Noninvasive Risk Stratification Research Articles

Abstract 4119146: Pulmonary Artery Enlargement Predicts Mortality in Patients with Severe Aortic Stenosis after Transcatheter Aortic Valve Replacement.

Background: Pulmonary hypertension is associated with extensive remodeling of the pulmonary tree, with key finding the enlargement of the pulmonary artery (PA). Although the independent predictive value of the pulmonary hypertension in patients undergoing transcatheter aortic valve replacement for severe aortic stenosis has been clearly demonstrated, data about the potential predictive value of the pre-interventional PA enlargement on mortality are scarce. Research questions: The aim of the present study was to assess the prognostic significance of the pre-interventional PA enlargement, assessed by cardiac CT scan, in patients undergoing transcatheter aortic valve replacement for severe aortic stenosis. Methods: We conducted a retrospective study and recruited 337 patients with aortic stenosis, who underwent a right heart catheterization and a cardiac CT scan before the transcatheter aortic valve replacement. The cohort was divided in two groups according to the presence of an enlarged PA (PA e ), (main PA axial diameter ≥ 29 mm for males and ≥ 27 mm for females, n=192, mean age 83±6 years, 41% males) or not (PA n ), ( n=145, mean age 84±6 years, 46% males). The primary endpoint was all-cause mortality at 1 year. Results: Globally, a strong correlation between invasive mean PA pressure and main PA axial diameter was noted (Pearson r=0.393, p<0 .001). Compared to the PA n group, patients with enlarged PA exhibited higher 1 year mortality rates (hazard ratio 2.51, 95% CI: 1.1-6.04, p=0.027). The prognostic significance was even stronger after indexing the PA to the ascending aorta diameter (hazard ratio 3.82, 95% CI: 1.86-7.87, p<0.001, figure 1). Conclusions: Pre interventional PA enlargement, is strongly associated with 1-year all-cause mortality in patients undergoing transcatheter aortic valve. This may pave the way for a more precise non-invasive risk stratification in patients with severe aortic stenosis.

Random survival forest algorithm for risk stratification and survival prediction in gastric neuroendocrine neoplasms.

This study aimed to construct and assess a machine-learning algorithm designed to forecast survival rates and risk stratification for patients with gastric neuroendocrine neoplasms (gNENs) after diagnosis. Data on patients with gNENs were extracted and randomly divided into training and validation sets using the Surveillance, Epidemiology, and End Results database. We developed a prediction model using 10 machine learning algorithms across 101 combinations to forecast cancer-related mortality in patients with gNENs, selecting the best model using the highest mean over a sequence of time-dependent area under the receiver operating characteristic (ROC) curve (AUC). The performance of the final model was assessed through time-dependent ROC curves for discrimination and calibration curves for calibration. The maximum selection rank method was used to determine the best prognostic risk score threshold for classifying patients into high- and low-risk groups. Afterward, Kaplan-Meier analysis and log-rank test were used to compare survival rates among these groups. Our study examined 775 patients with gNENs, dividing them into training and validation sets. A training set comprised 543 patients, with a median follow-up of 42 months and cumulative mortality rates of 40.0% at 1 year, 48.6% at 3 years, and 54.0% at 5 years. A validation set comprised 232 patients, with cumulative mortality rates of 29.1% at 1 year, 43.5% at 3 years, and 53.2% at 5 years. The optimal random survival forest (RSF) model (mtry = 4, node size = 5) achieved an AUC of 0.839 for survival prediction in the training set. Comprising 11 variables such as demographics, treatment details, tumor characteristics, T staging, N staging, and M staging, the RSF model revealed high predictive accuracy with AUCs of 0.92, 0.96, and 0.96 for 1-, 3-, and 5-year survival, respectively, which was consistently reflected in the validation set with AUCs of 0.88, 0.92, and 0.89, respectively. Moreover, patients were risk-stratified. Although our RSF model effectively stratified patients into different prognostic groups, it needs external validation to confirm its utility for noninvasive prognostic prediction and risk stratification in gNENs. Further research is required to verify its broader clinical applicability.

CT feature of irregular extensive ulceration as a predictor of liver metastasis in gastric gastrointestinal stromal tumours.

This study aimed to investigate whether the computed tomography (CT) finding of irregular extensive ulceration (IEU) can serve as a predictor of liver metastasis (LIM) in patients with gastric gastrointestinal stromal tumours (GISTs). This study retrospectively collected clinical and imaging data from 286 patients diagnosed with low-, intermediate-, or high-risk gastric GISTs, or primary lesions with LIM from three medical institutions. The patients were categorised into non-LIM and LIM groups according to whether they had synchronous or metachronous LIM. Multivariate logistic regression analyses were performed to identify significant predictors of LIM. Additionally, receiver operating characteristic (ROC) curve, subgroup, and pathologic-radiologic correlation analyses were conducted. A total of 124 patients were ultimately enroled. There were significant differences in sex, site, growth pattern, size, shape, ulceration and Ki-67 expression between LIM and non-LIM groups. ROC curve analysis demonstrated that IEU had the highest area under the curve for predicting LIM (AUC = 0.842; 95% CI: 0.760-0.924; p < 0.001). Multivariate analysis indicated that IEU was the most significant independent predictor of high LIM risk (OR = 88.62; 95% CI: 2.80-2803.54; p = 0.011). Subgroup analysis showed that IEU was more frequently associated with male sex, age ≤ 55 years, proximal sites, irregular shapes, mixed growth patterns, and a high Ki-67 expression. The CT feature of IEU serves as an independent predictor of LIM in gastric GISTs and is strongly associated with high Ki-67 expression. Question Accurate assessment of LIM risk in patients with gastric GISTs is crucial, yet current non-invasive predictors remain inadequate. Findings IEU on CT is an independent predictor of LIM, with high diagnostic accuracy and a significant association with elevated Ki-67 expression. Clinical relevance IEU on CT scans enables non-invasive risk stratification for LIM in gastric GISTs. Our study refined the assessment of ulceration types, highlighting significant heterogeneity, which may guide personalised treatment strategies.

Risk assessment models and survival in pulmonary arterial hypertension: a SPAHR analysis

BackgroundMulticomponent improvement (MCI) is a novel endpoint for predicting survival in patients with pulmonary arterial hypertension (PAH), included in the sotatercept clinical program. For the first time, we investigated the prognostic value of MCI, ESC/ERS 4-strata risk (4SR) assessment, and the non-invasive French risk stratification score (FRS), for predicting survival in PAH patients in Sweden. All risk prediction models are based on three parameters: WHO-FC (World Health Organization Functional Class), NT-proBNP, and 6MWD (6-minute walk distance). MethodsData from the Swedish PAH & CTEPH Registry (SPAHR) collected 2008-2021 were used for the analyses. The association of MCI achievement, 4SR, and FRS calculated at 6 months (6M), with transplant-free (TF) survival was investigated in the whole cohort, as well as categorized by age (<65 and ≥65 years). All risk prediction models are based on three parameters: WHO-FC (World Health Organization Function Class), NT-proBNP, and 6MWD (6-minute walk distance). Kaplan-Meier estimate/Log-Rank test and Cox proportional model were used for survival analyses. ResultsThe study included 411 patients (70% women) with a median [IQR] age of 66y.21 At 6M, the mean (SD) NT-proBNP decrease was 808 (603) and the mean 6MWD increase was 44 (11) meters. Median survival/follow-up time was 3.5y [1.7, 5.4]. After adjustment for sex and comorbidities, achievement of MCI independently predicted TF-survival; one MCI-criterion met (HR 0.65; CI 0.46-0.92, p=0.015); two MCI-criteria met (HR 0.45; CI 0.31-0.66, p<0.001); all three MCI-criteria met (HR 0.32; CI 0.21-0.52, p<0.001). Likewise, 4SR and FRS demonstrated a strong association with TF-survival with patients achieving lower risk scores exhibiting longer survival compared to those with higher risk scores. Patients ≥65Y more often had connective tissue disease-associated PAH, lower DLCO, more pronounced comorbidity burden, higher risk at baseline, less improvement during follow-up, and worse TF-survival then patients <65Y. ConclusionAll models were found to have prognostic relevance for TF-survival. Risk prediction was incremental with the number of low-risk criteria met, while improvements in only one of 6MWD, NT-proBNP, or FC showed a modest association with survival. The risk assessment tools predicted outcome in patients across both age categories.

Aggressiveness classification of clear cell renal cell carcinoma using registration-independent radiology-pathology correlation learning.

Renal cell carcinoma (RCC) is a common cancer that varies in clinical behavior. Clear cell RCC (ccRCC) is the most common RCC subtype, with both aggressive and indolent manifestations. Indolent ccRCC is often low-grade without necrosis and can be monitored without treatment. Aggressive ccRCC is often high-grade and can cause metastasis and death if not promptly detected and treated. While most RCCs are detected on computed tomography (CT) scans, aggressiveness classification is based on pathology images acquired from invasive biopsy orsurgery. CT imaging-based aggressiveness classification would be an important clinical advance, as it would facilitate non-invasive risk stratification and treatment planning. Here, we present a novel machine learning method, Correlated Feature Aggregation By Region (CorrFABR), for CT-based aggressiveness classification ofccRCC. CorrFABR is a multimodal fusion algorithm that learns from radiology and pathology images, and clinical variables in a clinically-relevant manner. CorrFABR leverages registration-independent radiology (CT) and pathology image correlations using features from vision transformer-based foundation models to facilitate aggressiveness assessment on CT images. CorrFABR consists of three main steps: (a) Feature aggregation where region-level features are extracted from radiology and pathology images at widely varying image resolutions, (b) Fusion where radiology features correlated with pathology features (pathology-informed CT biomarkers) are learned, and (c) Classification where the learned pathology-informed CT biomarkers, together with clinical variables of tumor diameter, gender, and age, are used to distinguish aggressive from indolent ccRCC using multi-layer perceptron-based classifiers. Pathology images are only required in the first two steps of CorrFABR, and are not required in the prediction module. Therefore, CorrFABR integrates information from CT images, pathology images, and clinical variables during training, but for inference, it relies solely on CT images and clinical variables, ensuring its clinical applicability. CorrFABR was trained with heterogenous, publicly-available data from 298 ccRCC tumors (136 indolent tumors, 162 aggressive tumors) in a five-fold cross-validation setup and evaluated on an independent test set of 74 tumors with a balanced distribution of aggressive and indolent tumors. Ablation studies were performed to test the utility of each component ofCorrFABR. CorrFABR outperformed the other classification methods, achieving an ROC-AUC (area under the curve) of 0.855±0.0005 (95% confidence interval: 0.775, 0.947), F1-score of 0.793±0.029, sensitivity of 0.741±0.058, and specificity of 0.876±0.032 in classifying ccRCC as aggressive or indolent subtypes. It was found that pathology-informed CT biomarkers learned through registration-independent correlation learning improves classification performance over using CT features alone, irrespective of the kind of features or the classification model used. Tumor diameter, gender, and age provide complementary clinical information, and integrating pathology-informed CT biomarkers with these clinical variables further improvesperformance. CorrFABR provides a novel method for CT-based aggressiveness classification of ccRCC by enabling the identification of pathology-informed CT biomarkers, and integrating them with clinical variables. CorrFABR enables learning of these pathology-informed CT biomarkers through a novel registration-independent correlation learning module that considers unaligned radiology and pathology images at widely varying imageresolutions.

Electrocardiographic Clues for Early Diagnosis of Ventricular Pre-Excitation and Non-Invasive Risk Stratification in Athletes: A Practical Guide for Sports Cardiologists.

Ventricular pre-excitation (VP) is a cardiac disorder characterized by the presence of an accessory pathway (AP) that bypasses the atrioventricular node (AVN), which, although often asymptomatic, exposes individuals to an increased risk of re-entrant supraventricular tachycardias and sudden cardiac death (SCD) due to rapid atrial fibrillation (AF) conduction. This condition is particularly significant in sports cardiology, where preparticipation ECG screening is routinely performed on athletes. Professional athletes, given their elevated risk of developing malignant arrhythmias, require careful assessment. Early identification of VP and proper risk stratification are crucial for determining the most appropriate management strategy and ensuring the safety of these individuals during competitive sports. Non-invasive tools, such as resting electrocardiograms (ECGs), ambulatory ECG monitoring, and exercise stress tests, are commonly employed, although their interpretation can sometimes be challenging. This review aims to provide practical tips and electrocardiographic clues for detecting VP beyond the classical triad (short PR interval, delta wave, and prolonged QRS interval) and offers guidance on non-invasive risk stratification. Although the diagnostic gold standard remains invasive electrophysiological study, appropriate interpretation of the ECG can help limit unnecessary referrals for young, often asymptomatic, athletes.

Cost-effective identification of Barrett's esophagus in the community: A first step towards screening.

The first step towards developing a screening strategy for Barrett's esophagus (BE) is the identification of individuals in the community. Currently available tools include endoscopy, less-invasive non-endoscopic devices, and non-invasive risk stratification models. We evaluated the cost of potential strategies for identification of BE as a first step towards screening. Two hypothetical cohorts of the general population aged ≥50years with BE prevalence rates of 1.9% and 6.8% were modeled. Four potential screening tools were evaluated: (i) risk stratification based on non-weighted clinical factors according to US/European guidelines, (ii) weighted risk stratification using algorithmic models, (iii) less-invasive devices such as Cytosponge+trefoil factor 3 (TFF3), and (iv) endoscopy. Using a decision-analytic model, the cost per BE case identified and the cost-effectiveness were compared for six potential BE screening strategies based on combinations of the four screening tools; (i)+(iv), (ii)+(iv), (iii)+(iv), (i)+(iii)+(iv), (ii)+(iii)+(iv), and only (iv). The cost per BE case identified was lowest for the weighted risk stratification followed by Cytosponge-TFF3 then endoscopy strategy at both 1.9% and 6.8% BE prevalences (US$9282 and US$3406, respectively) although it was sensitive to the cost of less-invasive devices. This strategy was also most cost-effective for a BE prevalence of 1.9%. At BE prevalence of 6.8%, the Cytosponge-TFF3 followed by endoscopy strategy was most cost-effective. Incorporating weighted risk stratification and less-invasive devices such as Cytosponge-TFF3 into BE screening strategies has a potential to cost-effectively identify BE in the community although device cost and the community prevalence of BE will impact the optimal strategy.

Additive value of invasive haemodynamic assessment for predicting post-operative outcomes after Fontan.

Routine pre-Fontan cardiac catheterization remains standard practice at most centres. However, with advances in non-invasive risk assessment, an invasive haemodynamic assessment may not be necessary for all patients.Using retrospective data from patients undergoing Fontan palliation at our institution, we developed a multivariable model to predict the likelihood of a composite adverse post-operative outcome including prolonged length of stay ≥ 30 days, hospital readmission within 6 months, and death and/or transplant within 6 months. Our baseline model included non-invasive risk factors obtained from clinical history and echocardiogram. We then incrementally incorporated invasive haemodynamic data to determine if these variables improved risk prediction.Our baseline model correctly predicted favourable versus adverse post-Fontan outcomes in 118/174 (68%) patients. Covariates associated with adverse outcomes included the presence of a systemic right ventricle (adjusted adds ratio [aOR] 2.9; 95% CI 1.4, 5.8; p = 0.004), earlier surgical era (aOR 3.1 for era 1 vs 2; 95% CI 1.5, 6.5; p = 0.002), and performance of concomitant surgical procedures at the time of Fontan surgery (aOR 2.5; 95% CI 1.1, 5.0; p = 0.026). Incremental addition of invasively acquired haemodynamic data did not improve model performance or percentage of outcomes predicted.Invasively acquired haemodynamic data does not add substantially to non-invasive risk stratification in the majority of patients. Pre-Fontan catheterization may still be beneficial for angiographic evaluation of anatomy, for therapeutic intervention, and in select patients with equivocal risk stratification.

Clinical Updates in Coronary Artery Disease: A Comprehensive Review.

Despite significant goals achieved in diagnosis and treatment in recent decades, coronary artery disease (CAD) remains a high mortality entity and continues to pose substantial challenges to healthcare systems globally. After the latest guidelines, novel data have emerged and have not been yet considered for routine practice. The scope of this review is to go beyond the guidelines, providing insights into the most recent clinical updates in CAD, focusing on non-invasive diagnostic techniques, risk stratification, medical management and interventional therapies in the acute and stable scenarios. Highlighting and synthesizing the latest developments in these areas, this review aims to contribute to the understanding and management of CAD helping healthcare providers worldwide.

Imaging of Chromophobe Renal Cell Carcinoma with 99mTc-Sestamibi SPECT/CT: Considerations Regarding Risk Stratification and Histologic Reclassification.

Indeterminate renal masses are increasingly incidentally found on cross-sectional imaging. 99mTc-sestamibi single-photon emission computed tomography/computed tomography (SPECT/CT) scans can be used to identify oncocytomas and oncocytic renal neoplasms, including a subset of chromophobe renal cell carcinomas (chRCCs), which are viewed as false-positive. Patients imaged with renal sestamibi scans between 2014 and 2023 were reviewed. Those patients with solitary tumors that were originally classified as chRCC were included in the analysis. Imaging with SPECT/CT from the liver dome down had been carried out 75min after the administration of 925MBq of 99mTc-sestamibi. All available H&E and immunostained slides were re-reviewed and classified according to WHO 2022 criteria. Confirmatory immunohistochemical stains were performed in tumors considered morphologically suspicious for non-chRCC entities. A total of 18 patients with solitary tumors were included in the final analysis. 13/18 (72.2%) tumors in this cohort remained classified as chRCC, with 4/18 (22.2%) being eosinophilic-variant chRCC. The reclassified tumors (5/18 [27.8%]) included 2/18 (11.1%) low-grade oncocytic tumor (LOT), 1/18 (5.5%) eosinophilic vacuolated tumor (EVT), and 2/18 (11.1%) unclassified low-grade oncocytic neoplasms. As such, only 2/9 (22.2%) qualitatively "hot" tumors were chRCC other than eosinophilic-variant and only 1/9 (11.1%) "cold" tumors was a histology other than chRCC. Based on current histopathologic classification methods, it is likely that the "false-positive" rate of uptake on renal sestamibi scans with chRCC has been over-stated. Further study is warranted to better refine the optimal utility of renal sestamibi scans for non-invasive risk stratification of indeterminate renal masses.

Three-dimensional cardiac magnetic resonance allows the identification of slow-conducting anatomical isthmuses in tetralogy of Fallot.

Patients with repaired tetralogy of Fallot remain at risk of life-threatening ventricular tachycardia related to slow-conducting anatomical isthmuses (SCAIs). Preventive ablation of SCAI identified by invasive electroanatomical mapping is increasingly performed. This study aimed to non-invasively identify SCAI using 3D late gadolinium enhancement cardiac magnetic resonance (3D-LGE-CMR). Consecutive tetralogy of Fallot patients who underwent right ventricular electroanatomical mapping (RV-EAM) and 3D-LGE-CMR were included. High signal intensity threshold for abnormal myocardium was determined based on direct comparison of bipolar voltages and signal intensity by co-registration of RV-EAM with 3D-LGE-CMR. The diagnostic performance of 3D-LGE-CMR to non-invasively identify SCAI was determined, validated in a second cohort, and compared with the discriminative ability of proposed risk scores. The derivation cohort consisted of 48 (34 ± 16 years) and the validation cohort of 53 patients (36 ± 18 years). In the derivation cohort, 78 of 107 anatomical isthmuses (AIs) identified by EAM were normal-conducting AI, 22 were SCAI, and 7 blocked AI. High signal intensity threshold was 42% of the maximal signal intensity. The sensitivity and specificity of 3D-LGE-CMR for identifying SCAI or blocked AI were 100% and 90%, respectively. In the validation cohort, 85 of 124 AIs were normal-conducting AI, 36 were SCAI, and 3 blocked AI. The sensitivity and specificity of 3D-LGE-CMR were 95% and 91%, respectively. All risk scores showed an at best modest performance to identify SCAI (area under the curve ≤ .68). 3D late gadolinium enhancement cardiac magnetic resonance can identify SCAI with excellent accuracy and may refine non-invasive risk stratification and patient selection for invasive EAM in tetralogy of Fallot.

Resistin predicts disease severity and survival in patients with pulmonary arterial hypertension

BackgroundAbnormal remodeling of distal pulmonary arteries in patients with pulmonary arterial hypertension (PAH) leads to progressively increased pulmonary vascular resistance, followed by right ventricular hypertrophy and failure. Despite considerable advancements in PAH treatment prognosis remains poor. We aim to evaluate the potential for using the cytokine resistin as a genetic and biological marker for disease severity and survival in a large cohort of patients with PAH.MethodsBiospecimens, clinical, and genetic data for 1121 adults with PAH, including 808 with idiopathic PAH (IPAH) and 313 with scleroderma-associated PAH (SSc-PAH), were obtained from a national repository. Serum resistin levels were measured by ELISA, and associations between resistin levels, clinical variables, and single nucleotide polymorphism genotypes were examined with multivariable regression models. Machine-learning (ML) algorithms were applied to develop and compare risk models for mortality prediction.ResultsResistin levels were significantly higher in all PAH samples and PAH subtype (IPAH and SSc-PAH) samples than in controls (P < .0001) and had significant discriminative abilities (AUCs of 0.84, 0.82, and 0.91, respectively; P < .001). High resistin levels (above 4.54 ng/mL) in PAH patients were associated with older age (P = .001), shorter 6-min walk distance (P = .001), and reduced cardiac performance (cardiac index, P = .016). Interestingly, mutant carriers of either rs3219175 or rs3745367 had higher resistin levels (adjusted P = .0001). High resistin levels in PAH patients were also associated with increased risk of death (hazard ratio: 2.6; 95% CI: 1.27–5.33; P < .0087). Comparisons of ML–derived survival models confirmed satisfactory prognostic value of the random forest model (AUC = 0.70, 95% CI: 0.62–0.79) for PAH.ConclusionsThis work establishes the importance of resistin in the pathobiology of human PAH. In line with its function in rodent models, serum resistin represents a novel biomarker for PAH prognostication and may indicate a new therapeutic avenue. ML-derived survival models highlighted the importance of including resistin levels to improve performance. Future studies are needed to develop multi-marker assays that improve noninvasive risk stratification.

Saliva antibody profiles are associated with reaction threshold and severity of peanut allergic reactions

BackgroundReaction threshold and severity in food allergy are difficult to predict, and there is a lack of non-invasive predictors. ObjectivesWe sought to determine the relationships between pre-challenge levels of peanut (PN)-specific antibodies in saliva and reaction threshold, severity, and organ-specific symptoms during peanut allergic reactions. MethodsWe measured PN-specific antibody levels in saliva collected from 127 children with suspected peanut allergy prior to double-blind, placebo-controlled peanut challenges where reaction threshold, severity, and symptoms were rigorously characterized. Low-threshold peanut allergy was defined as reaction to <300mg of peanut protein cumulatively consumed. A consensus severity grading system was used to grade severity. We analyzed associations between antibody levels and reaction threshold, severity, and organ-specific symptoms. ResultsAmong the 127 children, those with high pre-challenge saliva PN IgE had higher odds of low-threshold peanut allergy (OR 3.9, 95%CI 1.6-9.5), while those with high saliva PN IgA: PN IgE or PN IgG4:PN IgE had lower odds of low-threshold peanut allergy (OR 0.3, 95%CI 0.1-0.8, and OR 0.4, 95%CI 0.2-0.9, respectively). Children with high pre-challenge saliva PN IgG4 had lower odds of severe peanut reactions (OR 0.4, 95%CI 0.2-0.9). Those with high saliva PN IgE had higher odds of respiratory symptoms (OR 8.0, 95%CI 2.2-26.8). Saliva PN IgE modestly correlated with serum PN IgE levels (Pearson r=0.31, P=0.0004). High and low saliva PN IgE levels further distinguished reaction threshold and severity in participants stratified by serum PN IgE, suggesting endotypes. ConclusionSaliva PN antibodies could aid in non-invasive risk stratification of peanut allergy threshold, severity, and organ-specific symptoms.

Repolarization dynamics stability in Brugada type-1 pattern subjects with positive and negative genetics

Abstract Background Brugada type-1 pattern (Brgd1) risk stratification in asymptomatic subjects and its repolarization dynamics are complex. Purpose This study aims to analyze, from alternative standpoints, superficial 12-lead ECG V1 repolarization patterns (repol.). This method diverges from standard ECG pattern characterization. It rather exploits nonlinear dynamics and stability of ECG repol. to assess stability driving cardiac dynamics. This work consists in analyzing 2 subjects with Brgd1: Subject1 with negative genetics and negative EP study. Subect2 with positive genetics. Target To deepen knowledge/insight and applications of control-and-stability-theory applied to 12-lead ECG, aiming to eventually diminish the number of invasive EP studies. Methods A stability control closed loop system has been designed to model electrophysiology dynamics of cardiac conduction: ECG segments being the input-output of the system. (Figure). A sequence of V1 repol. have been scanned, digitized, and quantitatively processed yielding stability parameters (poles in S-plane). Scanning and signal processing performed by Matlab R_2023b. Results Figures. Conclusions Based on our data, 12-lead-ECG repol. seem to be associated with increasing stability in Subject1, exhibiting negative genetics. On the other hand, less stability seems to be associated with Subject2, reporting positive genetic tests. In conclusion, furthering control-and-stability-theory applied to 12-lead ECGs aims to deeper comprehension of physiological and pathological substrates of malignant arrhythmias, and to focus on noninvasive risk stratification strategies for ICD implantation indication in BrS. Further development of this technique will be largely facilitated by the implementation of AI methodologies in the near future.

Identification of clear cell renal cell carcinoma subtypes by integrating radiomics and transcriptomics

ObjectiveThis study aimed to delineate the clear cell renal cell carcinoma (ccRCC) intrinsic subtypes through unsupervised clustering of radiomics and transcriptomics data and to evaluate their associations with clinicopathological features, prognosis, and molecular characteristics. MethodsUsing a retrospective dual-center approach, we gathered transcriptomic and clinical data from ccRCC patients registered in The Cancer Genome Atlas and contrast-enhanced computed tomography images from The Cancer Imaging Archive and local databases. Following the segmentation of images, radiomics feature extraction, and feature preprocessing, we performed unsupervised clustering based on the “CancerSubtypes” package to identify distinct radiotranscriptomic subtypes, which were then correlated with clinical-pathological, prognostic, immune, and molecular characteristics. ResultsClustering identified three subtypes, C1, C2, and C3, each of which displayed unique clinicopathological, prognostic, immune, and molecular distinctions. Notably, subtypes C1 and C3 were associated with poorer survival outcomes than subtype C2. Pathway analysis highlighted immune pathway activation in C1 and metabolic pathway prominence in C2. Gene mutation analysis identified VHL and PBRM1 as the most commonly mutated genes, with more mutated genes observed in the C3 subtype. Despite similar tumor mutation burdens, microsatellite instability, and RNA interference across subtypes, C1 and C3 demonstrated greater tumor immune dysfunction and rejection. In the validation cohort, the various subtypes showed comparable results in terms of clinicopathological features and prognosis to those observed in the training cohort, thus confirming the efficacy of our algorithm. ConclusionUnsupervised clustering based on radiotranscriptomics can identify the intrinsic subtypes of ccRCC, and radiotranscriptomic subtypes can characterize the prognosis and molecular features of tumors, enabling noninvasive tumor risk stratification.

Usefulness of risk assessment tools in predicting hemodynamic outcome after balloon pulmonary angioplasty: a comparative analysis

ABSTRACT Objectives Several parameters of widely used risk assessment tools for pulmonary arterial hypertension (PAH) have been linked to hemodynamic outcomes of balloon pulmonary angioplasty (BPA). Therefore, we aimed to determine whether these risk assessment tools could be used to predict hemodynamic outcomes following BPA. Methods In this retrospective study, we included 139 patients with chronic thromboembolic pulmonary hypertension who had undergone BPA at Center for Pulmonary Vascular Diseases, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College (Beijing, China). We compared the accuracies of seven well-validated risk assessment tools for predicting hemodynamic outcomes following BPA. A favorable hemodynamic outcome was defined as a mean pulmonary arterial pressure < 30 mmHg at follow-up. Results The baseline risk profiles varied significantly among the risk assessment tools. The US Registry to Evaluate Early and Long-Term PAH Disease Management risk scales and the French risk assessment tools rated most patients as high-risk, while the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) series and laboratory examination-based risk scales categorized most patients as having intermediate-risk profile. COMPERA 2.0 (4-strata) exhibited the highest predictive power among all risk stratifications. Noninvasive risk stratification (COMPERA 2.0 [3-strata]) showed a comparable predictive ability to that of invasive risk stratification (COMPERA 1.0) (area under the curve 0.649 vs. 0.648). Moreover, incorporating diffusing capacity of the lungs for carbon monoxide and tricuspid regurgitation velocity into COMPERA 2.0 (4-strata) further enhanced its predictive power (net reclassification index 0.153, 95% confidence interval 0.009–0.298, p = 0.038). Additionally, this refined COMPERA version had a high calibration accuracy (slope 0.96). Conclusion Although the risk strata distribution varied among different risk assessment tools, the proportion of patients achieving favorable hemodynamics decreased with the escalation of risk stratification in most models. The well-validated risk assessment tools for PAH could also predict hemodynamic outcomes following BPA, and the refined COMPERA 2.0 model exhibited the highest predictive ability among these. Applying risk assessment tools before BPA can facilitate early identification of patients in need of closer monitoring and more intensive interventions, contributing to a better prognosis after BPA.

Risk stratification refinements with inclusion of haemodynamic variables at follow-up in patients with pulmonary arterial hypertension.

Haemodynamic variables are prognostic factors in pulmonary arterial hypertension (PAH). However, right heart catheterisation (RHC) is not systematically recommended to assess the risk status during follow-up. This study aimed to assess the added value of haemodynamic variables in prevalent patients to predict the risk of death or lung transplantation according to their risk status assessed by the non-invasive four-strata model as recommended by the European guidelines. We evaluated incident patients with PAH enrolled in the French pulmonary hypertension registry between 2009 and 2020 who had a first follow-up RHC. Cox regression identified, in each follow-up risk status, haemodynamic variables significantly associated with transplant-free survival. Optimal thresholds were determined by time-dependent receiver operating characteristics. Several multivariable Cox regression models were performed to identify the haemodynamic variables improving the non-invasive risk stratification model. We analysed 1240 incident patients reassessed within 1 year by RHC. None of the haemodynamic variables were significantly associated with transplant-free survival among low-risk (n=386) or high-risk (n=71) patients. Among patients at intermediate (intermediate-low, n=483 and intermediate-high, n=300) risk at first follow-up, multivariable models including either stroke volume index (SVI) or mixed venous oxygen saturation (S vO2 ) were the best. The prognostic performance of a refined six-strata risk stratification model including the non-invasive four-strata model and SVI >37 mL·m-2 and/or S vO2 >65% for patients at intermediate risk (area under the curve (AUC) 0.81; c-index 0.74) was better than that of the four-strata model (AUC 0.79, p=0.009; c-index 0.72). Cardiopulmonary haemodynamics may improve risk stratification at follow-up in patients at intermediate risk.

Assessment of Liver Fibrosis Grading with Non Invasive Scoring Systems Among Non Alcoholic Fatty Liver Disease (NAFLD) Patients

Background: The evaluation of degree of liver fibrosis is an important prognostic factor in the management of NAFLD patients. Noninvasive risk stratification is needed to triage individuals with advanced fibrosis for optimal treatment and surveillance. Objective: To find the frequency of fibrosis in NAFLD patients using multiple non-invasive tests and to match the performance of noninvasive tests in diagnosing liver fibrosis. Methods: An observational study done in Out-patient Clinics of National Institute of Liver and GI diseases (NILGID), Dow University Hospital from January 2021 to January 2022. All consecutive NAFLD patients during the study period were enrolled. Liver fibrosis was assessed in each patient with non invasive scoring including APRI score (AST to platelet ratio index), AST/ALT ratio, NFS (NAFLD fibrosis scores) and FIB-4. Results: 322 patients (mean age 44.84 ±10.94 years) were included. The mean values for ALT and AST levels were 40.67 ± 31.69IU/L and 31.85 ± 22.60 respectively. The best diagnostic accuracy was observed with FIB4 score having AUROC of 0.904. A robust correlation was observed between APRI index and FIB-4 score (r = 0.734, p-value &lt; 0.01), and between FIB-4 score and NFS score (r = 0.700, p- value &lt;0.01). Whereas correlation of APRI index with NFS score was moderate (r=0.394), and with AST/ALT ratio was weak (r = 0.201). Conclusion: Noninvasive markers, FIB4 and NFS score demonstrate a dependable ability to diagnose and rule out liver fibrosis, which proves to be an economical approach in management of patients with NAFLD.

Atrial and Ventricular Strain Imaging Using CMR in the Prediction of Ventricular Arrhythmia in Patients with Myocarditis

(1) Objective: Myocarditis can be associated with ventricular arrhythmia (VA), individual non-invasive risk stratification through cardiovascular magnetic resonance (CMR) is of great clinical significance. Our study aimed to explore whether left atrial (LA) and left ventricle (LV) myocardial strain serve as independent predictors of VA in patients with myocarditis. (2) Methods: This retrospective study evaluated CMR scans in 141 consecutive patients diagnosed with myocarditis based on the updated Lake Louise criteria (29 females, mean age 41 ± 20). The primary endpoint was VA; this encompassed ventricular fibrillation, sustained ventricular tachycardia, nonsustained ventricular tachycardia, and frequent premature ventricular complexes. LA and LV strain function were performed on conventional cine SSFP sequences. (3) Results: After a median follow-up time of 23 months (interquartile range (18–30)), 17 patients with acute myocarditis reached the primary endpoint. In the multivariable Cox regression analysis, LA reservoir (hazard ratio [HR] and 95% confidence interval [CI]: 0.93 [0.87–0.99], p = 0.02), LA booster (0.87 95% CI [0.76–0.99], p = 0.04), LV global longitudinal (1.26 95% CI [1.02–1.55], p = 0.03), circumferential (1.37 95% CI [1.08–1.73], p = 0.008), and radial strain (0.89 95% CI [0.80–0.98], p = 0.01) were all independent determinants of VA. Patients with LV global circumferential strain &gt; −13.3% exhibited worse event-free survival compared to those with values ≤ −13.3% (p &lt; 0.0001). (4) Conclusions: LA and LV strain mechanism on CMR are independently associated with VA events in patients with myocarditis, independent to LV ejection fraction, and late gadolinium enhancement location. Incorporating myocardial strain parameters into the management of myocarditis may improve risk stratification.

AI-CADR: Artificial Intelligence Based Risk Stratification of Coronary Artery Disease using Novel Non-invasive Biomarkers.

Coronary artery disease (CAD) is one of the most common causes of sudden cardiac arrest, accounting for a large percentage of global mortality. A timely diagnosis and detection may save a person's life. The research suggests a methodological framework for non-invasive risk stratification based on information only possible after invasive coronary angiography. Novel clinical, chemical, and molecular cardiac biomarkers were used as input features from an especially collected dataset. Following a thorough evaluative search in the biomarker feature space, the optimum feature and classifier or regression technique (regressor) set were selected using K-fold cross-validation. Ten machine learning (ML) classifiers were employed in classification tasks to determine the number of affected cardiac vessels, the Gensini group, and the severity of CAD with 82.58%, 86.26%, and 90.91% accuracy, respectively. Eleven approaches were used in regression tasks to calculate stenosis percentage and Gensini score, with R-squared values of 0.58 and 0.56, respectively. Following a thorough evaluative search in the biomarkers feature space, the optimum feature and classifier or regressor set were selected using K-fold cross-validation. The biomarkers and classifier or regressor combinations serve as the foundation for the proposed risk stratification framework, incorporating clinical protocol. Finally, our proposed framework is compared to state-of-the-art studies, offering a robust, well-rounded, early detection capable, and novel 'biomarkers-ML combination' approach to risk stratification.