Stratification Of Patients Research Articles

Clinical predictive value of the age, creatinine, and ejection fraction score in patients in acute type A aortic dissection after total arch replacement

The age, creatinine, and ejection fraction (ACEF) score has been accepted as a predictor of poor outcome in elective operations. This study aimed to investigate the predictive value of ACEF score in acute type A aortic dissection (AAAD) patients after total arch replacement. A total of 227 AAAD patients from July 2021 and June 2022 were enrolled and divided into Tertiles 1 (ACEF ≤ 0.73), Tertiles 2 (0.73 < ACEF ≤ 0.95), and Tertiles 3 (ACEF > 0.95). Using inverse probability processing weighting (IPTW) to balance the baseline characteristics and compare the outcomes. Cox logistic regression was used to further evaluate the survival prediction ability of ACEF score. The in-hospital mortality was 9.8%. After IPTW, in the baseline characteristics reached an equilibrium, a higher ACEF score before operation still associated with higher in-hospital mortality. After 1 year follow-up, 184 patients (90.6%) survival. Multivariable analysis revealed that ACEF score (adjusted hazard ratio 1.68; 95% confidence interval 1.34–4.91; p = 0.036) and binary ACEF score (adjusted HR 2.26; 95% CI 1.82–6.20; p < 0.001) was independently associated with 1-year survival. In addition, net reclassification improvement (NRI) and integrated differentiation improvement (IDI) verified that the ACEF score and binary ACEF score is an accurate predictive tool in clinical settings. In conclusions, ACEF score could be considered as a useful tool to risk stratification in patients with AAAD before operation in daily clinical work.

Key messages on obesity care from the 2023 ASPEN Physician Preconference Course: A narrative review.

Obesity is a challenging chronic disease process that continues to affect a large percentage of the population at large. With the advent of new therapeutic options and interventions and a deeper scientific understanding of obesity as a complex illness, there is hope in curtailing this evolving pandemic. In this article, we present key medical information to engage and empower nutrition-focused providers to manage obesity and its nutrition complications. The topics summarized here were presented during the 2023 American Society for Parenteral and Enteral Nutrition Preconference Physician Course and include pathophysiology and hormonal regulation of obesity, multidisciplinary care planning and nutrition risk stratification of patients, and common approaches to treatment, including lifestyle modifications, antiobesity medications, and procedures from the perspective of the nutrition specialist.

Nomogram for predicting post-therapy recurrence in BCLC A/B hepatocellular carcinoma with Child-Pugh B cirrhosis

IntroductionThis study conducts a retrospective analysis on patients with BCLC stage A/B hepatocellular carcinoma (HCC) accompanied by Child-Pugh B cirrhosis, who underwent transarterial chemoembolization (TACE) in combination with local ablation therapy. Our goal was to uncover risk factors contributing to post-treatment recurrence and to develop and validate an innovative 1-, 3-, and 5-year recurrence free survival (RFS) nomogram.MethodsData from 255 BCLC A/B HCC patients with Child-Pugh B cirrhosis treated at Beijing You’an Hospital (January 2014 - January 2020) were analyzed using random survival forest (RSF), LASSO regression, and multivariate Cox regression to identify independent risk factors for RFS. The prognostic nomogram was then constructed and validated, categorizing patients into low, intermediate, and high-risk groups, with RFS assessed using Kaplan-Meier curves.ResultsThe nomogram, integrating the albumin/globulin ratio, gender, tumor number, and size, showcased robust predictive performance. Harrell’s concordance index (C-index) values for the training and validation cohorts were 0.744 (95% CI: 0.703–0.785) and 0.724 (95% CI: 0.644–0.804), respectively. The area under the curve (AUC) values for 1-, 3-, and 5-year RFS in the two cohorts were also promising. Calibration curves highlighted the nomogram’s reliability and decision curve analysis (DCA) confirmed its practical clinical benefits. Through meticulous patient stratification, we also revealed the nomogram’s efficacy in distinguishing varying recurrence risks.ConclusionThis study advances recurrence prediction in BCLC A/B HCC patients with Child-Pugh B cirrhosis following TACE combined with ablation. The established nomogram accurately predicts 1-, 3-, and 5-year RFS, facilitating timely identification of high-risk populations.

Expression of Anoikis-Related Genes and Potential Biomarkers in Colon Cancer Based on RNA-seq and scRNA-seq.

Colon cancer (CC) is a malignant tumor in the colon. Despite some progress in the early detection and treatment of CC in recent years, some patients still experience recurrence and metastasis. Therefore, it is urgent to better predict the prognosis of CC patients and identify new biomarkers. Recent studies have shown that anoikis-related genes (ARGs) play a significant role in the progression of many tumors. Hence, it is essential to confirm the role of ARGs in the development and treatment of CC by integrating scRNA-seq and transcriptome data. This study integrated transcriptome and single-cell sequencing (scRNA-seq) data from CC samples to evaluate patient stratification, prognosis, and ARG expression in different cell types. Specifically, differential expression of ARGs was identified through consensus clustering to classify CC subtypes. Subsequently, a CC risk model composed of CDKN2A, NOX4, INHBB, CRYAB, TWIST1, CD36, SERPINE1, and MMP3 was constructed using prognosis-related ARGs. Finally, using scRNA-seq data of CC, the expression landscape of prognostic genes in different cell types and the relationship between important immune cells and other cells were explored. Through the above analysis, two CC subtypes were identified, showing significant differences in prognosis and clinical factors. Subsequently, a risk model comprising aforementioned genes successfully categorized all CC samples into two risk groups, which also exhibited significant differences in prognosis, clinical factors, involved pathways, immune landscape, and drug sensitivity. Multiple pathways (cell adhesion molecules (CAMs), and extracellular matrix (ECM) receptor interaction) and immune cells/immune functions (B cell naive, dendritic cell activate, plasma cells, and T cells CD4 memory activated) related to CC were identified. Furthermore, it was found that prognostic genes were highly expressed in various immune cells, and B cells exhibited more and stronger interaction pathways with other cells. The results of this study may provide references for personalized treatment and potential biomarker identification in CC.

Machine learning with electrocardiograms to optimize mortality risk stratification in patients with suspected acute coronary syndrome

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Heart, Lung, and Blood Institute (NHLBI) and National Center for Advancing Translational Sciences (NCATS). Background The importance of risk stratification in patients with suspected acute coronary syndrome (ACS) extends beyond diagnosis and immediate treatment. It influences the precision of care delivery and the allocation of resources to those at highest risk of adverse events. Purpose We sought to evaluate the prognostic value of electrocardiogram (ECG) feature-based machine learning models to risk-stratify long-term mortality in those with suspected ACS. Methods This was a multicenter prospective observational cohort study of consecutive, non-traumatic patients evaluated at the emergency department for suspected ACS. The derivation cohort included 4,015 patients from a University Medical Center (age 59±16 years, 47% women, 80% training with 10-fold cross-validation and 20% testing). Ascertainment of all-cause death was based on numerous data sources, including the Centers for Disease Control and Prevention’s National Death Index registry. We trained six machine learning methods for survival analysis using 73 morphological ECG features from presenting prehospital 10-second 12-lead ECGs. We used predicted risk scores in a variational Bayesian Gaussian mixture model to define three risk groups corresponding to low, moderate, and high-risk and compared the resulting classification performance against the HEART score. The external testing cohort included 3,095 patients from a University (age 59±15 years, 44% women). Results The mortality rate was 20.3% in the derivation cohort after an average follow-up period of 3.53 years (IQR 1.75 - 5.32). Extra Survival Trees outperformed other forecasting models during cross-validation. In the internal testing cohort, the derived risk groups were significantly predictive of survival (Kaplan-Meier log-rank test statistic = 121.14, p&amp;lt;0.001), outperforming the classification performance using the HEART score (Figure 1). Our machine learning-based risk stratification model detected &amp;gt;90% of death events missed by low-risk HEART score (Figure 2). Ruling-out the low-risk group, it achieved a negative predictive value of 93.4% with a sensitivity of 85.9% (compared to 89.0% and 75.0%, respectively, for the HEART score). This classification performance generalized well to our external testing cohort. Compared to those in the low-risk group, patients at moderate (OR = 3.62 [1.35-9.74]) and high (OR = 6.12 [2.38-15.75]) risk had significantly higher odds of 30-day cardiovascular death (n=109, 3.5%). Conclusions Using only features from the 10-second 12-lead ECG, we derived and externally validated a machine learning model that stratifies the mortality risk of patients during long-term follow up. This model outperformed current standard of care based on the HEART score. Future work should validate the potential of this decision-support tool in influencing treatment plans and resource allocation.Figure 1.Clustering performanceFigure 2.Sankey diagram

Bleeding risk stratification in patients admitted for acute coronary syndrome

Abstract Funding Acknowledgements None. Introduction The risk of hemorrhagic events should always be accounted for in patients admitted with acute coronary syndromes (ACS). With the increasing frailty of these patients, it is important to identify them and minimize the risk of such events. Purpose Identify high-risk patients admitted for ACS for hemorrhagic events during their hospital stay. Methods Multicenter retrospective study, data collected from 1/10/2010 and 31/12/2020 with ACS diagnosis. Patients were divided into 3 groups according to hemoglobin (Hb) and platelet (Plat) levels. Group 1 – Hb &amp;lt;8g/dl, Plat &amp;lt;150x109/L; Group 2 – Hb 8-12g/dl, Plat 150-450x109/L; Group 3 – Hb ≥12g/dl, Plat ≥450x109/L. We further divided these patients according to a composite endpoint. Group A without the composite endpoint; Group B with composite endpoint. This composite endpoint was defined as major hemorrhage, intracranial hemorrhage, need of blood transfusions and a Hb decrease of ≥ 2g/dl. Results 19409 patients were enrolled, of those 4707 (24.3%) had the composite endpoint. Group B was older (68±13 vs 66±12, p&amp;lt;0.001), had higher rates of arterial hypertension (72.5% vs 68.2%, p&amp;lt;0.001), diabetes mellitus (35.6% vs 30.7%, p&amp;lt; 0.001), stroke (9.0% vs 6.6%, p &amp;lt; 0.001) and previous haemorrhage (p= 0.004). No statistically significant differences regarding previous MI (p= 0.240). Hb levels &amp;lt;8g/dL (8.1% vs 0.6%, p&amp;lt;0.001) were associated with a higher risk of hemorrhagic events but not lower levels of Plat, instead higher levels (&amp;gt; 450x109/L) were event associated (1.8% vs 0.9%, p&amp;lt;0.001). The choice of femoral access was related to an increased risk of hemorrhagic events (p&amp;lt;0.001). Logistic regression confirmed that the Hb levels &amp;lt; 12g/dL when compared with normal levels were related to the composite endpoint proportionally to the degree of anemia, the lower the level of Hb the more likely these events occur, Hb &amp;lt;8g/dL p&amp;lt;0.001, OR 4.02, CI (95%) 2.50-6.47 and Hb 8-12g/dL, p&amp;lt;0.001, OR 0.761, CI (95%) 0.693-0.837. Survival analysis also showed that cardiovascular (CV) mortality and re-admission at 1 year were related to the degree of anemia. Conclusion Lower levels of Hb correlated well and proportionally to the occurrence of the composite endpoint as well as cardiovascular (CV) mortality and re-admission at 1 year. Contrarily to expected lower levels of Plat did not correlate with a higher risk of hemorrhagic events but instead thrombocytosis did, as a paradoxical risk of bleeding has been noted in patients with thrombocytosis, especially extreme thrombocytosis.

Rediscovery of the implication of albuminuria in heart failure: emerging classic index for cardiorenal interaction.

The development of new drugs and device therapies has led to remarkable advancements in heart failure (HF) treatment in the past couple of decades. However, it becomes increasingly evident that guideline-directed medical therapy cannot be one-size-fits-all across a wide range of ejection fractions (EFs) and various aetiologies. Therefore, classifications solely relying on EF and natriuretic peptide make optimization of treatment challenging, and there is a growing exploration of new indicators that enable efficient risk stratification of HF patients. Particularly when considering HF as a multi-organ interaction syndrome, the cardiorenal interaction plays a central role in its pathophysiology, and albuminuria has gained great prominence as its biomarker, independent from glomerular filtration rate. Albuminuria has been shown to exhibit a linear correlation with cardiovascular disease and HF prognosis in multiple epidemiological studies, ranging from normal (<30mg/g) to high levels (>300mg/g). However, on the other hand, it is only recently that the details of the pathological mechanisms that give rise to albuminuria have begun to be elucidated, including the efficient compaction/tightening of the glomerular basement membrane by podocytes and mesangial cells. Interestingly, renal disease, diabetes, and HF damage these components associated with albuminuria, and experimental models have demonstrated that recently developed HF drugs reduce albuminuria by ameliorating these pathological phenotypes. In this review, facing the rapid expansion of horizons in HF treatment, we aim to clarify the current understanding of the pathophysiology of albuminuria and explore the comprehensive understanding of albuminuria by examining the clinically established evidence to date, the pathophysiological mechanisms leading to its occurrence, and the outcomes of clinical studies utilizing various drug classes committed to specific pathological mechanisms to put albuminuria as a novel axis to depict the pathophysiology of HF.

Development of a Prognostic Risk Model Based on Oxidative StressRelated Genes for Platinum-Resistant Ovarian Cancer Patients.

Ovarian Cancer (OC) is a heterogeneous malignancy with poor outcomes. Oxidative stress plays a crucial role in developing drug resistance. However, the relationships between Oxidative Stress-related Genes (OSRGs) and the prognosis of platinum-resistant OC remain unclear. This study aimed to develop an OSRGs-based prognostic risk model for platinum-resistant OC patients. Gene Set Enrichment Analysis (GSEA) was performed to determine the expression difference of OSRGs between platinum-resistant and -sensitive OC patients. Cox regression analyses were used to identify the prognostic OSRGs and establish a risk score model. The model was validated by using an external dataset. Machine learning was used to determine the prognostic OSRGs associated with platinum resistance. Finally, the biological functions of selected OSRG were determined via in vitro cellular experiments. Three gene sets associated with oxidative stress-related pathways were enriched (p < 0.05), and 105 OSRGs were found to be differentially expressed between platinum-resistant and - sensitive OC (p < 0.05). Twenty prognosis-associated OSRGs were identified (HR: 0:562-5.437; 95% CI: 0.319-20.148; p < 0.005), and seven independent OSRGs were used to construct a prognostic risk score model, which accurately predicted the survival of OC patients (1-, 3-, and 5-year AUC=0.69, 0.75, and 0.67, respectively). The prognostic potential of this model was confirmed in the validation cohort. Machine learning showed five prognostic OSRGs (SPHK1, PXDNL, C1QA, WRN, and SETX) to be strongly correlated with platinum resistance in OC patients. Cellular experiments showed that WRN significantly promoted the malignancy and platinum resistance of OC cells. The OSRGs-based risk score model can efficiently predict the prognosis and platinum resistance of OC patients. This model may improve the risk stratification of OC patients in the clinic.

In-hospital trimethylamine N-oxide (TMAO) variation predicts major adverse cardiovascular events in myocardial infarction survivors

Abstract Funding Acknowledgements None. Background Trimethylamine N-oxide (TMAO), a gut microbiota result of the fermentation of animal foods rich in choline, L-carnitine and phosphatidylcholine (red meat, egg yolk, dairy food), is associated with alterations in lipid metabolism, increased foam cell formation, promotion of inflammation, oxidative stress, platelet aggregation and thrombosis. To date the data evaluating TMAO as a predictor of mortality and ischemic events in patients with acute myocardial infarction (AMI) based on one-time measurement of TMAO levels lead to conflicting results. TMAO levels are fluctuating and in addition to diet, genetic factors, general condition and some co-morbidities affect its circulating levels. Purpose Since, there is a need for better risk stratification of AMI patients before the hospital discharge for more personalised treatment, we evaluated in-hospital variation in TMAO levels and its impact on outcome. Methods Blood samples from 149 consecutive AMI patients were collected on admission and on discharge. The endpoint was a three-point major adverse cardiovascular events (MACE) composed as all-cause mortality, reinfarction and development of heart failure. Results The majority of AMI patients were male (77%), hospitalized for first coronary event, with mean age 64(11) years. Median circulating TMAO level was significantly higher on admission than on discharge, (7.81 [3.47 –19.98] vs 3.45 [2.3 – 4.78] μM, respectively, p&amp;lt;0.001). In order to evaluate the impact of TMAO variation during the hospital stay on outcome, we estimated TMAO cut-off value by continuous hazard ratio analysis; values above the 3.45 μM were associated with incident of MACE in a linear fashion (Fig. 1a). Subsequently, we stratified AMI patients in 2 groups: 1) LL/HL (low-low/high-low) that included 75 (50.3%) patients whose TMAO levels remained low or were high and decreased during hospitalization and 2) HH/LH (high-high/low-high) including 74 patients (49.7%) patients whose TMAO levels remained elevated or were initially low, but rose above the estimated threshold during hospitalisation (Fig. 1b). Thirty-two (21.5%) patients reached the combined endpoint during the median follow-up of 30 months. The Kaplan-Meier analysis demonstrated that patients from the HH/LH group had a higher likelihood of experiencing MACE (p=0.05) (Fig. 2a). The multivariate Cox analysis revealed that patients in the HH/LH group had over twice the risk of MACE compared to those in the LL/HL group. Furthermore, older age and worse left ventricular systolic function were identified as independent predictors of MACE during long-term follow-up (Fig. 2b). Conclusions AMI patients with permanently elevated or increasing levels of TMAO have a worse outcome. The novelty and the strength of our study is the fact that the direction of variation of TMAO during the index hospitalisation identifies patients at higher risk of MACE during 30 months follow-up.

Incremented Amyloid-beta values predict mortality during long-term follow-up in patients with acute myocardial infarction, both STEMI and NSTEMI

Abstract Funding Acknowledgements None. Background Both the amyloid-β (Aβ) isoforms, namely Aβ1-40 and Aβ1-42 are implicated in the pathogenesis of Alzheimer’s disease through different mechanisms. While Aβ42 easily forms oligomers in brain, Aβ1-40 is more abundant in circulation where it exerts a pro-inflammatory role. Recently, Aβ1-40 has raised interest in the cardiovascular field for its contribution to atherosclerosis, more specifically through plaque formation and destabilization. However, to date, the prognostic role of Aβ1-40 in patients with acute myocardial infarction (AMI) is limited to NSTEMI. Purpose Since there is a need for better risk stratification of AMI patients, we analyzed the impact of plasma Aβ1-40 on long-term mortality in a cohort of real-world patients with AMI, including STEMI and NSTEMI. Further, we sought to identify predictors for elevated Aβ1-40 levels. Methods Blood samples were obtained from patients hospitalized for AMI and Aβ1-40 levels were evaluated with Human Aβ40 ELISA Kit. The endpoint was all-cause mortality. Results Our population included 1119 consecutive patients, with a mean age of 67 years, 72% male, 2/3 of them had a first ischemic event and 68.4% had STEMI diagnosis. The median plasma Aβ1-40 concentration on admission was 86.9 [54.5-128.9] pg/ml and there was no difference in Aβ1-40 levels between NSTEMI and STEMI (93.6 [61.2-130.9] Vs. 85.3 [52.9-128.7] pg/ml, respectively, p=0.1). At logistic regression multivariate analysis, higher Aβ1-40 levels were predicted by older age, worse left ventricular systolic function, HbA1C &amp;gt; 39 mmol/mol and GFR&amp;lt;60ml/min/m 2 (Fig.1a). From the final multivariable model, a nomogram for prediction of high levels of Aβ1-40 was computed (Fig.1b). One hundred ninety-three patients (17.2%) died, during the median follow-up of 57 [30 - 80] months. Patients who died had significantly higher levels of Aβ1-40 compared to alive patients (113.1 [67.9 – 159.5] Vs. 83.9 [52.5 – 122.6] pg/ml, p&amp;lt;0.01). Higher plasma Aβ1-40 values were associated with increased mortality risk, in both STEMI and NSTEMI (p&amp;lt;0.01) (Fig.2a). Further, at Cox multivariate analysis including the whole population, Aβ1-40 levels were predictive of mortality (HR 1.03, p=0.02), together with older age, higher Il-1β, GFR&amp;lt;60ml/min/m 2 and worse left ventricular systolic function (Fig.2b). The predictive value of our final model was superior to GRACE score for up to 60 months (AUC 0.830 (0.793 – 0.868) Vs 0.797 (0.756 – 0.837), respectively, p=0.03, Fig.2c), after which they were comparable. Conclusions Elevated levels of Aβ1-40 values are independent predictors of long-term mortality in a real-world cohort including both STEMI and NSTEMI patients.

Transcriptome deconvolution reveals absence of cancer cell expression signature in immune checkpoint blockade response

Abstract Immune-checkpoint therapy (ICB) has conferred significant and durable clinical benefit to some cancer patients. However, most patients do not respond to ICB, and reliable biomarkers of ICB response are needed to improve patient stratification. Here, we performed a transcriptome-wide meta-analysis across 1,486 tumors from ICB-treated patients and tumors with expected ICB outcomes based on microsatellite status. Using a robust transcriptome deconvolution approach, we inferred cancer and stroma-specific gene expression differences and identified cell-type specific features of ICB response across cancer types. Consistent with current knowledge, stromal expression of CXCL9, CXCL13, and IFNG were the top determinants of favorable ICB response. In addition, we identified a group of potential immune-suppressive genes, including FCER1A, associated with poor response to ICB. Strikingly, PD-L1 expression in stromal cells, but not cancer cells, is correlated with ICB response across cancer types. Furthermore, the unbiased transcriptome-wide analysis failed to identify cancer-cell intrinsic expression signatures of ICB response conserved across tumor types, suggesting that cancer cells lack tissue-agnostic transcriptomic features of ICB response.

The impact of PET/CT and brain MRI for metastasis detection among patients with clinical T1-category lung cancer: Findings from a large-scale cohort study

Abstract Purpose [18F]-FDG PET/CT and brain MRI are common approaches to detect metastasis in patients of lung cancer. Current guidelines for the use of PET/CT and MRI in clinical T1-category lung cancer lack risk-based stratification and require optimization. This study stratified patients based on metastatic risk in terms of the lesions' size and morphological characteristics. Methods The detection rate of metastasis was measured in different sizes and morphological characteristics (solid and sub-solid) of tumors. To confirm the cut-off value for discriminating metastasis and overall survival (OS) prediction, the receiver operating characteristic (ROC) analysis was performed based on PET/CT metabolic parameters (SUVmax/SUVmean/SULpeak/MTV/TLG), followed by Kaplan–Meier analysis for survival in post-operation patients with and without PET/CT plus MRI. Results 2,298 patients were included. No metastasis was observed in patients with solid nodules &lt; 8.0 mm and sub-solid nodules &lt; 10.0 mm. The cut-off of PET/CT metabolic parameters on discriminating metastasis were 1.09 (SUVmax), 0.26 (SUVmean), 0.31 (SULpeak), 0.55 (MTV), and 0.81 (TLG), respectively. Patients undergoing PET/CT plus MRI exhibited longer OS compared to those who did not receive it in solid nodules ≥ 8.0 mm &amp; sub-solid nodules ≥ 10.0 mm (HR, 0.44; p &lt; 0.001); in solid nodules ≥ 8.0 mm (HR, 0.12; p&lt;0.001) and in sub-solid nodules ≥ 10.0 mm (HR; 0.61; p=0.075), respectively. Compared to patients with metabolic parameters lower than cut-off values, patients with higher metabolic parameters displayed shorter OS: SUVmax (HR, 12.94; p &lt; 0.001), SUVmean (HR, 11.33; p &lt;0.001), SULpeak (HR, 9.65; p &lt; 0.001), MTV (HR, 9.16; p = 0.031), and TLG (HR, 12.06; p &lt; 0.001). Conclusion The necessity of PET/CT and MRI should be cautiously evaluated in patients with solid nodules &lt; 8.0 mm and sub-solid nodules &lt; 10.0 mm, however, these examinations remained essential and beneficial for patients with solid nodules ≥ 8.0 mm and sub-solid nodules ≥ 10.0 mm.

Predicting long-term outcomes in patients with classical trigeminal neuralgia following microvascular decompression with an MRI-based radiomics nomogram: a multicentre study.

This study aimed to develop a clinical-radiomics nomogram to predict the long-term outcomes of patients with classical trigeminal neuralgia (CTN) following microvascular decompression (MVD). This retrospective study included 455 patients with CTN who underwent MVD from three independent institutions A total of 2030 radiomics features from the cistern segment of the trigeminal nerve were extracted computationally from the three-dimensional steady-state free precession and three-dimensional time-of-flight magnetic resonance angiography sequences. Using the least absolute shrinkage and selection operator regression, 16 features were chosen to develop radiomics signatures. A clinical-radiomics nomogram was subsequently developed in the development cohort of 279 patients via multivariate Cox regression. The predictive performance and clinical application of the nomogram were assessed in an external cohort consisting of 176 patients. Sixteen highly outcome-related radiomics features extracted from multisequence images were used to construct the radiomics model, with concordance indices (C-index) of 0.804 and 0.796 in the development and test cohorts, respectively. Additionally, a clinical-radiomics nomogram was developed by incorporating both radiomics features and clinical characteristics (i.e., pain type and degree of neurovascular compression) and yielded higher C-indices of 0.865 and 0.834 in the development and test cohorts, respectively. K‒M survival analysis indicated that the nomogram successfully stratified patients with CTN into high-risk and low-risk groups for poor outcomes (hazard ratio: 37.18, p < 0.001). Our study findings indicated that the clinical-radiomics nomogram exhibited promising performance in accurately predicting long-term pain outcomes following MVD. This model had the potential to aid clinicians in making well-informed decisions regarding the treatment of patients with CTN. Trigeminal neuralgia recurs in about one-third of patients after undergoing MVD. The clinical-radiomics nomogram stratified patients into high- and low-risk groups for poor surgical outcomes. Using this nomogram could better inform patients of recurrence risk and allow for discussion of alternative treatments.

Association of Triglyceride Glucose-Derived Indexes with Recurrent Events Following Atherosclerotic Cardiovascular Disease.

Triglyceride glucose (TyG) and TyG-body mass index (TyG-BMI) are reliable surrogate indexes of insulin resistance and used for risk stratification and outcome prediction in patients with atherosclerotic cardiovascular disease (ASCVD). Here, we inserted estimated average glucose (eAG) into the TyG (TyAG) and TyG-BMI (TyAG-BMI) as derived parameters and explored their clinical significance in cardiovascular risk prediction. This was a population-based cohort study of 9,944 Chinese patients with ASCVD. The baseline admission fasting glucose and A1C-derived eAG values were recorded. Cardiovascular events (CVEs) that occurred during an average of 38.5 months of follow-up were recorded. We stratified the patients into four groups by quartiles of the parameters. Baseline data and outcomes were analyzed. Distribution of the TyAG and TyAG-BMI indexes shifted slightly toward higher values (the right side) compared with TyG and TyG-BMI, respectively. The baseline levels of cardiovascular risk factors and coronary severity increased with quartile of TyG, TyAG, TyG-BMI, and TyAG-BMI (all P<0.001). The multivariate-adjusted hazard ratios for CVEs when the highest and lowest quartiles were compared from low to high were 1.02 (95% confidence interval [CI], 0.77 to 1.36; TyG), 1.29 (95% CI, 0.97 to 1.73; TyAG), 1.59 (95% CI, 1.01 to 2.58; TyG-BMI), and 1.91 (95% CI, 1.16 to 3.15; TyAG-BMI). The latter two showed statistical significance. This study suggests that TyAG and TyAG-BMI exhibit more information than TyG and TyG-BMI in disease progression among patients with ASCVD. The TyAG-BMI index provided better predictive performance for CVEs than other parameters.

Overall Survival and Prognostic Factors in Metastatic Triple-Negative Breast Cancer: A National Cancer Database Analysis

Background: Metastatic triple-negative breast cancer (TNBC) is aggressive with poor median overall survival (OS) ranging from 8 to 13 months. There exists considerable heterogeneity in survival at the individual patient level. To better understand the survival heterogeneity and improve risk stratification, our study aims to identify the factors influencing survival, utilizing a large patient sample from the National Cancer Database (NCDB). Methods: Women diagnosed with metastatic TNBC from 2010 to 2020 in the NCDB were included. Demographic, clinicopathological, and treatment data and overall survival (OS) outcomes were collected. Kaplan–Meier curves were used to estimate OS. The log-rank test was used to identify OS differences between groups for each variable in the univariate analysis. For the multivariate analysis, the Cox proportional hazard model with backward elimination was used to identify factors affecting OS. Adjusted hazard ratios and 95% confidence intervals are presented. Results: In this sample, 2273 women had a median overall survival of 13.6 months. Factors associated with statistically significantly worse OS included older age, higher comorbidity scores, specific histologies, higher number of metastatic sites, presence of liver or other site metastases in those with only one metastatic site (excluding brain metastases), presence of cranial and extra-cranial metastases, lack of chemotherapy, lack of immunotherapy, lack of surgery to distant sites, lack of radiation to distant sites, and receipt of palliative treatment to alleviate symptoms. In the multivariate analysis, comorbidity score, histology, number of metastatic sites, immunotherapy, and chemotherapy had a statistically significant effect on OS. Conclusions: Through NCDB analysis, we have identified prognostic factors for metastatic TNBC. These findings will help individualize prognostication at diagnosis, optimize treatment strategies, and facilitate patient stratification in future clinical trials.

Evolutionary changes in metabolic dysfunction-associated steatotic liver disease and the risk of hepatocellular carcinoma: a nationwide cohort study.

To determine the association between evolutionary changes in metabolic dysfunction-associated steatotic liver disease (MASLD) status and the risk of hepatocellular carcinoma (HCC) in a nationwide population-based cohort. Information on study participants were derived from the Korea National Health Insurance Service database. The study population consisted of 5,080,410 participants who underwent two consecutive biennial health screenings between 2009 and 2012. All participants were followed up until HCC, death, or 31 December 2020. Association of evolutionary changes in MASLD status as assessed by fatty liver index and cardiometabolic risk factors, including persistent non-MASLD, resolved MASLD, incident MASLD, and persistent MASLD, with HCC risk was evaluated using the multivariable-adjusted Cox proportional hazards regression. Among the 5,080,410 participants with 39,910,331 person-years of follow-up, 4,801 participants developed HCC. The incidence of HCC in participants with resolved, incident, and persistent MASLD was approximately 2.2-, 2.3-, and 4.7-fold higher, respectively, than that in those with persistent non-MASLD among the Korean adult population. When stratifying the participants according to the evolutionary change in MASLD status, persistent (adjusted hazard ratio [aHR], 2.94; 95% confidence interval [CI], 2.68-3.21; P<0.001), incident (aHR, 1.85; 95% CI, 1.63-2.10; P<0.001), and resolved MASLD (aHR, 1.33; 95% CI, 1.18-1.50; P<0.001) had an increased risk of HCC than that of persistent non-MASLD. The evolutionary changes in MASLD were associated with the differential risk of HCC independent of metabolic risk factors and concomitant medications, providing additional information on the risk of HCC stratification in patients with MASLD.

Establishing subdivisions of M1 stage nasopharyngeal carcinoma based on decision tree classification: A multicenter retrospective study

ObjectivesTo meet the demand for personalized treatment, effective stratification of patients with metastatic nasopharyngeal carcinoma (mNPC) is essential. Hence, our study aimed to establish an M1 subdivision for prognostic prediction and treatment planning in patients with mNPC. Materials and methodsThis study included 1239 patients with mNPC from three medical centers divided into the synchronous mNPC cohort (smNPC, n = 556) to establish an M1 stage subdivision and the metachronous mNPC cohort (mmNPC, n = 683) to validate this subdivision. The primary endpoint was overall survival. Univariate and multivariate Cox analyses identified covariates for the decision-tree model, proposing an M1 subdivision. Model performance was evaluated using time-dependent receiver operating characteristic curves, Harrell’s concordance index, calibration plots, and decision curve analyses. ResultsThe proposed M1 subdivisions were M1a (≤5 metastatic lesions), M1b (>5 metastatic lesions + absent liver metastases), and M1c (>5 metastatic lesions + existing liver metastases) with median OS of 34, 22, and 13 months, respectively (p < 0.001). This M1 subdivision demonstrated superior discrimination (C-index = 0.698; 3-year AUC = 0.707) and clinical utility over those of existing staging systems. Calibration curves exhibited satisfactory agreement between predictions and actual observations. Internal and mmNPC cohort validation confirmed the robustness. Survival benefits from local metastatic treatment were observed in M1a, while immunotherapy improved survival in patients with M1b and M1c disease. ConclusionThis novel M1 staging strategy provides a refined approach for prognostic prediction and treatment planning in patients with mNPC, emphasizing the potential benefits of local and immunotherapeutic interventions based on individualized risk stratification.

Evaluation of Central Sensitisation in Bladder Pain Syndrome: A Systematic Review.

Bladder pain syndrome (BPS) is a debilitating condition characterised by exaggerated bladder sensations and altered bladder function. It is still unknown whether the condition is a peripheral sensory problem or due to abnormal central sensory processing as seen in central sensitisation. This systematic review, which followed a published and Prospective Register of Systematic Reviews-registered protocol (CRD42021229962), is aimed at establishing the scope of central sensitisation in patients with BPS to aid optimal management and treatment. Four databases were searched, and appraisal of the identified studies was conducted by two independent reviewers based on eligibility criteria: patients with BPS being investigated for central sensitisation with or without comparison of controls, English-language articles, full text and publication in a peer-reviewed journal. The Methodological Index for non-Randomised Studies was used to determine study quality. We identified 763 papers in total, with 15 studies included in the final analysis. All studies were observational and had a low risk of bias. Measures included in the evaluation of CS were questionnaires, urodynamics, and quantitative sensory testing methods. There was evidence of central sensitisation in patients with BPS in all papers evaluated (15 out of 15). In addition, more significant central sensitisation correlated with severe disease presentation (3 out of 3 papers) and concomitant chronic pain conditions (5 out of 5 papers). Central sensitisation plays an integral role in BPS patient pathology. Many secondary measures are used to evaluate this condition. Stratification of patients based on their pathology (peripheral, central or a combination of the two) will aid in implementing an individualised management strategy.

Τ cell-mediated adaptive immunity in the transition from metabolic dysfunction-associated steatohepatitis to hepatocellular carcinoma

Metabolic dysfunction-associated steatohepatitis (MASH) is the progressed version of metabolic dysfunction-associated steatotic liver disease (MASLD) characterized by inflammation and fibrosis, but also a pathophysiological “hub” that favors the emergence of liver malignancies. Current research efforts aim to identify risk factors, discover disease biomarkers, and aid patient stratification in the context of MASH-induced hepatocellular carcinoma (HCC), the most prevalent cancer among MASLD patients. To investigate the tumorigenic transition in MASH-induced HCC, researchers predominantly exploit preclinical animal-based MASH models and studies based on archived human biopsies and clinical trials. Recapitulating the immune response during tumor development and progression is vital to obtain mechanistic insights into MASH-induced HCC. Notably, the advanced complexity behind MASLD and MASH pathogenesis shifted the research focus towards innate immunity, a fundamental element of the hepatic immune niche that is usually altered robustly in the course of liver disease. During the last few years, however, there has been an increasing interest for deciphering the role of adaptive immunity in MASH-induced HCC, particularly regarding the functions of the various T cell populations. To effectively understand the specific role of T cells in MASH-induced HCC development, scientists should urgently fill the current knowledge gaps in this field. Pinpointing the metabolic signature, sketching the immune landscape, and characterizing the cellular interactions and dynamics of the specific T cells within the MASH-HCC liver are essential to unravel the mechanisms that adaptive immunity exploits to enable the emergence and progression of this cancer. To this end, our review aims to summarize the current state of research regarding the T cell functions linked to MASH-induced HCC.

Sphingolipids in prostate cancer prognosis: integrating single-cell and bulk sequencing.

Stratifying patient risk and exploring the tumor microenvironment are critical endeavors in prostate cancer research, essential for advancing our understanding and management of this disease. Single-cell sequencing data for prostate cancer were sourced from the pradcellatlas website, while bulk transcriptome data were obtained from the TCGA database. Dimensionality reduction cluster analysis was employed to investigate heterogeneity in single-cell sequencing data. Gene set enrichment analysis, utilizing GO and KEGG pathways, was conducted to explore functional aspects. Weighted gene coexpression network analysis (WGCNA) identified key gene modules. Prognostic models were developed using Cox regression and LASSO regression techniques, implemented in R software. Validation of key gene expression levels was performed via PCR assays. Through integrative analysis of single-cell and bulk transcriptome data, key genes implicated in prostate cancer pathogenesis were identified. A prognostic model focused on sphingolipid metabolism (SRSR) was constructed, comprising five genes: "FUS," "MARK3," "CHTOP," "ILF3," and "ARIH2." This model effectively stratified patients into high-risk and low-risk groups, with the high-risk cohort exhibiting significantly poorer prognoses. Furthermore, distinct differences in the immune microenvironment were observed between these groups. Validation of key gene expression, exemplified by ILF3, was confirmed through PCR analysis. This study contributes to our understanding of the role of sphingolipid metabolism in prostate cancer diagnosis and treatment. The identified prognostic model holds promise for improving risk stratification and patient outcomes in clinical settings.