Enhance Risk Stratification Research Articles

Predicting Survival in Mucinous Adenocarcinoma of the Appendix: Demographics, Disease Presentation, and Treatment Methodology

BackgroundMucinous adenocarcinoma of the appendix (MACA) follows a complex disease course with variable survival. Large-scale predictive modeling may determine subtle yet important prognostic factors otherwise unseen in smaller cohort analyses.MethodsPatients with MACA were identified from the Surveillance, Epidemiology, and End Results (SEER) Research Plus database (2005–2019). Primary, secondary, and tertiary outcomes were disease-specific survival (DSS), overall survival (OS), and average annual percent change (AAPC) in incidence.ResultsAmong 4,258 included patients, MACA was most frequently diagnosed at 50 to 69 years (52.0%), with female preponderance (55.9%). MACA incidence AAPC was 3.8 (95% confidence interval [CI] 1.9–5.9). For patients with exclusive, first-diagnosis MACA included in survival analysis (3,222 patients), median DSS and OS were 118 and 88 months, respectively. In DSS-based multivariable analysis, worse prognosis was associated with non-Hispanic Black background (HR 1.36, 95% CI 1.02–1.82; p = 0.036), high grade (grade 3 HR 3.10, 95% CI 2.44–3.92; p < 0.001), lymphatic spread (HR 2.73, 95% CI 2.26–3.30; p < 0.001), and distant metastasis (HR 5.84, 95% CI 3.86–8.83; p < 0.001). In subcohort analysis of patients with rationale for cytoreductive surgery and hyperthermic intraperitoneal chemotherapy (CRS-HIPEC, 2,387 patients), CRS-HIPEC was associated with survival benefit compared with surgery alone but only for moderate-grade tumors (median DSS/OS 138/138 vs. 116/87 months; p < 0.001).ConclusionsMucinous adenocarcinoma of the appendix incidence is increasing in the United States. Survival rates are affected by both demographics and classical risk factors, and CRS-HIPEC-associated survival benefit predominantly occurs in moderate-grade tumors. Further exploration of biologic and clinicopathologic features may enhance risk stratification for this disease.

The Stress Index as a Predictor of Mortality in Patients with Isolated Moderate to Severe Traumatic Brain Injury.

The Stress Index (SI), calculated as the ratio of blood glucose to serum potassium levels, is a promising prognostic marker in various acute care settings. This study aimed to evaluate the utility of the SI for predicting mortality in patients with isolated moderate-to-severe traumatic brain injury (TBI). This retrospective cohort study included adult trauma patients (aged ≥ 20 years) with isolated moderate to severe TBI (Abbreviated Injury Scale ≥ 3 for only head region) treated from 2009-2022. The SI was computed from the initial glucose and potassium levels upon arrival at the emergency department. Logistic regression models were used to assess the association between the SI and mortality after adjusting for relevant covariates. The most effective threshold value of the SI for predicting mortality was identified using receiver operating characteristic (ROC) analysis. Among the 4357 patients with isolated moderate and severe TBI, 463 (10.6%) died. Deceased patients had a significantly higher SI (61.7 vs. 44.1, p < 0.001). In multivariate analysis, higher SI independently predicted greater mortality risk (odds ratio (OR) 6.70, 95% confidence interval (CI) 1.66-26.99, p = 0.007). The optimal SI cutoff for predicting mortality was 48.50 (sensitivity 62.0%, specificity 71.4%, area under the curve 0.724). Patients with SI ≥ 48.5 had nearly two-fold higher adjusted mortality odds compared to those below the threshold (adjusted OR 1.94, 95% CI 1.51-2.50, p < 0.001). SI is a useful predictor of mortality in patients with isolated moderate-to-severe TBI. Incorporating SI with standard clinical assessments could enhance risk stratification and management approaches for this patient population.

Killip scale reclassification according to lung ultrasound: Killip pLUS.

The Killip scale remains a fundamental tool for prognostic assessment in ST-segment elevation myocardial infarction (STEMI) due to its simplicity and predictive value. Lung ultrasound (LUS) has emerged as a valuable adjunct for diagnosing and predicting outcomes in heart failure (HF) and STEMI patients, even those with subclinical congestion. We created a new classification (Killip pLUS), which reclassifies Killip I and II patients into an intermediate category (Killip I pLUS) based on LUS results. This category included Killip I patients and ≥1 positive zone (≥3 B-lines) and Killip II with 0 positive zones. We aimed to evaluate this new classification by comparing it with the Killip scale and a previous LUS-based reclassification scale (LUCK scale). Lung ultrasound was performed within 24 h of admission in a multicentre cohort of 373 patients admitted for STEMI. In-hospital mortality and major adverse cardiovascular events within one year after admission, comprising mortality or readmission for HF, acute coronary syndrome, or stroke, were analysed. When predicting in-hospital mortality, the global comparison of these three classifications was statistically significant: Killip pLUS area under the curve (AUC) 0.90 (95% CI 0.85-0.95) vs. Killip AUC 0.85 (95% CI 0.73-0.96) vs. LUCK 0.83 (95% CI 0.70-0.95), P = 0.024. To predict events during follow-up, the comparison between scales was also significant: Killip pLUS 0.77 (95% CI 0.71-0.85) vs. Killip 0.72 (95% CI 0.65-0.79) vs. LUCK 0.73 (95% CI 0.66-0.81), P = 0.033. The Killip pLUS scale provides enhanced risk stratification compared to the Killip and LUCK scales while preserving simplicity.

Clinical characteristics and independent prognostic factors of patients with mantle cell lymphoma over last two decades with a focus on the interaction between tumor stage and age.

e19065 Background: Mantle Cell Lymphoma (MCL) poses challenges in clinical management due to its rarity and aggressiveness. Understanding its epidemiology and outcomes is crucial. This study examines demographic and clinicopathologic profiles of 746 MCL patients in the USA from 2000-2015, focusing on the interplay between age and tumor stage. Methods: Using SEER data, this retrospective study analyzes demographic and clinical variables to assess mortality risks. Univariate and multivariate Cox regression models evaluate overall mortality (OM) and MCL-specific mortality (CSM), identifying independent prognostic factors (HR &gt;1). The study explores age-tumor stage interaction. Results: Multivariate Cox proportional hazard regression analysis revealed age as pivotal, with those ≥80 facing higher risks for overall mortality (HR=3.93) and MCL-related mortality (HR=4.02) . Advanced Ann Arbor staging (II, III, IV) predicts heightened risks for both mortality types. Widowed status significantly influences outcomes ,with widowed individuals facing notably higher risks of overall mortality (HR=1.85) and MCL-related mortality (HR=1.81).Stage II and age groups 60-79 and ≥80 show substantial mortality risks, with similar trends in Stage IV combined with these age groups. Intriguingly, Stage III lacks significant age interactions, suggesting heterogeneous clinical profiles and treatment responses compared to patients in Stages II and IV. Therefore, the influence of age on mortality outcomes may be obscured by the complexities inherent in Stage III disease presentation and management. Conclusions: This study underscores age, staging, and marital status as key factors influencing MCL mortality. Widowed status emerges as notable, emphasizing social support's importance. Integrating these insights into clinical practice can enhance risk stratification and guide tailored interventions for improved outcomes. Our findings, particularly the lack of significant interactions between Stage III and age groups, highlight the intricate interplay between age and disease stage in MCL patients. This observation prompts us to advocate for further research aimed at elucidating the underlying mechanisms driving these complex interactions. Understanding the biological and clinical factors that influence the relationship between age and disease stage could provide valuable insights into MCL pathogenesis and progression. Further research in this area is warranted to inform personalized therapeutic approaches and ultimately enhance patient care and outcomes.

Improving Prediction of Survival and Progression in Metastatic Non-Small Cell Lung Cancer After Immunotherapy Through Machine Learning of Circulating Tumor DNA.

To use modern machine learning approaches to enhance and automate the feature extraction from the longitudinal circulating tumor DNA (ctDNA) data and to improve the prediction of survival and disease progression, risk stratification, and treatment strategies for patients with 1L non-small cell lung cancer (NSCLC). Using IMpower150 trial data on patients with untreated metastatic NSCLC treated with atezolizumab and chemotherapies, we developed a machine learning algorithm to extract predictive features from ctDNA kinetics, improving survival and progression prediction. We analyzed kinetic data from 17 ctDNA summary markers, including cell-free DNA concentration, allele frequency, tumor molecules in plasma, and mutation counts. Three hundred and ninety-eight patients with ctDNA data (206 in training and 192 in validation) were analyzed. Our models outperformed existing workflow using conventional temporal ctDNA features, raising overall survival (OS) concordance index to 0.72 and 0.71 from 0.67 and 0.63 for C3D1 and C4D1, respectively, and substantially improving progression-free survival (PFS) to approximately 0.65 from the previous 0.54-0.58, a 12%-20% increase. Additionally, they enhanced risk stratification for patients with NSCLC, achieving clear OS and PFS separation. Distinct patterns of ctDNA kinetic characteristics (eg, baseline ctDNA markers, depth of ctDNA responses, and timing of ctDNA clearance, etc) were revealed across the risk groups. Rapid and complete ctDNA clearance appears essential for long-term clinical benefit. Our machine learning approach offers a novel tool for analyzing ctDNA kinetics, extracting critical features from longitudinal data, improving our understanding of the link between ctDNA kinetics and progression/mortality risks, and optimizing personalized immunotherapies for 1L NSCLC.

Heartbeat Chronicles: Decoding the Interplay of Echocardiography and Heart Rate Variability in Chronic Heart Failure Patients – Unraveling the Mysteries with Traditional and Advanced 24-Hour Holter ECG Parameters

Abstract Objective Chronic heart failure (CHF) is a clinical syndrome that encompasses individuals who either have received a definitive diagnosis of heart failure or display a gradual escalation of symptoms as time elapses. Echocardiography, particularly evaluating left ventricular function, is crucial for diagnosis and prognosis. However, 24-hour Holter monitoring, focusing on heart rate variability (HRV), provides insights into autonomic dynamics and vulnerability. Recent HRV parameters offer nuanced information, enhancing risk stratification and guiding personalized interventions in CHF. The interplay between echocardiography and HRV enables a comprehensive approach, refining the management of CHF by considering both cardiac structure and autonomic regulation. Methods This prospective study at “St. Spiridon” County Hospital involved 80 patients with left ventricular ejection fraction (LVEF) &lt; 50%. The diagnosis was according to standard clinical echocardiography, laboratory panel, and Holter ECG monitoring. Results Unexpectedly, no statistically significant relationship was found between commonly used HRV parameters and echocardiographic parameters. Further analyses showed statistically significant associations between non-traditional HRV parameters and E/A ratio, E/E’, and S’ lateral and septal. Additionally, modifications in HRV parameters were correlated with mitral valve deceleration time, left atrial volume index, estimated pulmonary artery systolic pressure, and cardiac output. Conclusions Less commonly used Holter ECG parameters, such as acceleration capacity, deceleration capacity, and triangular index, demonstrated significant diagnostic efficacy, especially when conventional HRV parameters were normal. This highlights the importance of incorporating non-traditional HRV parameters in CHF patient risk stratification, urging further exploration through comprehensive multicenter studies for long-term prognostic implications.

Enhanced reliability and time efficiency of deep learning-based posterior tibial slope measurement over manual techniques.

Multifaceted factors contribute to inferior outcomes following anterior cruciate ligament (ACL) reconstruction surgery. A particular focus is placed on the posterior tibial slope (PTS). This study introduces the integration of machine learning and artificial intelligence (AI) for efficient measurements of tibial slopes on magnetic resonance imaging images as a promising solution. This advancement aims to enhance risk stratification, diagnostic insights, intervention prognosisand surgical planning for ACL injuries. Images and demographic information from 120 patients who underwent ACL reconstruction surgery were used for this study. An AI-driven model was developed to measure the posterior lateral tibial slope using the YOLOv8 algorithm. The accuracy of the lateral tibial slope, medial tibial slopeand tibial longitudinal axis measurements was assessed, and the results reached high levels of reliability. This study employed machine learning and AI techniques to provide objective, consistentand efficient measurements of tibial slopes on MR images. Three distinct models were developed to derive AI-based measurements. The study results revealed a substantial correlation between the measurements obtained from the AI models and those obtained by the orthopaedic surgeon across three parameters: lateral tibial slope, medial tibial slopeand tibial longitudinal axis. Specifically, the Pearson correlation coefficients were 0.673, 0.850and 0.839, respectively. The Spearman rank correlation coefficients were 0.736, 0.861and 0.738, respectively. Additionally, the interclass correlation coefficients were 0.63, 0.84and 0.84, respectively. This study establishes that the deep learning-based method for measuring posterior tibial slopes strongly correlates with the evaluations of expert orthopaedic surgeons. The time efficiency and consistency of this technique suggest its utility in clinical practice, promising to enhance workflow, risk assessmentand the customization of patient treatment plans. Level III, cross-sectional diagnostic study.

Long-term prognostic role of pre-ICD implantation magnetic resonance: a longitudinal cohort study

Abstract Background While it's has already been demonstrated that cardiovascular mortality increases in patients with reduced ejection fraction (EF), there is no evidence that this parameter identifies with high sensitivity those who will experience sudden cardiac death (SCD). Cardiac magnetic resonance (CMR), through myocardial scar characterization, could enhance risk stratification for arrhythmic events in such patients. Purpose The study's objective was to identify factors, independent of EF, capable of stratifying arrhythmic risk in a population of patients undergoing implantable cardioverter-defibrillator (ICD) placement. Methods This is an observational, retrospective, longitudinal, cohort study. Demographic, clinical, and radiological data were collected for all patients undergoing ICD implantation between 2014 and 2021. Deaths from all causes and significant arrhythmic events (non-sustained ventricular tachycardia, sustained ventricular tachycardia, ventricular fibrillation) were recorded during routine device controls and remote monitoring. To define the prognostic value of various factors was performed an univariate Kaplan-Meier analysis in relation to the composite primary endpoint, consisting of ventricular arrhythmias [VA] and all-cause mortality. Results Among 700 patients (82% males, mean age 69 ± 13 years) who underwent ICD implantation (46% single-chamber, 15% dual-chamber, 36% cardiac resynchronization therapy, 3% subcutaneous ICD) pre-implantation CMR was available in 354 of the total, with 69% of them implanted for primary prevention and 52% with non-ischemic cardiomyopathy. Late gadolinium enhancement (LGE) was present in 67% of them; and 30% of these patients had it in fewer than 5 segments. At the end of the mean 8-year follow-up, 30.6% of patients died. Patients with LGE showed increased all-cause mortality and incidence of VA compared to those without LGE (HR=2.17 [1.78-2.60]; p&amp;lt;0.05); this data was mainly driven by the difference in mortality, as the incidence of VA alone did not exhibit significant variation between the two groups (HR=0.7 [0.65-1.02]; p=0.23). In patients with fewer than 5 cardiac segments involved by LGE, the risk of all-cause death and significant VA was higher than in those with more than 5 segments involved (HR=2.46 [2.09-9.07]; p&amp;lt;0.05). Conclusions The presence of LGE emerged as a significant prognostic factor for both arrhythmic events and overall mortality. This suggests the possibility that assessing myocardial fibrosis serves as a comprehensive predictor of the patient's future clinical outcome, extending beyond merely predicting arrhythmic events. Notably, the relevance of LGE is heightened when observed in fewer than 5 segments, indicating a focal substrate, making it a crucial prognostic factor for anticipating arrhythmic events. In patients with reduced EF, pre-implantation CMR proves valuable in effectively stratifying arrhythmic risk and all-cause mortality.

Therapeutic options for different metastatic sites arising from renal cell carcinoma: A review.

Renal cell carcinoma (RCC) stands among the top 10 malignant neoplasms with the highest fatality rates. It exhibits pronounced heterogeneity and robust metastatic behavior. Patients with RCC may present with solitary or multiple metastatic lesions at various anatomical sites, and their prognoses are contingent upon the site of metastasis. When deliberating the optimal therapeutic approach for a patient, thorough evaluation of significant risk factors such as the feasibility of complete resection, the presence of oligometastases, and the patient's functional and physical condition is imperative. Recognizing the nuanced differences in RCC metastasis to distinct organs proves advantageous in contemplating potential treatment modalities aimed at optimizing survival outcomes. Moreover, discerning the metastatic site holds promise for enhancing risk stratification in individuals with metastatic RCC. This review summarizes the recent data pertaining to the current status of different RCC metastatic sites and elucidates their role in informing clinical management strategies across diverse metastatic locales of RCC.

Abstract A025: Integrating genomic alterations and histopathological features for enhanced risk stratification in non-muscle invasive bladder cancer

Abstract Urothelial carcinoma is the most prevalent type of bladder cancer, where non-muscle invasive bladder cancer (NMIBC) accounts for about 75% of the initial diagnosis. Recognizing the limitations of current WHO04/16 grading and TNM staging systems, this study investigates genomic alterations, mitotic activity index (MAI) and immunohistochemical (IHC) markers CK20, p53, and CD25 to identify better prognostic biomarkers in NMIBC. In this retrospective population-based study of NMIBC patients (n=287), a targeted next-generation sequencing (NGS) panel analysis was performed using Oncomine™ Focus Assay on Ion GeneStudio S5 platform to identify genomic changes, including single nucleotide variations (SNVs) and copy number variations (CNVs) across 52 genes. The results revealed that the most frequent gene variants were identified in FGFR3 (n=183, 45%) and PIK3CA (n=98, 24%), while AKT1 (n=39, 20%), ERBB2 (n=24, 12%), MYCN (n=23, 12%) and CCND1 (n=22, 11%) were among the most amplified genes. Distinct genetic associations were observed among Ta, T1, low-grade (LG), and high-grade (HG) tumors. Ta tumors were significantly associated with FGFR3 and PIK3CA mutations, while T1 tumors were linked to ERBB2 mutations (p&amp;lt;0.001). Additionally, LG tumors were enriched with FGFR3 mutations, while HG tumors were significantly associated with FGFR1, CCND1, and ERBB2 mutations (p&amp;lt;0.001). In the analysis of tumor recurrence, patients with FGFR3 mutations had significantly shorter recurrence-free survival (RFS) (p=0.033). Analysis of stage progression revealed a significant association between FGFR3 mutations and improved PFS (p&amp;lt;0.001), while MYC, ERBB2 (p&amp;lt;0.001), and MYCN (p=0.011) were linked to shorter PFS. In gene set enrichment analysis, gene sets like PIK3CA and ERBB2 (p=0.003), and PIK3CA and MYC (p=0.005), were inversely associated with progression-free survival (PFS). In the multivariate Cox regression analysis, MAI was the strongest predictor for PFS (p&amp;lt;0.001). The study emphasizes the potential of combining genomic alterations with histopathological features for enhanced risk stratification in NMIBC patients. Citation Format: Melinda Lillesand, Vebjørn Kvikstad, Einar Gudlaugsson, Ivar Skaland, Aida Slewa Johannessen, Almaz Nigatu Tesfahun, Sigmund Vegard Sperstad, Emiel A.M. Janssen, Marie Austdal. Integrating genomic alterations and histopathological features for enhanced risk stratification in non-muscle invasive bladder cancer [abstract]. In: Proceedings of the AACR Special Conference on Bladder Cancer: Transforming the Field; 2024 May 17-20; Charlotte, NC. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(10_Suppl):Abstract nr A025.

Left and right ventricular longitudinal systolic function following aortic valve replacement in the PARTNER 2 trial and registry.

Evaluation of left and right ventricular (RV) longitudinal systolic function may enhance risk stratification following aortic valve replacement (AVR). The study objective was to evaluate the changes in left and RV longitudinal systolic function and RV-pulmonary artery (RV-PA) coupling from baseline to 30 days and 1 year after AVR. Left ventricular (LV) longitudinal strain (LS), tricuspid annulus plane systolic excursion (TAPSE), and RV-PA coupling were evaluated in patients from the PARTNER 2A surgical AVR (SAVR) arm (n = 985) and from the PARTNER 2 SAPIEN 3 registry (n = 719). TAPSE and RV-PA coupling decreased significantly following SAVR, but remained stable following TAVR. Lower LV LS, TAPSE, or RV-PA coupling at baseline was associated with increased risk of the composite of death, hospitalization, and stroke at 5 years [adjusted hazard ratios (HRs) for LV LS < 15%: 1.24, 95% confidence interval (CI) 1.05-1.45, P = 0.001; TAPSE < 14 mm: 1.44, 95% CI 1.21-1.73, P < 0.001; RV-PA coupling < 0.55 mm/mmHg: 1.32, 95% CI 1.07-1.63, P = 0.011]. Reduced TAPSE at baseline was the most powerful predictor of the composite endpoint at 5 years. Patients with LV ejection fraction <50% at baseline had increased risk of the primary endpoint with SAVR (HR: 1.34, 95% CI 1.08-1.68, P = 0.009) but not with TAVR (HR: 1.12, 95% CI 0.88-1.42). Lower RV-PA coupling at 30 days showed the strongest association with cardiac mortality. SAVR but not TAVR was associated with a marked deterioration in RV longitudinal systolic function and RV-PA coupling. Lower TAPSE and RV-PA coupling at 30 days were associated with inferior clinical outcomes at 5 years. In patients with LVEF < 50%, TAVR was associated with superior 5-year outcomes.

The Role of Neutrophil-to-Lymphocyte Ratio in Predicting Outcomes of Acute Organophosphorus Poisoning: A Comprehensive Review.

Organophosphorus poisoning (OPP) poses a significant threat to human health, necessitating accurate prognostic markers for timely intervention and improved outcomes. This review evaluates the potential of the neutrophil-to-lymphocyte ratio (NLR) as a prognostic indicator in acute organophosphorus poisoning (AOPP). A comprehensive analysis of existing literature reveals that elevated NLR values correlate with increased severity of poisoning and adverse clinical outcomes, including mortality and morbidity. NLR assessment offers valuable prognostic information beyond traditional markers, aiding risk stratification and guiding clinical decision-making. Integration of NLR into clinical practice holds promise for optimizing patient care through the early identification of high-risk individuals and tailored therapeutic interventions. Further research is needed to validate the utility of NLR in larger patient cohorts and standardize its incorporation into clinical guidelines. Leveraging NLR as a prognostic tool can enhance risk stratification, optimize treatment strategies, and ultimately improve outcomes in AOPP.

The unique association between the level of plateletcrit and the prevalence of diabetic kidney disease: a cross-sectional study.

The activation of platelets in individuals with type 2 diabetes mellitus (T2DM) triggers inflammation and hemodynamic abnormalities, contributing to the development of diabetic kidney disease (DKD). Despite this, research into the relationship between plateletcrit (PCT) levels and DKD is sparse, with inconsistent conclusions drawn regarding the connection between various platelet parameters and DKD. This highlights the necessity for comprehensive, large-scale population studies. Therefore, our objective is to explore the association between PCT levels and various platelet parameters in relation to DKD. In this cross-sectional study, hematological parameter data were collected from a cohort of 4,302 hospitalized Chinese patients. We analyzed the relationships between PCT, platelet count (PLT), mean platelet volume (MPV), platelet distribution width (PDW), platelet large cell ratio (P-LCR), and DKD, along with the urinary albumin-to-creatinine ratio (UACR), and estimated glomerular filtration rate (eGFR). Receiver operating characteristic (ROC) curve analysis was conducted to evaluate the diagnostic potential of these parameters. DKD patients exhibited significantly higher PCT levels compared to those without DKD. Multivariate regression analysis identified elevated PCT and PLT levels as potential independent risk factors for both DKD and UACR, while lower MPV levels might serve as independent protective factors for eGFR. The areas under the ROC curve for PCT in relation to DKD and UACR (≥30 mg/g) were 0.523 and 0.526, respectively. The area under the ROC curve for PLT in relation to UACR (≥30 mg/g) was 0.523. PCT demonstrates a weak diagnostic value for T2DM patients at risk of developing DKD and experiencing proteinuria, and PLT shows a similarly modest diagnostic utility for detecting proteinuria. These insights contribute to a deeper understanding of the complex dynamics involved in DKD. Additionally, incorporating these markers into routine clinical assessments could enhance risk stratification, facilitating early interventions and personalized management strategies.

Gene Expression Assays to Tailor Adjuvant Endocrine Therapy for HR+/HER2- Breast Cancer.

Adjuvant endocrine therapy (ET) represents the standard of care for almost all hormone receptor (HR)+/HER2- breast cancers, and different agents and durations are currently available. In this context, the tailoring and optimization of adjuvant endocrine treatment by reducing unnecessary toxic treatment while taking into account the biological heterogeneity of HR+/HER2- breast cancer represents a clinical priority. There is therefore a significant need for the integration of biological biomarkers in the choice of adjuvant ET beyond currently used clinicopathological characteristics. Several gene expression assays have been developed to identify patients with HR+/HER2- breast cancer who will not derive benefit from the addition of adjuvant chemotherapy. By enhancing risk stratification and predicting therapeutic response, genomic assays have also shown to be a promising tool for optimizing endocrine treatment decisions. In this study, we review evidence supporting the use of most common commercially available gene expression assays [Oncotype DX, MammaPrint, Breast Cancer Index (BCI), Prosigna, and EndoPredict] in tailoring adjuvant ET. Available data on the use of genomic tests to inform extended adjuvant treatment choice based on the risk of late relapse and on the estimated benefit of a prolonged ET are discussed. Moreover, preliminary evidence regarding the use of genomic assays to inform de-escalation of endocrine treatment, such as shorter durations or omission, for low-risk patients is reviewed. Overall, gene expression assays are emerging as potential tools to further personalize adjuvant treatment for patients with HR+/HER2- breast cancers.

Treatment of Myelodysplastic Syndromes for Older Patients: Current State of Science, Challenges, and Opportunities.

Myelodysplastic syndromes/neoplasms (MDS) represent a diverse group of pathologically distinct diseases with varying prognoses and risks of leukemia progression. This review aims to discuss current treatment options for elderly patients with MDS, focusing on patients ineligible for intensive chemotherapy or allogenic hematopoietic stem cell transplantation (HSCT). The challenges associated with treatment in this population and emerging therapeutic prospects are also explored. Recent advancements in molecular diagnostics have enhanced risk stratification by incorporating genetic mutations, notably through the molecular International Prognostic Scoring System (IPSS-M). Lower-risk MDS (LR-MDS) treatment ranges from observation to supportive measures and erythropoiesis-stimulating agents (ESAs), with emerging therapies like luspatercept showing promise. High-risk MDS (HR-MDS) is treated with hypomethylating agents (HMAs) or allogenic HSCT, but outcomes remain poor. Elderly MDS patients, often diagnosed after 70, pose challenges in treatment decision-making. The IPSS-M aids risk stratification, guiding therapeutic choices. For LR-MDS, supportive care, ESAs, and novel agents like luspatercept are considered. Treatment of HR-MDS involves HMAs or allogenic HSCT. Emerging treatments, including oral HMAs and novel agents targeting FLT3, and IDH 1/2 mutations, show promise. Future research should refine treatment strategies for this elderly population focusing on quality-of-life improvement.

Prostate cancer and liquid biopsies: Clinical applications and challenges.

Liquid biopsy has emerged as a valuable and minimally invasive tool for real-time detection of clinically actionable abnormalities across various cancer types. Its applicability is particularly compelling in the realm of prostate cancer, where novel therapeutic agents, including those targeting DNA repair systems, are under development. Despite these advancements, challenges persist in effectively screening for prostate cancer, enhancing risk stratification, and determining optimal approaches for treating advanced disease. Consequently, there is a pressing need for improved biomarkers to aid clinicians in decision-making within these contexts. Cell-free DNA and extracellular vesicle analysis have demonstrated promise in diagnosis, prognostication, assessment of treatment responses, and identification of emerging mechanisms of resistance. Nevertheless, obstacles must be addressed before liquid biopsies can be integrated into routine clinical practice. These challenges encompass preanalytical considerations such as sample collection and storage, methods of extracellular vesicle isolation and enrichment, and the need for enhanced interpretation of generated sequencing data. This review provides a comprehensive overview of current clinical opportunities in managing prostate cancer through blood-based liquid biopsy, highlighting the progress made, and acknowledging the challenges that remain. Additionally, we discuss the next steps required for the effective implementation of liquid biopsies in guiding personalized treatment strategies for prostate cancer.

Impact of Preformed Donor-specific Antibodies in Comparison to ABO Incompatibility in Living Donor Liver Transplantation: A Propensity Score-Matched Analysis.

Immunological factors play a pivotal role in the outcomes of solid organ transplantation. We aimed to elucidate the effects of donor-specific antibodies (DSAs) and ABO compatibility on living donor liver transplantation (LDLT) outcomes. A retrospective analysis was conducted on 584 LDLT recipients from 2015 to 2020. The recipients were stratified into 3 groups: ABO-compatible recipients without DSAs (group 1), ABO-compatible recipients with DSAs (group 2), and ABO-incompatible recipients without DSAs (group 3). Propensity score matching was used for balanced comparisons. In the matched comparisons, group 2 exhibited a higher incidence of T cell-mediated rejection compared with group 1 (22.7% versus 4.5%, P = 0.030). Despite this, the 5-y survival rates were similar between groups 1 and 2 (81.6% versus 95.5%, P = 0.085). Group 3, in comparison with group 1, showed elevated rates of cytomegalovirus infection (23.2% versus 7.3%, P = 0.008), T cell-mediated rejection (28.0% versus 7.3%, P = 0.001), and antibody-mediated rejection (13.4% versus 0%, P = 0.001). However, the survival rates were comparable between group 3 and group 1 (82.0% versus 86.5%, P = 0.220, respectively). Comparisons between group 2 and group 3 did not reveal significant differences in postoperative outcomes or survival rates ( P > 0.05). DSA positivity and ABO incompatibility contribute to distinct posttransplant complications in LDLT. The integrated consideration of both factors in pretransplant assessment may enhance risk stratification and inform tailored interventions. Further research is required to corroborate these findings and provide mechanistic insights.

Escalated complement activation during hospitalization is associated with higher risk of 60-day mortality in SARS-CoV-2-infected patients.

The complement system, an upstream recognition system of innate immunity, is activated upon SARS-CoV-2 infection. To gain a deeper understanding of the extent and duration of this activation, we investigated complement activation profiles during the acute phase of COVID-19, its persistence post-recovery and dynamic changes in relation to disease severity. Serial blood samples were obtained from two cohorts of hospitalized COVID-19 patients (n=457). Systemic complement activation products reflecting classical/lectin (C4d), alternative (C3bBbP), common (C3bc) and terminal pathway (TCC and C5a) were measured during hospitalization (admission, days 3-5 and days 7-10), at 3 months and after 1 year. Levels of activation and temporal profiles during hospitalization were related to disease severity defined as respiratory failure (PO2/FiO2 ratio <26.6kPa) and/or admission to intensive care unit, 60-day total mortality and pulmonary pathology after 3 months. During hospitalization, TCC, C4d, C3bc, C3bBbP and C5a were significantly elevated compared to healthy controls. Severely ill patients had significantly higher levels of TCC and C4d (p<0.001), compared to patients with moderate COVID-19. Escalated levels of TCC and C4d during hospitalization were associated with a higher risk of 60-day mortality (p<0.001), and C4d levels were additionally associated with chest CT changes at 3 months (p<0.001). At 3 months and 1 year, we observed consistently elevated levels of most complement activation products compared to controls. Hospitalized COVID-19 patients display prominent and long-lasting systemic complement activation. Optimal targeting of the system may be achieved through enhanced risk stratification and closer monitoring of in-hospital changes of complement activation products.

Clinical value of a comprehensive clinical- and echocardiography-based risk score on predicting cardiovascular outcomes in ischemic heart failure patients with reduced ejection fraction.

The present study aimed to develop a comprehensive clinical- and echocardiography-based risk score for predicting cardiovascular (CV) adverse outcomes in patients with ischemic heart failure (IHF) and reduced left ventricular ejection fraction (LVEF). This retrospective cohort study included 1341 hospitalized patients with IHF and LVEF < 50% at our hospital from 2009 to 2017. Cox regression models and nomogram were utilized to develop a comprehensive prediction model (C&E risk score) for CV mortality and CV-related events (hospitalization or death). Over a median 26-month follow-up, CV mortality and CV events rates were 17.4% and 40.9%, respectively. The C&E risk score, incorporating both clinical and echocardiographic factors, demonstrated superior predictive performance for CV outcomes compared to models using only clinical or echocardiographic factors. Internal validation confirmed the stable predictive ability of the C&E risk score, with an AUC of 0.740 (95% CI 0.709-0.775, P < 0.001) for CV mortality and an AUC of 0.678 (95% CI 0.642-0.696, P < 0.001) for CV events. Patients were categorized into low-, intermediate-, and high-risk based on the C&E risk score, with progressively increasing CV mortality (5.3% vs. 14.6% vs. 31.9%, P < 0.001) and CV events (28.8% vs. 38.2% vs. 55.0%, P < 0.001). External validation also confirmed the risk score's prognostic efficacy within additional IHF patient datasets. This study establishes and validates the novel C&E risk score as a reliable tool for predicting CV outcomes in IHF patients with reduced LVEF. The risk score holds potential for enhancing risk stratification and guiding clinical decision-making for high-risk patients.

CardioXNet: Representation Learning for Accurate Cardiovascular Disease Diagnosis from X-ray Images

Cardiovascular Disease (CVD) is a prevalent, incurable condition affecting the heart and blood vessels. Due to its significant impact on mortality in the United States, there is a pressing need for enhanced risk stratification methods. Coronary Artery Calcium Score (CACS), reliant on CT scans, is most commonly employed as a risk stratification method but suffers from limitations, including accessibility and early detection challenges. To address the problem, this research study proposes a representation learning-based CVD diagnosis framework utilizing X-ray images, offering expedited and earlier detection, as well as improved accessibility. This system comprises two distinct stages: representation learning to extract CVD-related features and transfer learning to train a CVD classifier. Representation learning enhances the quality of extracted features, thereby leading to more precise results in the subsequent CVD diagnosis network. Comprehensive experiments and training validate the efficacy of the proposed method, demonstrating its superiority over the existing methods. These promising results suggest the potential utility of X-rays as a valuable biomarker for diagnosing CVD disease.