Current Risk Stratification Research Articles

Enhancing risk stratification models in localized prostate cancer by novel validated tissue biomarkers.

Localized prostate cancer (PCa) is a largely heterogeneous disease regarding its clinical behavior. Current risk stratification relies on clinicopathological parameters and distinguishing between indolent and aggressive cases remains challenging. To improve risk stratification, we aimed to identify new prognostic markers for PCa. We performed an in silico analysis on publicly available PCa transcriptome datasets. The top 20 prognostic genes were assessed in PCa tissue samples of our institutional cohort (n = 92) using the NanoString nCounter technology. The three most promising candidates were further assessed by immunohistochemistry (IHC) in an institutional (n = 121) and an independent validation cohort from the EMPACT consortium (n = 199). Cancer-specific survival (CSS) and progression-free survival (PFS) were used as endpoints. Our in silico analysis identified 113 prognostic genes. The prognostic values of seven of the top 20 genes were confirmed in our institutional radical prostatectomy (RPE) cohort. Low CENPO, P2RX5, ABCC5 as well as high ASF1B, NCAPH, UBE2C, and ZWINT gene expressions were associated with shorter CSS. IHC analysis confirmed the significant associations between NCAPH and UBE2C staining and worse CSS. In the external validation cohort, higher NCAPH and ZWINT protein expressions were associated with shorter PFS. The combination of the newly identified tissue protein markers improved standard risk stratification models, such as D'Amico, CAPRA, and Cambridge prognostic groups. We identified and validated high tissue levels of NCAPH, UBE2C, and ZWINT as novel prognostic risk factors in clinically localized PCa patients. The use of these markers can improve routinely used risk estimation models.

Immune-based subgroups uncover diverse tumor immunogenicity and implications for prognosis and precision therapy in acute myeloid leukemia

BackgroundAlthough a considerable proportion of acute myeloid leukemia (AML) patients achieve remission through chemotherapy, relapse remains a recurring and significant event leading to treatment failure. This study aims to investigate the immune landscape in AML and its potential implications for prognosis and chemo-/immune-therapy.MethodsIntegrated analyses based on multiple sequencing datasets of AML were performed. Various algorithms estimated immune infiltration in AML samples. A subgroup prediction model was developed, and comprehensive bioinformatics and machine learning algorithms were applied to compare immune-based subgroups in relation to clinical features, mutational landscapes, immune characterizations, drug sensitivities, and cellular hierarchies at the single-cell level.ResultsTwo immune-based AML subgroups, G1 and G2, were identified. G1 demonstrated higher immune infiltration, a more monocytic phenotype, increased proportions of monocytes/macrophages, and higher FLT3, DNMT3A, and NPM1 mutation frequencies. It was associated with a poorer prognosis, lower proportions of various immune cell types and a lower T cell infiltration score (TIS). AML T-cell-based immunotherapy target antigens, including CLEC12A, Folate receptor β, IL1RAP and TIM3, showed higher expression levels in G1, while CD117, CD244, CD96, WT and TERT exhibited higher expression levels in G2. G1 samples demonstrated higher sensitivity to elesclomol and panobinostat but increased resistance to venetoclax compared to G2 samples. Moreover, we observed a positive correlation between sample immune infiltration and sample resistance to elesclomol and panobinostat, whereas a negative correlation was found with venetoclax resistance.ConclusionOur study enriches the current AML risk stratification and provides guidance for precision medicine in AML.

Prognostic role of lipoprotein(a) in atherosclerotic cardiovascular disease risk from a perspective on current risk stratification.

Lipoprotein(a) [Lp(a)] is an emerging predictor for atherosclerotic cardiovascular disease (ASCVD) but the association from a perspective on current risk stratification was unknown. A cohort of 9944 Chinese patients with ASCVD was recruited and refined into very-high-risk (VHR) and non-VHR subgroups according to current guideline. Lp(a) plasma levels were divided by its concentration (<30, 30-50, 50-75, and ≥75mg/dL) and percentile zones (<25th, 25-50th, 50-75th, 75-90th, ≥90th). Cardiovascular events (CVEs) occurred during an average of 38.5 months' follow-up were recorded. We found that Lp(a) was increased with risk stratification of ASCVD increasing. Prevalence of CVEs had a significantly increasing trend with gradients of Lp(a) elevation in VHR but not in non-VHR subgroup. The adjusted HRs (95%CIs) for CVEs were 1.75(1.25-2.46) in the highest group of Lp(a) ≥75mg/dL compared with the group of Lp(a)<30mg/dL as the reference in overall patients, 2.18(1.32-3.58) in VHR subgroup and 1.43(0.93-2.18) in non-VHR subgroup, respectively. The adjusted HRs (95%CIs) at the highest grade of Lp(a) levels (≥90th) were 1.72(1.19-2.50) in overall population, 2.83(1.53-5.24) in VHR subgroup and 1.38(0.86-2.12) in non-VHR subgroup, respectively. These findings suggested that Lp(a) might contribute more to CVEs risk in VHR subgroup of ASCVD.

Effectiveness of contemporary risk stratification in patients with hypertrophic cardiomyopathy: a pilot study

Abstract Introduction Risk stratification and primary prevention of sudden cardiac death (SCD) in patients with hypertrophic cardiomyopathy (HCM) is a challenging discipline. When using current stratification systems, there is always a compromise between sensitivity and specificity. Objective In our pilot study, we sought to determine the incidence of SCD and the effectiveness of stratification systems, i.e., the ESC HCM Risk-SCD score and the system of individual major risk factors (RF) according to ACC/AHA in patients followed in an expert center. Methods Patients with HCM were risk-stratified and followed for 10 ± 8 years at the referral HCM center. Results Five hundred and five patients were stratified, and 71 (14%) were implanted with a cardioverter-defibrillator (ICD) based on risk stratification. ICD patients were stratified at age 39 ± 17 years, of which 26 (35%) were women, maximal left ventricular wall thickness (MLWT) was 23 ± 7 mm, and left atrial dimension (LA) 46 ± 7 mm. During follow-up, 12 (17%) patients had ≥ 1 appropriate ICD therapy at age 46 ± 19 years. Patients with ≥ 1 appropriate ICD therapy had an average 5-year ESC score of 1.8 ± 0.4% and an average ACC/AHA major RF of 1.4 ± 0.8, MLWT 25 ± 6 mm, LA 47 ± 7 mm. None of the ICD patients died of SCD. Five (7%) patients suffered from ICD complications, including 4 (6%) inappropriate shock, and one patient experienced infective endocarditis requiring system extraction. During follow-up, 118 (23%) patients died, of which 28 (6%) from HCM-related causes, 12 (2%) from stroke, 11 (2%) from advanced heart failure, one perioperatively (myectomy) and 4 (1%) patients died suddenly (SCD). The mean age of SCD patients at the time of stratification was 59 ± 14 years; all were male, MLWT 18 ± 5 mm, LA 49 ± 2 mm, mean ESC score 2.0 ± 0.9%, and three (75%) had no RF present according to ACC/AHA. The ESC HCM Risk-SCD score was markedly less sensitive (sensitivity 56%; 95% CI 21-86) but more specific (specificity 98%; 95% CI 96-99) than the ACC/AHA approach (sensitivity 77%; 95% CI 46-95; and specificity 71%; 95% CI 67-75) (Figure). Conclusion With current risk stratification, albeit imperfect, SCD is rare in HCM patients managed at an expert center.

Serum metabolomics improve risk stratification for incident heart failure

Abstract Background The prediction and early detection of heart failure (HF) is vital to reduce its substantial impact on quality of life, survival and healthcare expenditure. Current incident HF risk stratification strategies achieve only modest performance. Moreover, state-of-the-art risk scores focus on commonly acknowledged clinical risk factors, thus necessitating the integration of heterogenous data sources and prioritizing individuals with obvious risk constellations. Purpose Here, we explored the predictive value of serum metabolomics (168 metabolites detected by proton nuclear magnetic resonance (1H-NMR) spectroscopy) for incident HF. Methods Leveraging data of n = 68,311 individuals and &amp;gt; 0.8 million person-years of follow-up from the UK Biobank (UKB) cohort, we (I) evaluated the association of individual metabolites with incident HF via fitting of per-metabolite COX proportional hazards models and (II) trained and validated elastic net (EN) models to predict incident HF using the serum metabolome. Discriminative performance was benchmarked against a comprehensive, well-validated clinical risk score (Pooled Cohort Equations to Prevent HF, PCP-HF). External validation was conducted in the independent FINRISK study. Results Several metabolites were independently associated with incident HF (90/168 adjusting for age and sex, 48/168 adjusting for PCP-HF). Performance-optimized risk models effectively retained key predictors representing highly correlated clusters (≈ 80 % feature reduction). The addition of metabolomics to PCP-HF improved predictive performance (Harrel’s C: 0.768 vs. 0.755, ΔC = 0.013 (95% confidence interval (CI) 0.004 – 0.022), continuous net reclassification improvement (NRI) = 0.287 (95% CI 0.200 – 0.367), relative integrated discrimination improvement (IDI): 17.47 % (95% CI 9.463 - 27.825)). Simplified models only including age, sex and metabolomics performed almost as well as the PCP-HF model (Harrel’s C: 0.745 vs. 0.755, ΔC = 0.010 (95% CI -0.004 - 0.027), continuous NRI: 0.097 (95% CI -0.025 - 0.217), relative IDI: 13.445 % (95% CI -10.608 - 41.454)). Risk and survival stratification was improved by integrating metabolomics. External validation within FINRISK revealed similar patterns using the original model coefficients. Conclusions Serum metabolomics improve the prediction of incident HF risk. Scores obtained from the combination of age, sex and metabolomics exhibit similar predictive power as clinical risk models. Offering serum metabolomics to a middle-aged cohort might significantly improve HF risk stratification, thus facilitating preventive efforts. Via a single blood draw, serum metabolomics displays a highly cost- and time-effective, standardizable, and scalable alternative to clinical risk scores involving physical measurements and history taking in addition to clinical chemistry.Relative performance (test split)ROC &amp; DCA plots (test split)

Left atrial strain predicts adverse events in hypertrophic cardiomyopathy: preliminary results from a CMR study

Abstract Background Hypertrophic cardiomyopathy (HCM) is associated with an increased risk of major adverse cardiovascular events (MACE), however, current risk stratification methods are imperfect. Left atrial global longitudinal strain (LA GLS) is an increasingly acknowledged predictive parameter, yet its potential role in HCM is not well defined. Purpose This study aims to investigate the prognostic role of cardiac magnetic resonance (CMR)-derived LA GLS for the occurrence of MACE in patients with HCM. Methods A retrospective, single-centre, CMR study of HCM patients was conducted. Clinical data was collected from electronic medical record and CMR images were analysed by two blinded investigators. LA GLS was derived from CMR two-chamber cine images by a semiautomatic method, and was categorized according to its median value. The endpoint was a composite of MACE, including all-cause death, sudden cardiac death (SCD), sustained and non-sustained ventricular tachycardias (VT), appropriate implantable cardiac defibrillator (ICD) shocks, heart failure (HF) hospitalization, and new-onset atrial fibrillation (AF). Cox regression analysis was performed. The preliminary results of the study are now reported. Results A total of 54 HCM patients were included, mean age was 58.5 years ± 15.9 and 38.9% were female. Average maximal wall thickness (MWT) was 18.0 mm ± 3.9, 37% had left ventricular outflow obstruction (LVOTO) and 45.1% had positive late gadolinium enhancement (LGE). After a mean follow-up of 6.9 ± 3.0 years, 26 patients (48.1%) experienced the composite endpoint. Figure 1 shows the clinical and CMR differences according to the occurrence of MACE. LA GLS was significantly lower in patients with MACE compared to those without (26.5 ± 19% vs 36.7 ± 13%, p=0.026), with a moderate predictive value (AUC = 0.68, Figure 2A). Multivariate Cox regression analysis revealed that a decrease in LA GLS (HR 1.09 per each 1% decrease, 95% CI 1.03-1.15, p=0.002), the presence of LGE (HR 6.9, 95% CI 1.7-28, p=0.007), the presence of LVOTO (HR 3.4, 95% CI 1.12-10.2, p=0.031) and an increase in MWT (HR 1.23 per each 1mm increase, 95% CI 1.05-1.45, p=0.011) were independently associated with MACE. Among low-risk HCM patients (absence of LGE or LVOTO, or MWT below our median value), LA GLS had an additional prognostic role, and those with an LA GLS above the median value (LA GLS&amp;gt;29.7%) presented a remarkably favourable prognosis (Figure 2B-D). Conclusions CMR-derived LA GLS is a predictor of outcomes in HCM beyond established imaging prognostic factors. Particularly, LA GLS appears to be useful in risk-stratifying low-risk HCM patients: those with normal LA GLS show an excellent prognosis. If confirmed in larger studies, LA GLS could be used to individualise HCM management.Figure 2

Combined mammographic breast density and breast arterial calcification is incrementally predictive of coronary artery disease beyond traditional risk factors

Abstract Background Coronary artery disease (CAD) risk is underestimated in women using current risk stratification tools. The mammographic finding of breast arterial calcification (BAC) associates with CAD. Furthermore, breast adipose tissue, measured through breast density (BD) on mammography (low breast density indicates high adiposity) has shown potential to act as a pro-inflammatory, pro-atherogenic fat deposit. Given its uptake in screening programs, mammography may therefore represent a novel risk stratification tool for cardiovascular disease in women. Purpose To evaluate the association between the combined mammographic features of BD and BAC, with CAD. Methods Single-centre, retrospective, cross-sectional study, including 153 women, mean age 62±10, who had both clinically indicated mammography, and coronary computed tomography angiogram (CCTA) for suspected CAD. CAD risk was identified by the CAD Consortium Score, with a 15% threshold for low and high risk. BD was visually assessed and categorised by 4-level BI-RADS grade with grade A-B representing low density, and C-D representing high density (Figure 1). BAC was visually assessed and categorised as present/absent (Figure 1). CAD was categorised as presence/absence of coronary artery plaque on CCTA. Logistic regression was performed with results presented as Odds Ratio (OR) and [95% Confidence Intervals]. Receiver operator characteristic area under the curve (AUC) was used for model discrimination. Results Low BD (n=103 (67%)) was associated with CAD (OR 3.20 [1.58-6.53], p=0.001, as was BAC presence (n=37 (24%), OR 4.36 [1.58-12], p=0.004). There were 51 (33%) with elevated CAD risk. Participants were categorised into 4 subgroups based on low/high BD and presence/absence of BAC: 29 (19%) had low BD and BAC, 74 (48%) had low BD and no BAC, 8 (5%) had high BD and BAC and 42 (27%) had high BD and no BAC. Significantly higher proportions of CAD were noted with low BD and BAC alone, as well as combined low BD and BAC (Figure 2). Compared with high BD/BAC negative, the presence of low BD and BAC independently associated with CAD (OR 5.27 [1.19-23.3], p=0.03). Significant incremental benefit was seen after adding BD/BAC status to CAD Consortium Score (AUC 0.65 vs. 0.72, p=0.004). Conclusions Combined, and individual mammographic features of low BD and BAC presence are associated with CAD and improve risk prediction beyond standard coronary risk probabilities. Standardised reporting of these features to inform risk identification may be of further benefit and should be tested in prospective screening studies.

Right ventricular-pulmonary artery coupling for prognostication in acute pulmonary embolism.

Acute pulmonary embolism (PE) increases pulmonary pressure and impair right ventricular (RV) function. Echocardiographic investigation can quantify this mismatch as the tricuspid annular plane systolic excursion (TAPSE) to pulmonary arterial systolic pressure (PASP) ratio. The aim of the study was to investigate the prognostic capabilities of TAPSE/PASP ratio in patients with acute PE. We utilized the RIETE registry to analyze consecutive hemodynamically stable PE patients. We used multivariable logistic regression analyses to assess the association between the TAPSE/PASP ratio and 30-day all-cause mortality across the strata of European Society of Cardiology (ESC) risk categories. We included 4,478 patients, of whom 1,326 (30%) had low-risk, 2,425 (54%) intermediate-low risk, and 727 (16%) intermediate-high risk PE. Thirty-day mortality rates were 0.7%, 2.3% and 3.4%, respectively. Mean TAPSE/PASP ratio was 0.65±0.29 in low-risk patients, 0.46±0.30 in intermediate-low risk, and 0.33±0.19 in intermediate-high risk patients. In multivariable analyses, there was an inverse association between TAPSE/PASP ratio and 30-day mortality (adjusted OR 1.32 [95%CI 1.14-1.52] per 0.1 decrease in TAPSE/PASP). TAPSE/PASP ratio below optimal cut-points was associated with increased mortality in low- (<0.40, aOR: 5.88; 95%CI:1.63-21.2), intermediate-low (<0.43, aOR: 2.96; 95%CI:1.54-5.71) and intermediate-high risk patients (<0.34, aOR: 4.37; 95%CI:1.27-15.0). TAPSE/PASP <0.44 showed net reclassification improvement of 18.2% (95%CI:0.61-35.8) vs. RV/LV ratio >1, and 27.7% (95%CI:10.2-45.1) vs. ESC risk strata. Decreased TAPSE/PASP ratio was associated with increased mortality. The ratio may aid in clinical decision-making, particularly for intermediate-risk patients for whom the discriminatory capability of the current risk stratification tools is limited.

Utility of respiratory viral testing in the risk stratification of young febrile infants presenting to emergency care settings: a protocol for systematic review and meta-analysis

IntroductionFebrile infants under 3 months of age are at risk of invasive bacterial infection (IBI). It is currently unclear if testing for respiratory viruses may have a role in IBI...

Prostate Cancer Risk Stratification in NRG Oncology Phase III Randomized Trials Using Multimodal Deep Learning With Digital Histopathology.

Current clinical risk stratification methods for localized prostate cancer are suboptimal, leading to over- and undertreatment. Recently, machine learning approaches using digital histopathology have shown superior prognostic ability in phase III trials. This study aims to develop a clinically usable risk grouping system using multimodal artificial intelligence (MMAI) models that outperform current National Comprehensive Cancer Network (NCCN) risk groups. The cohort comprised 9,787 patients with localized prostate cancer from eight NRG Oncology randomized phase III trials, treated with radiation therapy, androgen deprivation therapy, and/or chemotherapy. Locked MMAI models, which used digital histopathology images and clinical data, were applied to each patient. Expert consensus on cut points defined low-, intermediate-, and high-risk groups on the basis of 10-year distant metastasis rates of 3% and 10%, respectively. The MMAI's reclassification and prognostic performance were compared with the three-tier NCCN risk groups. The median follow-up for censored patients was 7.9 years. According to NCCN risk categories, 30.4% of patients were low-risk, 25.5% intermediate-risk, and 44.1% high-risk. The MMAI risk classification identified 43.5% of patients as low-risk, 34.6% as intermediate-risk, and 21.8% as high-risk. MMAI reclassified 1,039 (42.0%) patients initially categorized by NCCN. Despite the MMAI low-risk group being larger than the NCCN low-risk group, the 10-year metastasis risks were comparable: 1.7% (95% CI, 0.2 to 3.2) for NCCN and 3.2% (95% CI, 1.7 to 4.7) for MMAI. The overall 10-year metastasis risk for NCCN high-risk patients was 16.6%, with MMAI further stratifying this group into low-, intermediate-, and high-risk, showing metastasis rates of 3.4%, 8.2%, and 26.3%, respectively. The MMAI risk grouping system expands the population of men identified as having low metastatic risk and accurately pinpoints a high-risk subset with elevated metastasis rates. This approach aims to prevent both overtreatment and undertreatment in localized prostate cancer, facilitating shared decision making.

Comparing Carotid Artery Velocities with Current ASCVD Risk Stratification: A Novel Approach to Simpler Risk Assessment.

To explore the potential of a novel approach to simplify risk assessment by comparing carotid artery velocities with current atherosclerotic cardiovascular disease (ASCVD) risk stratification method using nonlinear measurements. In this prospective study conducted at a medical center in southern Taiwan from January 1, 2020, to December 31, 2021, 1636 participants aged 40-75 years without prior ASCVD events were enrolled. Carotid flow velocity was obtained through duplex ultrasonography. ASCVD risk was categorized into two groups according to the 2022 USPSTF guidelines for primary prevention. We analyzed associations between flow indices and ASCVD risk using logistic regression and generalized additive models (GAMs). The end diastolic velocity (EDV) of common carotid artery (CCA) and the peak systolic velocity (PSV) of internal carotid artery (ICA) were inversely and nonlinearly associated with cardiovascular event risk. Multivariate logistic regression analysis with ROC curves revealed that the optimal speed for the EDV of CCA was approximately 23.75cm/s, and the optimal PSV and EDV of ICA were approximately 81.75cm/s and 26.75cm/s, respectively. The GAMs showed U-shaped relationships between elevated ASCVD risk and blood flow velocity in the carotid arteries, with inflection points of approximately 82cm/s in the PSV of ICA and near 25cm/s in the EDV of CCA. Both methods revealed similar results. The EDVs and PSVs of the CCA and ICA are associated with the development of cardiovascular events. Optimal velocity ranges were identified; however, further hemodynamic investigations are warranted.

RiskPath: Explainable deep learning for multistep biomedical prediction in longitudinal data.

Predicting individual and population risk for disease outcomes and identifying persons at elevated risk is a key prerequisite for targeting interventions to improve health. However, current risk stratification tools for the common, chronic diseases that develop over the lifecourse and represent the majority of disease morbidity, mortality and healthcare costs are aging and achieve only moderate predictive performance. In some common, highly morbid conditions such as mental illness no risk stratification tools are yet available. There is an urgent need to improve predictive performance for chronic diseases and understand how cumulative, multifactorial risks aggregate over time so that intervention programs can be targeted earlier and more effectively in the disease course. Chronic diseases are the end outcomes of multifactorial risks that increment over years and represent cumulative, temporally-sensitive risk pathways. However, tools in current clinical use were constructed in older data and utilize inputs from a single data collection step. Here, we present RiskPath, a multistep deep learning method for temporally-sensitive biomedical risk prediction tailored for the constraints and demands of biomedical practice that achieves very strong performance and full translational explainability. RiskPath delineates and quantifies cumulative multifactorial risk pathways and allows the user to explore performance-complexity tradeoffs and constrain models as required by clinical use cases. Our results highlight the potential for developing a new generation of risk stratification tools and risk pathway mapping in time-dependent diseases and health outcomes by leveraging powerful timeseries deep learning methods in the wealth of biomedical data now appearing in large, longitudinal open science datasets.

Risk Stratification and Management of Intermediate- and High-Risk Pulmonary Embolism.

Acute pulmonary embolism (PE) is a leading cause of mortality. Not only is PE associated with short-term mortality, but up to ~20% of patients might suffer from long-term consequences such as post-PE syndrome and chronic thromboembolic pulmonary hypertension. Current risk stratification tools poorly predict those who are at risk for short-term deterioration and those who develop long-term consequences. Traditionally, systemic thrombolysis has been considered the first-line therapy for patients with high-risk PE without contraindications; however, it comes with the risk of major bleeding (notably intracranial hemorrhage). The use of catheter-directed interventions (embolectomy or thrombolysis) has been increasing owing to their low bleeding risk; however, randomized trial data supporting their efficacy in improving clinical outcomes are limited. In this review, we highlight the evidence supporting the available advanced therapies for high- and intermediate-risk PE and summarize the ongoing trials which are evaluating these therapies.

Galectin-3 Predicts Long-Term Risk of Cerebral Disability and Mortality in Out-of-Hospital Cardiac Arrest Survivors.

Out-of-hospital cardiac arrest (OHCA) is associated with high mortality and cerebral disability in survivors. Current models of risk prediction and survival are mainly based on resuscitation duration. We examined the prognostic value of circulating biomarkers in predicting mortality and severe cerebral disability for OHCA survivors, alongside traditional clinical risk indicators. Biomarkers including BNP, troponin I, and galectin-3 were measured at hospital admission in resuscitated OHCA patients. Prognostic significance for mortality and cerebral disability involving circulating biomarkers, resuscitation duration, demographics, and laboratory and clinical characteristics was examined via univariate and multivariate Cox proportional hazards regression models. The incremental prognostic value of the index covariates was examined through model diagnostics, focusing on the Akaike information criterion (AIC) and Harrell's concordance statistic (c-statistic). In a combinatorial analysis of 144 OHCA survivors (median follow-up 5.7 years (IQR 2.9-6.6)), BNP, galectin-3, arterial pH, and resuscitation time were significant predictors of all-cause death and severe cerebral disability, whereas troponin I levels were not. Multivariate regression, adjusting for BNP, arterial pH, and resuscitation time, identified galectin-3 as an independent predictor of long-term mortality. Multiple linear regression models also confirmed galectin-3 as the strongest predictor of cerebral disability. The incorporation of galectin-3 into models for predicting mortality and cerebral disability enhanced fit and discrimination, demonstrating the incremental value of galectin-3 beyond traditional risk predictors. Galectin-3 is a significant, independent long-term risk predictor of cerebral disability and mortality in OHCA survivors. Incorporating galectin-3 into current risk stratification models may enhance early prognostication and guide targeted clinical interventions.

Neuroblastoma with high ASPM reveals pronounced heterogeneity and poor prognosis

ObjectiveWe explored the preliminary value of abnormal spindle-like microcephaly- associated (ASPM) protein in aiding precise risk sub-stratification, prediction of metabolic heterogeneity, and prognosis of neuroblastoma (NB).MethodsThis retrospective study enrolled newly diagnosed patients with NB who underwent positron emission tomography/computed tomography (PET/CT) before therapy, and tumor tissue was collected after surgery. Regression analysis was used to evaluate ASPM expression and risk stratification in patients with NB. The expression levels of ASPM, clinical information, and PET/CT text features were analyzed using univariate and multivariate survival analyses. Finally, a correlation analysis was used to explore the relationship between ASPM and tumor metabolic heterogeneity.ResultsThere were 48 patients with NB in this study (35 boys and 13 girls); 22 patients progressed and 16 died. We found that the level of ASPM was highly associated with risk stratification (OR = 5.295, 95%IC: 1.348–41.722, p = 0.021). Patients with NB and high-risk stratification with high ASPM level had a lower 3-year progression-free survival (PFS) rate (14.28%) and 1-year PFS rate (57.14%) than those with low ASPM level (57.14% and 93.75%, respectively). Using univariate and multivariate survival analyses, this study revealed that ASPM and LDH were independent risk factors for both PFS and overall survival (OS), whales GLZLM_ZLNU was only a risk factor for PFS.ConclusionASPM holds promise as a novel biomarker for refining current risk stratification and predicting prognosis in neuroblastoma. Elevated levels of ASPM, LDH, and GLZLM_ZLNU may be associated with poorer survival outcomes in neuroblastoma patients.

Molecular biomarkers of progression in non-muscle-invasive bladder cancer - beyond conventional risk stratification.

The global incidence of bladder cancer is more than half a million diagnoses each year. Bladder cancer can be categorized into non-muscle-invasive bladder cancer (NMIBC), which accounts for ~75% of diagnoses, and muscle-invasive bladder cancer(MIBC). Up to 45% of patients with NMIBC develop disease progression to MIBC, which is associated with a poor outcome, highlighting a clinical need to identify these patients. Current risk stratification has a prognostic value, but relies solely on clinicopathological parameters that might not fully capture the complexity of disease progression. Molecular research has led to identification of multiple crucial players involved in NMIBC progression. Identified biomarkers of progression are related to cell cycle, MAPK pathways, apoptosis, tumour microenvironment, chromatin stability and DNA-damage response. However, none of these biomarkers has been prospectively validated. Reported gene signatures of progression do not improve NMIBC risk stratification. Molecular subtypes of NMIBC have improved our understanding of NMIBC progression, but these subtypes are currently unsuitable for clinical implementation owing to a lack of prospective validation, limited predictive value as a result of intratumour subtype heterogeneity, technical challenges, costs and turnaround time. Future stepsinclude thedevelopment of consensus molecular NMIBC subtypes that mightimprove conventional clinicopathological risk stratification. Prospective implementation studies of biomarkers and the design ofbiomarker-guided clinical trials are required for the integration of molecular biomarkersinto clinical practice.

Opportunities and Challenges in Soft Tissue Sarcoma Risk Stratification in the Era of Personalised Medicine.

Soft tissue sarcomas (STS) are a rare and heterogeneous group of cancers. Treatment options have changed little in the past thirty years, and the role of neoadjuvant chemotherapy is controversial. Accurate risk stratification is crucial in STS in order to facilitate clinical discussions around peri-operative treatment. Current risk stratification tools used in clinic, such as Sarculator, use clinicopathological characteristics and may be specific to anatomical site or to histology. More recently, risk stratification tools have been developed using molecular or immunological data. Combining Sarculator with other risk stratification tools may identify novel patient groups with differential clinical outcomes. There are several considerations when translating risk stratification tools into widespread clinical use, including establishing clinical utility, health economic value, being applicable to existing clinical pathways, having strong real-world performance, and being supported by investment into infrastructure. Future work may include incorporation of novel modalities and data integration techniques.

Machine Learning in Hypertrophic Cardiomyopathy: Nonlinear Model From Clinical and CMR Features Predicting Cardiovascular Events

BackgroundThe cumulative burden of hypertrophic cardiomyopathy (HCM) is significant, with a noteworthy percentage (10%-15%) of patients with HCM per year experiencing major adverse cardiovascular events (MACEs). A current risk stratification scheme for HCM had only limited accuracy in predicting sudden cardiac death (SCD) and failed to account for a broader spectrum of adverse cardiovascular events and cardiac magnetic resonance (CMR) parameters. ObjectivesThis study sought to develop and evaluate a machine learning (ML) framework that integrates CMR imaging and clinical characteristics to predict MACEs in patients with HCM. MethodsA total of 758 patients with HCM (67% male; age 49 ± 14 years) who were admitted between 2010 and 2017 from 4 medical centers were included. The ML model was built on the internal discovery cohort (533 patients with HCM, admitted to Fuwai Hospital, Beijing, China) by using the light gradient-boosting machine and internally evaluated using cross-validation. The external test cohort consisted of 225 patients with HCM from 3 medical centers. A total of 14 CMR imaging features (strain and late gadolinium enhancement [LGE]) and 23 clinical variables were evaluated and used to inform the ML model. MACEs included a composite of arrhythmic events, SCD, heart failure, and atrial fibrillation–related stroke. ResultsMACEs occurred in 191 (25%) patients over a median follow-up period of 109.0 months (Q1-Q3: 73.0-118.8 months). Our ML model achieved areas under the curve (AUCs) of 0.830 and 0.812 (internally and externally, respectively). The model outperformed the classic HCM Risk-SCD model, with significant improvement (P < 0.001) of 22.7% in the AUC. Using the cubic spline analysis, the study showed that the extent of LGE and the impairment of global radial strain (GRS) and global circumferential strain (GCS) were nonlinearly correlated with MACEs: an elevated risk of adverse cardiovascular events was observed when these parameters reached the high enough second tertiles (11.6% for LGE, 25.8% for GRS, −17.3% for GCS). ConclusionsML-empowered risk stratification using CMR and clinical features enabled accurate MACE prediction beyond the classic HCM Risk-SCD model. In addition, the nonlinear correlation between CMR features (LGE and left ventricular pressure gradient) and MACEs uncovered in this study provides valuable insights for the clinical assessment and management of HCM.

A retrospective study of prognostic factors and treatment outcome in advanced-stage Mycosis Fungoides and Sezary Syndrome

ABSTRACT Background: Mycosis fungoides (MF) and Sezary Syndrome (SS) comprise over half of all Cutaneous T-cell lymphoma diagnoses. Current risk stratification is largely based on TNMB staging, few research investigated the prognostic value of clinical exams. Current systemic therapy for advanced disease includes immunomodulatory drugs, chemotherapy, and HADC inhibitors. Few clinical trials or retrospective research compared the efficacy of different drugs. Method: Here, we performed a retrospective analysis of prognostic factors and treatment outcomes of 92 patients diagnosed with MF/SS at the Peking Union Medical College Hospital from 2013-2023. Results: Cox regression analysis identified that age ≥ 50 years, WBC ≥ 8 × 109/L, serum LDH ≥ 250U/L, β2-MG ≥ 4.50 mg/L, and stage IV were associated with reduced overall survival, age ≥ 50 years, serum LDH ≥ 250U/L and stage IV were associated with reduced progression free survival. Kaplan-Meier analysis established that immunomodulatory therapy was associated with longer progression free survival. Conclusion: These results suggested new factors in predicting prognosis and selecting appropriate treatments in patients with advanced MF/SS.

Atrial Fibrillation, Atrial Myopathy, and Thromboembolism: The Additive Value of Echocardiography and Possible New Horizons for Risk Stratification.

Atrial fibrillation (AF) is the most common cardiac sustained arrhythmia, and it is associated with increased stroke and dementia risk. While the established paradigm attributes these complications to blood stasis within the atria and subsequent thrombus formation with cerebral embolization, recent evidence suggests that atrial myopathy (AM) may play a key role. AM is characterized by structural and functional abnormalities of the atria, and can occur with or without AF. Moving beyond classifications based solely on episode duration, the 4S-AF characterization has offered a more comprehensive approach, incorporating patient's stroke risk, symptom severity, AF burden, and substrate assessment (including AM) for tailored treatment decisions. The "ABC" pathway emphasizes anticoagulation, symptom control, and cardiovascular risk modification and emerging evidence suggests broader benefits of early rhythm control strategies, potentially reducing stroke and dementia risk and improving clinical outcomes. However, a better integration of AM assessment into the current framework holds promise for further personalizing AF management and optimizing patient outcomes. This review explores the emerging concept of AM and its potential role as a risk factor for stroke and dementia and in AF patients' management strategies, highlighting the limitations of current risk stratification methods, like the CHA2DS2-VASc score. Echocardiography, particularly left atrial (LA) strain analysis, has shown to be a promising non-invasive tool for AM evaluation and recent studies suggest that LA strain analysis may be a more sensitive risk stratifier for thromboembolic events than AF itself, with some studies showing a stronger association between LA strain and thromboembolic events compared to traditional risk factors. Integrating it into routine clinical practice could improve patient management and targeted therapies for AF and potentially other thromboembolic events. Future studies are needed to explore the efficacy and safety of anticoagulation in AM patients with and without AF and to refine the diagnostic criteria for AM.