecular clusters, supporting the hypothesis of physiologically driven HFpEF subphenotypes. Conclusion: This study shows that a multi-omic ML method outperforms existing clinical models for predicting outcomes in HFpEF. The ability to combine molecular and clinical data may pave the way for physiologically informed, personalized HFpEF therapy. Prospective validation and implementation studies are required to translate these results into clinical practice.

Background: Venous thromboembolism (VTE), which includes deep vein thrombosis (DVT) and pulmonary embolism (PE), is a substantial but underappreciated problem in sickle cell disease (SCD) patients. Despite the hypercoagulable state associated with SCD, national patterns of VTE diagnosis, management, and outcomes in this population are still poorly understood, particularly across racial, regional, and hospital-level variables. Objective: To evaluate disparities in the diagnosis and management of VTE among hospitalized patients with sickle cell disease using a nationally representative dataset. Methods: We conducted a retrospective study from 2019 to 2021 using the National Inpatient Sample (NIS), identifying hospitalizations with a main or secondary diagnosis of sickle cell disease (ICD-10 D57.x) and a co-diagnosis of VTE (ICD-10 I26.x, I82.x, I80.x). Patient demographics (age, gender, race, income quartile, insurance) were explored, as were hospital aspects (region, teaching status), and VTE-related treatment (inferior vena cava [IVC] filter installation, systemic anticoagulation, thrombolysis). Multivariable logistic regression was used to identify predictors of intervention usage and in-hospital mortality while accounting for comorbidities and hospital-level characteristics. National figures were determined using survey weights. Results: African American patients accounted for 88.1% of the estimated 18,672 SCD and VTE hospitalizations, with a median age of 33 years. African American patients had reduced chances of obtaining anticoagulant medication (aOR 0.72, 95% CI 0.61-0.84, p<0.001) and higher odds of IVC filter installation (aOR 1.34, 95% CI 1.12-1.61, p=0.002). Patients treated at non-teaching or Southern US institutions were also much less likely to get systemic anticoagulation. In-hospital mortality was 2.3% overall but higher among those not on anticoagulation (4.9% vs. 1.8%, p<0.001). Medicaid recipients were less likely to get thrombolysis in instances of PE (aOR 0.68, 95% CI 0.51-0.89, p=0.005) than commercially insured patients. Conclusions: There are significant racial and institutional disparities in the treatment of VTE among hospitalized sickle cell patients in the United States. African American patients and those treated at non-teaching or Southern facilities were less likely to get evidence-based anticoagulants. These changes may have negative consequences, including increased mortality. Our results show the need for standardized VTE treatment procedures and targeted quality improvement initiatives to ensure fairness in care for people with SCD.