Improve Risk Stratification Research Articles

Single-cell RNA sequencing and multi-omics analysis of prognosis-related staging in papillary thyroid cancer

BackgroundPapillary thyroid cancer (PTC) is the most common thyroid cancer, but current molecular features inadequately stratify its risk. Whether distinct underlying mechanisms can further classify PTC and improve prognostic precision remains unclear.MethodsWe integrated single-cell RNA sequencing data (158,577 cells from 11 PTC patients; GEO: GSE184362) with bulk-RNA sequencing data from The Cancer Genome Atlas Thyroid Carcinoma (TCGA-THCA) cohort (501 patients). Multi-omics analyses were employed to elucidate PTC heterogeneity, identify malignant cell differentiation and prognosis-related genes (MCD&PRGs), and construct a novel molecular classification, the Oncogenic Signature Of Papillary Thyroid Carcinoma Classification (OSPTCC). A prognostic risk score was developed, and the classification's prognostic relevance was further explored in an independent institutional cohort using qRT-PCR.ResultsSingle-cell analysis revealed three malignant cell differentiation states (PTC1-3) and a 34-gene signature (MCD&PRGs). This formed the basis of our Oncogenic Signature Of Papillary Thyroid Carcinoma Classification (OSPTCC), defining three subtypes: Inflammation-associated (IPTCC), BRAF/autophagy-related (BAPTCC), and lipid metabolism-related (LPTCC). These subtypes showed distinct molecular profiles and significantly different progression-free survival (IPTCC poorest, P = 0.044). A 7-gene risk score derived from MCD&PRGs independently predicted prognosis (multivariate HR = 21.511, P < 0.001). qRT-PCR validation in an independent cohort (n = 48) using key markers (DEPTOR, APOE, APOC1) confirmed that OSPTCC-based risk stratification correlated with adverse clinical features, including higher recurrence rates in the high-risk group (P = 0.007).ConclusionsThis study introduces OSPTCC, a prognostically significant molecular classification for PTC based on tumor cell differentiation states. The identified subtypes, characterized by distinct biological mechanisms, provide deeper insights into PTC's molecular pathology and offer a framework for improved risk stratification and potential precision therapies.

Exploration of mortality-associated creatinine thresholds for acute kidney injury in cardiac surgery patients with impaired kidney function: a single-center retrospective cohort study

BackgroundAcute kidney injury (AKI) is a frequent and severe complication following cardiac surgery, particularly in patients with impaired kidney function. The existing Kidney Disease: Improving Global Outcomes (KDIGO) criteria do not specifically address acute-on-chronic kidney injury in this high-risk population. Previous studies have proposed alternative diagnostic thresholds that identify more AKI cases than KDIGO and are associated with adverse outcomes. However, their association with endpoints including mortality and clinical utility in cardiac surgery patients remain unclear. This study aims to explore optimal perioperative serum creatinine (SCr) change thresholds associated with in-hospital mortality and compare their predictive performance with KDIGO and other proposed thresholds.MethodsThis retrospective cohort study included 1,081 adult cardiac surgery patients with impaired preoperative kidney function (eGFR 15–60 mL/min/1.73 m²). Postoperative SCr changes were assessed as maximum absolute increases within 48 h and maximum fold increases within 7 days. Multivariable Cox regression and restricted cubic spline (RCS) analyses were used to evaluate associations with endpoints including in-hospital mortality, the initiation of KRT, failure of kidney function recovery by hospital discharge (or death), and major adverse kidney events (MAKE), defined as a composite of in-hospital mortality, dialysis dependence at discharge, or non-recovery of kidney function by hospital discharge (or death). Optimal thresholds were derived using receiver operating characteristic (ROC) curve analysis and Youden’s index. The predictive performance and net clinical benefit for in-hospital mortality were compared across KDIGO definition, previously proposed thresholds, and the newly derived thresholds using ROC and decision curve analysis (DCA).ResultsBoth the absolute increase in SCr within 48 h and the fold increase within 7 days were independently associated with in-hospital mortality (HR 1.66 and 1.59, respectively), RRT (OR 3.10 and 3.62, respectively), kidney function non-recovery (OR 1.43 and 1.38, respectively), and MAKE (OR 2.32 and 2.24, respectively). For in-hospital mortality, the optimal thresholds identified were 38 µmol/L and 2.177-fold, respectively. ROC analysis showed comparable predictive performance with KDIGO definition and other standards. For in-hospital mortality, decision curve analysis suggested a marginally higher net benefit for the new thresholds within the 10–30% threshold probability range.ConclusionsThis study proposes new SCr thresholds specific to cardiac surgery patients with impaired kidney function. If externally validated, these thresholds may aid in improving risk stratification and guiding perioperative management. Nonetheless, further studies are warranted to refine diagnostic approaches to AKI in this high-risk population.

Effect of FRAXplus adjustments on fracture risk reclassification in older Swedish women-results from the SUPERB-study.

FRAXplus enables adjustments based on additional clinical information, such as recency of osteoporotic fractures, high-dose oral glucocorticoids, T2DM duration, lumbar spine (LS) bone mineral density (BMD), trabecular bone score (TBS), falls in the previous year, and hip axis length. We aimed to determine how these adjustments alter treatment eligibility in older Swedish women. Ten-year fracture probabilities with femoral neck BMD were calculated using FRAX and adjusted by FRAXplus in the SUPERB cohort of 3028 Swedish women aged 75 to 80years. Clinical risk factors (CRFs) and outcomes were collected via questionnaires and national registers over 8years, with incident X-ray-verified MOFs. FRAXplus adjustments were applied one factor at a time; if multiple were available, the most influential factor was used. Net reclassification improvement (NRI) was calculated. Overall, 90% (n = 2723) had their 10-year MOF probability adjusted upwards, with a mean (± SD) change of 4.25% (5.12%). Common adjustments included HAL (31%), TBS (23%), falls (20%), LS BMD (8%), and recent fracture (5%). Similar patterns were observed for hip fracture probabilities. Among those below the IT using FRAX alone, 1785 remained below, with 365 (20.4%) experiencing incident MOFs. Of 339 women uplifted above the IT using FRAXplus, 119 (35.1%) sustained incident MOFs. Among 904 above the IT with both FRAX and FRAXplus, 324 (35.8%) experienced incident MOFs. The NRI was 4.82% (95% CI: 1.87-7.77%; p < 0.01). FRAXplus improved risk stratification, with a significant proportion of older Swedish women having their fracture probabilities uplifted above the IT, more accurately reflecting their elevated fracture risk, thereby enhancing the utility of risk assessment tools and improving patient management.

Early cardiovascular changes in hypertensive pregnancies: insights from left atrial strain and compliance

BackgroundHypertensive disorders of pregnancy (HDP), which include gestational hypertension and preeclampsia, are associated with adverse maternal and fetal outcomes and may result in subclinical cardiac dysfunction. Traditional echocardiographic parameters, including the ejection fraction and Doppler indices, often fail to detect early myocardial and atrial abnormalities.ObjectiveTo evaluate early cardiovascular changes in pregnant women with hypertensive disorders, with an explicit focus on left atrial (LA) strain and compliance assessed through speckle-tracking echocardiography.MethodsA prospective observational study was conducted at a tertiary care center, enrolling 73 pregnant women from 20 weeks of gestation onward: 38 with HDPs and 35 with normotensive pregnancies. Transthoracic echocardiography, including two-dimensional speckle tracking, was used to assess global longitudinal strain (GLS), LA strain (reservoir, conduit, contraction), and LA compliance (LASr/E/e′). Standard Doppler and tissue Doppler indices were also evaluated. Logistic regression was used to identify independent predictors of HDP.ResultsCompared with controls, women with HDPs presented significantly lower LA reservoir strain (31.00% vs. 35.00%, p = 0.002), conduit strain (18.00% vs. 24.00%, p < 0.001), and LA compliance (3.87 vs. 5.57, p < 0.001), despite having a preserved ejection fraction. GLS also decreased (19.00% vs. 21.00%, p < 0.001). In the multivariate analysis, decreased LA compliance (OR 0.50, 95% CI 0.33–0.76, p = 0.001) and GLS (OR 1.49, 95% CI 1.08–2.03, p = 0.014) were identified as independent predictors of HDP.ConclusionPregnant women with hypertensive disorders exhibit early subclinical cardiac dysfunction characterized by impaired left atrial (LA) strain and compliance. These advanced echocardiographic parameters may serve as sensitive markers for early cardiovascular changes, providing superior diagnostic value compared with conventional metrics. Routine evaluation of LA strain and compliance could improve risk stratification and guide management in hypertensive pregnancies.

Diastolic function and cardiovascular events in patients with preserved left ventricular ejection fraction. Improving risk stratification with left atrial strain

BackgroundA limited number of studies have examined the prognostic significance of diastolic function in patients with preserved left ventricular ejection fraction (LVEF) in a general population referred for transthoracic echocardiography. Our aim was to assess the association between diastolic function and a combined event in which the left atrium plays a pivotal role, including heart failure (HF), atrial fibrillation (AF) and ischemic stroke. The study sought to determine the incremental value of left atrial reservoir strain (LARS) in risk stratification.MethodsWe performed a retrospective analysis of 364 patients with preserved LVEF and sinus rhythm referred for transthoracic echocardiography and categorized them into four groups based on their diastolic function status according to the 2016 ASE/EACVI guidelines: normal diastolic function (NDF), indeterminate diastolic function and diastolic dysfunction with indeterminate filling pressure (IDT), grade 1 diastolic dysfunction (DD1), and diastolic dysfunction with elevated filling pressure (DD-EFP). The primary endpoint was a composite of HF, AF or ischemic stroke. LARS was measured by 2D speckle tracking. Clinical parameters, comorbidities and specific cardiac diseases were also assessed. Secondary endpoint was all-cause mortality.ResultsThe mean follow-up period was 2.4 years. IDT and DD-EFP diastolic function status were independently associated with the combined event. The incorporation of LARS enhanced risk stratification, particularly in IDT patients, with a cutoff of ≤24% identifying a high-risk population. Patients classified as high risk, defined as those with DD-EFP and IDT with LARS ≤ 24%, exhibited a notable event rate of 34% and 46%, respectively. Diastolic function and LARS were not independently associated with all-cause mortality.ConclusionsIn patients with preserved LVEF and sinus rhythm, diastolic function is strongly and independently associated with the combined event of HF, AF, or ischemic stroke. LARS provides a valuable tool for improving risk stratification in this population. Patients at high risk (DD-EFP and IDT with LARS ≤ 24%) demonstrated a significant event rate, underscoring the necessity for preventive measures. Diastolic function and LARS were not independently associated with all-cause mortality. Further studies are required to confirm these findings and validate the proposed approach.

Utility of CAD-RADS 2.0 Plaque Burden Grades and Stenosis Categories on Coronary CTA for Predicting Cardiac Events in Patients With Acute Chest Pain: A Multicenter Study.

Background: Coronary Artery Disease-Reporting and Data System (CAD-RADS) 2.0 incorporated reporting of plaque burden grades in addition to stenosis categories. Objective: To assess the utilities of plaque burden grades and CAD-RADS categories, determined on coronary CTA using CAD-RADS 2.0, in predicting cardiac events in patients presenting to the emergency department (ED) with acute chest pain. Methods: This retrospective study included patients who underwent coronary CTA after presenting to the ED with acute chest pain at one of four centers from January 2018 to December 2021. A single radiologist reviewed examinations at each center to assign a plaque burden grade by calculating a coronary artery calcium score on noncontrast images and to assign a CAD-RADS category by assessing vessel stenosis on contrast-enhanced CTA images, both using CAD-RADS 2.0; each center's reviewing radiologist also assessed examinations for presence of high-risk plaque. The EMR was reviewed for cardiac events, reflecting a composite outcome of cardiac-related death, myocardial infarction, or hospitalization for unstable angina. Prognostic models were compared. Results: The study included 2032 patients (1085 male, 752 women; mean age, 58.4 years). During median follow-up of 15.2 months, 63 (3.1%) patients experienced cardiac events. In a multivariable Cox model adjusting for clinical variables, cardiac events showed significant independent associations with CAD-RADS 3 (HR=7.1), CADRADS 4 (HR=13.6), CAD-RADS 5 (HR=17.6), and high-risk plaque (HR=2.5), but not with plaque burden grades. For predicting cardiac events, C-statistic was 0.67 for a model including clinical variables; 0.74 for a model including clinical variables and plaque burden grades; 0.86 for a model including clinical variables, CAD-RADS categories, and high-risk plaque; and 0.87 for a model including clinical variables, plaque burden grades, CAD-RADS categories, and high-risk plaque. The model with clinical variables, CAD-RADS categories, and high-risk plaque, but without plaque burden grades, showed highest net clinical benefit across threshold probabilities from 20-100%. Conclusion: Addition of plaque burden grades did not provide further prognostic benefit in models using CADRADS categories. Clinical Impact: Inclusion of an additional noncontrast acquisition for CAC scoring within coronary CTA protocols may not improve risk stratification for patients presenting to the ED with chest pain.

Improved survival in pediatric acute lymphoblastic leukemia through therapy intensification based on minimal residual disease and protocol-driven early response risk classification

PurposeMinimal residual disease (MRD)-guided therapy is the global standard treatment for pediatric acute lymphoblastic leukemia (ALL). We assessed the impact of MRD-driven intensification along with protocol-defined risk groups in pediatric ALL treatment.MethodsThis retrospective analysis included 209 patients with ALL (treated between January 2013 to June 2023). MRD was assessed using six- to eight-color flow cytometry at the end of each phase before the maintenance phase. Post-induction treatment was determined based on early response, National Cancer Institute risk, and cytogenetics. High-risk (HR) patients followed the Korean HR or CCG-1882 protocols and standard-risk (SR) patients followed the modified COG-AALL0331 protocol. Treatment was intensified if flow-MRD ≥ 0.1% was identified.ResultsOverall, 103 and 106 patients were classified as having SR and HR, respectively. The 5-year overall survival (OS) and event-free survival (EFS) were 92.5% and 84.3%, respectively. Thirty SR and 18 HR patients received intensified chemotherapy. Treatment intensification significantly improved EFS in patients with high MRD (94.2% vs. 75.5%, p = 0.04), particularly in post-induction patients with high MRD (90.0% vs. 19.0%, p = 0.035). The difference in survival between rapid early responder (RER) and slow early responder (SER) groups was eliminated after MRD-based intensification. The implementation rates of treatment intensification varied over time (9.1% before 2015, 28.6% during 2016–2019, and 13.9% during 2020–2023), reflecting improved risk stratification and therapy selection.ConclusionMRD-guided therapy intensification markedly improved survival outcomes in patients with pediatric ALL when combined with risk-based protocols, highlighting the importance of MRD monitoring for optimizing risk-adapted treatment strategies.

A scoring model integrating CXCL9, GDF15, FGF21, and NfL, predicts long-term mortality in type 2 diabetes: a retrospective study

BackgroundType 2 diabetes (T2D) is a chronic metabolic disorder associated with aging, systemic inflammation, and increased long-term mortality. Identifying prognostic biomarkers may improve risk stratification and guide personalized interventions. This study aimed to evaluate the long-term prognostic value of circulating biomarkers related to inflammation, metabolic stress, and organ damage in individuals with T2D.MethodsA retrospective study was conducted on a cohort of 478 individuals with T2D, followed for a median of 16.1 years. Ten circulating biomarkers (IL-6, IL-10, CD163, CXCL9, CCL22, GDF15, IL-33, FGF21, Follistatin, and neurofilament light chain [NfL]) were quantified using an automated immunoassay platform. Kaplan–Meier survival analysis and Cox proportional hazards models were used to assess their prognostic significance for all-cause mortality. A biomarker-based scoring model was developed by integrating independent predictors of mortality. Predictive performance was evaluated in comparison with the RECODe equation, a validated risk model for diabetes complications and mortality.ResultsDeceased individuals exhibited significantly higher levels of IL-6, IL-10, CXCL9, FGF21, NfL, and GDF15. Biomarker levels correlated with both microvascular and macrovascular complications, particularly neuropathy, nephropathy, retinopathy, and major adverse cardiovascular events (MACE). In multivariable Cox regression analysis, four biomarkers emerged as independent predictors of mortality: CXCL9 (HR per 1 SD increase 1.19, 95% CI 1.05–1.36, p = 0.006), GDF15 (HR 1.16, 95% CI 1.02–1.33, p = 0.032), NfL (HR 1.25, 95% CI 1.09–1.43, p = 0.001), and FGF21 (HR 1.20, 95% CI 1.04–1.37, p = 0.009). A composite biomarker score (range: 4–12) stratified individuals into distinct risk categories, with each 1-point increase in the score associated with a 55% higher mortality risk (HR 1.53, 95% CI 1.35–1.74, p < 0.001). The biomarker score remained independently predictive after adjusting for clinical covariates and significantly improved individual-level risk classification beyond the RECODe model, as demonstrated by net reclassification and discrimination improvement metrics.ConclusionsThese findings suggest that inflammatory and metabolic stress-related biomarkers independently predict long-term mortality in T2D. The biomarker-based scoring model enhances risk stratification and improves the prognostic performance of existing clinical tools, such as the RECODe equation, potentially informing targeted clinical interventions.

Biomarker and Prognostic Value of Super-ARMS Detection for EGFR Mutation in Advanced NSCLC

BackgroundctDNA is a non-invasive and convenient method for detecting EGFR mutations in non-small cell lung cancer (NSCLC). However, its sensitivity is lower than that of tissue-based testing. To enhance ctDNA detection efficiency, we identified the patient population most suitable for ctDNA testing, assessed the relationship between ctDNA and tumor markers, and examined the clinical significance of ctDNA in medical practice.MethodsA single-center retrospective study was conducted, including 135 patients with NSCLC who underwent histological and liquid Super-ARMS tests. Of these, 92 patients with EGFR mutations detected in both tumor tissue and plasma were classified into the EGFRt+, p+ group, while 43 patients with EGFR mutations detected only in tumor tissue were classified into the EGFRt+, p− group. The clinical features and outcomes between these two groups were compared.ResultsThe positivity rate of Super-ARMS test was 68.1% (92/135). The presence of EGFRt+, p+ in the Super-ARMS test was significantly associated with pleural effusion, bone, liver, and multiple organ metastases. Compared to the EGFRt+, p+ group, the EGFRt+, p− group had a significantly better PFS (P < 0.01). Carcinoembryonic antigen (CEA) levels demonstrated a strong predictive value for identifying plasma EGFR-mutated patients (AUC 0.828, sensitivity 68.8%, specificity 84.4%), while Maximum Standardized Uptake Value (SUVmax) also showed diagnostic value for plasma EGFR-mutated patients (AUC 0.78). Additionally, combination of TP53 and EGFR mutations in plasma provided improved risk stratification for PFS (P < 0.001).ConclusionPatients exhibiting metastasis, elevated levels of tumor markers and SUVmax are more suitable for plasma EGFR mutation testing in clinical NSCLC management. Moreover, a positive plasma ctDNA test not only guides targeted therapy but also predicts a worse prognosis.

Prognostic value of the triglyceride-glucose index for continuous ambulatory peritoneal dialysis patients

ObjectiveWe are committed to demonstrating that the initial triglyceride-glucose (TyG) index possesses prognostic importance for patients undergoing continuous ambulatory peritoneal dialysis (CAPD).Methods354 patients who utilized continuous CAPD at Wuhan No.1 Hospital were recruited. The participants were classified into three unique groups according to the tertiles of the TyG index. The main endpoints were overall and cardiovascular (CVD) mortality, whereas the secondary outcomes were peritonitis, technical failure, and early-onset peritonitis. The association between the TyG index and outcomes utilizing Cox proportional hazard and restricted cubic spline analysis.ResultsOver a median follow-up of 72 months, 92 patients (26.0%) and 60 patients (16.9%) succumbed to all-cause and CVD mortality, respectively. The three groups exhibited significant differences for all-cause death (16.8% in tertile 1, 24.8% in tertile 2, and 36.4% in tertile 3). After full adjustment, patients with the highest TyG index demonstrated a significantly increased all-cause death relative to those in the lowest tertile (hazard ratio, HR, 2.31, 95% confidence interval, 95%CI 1.18–4.56, P = 0.015). Similarly, each unit increase in the TyG index was associated with a 1.32-fold elevated risk of all-cause death (HR 1.32, 95% CI 1.06–1.87, P = 0.031). Restricted cubic spline analysis indicated a relationship between the TyG index and all-cause mortality (P for nonlinearity > 0.05). Furthermore, the same findings were also seen with CVD mortality and secondary outcomes.ConclusionThe TyG index may function as a meaningful and reliable prognostic indicator in CAPD patients, suggesting its potential importance in improving risk stratification in clinical environments.

Association between aortomitral continuity calcification and conduction disturbances following transcatheter aortic valve implantation with the balloon‐expandable Myval valve

BackgroundGiven the anatomical proximity of the cardiac conduction system, aortomitral continuity calcification (AMCC) may contribute to conduction disturbances (CD) during transcatheter aortic valve implantation (TAVI) due to radial force on the AMCC. This study aimed to investigate the impact of AMCC on new‐onset CD in patients undergoing TAVI with the balloon‐expandable Myval valve.MethodsThis retrospective study included 160 patients who underwent TAVI. AMCC was assessed using Agatston and calcium volume scores from preprocedural computed tomography (CT). Multivariable logistic regression was used to identify independent predictors of CD.ResultsHigh‐grade atrioventricular block (HAVB) occurred in 13.1% of patients, and 17.5% required permanent pacemaker implantation (PPM). Patients with HAVB and PPM exhibited a higher prevalence of AMCC and significantly higher AMCC scores. An AMCC score >180 was an independent predictor of HAVB (OR, 5.58; 95% CI, 1.43–21.70; p = .013) and PPM (OR, 5.39; 95% CI, 1.75–16.55; p = .002). When classified by AMCC proximity type, right fibrous trigone (RFT)‐dominant calcification was a strong independent predictor of HAVB (OR, 9.22; 95% CI, 1.63–51.99; p = .012) and PPM (OR, 7.62; 95% CI, 1.91–30.38; p = .004). Prolonged QRS duration, greater implantation depth, and shorter membranous septum length were also independent predictors.ConclusionAMCC is a strong independent predictor of HAVB and PPM following TAVI, particularly with scores >180 or when AMCC is anatomically close to the RFT. Preprocedural CT‐based assessment of AMCC burden and proximity may improve risk stratification and procedural planning.

A Deep Learning Model Integrating Clinical and MRI Features Improves Risk Stratification and Reduces Unnecessary Biopsies in Men with Suspected Prostate Cancer

Background: Accurate upfront risk stratification in suspected clinically significant prostate cancer (csPCa) may reduce unnecessary prostate biopsies. Integrating clinical and Magnetic Resonance Imaging (MRI) variables using deep learning could improve prediction. Methods: We retrospectively analysed 538 men who underwent MRI and biopsy between April 2019-September 2024. A fully connected neural network was trained using 5-fold cross-validation. Model 1 included clinical features (age, prostate-specific antigen [PSA], PSA density, digital rectal examination, family history, prior negative biopsy, and ongoing therapy). Model 2 used MRI-derived Prostate Imaging Reporting and Data System (PI-RADS) categories. Model 3 used all previous variables as well as lesion size, location, and prostate volume as determined on MRI. Results: Model 3 achieved the highest area under the receiver operating characteristic curve (AUC = 0.822), followed by Model 2 (AUC = 0.778) and Model 1 (AUC = 0.716). Sensitivities for detecting clinically significant prostate cancer (csPCa) were 87.4%, 91.6%, and 86.8% for Models 1, 2, and 3, respectively. Although Model 3 had slightly lower sensitivity than Model 2, it showed higher specificity, reducing false positives and avoiding 43.4% and 21.2% more biopsies compared to Models 1 and 2. Decision curve analysis showed M2 had the highest net benefit at risk thresholds ≤ 20%, while M3 was superior above 20%. Conclusions: Model 3 improved csPCa risk stratification, particularly in biopsy-averse settings, while Model 2 was more effective in cancer-averse scenarios. These models support personalized, context-sensitive biopsy decisions.

Patient risk evaluation for transcatheter aortic valve replacement (PRE-TAVR) - identification of real-time predictors of short- and long-term mortality.

The steadily increasing number of transcatheter aortic valve replacement (TAVR) procedures being performed on a heterogeneous patient population highlights the need for robust risk assessment. While EuroSCORE II is well established for surgical risks, it is less effective for TAVR, and the newer STS/ACC TAVR score has so far been validated mainly for in-hospital and 30-day mortality. This study aims to improve risk stratification for TAVR patients by identifying real-time predictors of 30-day and 1-year mortality that incorporate comprehensive, procedure-specific factors. Five-year data from 2256 transfemoral TAVR procedures performed at two German Heart Centers (2017-2022) were retrospectively analyzed. Predictors of 1-year and 30-day mortality were assessed using multivariable logistic and LASSO regression, considering a broad spectrum of patient demographics, comorbidities, and peri-procedural factors. The analyses revealed a predictor model (PRE-TAVR predictors) for 1-year mortality (AUC 0.770; 95% CI 0.731-0.809), including age (> 81.5years), NYHA stage IV, COPD (GOLD ≥ 2), atrial fibrillation, previous stroke or malignancy, elevated C-reactive protein (≥ 9.5mg/L), aortic valve ΔP mean ≥ 48.5mmHg, peripheral arterial disease (> stage 2) and low platelet count (≤ 228.5g/L). The accuracy of the model exceeded the EuroSCORE II (AUC 0.645; 95% CI 0.599-0.691) and the STS/ACC TAVR score (AUC 0.714; 95% CI 0.670-0.758). For 30-day mortality, NYHA class IV was the only significant predictor in the bivariate analyses. However, additional LASSO analyses identified pre-existing renal insufficiency (KDIGO stage ≥ 3) and pre-TAVR sodium levels as further significant predictors. The AUC was 0.699 (95% CI 0.611-0.788) compared to an AUC of 0.680 (95% CI 0.604-0.756) for EuroSCORE II and 0.7129 (95% CI 0.633-0.793) for the STS/ACC TAVR score. The PRE-TAVR study developed a robust model, particularly for predicting 1-year mortality. This model outperformed the EuroSCORE II and STS/ACC TAVR scores, despite requiring fewer variables. It provides a solid basis for future risk scores and enables more precise patient selection.

MELD 3.0 Improves Risk Stratification and Mortality Prediction in Hepatorenal Syndrome-Acute Kidney Injury: A Multicentre Study.

Hepatorenal syndrome-acute kidney injury (HRS-AKI) is a life-threatening complication in decompensated cirrhosis with limited predictive tools for short-term prognosis. Traditional models such as MELD, MELD-Na and Child-Turcotte-Pugh (CTP) have notable limitations in this population. This study aimed to evaluate the prognostic performance of the newly developed MELD 3.0 score in predicting 90-day mortality among patients with HRS-AKI. In this multicentre retrospective cohort study, 539 patients with HRS-AKI were enrolled from three tertiary hospitals between 2015 and 2023. The predictive performance of MELD 3.0 for 90-day mortality was systematically compared with MELD, MELD-Na, and CTP scores through ROC curve (AUC) and net reclassification improvement (NRI) analysis. MELD 3.0 demonstrated the highest predictive performance for 90-day mortality (AUC = 0.812), significantly surpassing MELD (AUC = 0.790, p = 0.006), MELD-Na (AUC = 0.791, p = 0.002) and CTP (AUC = 0.756, p = 0.005). It was an independent predictor of mortality (HR = 1.13 per point, 95% CI: 1.11-1.16, p < 0.001) and achieved significant NRI over MELD (7.05%), MELD-Na (6.01%) and CTP (13.75%). Its predictive performance remained robust across ACLF severity, age subgroups and treatment response profiles. This is the first large-scale study to validate MELD 3.0 in HRS-AKI. By offering enhanced discrimination and clinically meaningful reclassification, MELD 3.0 provides a more refined tool for early risk stratification, with potential to guide timely intervention, inform transplant decisions and improve personalized care in this critically ill population.

Prognostic Value of Inflammation and Nutrition-Based Scores in Low-Risk Myelodysplastic Syndrome: A Retrospective Cohort Study.

Background/Objectives: The pathogenesis of Myelodysplastic Syndrome (MDS) is diverse; however, increasing evidence suggests that inflammation and oxidative stress play a significant role in the development and progression of the disease. This study aimed to evaluate the prognostic impact of inflammation, nutritional status, and oxidative stress at diagnosis in patients with low-risk MDS. Methods: A retrospective analysis was conducted on 175 newly diagnosed low-risk MDS patients. Results: A low Prognostic Nutritional Index (PNI) and a high systemic oxidative stress (SOS) score were independently associated with poorer prognosis (PNI: HR 1.598, 95% CI 1.076-2.372, p = 0.02; SOS: HR 1.003, 95% CI 1.001-1.006, p = 0.002). The optimal PNI cut-off value for predicting mortality was identified as 47.47. Based on this cut-off, 92 patients had a low PNI score, while 83 patients had a high PNI score. The comparison between these groups revealed a statistically significant difference in median overall survival (OS), with 45.5 months for the low-PNI group and 75.1 months for the high-PNI group (p < 0.001). However, PNI was not significantly associated with progression to acute myeloid leukemia (AML) (p = 0.668). In the multivariate OS analysis, several factors were identified as independent predictors of prognosis, including a high Revised International Prognostic Scoring System (R-IPSS) score, low PNI, high SOS score, advanced age, male gender, and transformation to AML. Conclusions: Together, PNI and SOS may serve as simple, accessible tools to improve risk stratification in low-risk MDS patients.

Incidence and resource burden for the management of computed tomography-detected ground glass opacities at a tertiary lung cancer service in the United Kingdom.

The increased use of computed tomography (CT) for lung cancer screening has significantly raised awareness of pure ground glass opacity (pGGO) lesions. GGOs pose diagnostic challenges, ranging from benign findings to invasive adenocarcinomas. We aim to evaluate the incidence of pGGOs identified on CT at a tertiary UK lung cancer service and quantify their impact on time and healthcare resources. We conducted a retrospective review of patients with pGGOs discussed at multidisciplinary team (MDT) meetings between 2017 and 2019 at the Royal Free and Royal Brompton Hospitals. Demographics were recorded, and annual incidence along with management data was analyzed. A total of 3,731 patients were discussed at MDTs during the specified timeframe. On initial review 401 cases (11%) discussed were identified on CT as exhibiting areas of ground glass change, with the annual incidence increasing from 8% in 2017 to 13% in 2019. Following a second stage detailed assessment of the cases and application of exclusion criteria-such as the presence of a part solid component-259 patients were confirmed to have pure GGOs and were included for subsequent analysis in this study. The median number of MDT meetings per GGO was 2 (range: 1-3), with 3 CT scans (range: 2-4) over a median follow-up of 8.7 months (range: 2.0-24.1). PET scans were performed in 74 (27%) patients, 40 (15%) underwent biopsies (62.5% confirming malignancy), and 24 (9%) proceeded to surgery. The rising incidence of GGOs presents a growing resource burden, with repeated MDT discussions, imaging, and interventions. Revised guidelines with improved risk stratification models are essential to optimize management, reduce unnecessary follow-up, and improve resource utilization in lung cancer services.

Right ventricular-pulmonary coupling and tissue hypoperfusion: a combined echocardiographic and metabolic approach to prognosticate cardiogenic shock

BackgroundCardiogenic shock (CS) remains a high-mortality condition. Effective risk stratification is crucial for guiding management. This study evaluated the combined prognostic utility of the tricuspid annular plane systolic excursion(TAPS) to pulmonary artery systolic pressure (PASP) ratio, a measure of right ventricular-pulmonary coupling, and lactate, a marker of tissue hypoperfusion, in intensive care unit (ICU) patients with CS.MethodsA retrospective cohort study was conducted on 221 ICU patients diagnosed with CS (SCAI shock stages B-E). Echocardiographic parameters (including TAPS/PASP) and arterial lactate levels were obtained within 24 h of diagnosis. The primary outcome was 28-day mortality. Univariate and multivariate Cox proportional hazards regression, Kaplan-Meier survival analysis, and receiver operating characteristic (ROC) curve analysis were performed.ResultsBoth TAPS/PASP ratio (HR = 0.689, p = 0.003) and lactate (HR = 1.498, p = 0.003) were independently associated with 28-day mortality. Patients with low TAPS/PASP and high lactate had significantly lower survival. A combined model incorporating both parameters demonstrated superior predictive performance for 28-day mortality (AUC = 0.703) compared to TAPS/PASP (AUC = 0.671) or lactate (AUC = 0.650) alone. A non-linear relationship was observed between TAPS/PASP and lactate.ConclusionThe combined assessment of TAPS/PASP ratio and lactate levels provides enhanced prognostic information in patients with cardiogenic shock. This integrated approach, incorporating both cardiac function and metabolic biomarkers, may improve risk stratification and facilitate more targeted therapeutic interventions in this high-risk population.

MuTATE: an interpretable multi-endpoint machine learning framework for automated molecular subtyping in cancer

Effective and interpretable molecular subtyping is critical for cancer risk stratification and treatment, yet existing methods face key limitations. Traditional models cannot jointly model multiple clinical endpoints, limiting prognostic utility, while machine learning (ML) approaches often lack transparency. We developed MuTATE, an automated, interpretable decision-tree framework powered by ML that improves subtyping accuracy and enables multi-endpoint risk stratification. MuTATE was evaluated using 18,400 simulations and 682 patient biopsies from three TCGA cancers: lower-grade glioma (LGG), endometrial carcinoma (EC), and gastric adenocarcinoma (GA). Compared to established clinical models, MuTATE improved accuracy, interpretability, and biomarker discovery, and reclassified risk groups. In LGG, MuTATE reassigned 13% of “low-risk” IDH-1p19q cases into higher-risk subtypes, and 19% of “high-risk” IDH wild-type cases were reassigned to higher-risk categories. In GA, MuTATE refined the “intermediate-risk” genomically stable group into a higher-risk ARID1A wild-type subtype. In EC, 72% of “intermediate-risk” MSI/MLH1 cases were reassigned to the highest-risk category. These findings demonstrate MuTATE’s potential to reduce diagnostic bias, improve risk stratification, and support scalable integration of multi-endpoint ML into precision oncology workflows.

Predicting carotid atherosclerosis in latent autoimmune diabetes in adult patients using machine learning models: a retrospective study

BackgroundLatent autoimmune diabetes in adults (LADA) is a slowly progressing form of diabetes with autoimmune origins. Patients with LADA are at an elevated risk of developing cardiovascular diseases, including carotid atherosclerosis. While machine learning models have been widely used in predicting cardiovascular risks in Type 1 and Type 2 diabetes, research on LADA remains limited. Early prediction of carotid atherosclerosis using machine learning models could help in timely intervention and improved patient outcomes for this specific population.MethodsWe conducted a retrospective cross-sectional analysis involving 142 LADA patients diagnosed within the endocrinology department at Shanxi Bethune Hospital, China. Various clinical, demographic, and laboratory variables were analyzed using univariate and multivariate logistic regression, complemented by LASSO regression for feature selection. Additionally, eight machine learning algorithms—logistic regression (LR), decision tree (DT), random forests (RF), k-nearest neighbors (KNN), support vector machine (SVM), neural networks (NNET), eXtreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM)—were employed to predict carotid atherosclerosis.ResultsSignificant risk factors for carotid atherosclerosis were identified, including age, smoking history, BMI, ALB, HDL-C, and ALT. Among the various machine learning models evaluated, the LR model exhibited the highest performance, achieving an area under the curve (AUC) of 0.936, alongside an accuracy of 86%. NNET and SVM models also demonstrated robust predictive capacities with AUC values of 0.919 and 0.918, respectively.ConclusionsThis study highlights the critical role of identifying risk factors for carotid atherosclerosis in LADA patients. Our use of ML models builds on the growing body of work in diabetes-related cardiovascular risk prediction, and it offers a novel approach by specifically targeting the LADA population. Incorporating ML models into clinical practice can improve risk stratification and patient management in LADA. Future research should validate these models across diverse populations and investigate the underlying mechanisms linking LADA to cardiovascular risk.Clinical trial numberNot applicable.

Prognostic and therapeutic value of a 23-gene risk score tailored to the molecular characteristics of mucinous colorectal cancer.

Mucinous adenocarcinoma (MuC), a colorectal cancer (CRC) subtype, exhibits distinct molecular features and poorer response to chemoradiotherapy than non-mucinous adenocarcinoma (NMuC). Conventional treatments often fail to address CRC heterogeneity, particularly in stage II disease. Therefore, improved biomarkers for risk stratification and personalised treatment are needed. We analysed gene expression and mutation data from 259 CRC samples to identify characteristics of MuC. A 23-gene risk score (MuC-RS) was developed and validated across four independent cohorts (n = 1157). Statistical analyses, including generalised linear model likelihood ratio tests, Kaplan-Meier curves, log-rank tests, and Cox regression models, evaluated the prognostic utility of the MuC-RS. MuC showed significant upregulation of fibroblast-associated genes, pathways related to epithelial-mesenchymal transition, and mucin glycosylation. The MuC-RS effectively stratified patients into high-risk (MuC-H) and low-risk (MuC-L) groups, with multivariate analysis confirming its prognostic value (HR = 1.72, 95% CI = 1.31-2.25, P < 0.001). Stage II MuC-L patients had poorer outcomes after conventional chemotherapy, but responded better to immune checkpoint inhibitors (ICIs), linked to higher tumour mutation burden and immune activation. The MuC-RS effectively predicts recurrence and guides personalised treatment in CRC, particularly benefiting stage II MuC patients through improved risk stratification and treatment selection.