Predictor Of Prognosis Research Articles

Prognostic significance of right ventricular dysfunction in patients with TTR amyloidosis

Abstract Background Transthyretin amyloidosis (ATTR) is increasingly recognized as a cause of heart failure (HF) that now benefits from effective treatment. While the role of right ventricular (RV) systolic dysfunction has been widely accepted as an independent predictor of poor prognosis in HF patients, its implications in ATTR remain poorly understood. Right ventricular free wall strain (FWS) over pulmonary arterial systolic pressure (PAsP) has been recently proposed as an accurate and sensitive tool to evaluate RV function and its coupling to the afterload. Purpose Our study sought to analyze whether indexing FWS to PASP should integrate the set of fundamental data we have to assess the prognosis of ATTR patients. Methods 191 consecutive patients diagnosed with ATTR were enrolled. A comprehensive echocardiographic examination was part of the systematic evaluation. The primary outcome was a composite of cardiovascular mortality and hospitalization caused by heart failure worsening. Results After a median follow-up of 24 months, among patients with ATTR [median age 82 (77, 85), 89.5% male], the primary composite outcome occured in 26%. Cardiovascular mortality rate was 7.3% and the hospital event rate was 18.8%. Tricuspid annular plane systolic excursion (TAPSE), FWS, TAPSE/PAsP and PAsP were univariately significant risk factors for adverse outcome. In multivariate Cox regression analysis, after adjustment of confounding factors, FWS/PASP emerged as an independent predictor of the composite endpoint of cardiac death and rehospitalization (HR 1.61, 95% CI 1.07-2.42, p=0.021). Based on receiver-operating characteristic analysis, the optimal FWS/PASP cut-off value for event prediction was 42.5% (area under the curve = 0.749, p = 0.001). Conclusion Right ventricular function assessed with speckle-tracking echocardiography is an independent prognostic marker for cardiac mortality and heart failure hospitalization in patients with ATTR. FWS/PAsP should be integrated into regular clinical practice for a better evaluation and follow-up of ATTR patients.

Association of PCSK6 rs1531817(C/A) polymorphism with the prognosis and coronary stenosis in premature myocardial infarction patients: a prospective cohort study

Abstract Background An increasing proportion of younger patients are currently hospitalized for acute myocardial infarction. Exploring the factors affecting coronary artery lesions and prognosis in patients with premature myocardial infarction (PMI) from a genetic perspective is of great significance to clinical management. Proprotein convertase subtilisins/kexins 6(PCSK6), a member of the PCSK family, functions in protein hydrolytic cleavage. It has been shown that PCSK6 polymorphisms are associated with carotid atherosclerosis. Purpose This research intended to evaluate the relationship between the PCSK6 rs1531817 polymorphism and the prognosis of PMI patients and their coronary stenosis. Methods This study was a prospective cohort study, which consecutively included 605 patients with PMI who underwent emergency PCI from January 2017 to August 2022 at X Hospital. Using the Massarray technique, the single nucleotide polymorphisms(SNPs) of PCSK6 rs1531817 were identified, and the patients underwent follow-up for 12 to 72 months, with major adverse cardiovascular events (MACEs) as the outcome. Analyze the relationship between patients' SNPs, Gensini score(GS), Triple vessel diseases(TVD), and MACEs. Results In PMI patients with PCSK6 rs1531817 CA+AA genotypes, ApoA1/ApoB levels were higher than in patients with CC genotype, and TC/HDL levels were lower (p < 0.05). Logistic regression analysis showed that the PCSK6 rs1531817 polymorphism (Dominant model: CA+AA vs.CC) was significantly correlated with the GS (high vs. low GS, adjOR=0.297, 95%CI:0.139-0.632, p=0.002; high vs. medium GS, adjOR=0.449, 95%CI:0.211-0.956, p=0.038), and it was correlated with the number of coronary artery lesions (TVD vs. SVD, adjOR=0.420, 95%CI:0.226-0.782, p=0.006; TVD vs. DVD, adjOR=0.406, 95%CI:0.214-0.773, p=0.006). According to multivariate Cox regression analysis, the PCSK6 rs1531817 dominant model was associated with the MACEs in PMI patients (adjHR=0.454, 95%CI:0.269-0.766, p=0.003). The mediation effect results showed that ApoA1/ApoB played a partially mediating role in the association between PCSK6 rs1531817 polymorphism and the severity of coronary stenosis (the effect leading to high GS and TVD accounted for 14.6% and 8.3% respectively), TC/HDL and TVD partially parallel mediated the association between PCSK6 rs1531817 polymorphism and MACEs (effect proportions were 8.1% and 17.1%, respectively). Conclusion Carrying the PCSK6 rs1531817 mutant A allele is an independent protective predictor of severe coronary stenosis and long-term prognosis in patients with PMI. Patients with the PCSK6 CA+AA genotypes have milder coronary stenosis partially because they have higher levels of ApoA1/ApoB; in addition, these patients have a better long-term prognosis partially because they have lower TC/HDL levels and fewer TVD patients.Figure 1.ApoA1/ApoB and TC/HDL were compareFigure 2.Correlation of PCSK6 rs1531817 pol

Clinical characteristics of idiopathic inflammatory myopathies related to anti-SRP: a single center experience

Idiopathic inflammatory myopathy (IIM) with autoantibodies recognizing the signal recognition particle (SRP) is characterized by prominent proximal weakness, infrequent extramuscular involvement, dramatically elevated creatine kinase levels, and myofiber necrosis with few inflammatory cell infiltrates in muscle tissue. To enhance understanding of the clinically used diagnosis and treatment of this disease, this study presents a single-center experience and analysis of a Chinese cohort with anti-SRP IIM. The most recent European Neuromuscular Center criteria were used to include Anti-SRP IIM patients from September 2016 to November 2019. Prior to treatment initiation, all sera were collected for the detection of anti-SRP autoantibodies and other myositis-related autoantibodies. Muscle strength, concurrent autoimmune conditions, comorbidities like interstitial lung disease (ILD), treatment outcomes, and follow-up results were also documented. Univariate logistic regression was employed to determine factors affecting prognosis. Among 271 patients with IIM, we identified 23 (8.5%) patients with anti-SRP IIM. Lower limb muscle weakness was frequently more severe. Interstitial lung disease (ILD) was observed in 50% of anti-SRP IIM patients. The presence of ILD may serve as a predictor of a poor prognosis, as revealed by univariate logistic regression analysis (odds ratio, 3.8, 95% CI: 1.0-6.8, p = 0.05).This Chinese Anti-SRP IIM cohort from Hunan province seems to have higher incidences of ILD, and associated ILD may be a risk factor for a poor prognosis. To fully understand the specific role of the anti-SRP autoantibody in this unique subset with ILD, further research is necessary.

Imaging predictors of adverse prognosis in fabry disease cardiomyopathy: a systematic review and meta-analysis

Abstract Background Cardiac involvement represents the main cause of death in patients with Fabry disease (FD). Echocardiography and Cardiac Magnetic Resonance (CMR) have an established diagnostic role, but their prognostic value remains unresolved. This systematic review and meta-analysis sought to assess the prognostic implications of imaging parameters in FD. Methods PubMed, ClinicalTrials.gov, Embase and Cochrane Library databases were searched for studies published from inception through to October 1, 2023. Full-length original papers including FD patients undergoing baseline imaging assessment and clinical follow-up were selected. Pre-defined study outcomes were a cardiovascular endpoint and a composite clinical endpoint. A meta-analysis of the studies investigating the effect of single imaging parameters on a pre-specified cardiovascular endpoint and a separate analysis on a pre-specified composite clinical endpoint were performed to obtain the pooled estimates for the association between the imaging parameters and the study outcome. The study protocol was registered in PROSPERO. Results Thirteen studies including 1,399 FD patients (44.8% males) were selected. At pooled analysis, late gadolinium enhancement (HR 4.39; 95% CI 2.5-7.71), left atrium volume indexed (HR 1.02 per ml/m2; 95% CI 1.01-1.04), E/e’ (HR 1.14 per unit increase; 95% CI 1.08-1.21), left ventricular (LV) mass indexed (HR 1.014 per mg/m2; 95% CI 1.006-1.023), maximum LV wall thickness (HR 1.19 per mm, 95% CI 1.04-1.36), LV-global longitudinal strain (HR 1.2 per unit increase; 95% CI 1.2-1.27), were significantly associated with the cardiovascular endpoint, whereas T1-mapping (p=0.115) and LV-ejection fraction (p=0.305) were not. T1-mapping was associated with the composite endpoint (HR 0.986 per msec increase; 95% CI 0.976-0.997). Meta-regression analysis did not show any significant interaction between each of the potential effect modifiers. Conclusion This meta-analysis demonstrates a robust prognostic value regarding several imaging parameters in FD, specifically indexes of LV mass and wall thickness, diastolic dysfunction, LV-GLS, and LGE presence. These findings support multimodality cardiovascular imaging, including CMR, in FD disease patients to predict long-term prognosis and call for longitudinal multicentre studies to develop multi-modality-imaging derived risk-score algorithms.

Machine learning for prediction of all-cause mortality in acute coronary syndrome

Abstract Background Acute coronary syndrome (ACS) remains one of the leading causes of death globally. Accurate and reliable mortality risk prediction of ACS patients is essential for developing targeted treatment strategies and improve prognostication. Traditional models for risk stratification such as the Global Registry of Acute Coronary Events (GRACE) and the Thrombolysis In Myocardial Infarction (TIMI) risk scores offer moderate discriminative value, and do not incorporate contemporary predictors of ACS prognosis. Machine learning (ML) models have emerged as an alternate method that may offer improved risk assessment. This study aims to compare machine learning models with traditional risk scores for predicting all-cause mortality in patients with ACS. Methods PubMed, EMBASE, Web of Science and Cochrane databases were searched until 1st November 2023 for studies comparing ML models with traditional statistical methods for event prediction of ACS patients. The primary outcome was comparative discrimination measured by C-statistics with 95% confidence intervals between ML models and traditional methods in estimating the risk of all-cause mortality. Results Ten studies were included (239627 patients). The summary C-statistic of all ML models across all endpoints was 0.89 (95% CI, 0.86-0.92), compared to traditional methods 0.82 (95% CI, 0.79-0.85). The difference in C-statistic between all ML models and traditional methods was 0.07 (p<0.05). Three models undertook external validation, and calibration was inconsistently reported. Conclusion ML models demonstrated superior discrimination of all-cause mortality for ACS patients compared to traditional risk scores. Despite outperforming well-established prognostic tools such as the GRACE and TIMI scores, current clinical applications of ML approaches remain uncertain, particularly in view of the methodological limitations of existing models and the need for greater validation.

Utilizing unsupervised machine learning to uncover novel phenogroups within the broad QRS complex

Abstract Background Conduction disease can manifest as a broad QRS complex on the ECG. Broad QRS is conventionally categorised as left bundle branch block (LBBB) and right bundle branch block (RBBB) or non-specific intraventricular conduction delay (IVCD), based on QRS morphology. These subgroups were coined over a century ago and have been relevant for heart failure with reduced ejection fraction and cardiac resynchronization therapy (CRT). However, there may be more precise phenogroups underlying broad QRS complexes. Purpose Using unsupervised machine learning to define novel broad QRS phenogroups and characterise them using clinical variables and disease outcomes. Methods We extracted 51 relevant features using a Variational Autoencoder from 47,456 median-beat ECGs with broad QRS intervals (QRS ≥ 130ms) from a secondary care cohort. These were input into Discriminative Dimensionality Reduction via Learning a Tree (DDRTree), a clustering method that models the continuum of heterogeneity by projecting the underlying distribution as a tree structure in a low-dimensional space, while assigning distinct branch/cluster labels. Results Six distinct phenogroups were identified within broad QRS morphologies (Figure-1). Two branches were RBBB dominant (Branch 1: more males, high-comorbidity; Branch 6: low-comorbidity/healthy-RBBB). Two branches were LBBB dominant (Branch 4: mixed IVCD with CHB burden; Branch 5: more females, CRT responders). Two branches were IVCD/mixed-phenotype (Branch 2: IVCD-dominant, high risk of future cardiovascular events; Branch 3: average/core branch). Within LBBB, Branch 4 and 5 showed similar trends with improving echocardiography profiles and CRT response when adjusted for covariates, each compared against the core branch. Branch 5 was distinct due its high risk of future HF (HR 1.19 [1.07-1.33] p < 0.01), cardiovascular death (HR 1.35 [1.20-1.50] p < 0.0001), all-cause mortality (HR 1.08 [1.01-1.16] p < 0.05) and CHB (HR 1.35 [1.16-1.56] p < 0.0001) (Figure-2). Conversely within RBBB, Branch 1 and 6 showed stark differences across various traits. Branch 1 captured a high-risk profile associated to higher risk of cardiovascular death (HR 1.37 [1.24-1.52] p < 0.0001), all-cause mortality (HR 1.20 [1.12-1.29] p < 0.0001) and CHB (HR 1.15 [1.01-1.32] p < 0.05) (Figure-2), while Branch 6 retained a low-risk phenotype. The tree dimensions also revealed relevant trends in cardiac anatomy and valvular dysfunction; the top-left region in Figure-1 associated with higher left ventricular ejection fraction and lower left ventricular end-diastolic dimension, lower left ventricular end-systolic dimension and a greater CRT response. Conclusion Through unsupervised methods we have identified conduction disease phenogroups with additive value for disease prognosis and CRT response prediction beyond traditional LBBB and RBBB labels. These novel phenogroups may help guide precision care for patients with a broad QRS complex.Figure-1Figure-2

Extra cellular volume by computed tomography is useful for prediction of prognosis in cardiac sarcoidosis

Abstract Background Extracellular volume (ECV) fraction on magnetic resonance imaging (MRI) has been useful in evaluating the degree of myocardial fibrosis and has been reported to help diagnose and evaluate the prognosis of cardiomyopathy. However, there are also several contraindications in the cardiac MRI, and the performance of it takes time. Computed tomography (CT) helps screen for coronary artery disease, is more versatile than cardiac MRI, and is also useful in screening for coronary artery disease. Myocardial extracellular volume (ECV) analysis on CT has been recently eligible and has shown a good correlation with ECV on MRI. We hypothesized that ECV on CT, a new fibrosis indicator, would be useful in predicting prognosis in patients with cardiac sarcoidosis (CS). Purpose The purpose of this study is to evaluate the utility of ECV analysis on CT for predicting major adverse cardiac events (MACEs) in cases with cardiac sarcoidosis (CS). Methods As a single-center study, 48 patients (18 males, 67 ± 11 years old) diagnosed with CS based on the 2016 Japanese Circulation Society criteria and who underwent cardiac CT using 320-row detector CT or 256-row detector CT, and including late-phase images from December 2008 to January 2024 were analyzed retrospectively. We evaluated the ECV of left ventricular myocardium (LVM) using commercially available software (Figure A). The primary endpoint was MACEs, a composite of all-cause death, heart failure hospitalization, and fatal ventricular arrhythmia. Results Among all 48 patients, 14 combined events (10 fatal ventricular arrhythmias, three heart failure hospitalizations, and one all-cause death) were observed during a follow-up of 56 ± 57 months. ECV of LVM on CT was significantly higher in patients with MACEs than the others (37 ± 5.6% vs 33 ± 4.6%, P = 0.0087). The best cut-off value of ECV of LVM on CT for prediction of MACEs was 34.97%, and the sensitivity and specificity of LV-ECV to predict MACEs were 71% and 76% at the best cut-off, and the area under the curve was 0.73 based on the receiver operating characteristics curve analysis (P = 0.009) (Figure B). The cases with ECV ≥ 34.97% (n = 18) had significantly higher MACEs than the cases with ECV < 34.97% based on the Kaplan-Meier analysis (P = 0.0031) (Figure C). In the multivariate Cox proportional hazard model, ECV on LVM (Hazard ratio 1.11, 95% confidence interval 1.00-1.21, p=0.0493) and history of VT ablation (Hazard ratio 27.7, 95% confidence interval 4.27-180, p=0.0011) were the significant predictors of MACEs. Conclusion Adverse events mainly caused by fatal ventricular arrhythmia are relatively common in CS. Evaluation of ECV by CT may be useful for the prediction of MACEs in cases with CS.

Machine learning prediction for prognosis and long-term effectiveness for transcatheter ventricular septal defect closure: a 5-year single center experience

Abstract Background Transcatheter therapy, with minimal invasiveness and rapid recovery advantages, has emerged as the primary choice for treating perimembranous ventricular septal defect. However, patients receiving transcatheter occlusion may confront conduction abnormalities due to the occluder's proximity to the cardiac conduction bundle during implantation. Few have assessed the risks of postoperative conduction block (CB) via robust machine learning models. Purpose The purpose of this study was to evaluate our long-term follow up results of pmVSD occlusion and establish a prediction model to identify risk predictors of conduction abnormalities via robust machine learning methods. Methods Five methods including logistic regression with a stepwise selection of variables, lasso regularization; random forest; gradient descent boosting (GBM); and support vector machine, were used to train models for predicting risks of late-onset and persistent conduction block through 5 years of follow-up and were validated using 5-fold cross-validation. Receiver-operating characteristic curves and Brier scores were utilized to evaluate model discrimination and calibration, respectively. The top prediction variables were identified by using the best performing models, using the relative influence score of each variable in 5-fold cross-validation. Results The GBM model performed the best with an AUC of 0.82 (95% confidence interval [CI]: 0.75 to 0.89) for predicting late-onset CB (Brier score: 0.0204), and 0.70 (95% CI: 0.62 to 0.78) for persistent CB (Brier score: 0.0003) over entire study period. Conduction block on admission, advanced age and aortic regurgitation before discharge were strongly associated with late-onset CB, whereas decreased left ventricular ejection fraction, enlarged left ventricle end diastolic diameter at 1-month follow-up were the most significant predictors of persistent CB. Conclusions These models predict the risks of temporary and persistent postoperative conduction block in patients with pmVSD occlusion and shed light on preventing long-term complications.Study FlowchartCentral Illustration

Improved prognosis prediction of idiopathic pulmonary arterial hypertension using targeted plasma proteomics in disease management

Abstract Background Idiopathic pulmonary arterial hypertension (IPAH), a special subgroup of PAH with unclear etiology, is characterized by rapid progression and poor prognosis underscoring the need for optimization of IPAH management and the accurate prediction of prognosis. However, it is disappointing that specific plasma biomarkers for IPAH management and prognosis are lacking in clinical practice. Up to date, the plasma biomarkers used for assessment of IPAH severity and prognosis are relied solely on indicators such as N-terminal prohormone of brain natriuretic peptide, or brain natriuretic peptide, which could reflect cardiac function but fail to promptly response to the dynamic change of disease condition due to the complexity of IPAH pathogenesis. Purpose We evaluated the prognostic value of plasma proteins in addition to current clinical predictors for prognosis prediction of IPAH. Methods Olink Immune-Response panel was used to measure 92 plasma proteins in a prospective IPAH cohort with 169 patients and a mean follow-up period of 2.4 ± 0.9 years. Six machine learning methods were used to construct predictive models, including plasma proteins-based Olink models, clinical parameters-based clinical models, and integrative models. Results A total of 18 proteins showed differential expression between patients with or without adverse clinical outcomes. Three proteins (STC1, CXADR, and IL6) were strongly correlated with clinical indicators of IPAH severity and their concentrations showed upward trends along with increased risk stratification. Elevated levels of STC1, CXADR, and IL6 are all associated with an increased risk of adverse clinical events, indicated by survival analysis, restricted cubic splines, and multivariable Cox regression analysis (STC1 HR=3.474, 95%CI: 1.129-10.692, P=0.030; CXADR: HR=1.602, 95%CI: 1.074-2.387, P=0.021; IL6: HR=1.883, 95%CI: 1.376-2.576, P<0.001). The inclusion of these three proteins into the predictive model could significantly increase the predictive power on the basis of the clinical model (AUC: 0.746 vs 0.626). Conclusions High levels of STC1, CXADR, and IL6 in plasma were associated with severe clinical phenotype and increased risk of adverse clinical events in patients with IPAH. These plasma proteins could significantly improve the prognostic performance of pre-existing clinical models, facilitating the clinical management of IPAH patients.The predictive efficient of models

Left atrial function predicts long-term outcome in hypertension and diabetes across genders

Abstract Introduction In the last decade, peak atrial longitudinal strain (PALS) was ascertained to be superior to conventional echocardiographic indices for secondary cardiovascular (CV) prevention. Purpose Standard and advanced echocardiography including PALS was evaluated to find predictors of prognosis in patients with arterial hypertension and/or diabetes for the optimization of primary CV prevention. Methods Hypertensive and/or diabetic patients >40 years old and in sinus rhythm who underwent a complete cardiological evaluation at our centre in 2008-2015 were retrospectively included. Personal history, physical examination, standard and advanced (speckle tracking) echocardiographic data were collected. The exclusion criteria were previous cv events or cardiac surgery, active pacemaker, more than moderate valvular regurgitation and/or stenosis, missing informed consent. All patients were followed up for a mean time of 11.2±1.3 years for the development of first atrial fibrillation (AF) episode, congestive heart failure hospitalization, transient ischemic attack, stroke, myocardial infarction/coronary revascularization, and CV death. Univariate and multivariable stepwise Cox regressions were performed to estimate Hazard Ratio (HR). Kaplan Meier curves (KM) compared the survival between patients with different ranges of Global PALS. Log-rank test was performed to compare the KM curves. Results This retrospective study included 292 adults (mean age 63.0±9.0 years, 50% female), among which 210 hypertensives and 67 diabetics. Mean left ventricular (LV) ejection fraction (EF) was 58.2±4.9%, mean GLS was -17.0±6.6%, mean left atrial volume was 52.9±24.9ml and mean PALS 29.7±11.1%. During follow up, 110 patients developed at least one cv event: 52 all-cause deaths, 28 CV deaths, 30 heart failure admissions, 31 first AF episode, 18 TIA/strokes, 25 myocardial infarction/revascularization. Dividing the population according to events occurrence, patients with events had similar EF (p= 0.06), while being older (65.8±9.6 vs 61.3±9.0 years) and with a worse diastolic function (E/E’ ratio 10.6±5.0 vs 8.7±3.6), LV longitudinal strain (GLS, -17.7±2.9 vs -15.7±3.4%) and PALS (34.6±9.9 vs 21.6±7.7%, all p <0.001). From univariate statistical analysis, age, E/E’, PAPs, GLS and PALS were all predictors of events, but only age (HR 1.03, p=0.03) and PALS (PALS<22.5% HR 18.99 and PALS 22.5-30 HR 7.68, both p<0.001) remained independently associated with outcome at multivariate analysis. Patients with PALS<22.5 have an event-free survival of 82.4% at 1 year, 49.8% at 5 years and 25.3% at 10 years compared to 100% at 1 year, 98.5% at 5 years and 92.5% at 10 years if PALS PALS>30% (Fig.1). The same results were confirmed in female population (Fig.2). Conclusions If confirmed by larger studies, PALS could be a quick, feasible and reliable part of cardiologic evaluation for prognostic stratification in primary CV prevention in hypertensive and/or diabetic patients.Fig. 1Fig. 2

Biomarkers of cardiohepatic syndrome in the cardiac intensive care unit

Abstract Introduction Non-cardiovascular end-organ dysfunction is an important determinant of outcomes in cardiac intensive care unit (CICU) patients. We examined the association between in-hospital mortality with the severity and extent of admission liver function test (LFT) abnormalities in a heterogeneous CICU population. Purpose We hypothesized that a greater severity or extent of LFT abnormalities would be associated with higher in-hospital mortality. Methods We included consecutive unique adult CICU patients with available data for one or more of the admission LFT values of interest. Admission laboratory values were defined as those closest to CICU admission. We used the NIH Common Terminology Criteria for Adverse Events (CTCAE) version 5.0 to categorize the severity of each LFT abnormality from Grade 0 (normal) to Grade 4 (life-threatening). We defined the extent of LFT abnormality as the number of individual LFTs meeting criteria for CTCAE Grade 1 or greater. The primary outcome of interest was all-cause in-hospital mortality. Odds ratio (OR) and 95% confidence interval (CI) values for in-hospital mortality were estimated using logistic regression, before and after multivariable adjustment. Results Of 12,428 unique CICU patients, 5,144 were excluded due to a lack of available data for any admission LFT values. The remaining 7,284 patients comprised the final study population. Among patients with available data for each individual LFT, abnormal values were present in: AST, 2,743/5,733 (47.8%); ALT 1,576/5,512 (28.6%); ALK 611/3,684 (16.6%); and TB 898/5,155 (17.4%). A total of 864 (11.9%) patients died during hospitalization. In-hospital mortality was higher for patients with one or more LFTs meeting criteria for CTCAE Grade 1 or greater (17.9% vs. 6.4%, adjusted OR 1.54 [1.27-1.86], p <0.001). A stepwise increase in in-hospital mortality was observed with increasing CTCAE grade, both overall (adjusted OR 1.25 per each higher CTCAE grade [1.16-1.34], p <0.001); (Figure 1A), and for each individual LFT (Figure 1B). Conclusion Cardiohepatic syndrome is an important predictor of prognosis in CICU patients, as the extent and severity of LFT abnormalities are strongly associated with in-hospital mortality. This is the first large-scale study to examine the association between the magnitude of hepatic biomarker derangement with in-hospital mortality in unselected CICU patients. Patients with markedly elevated LFT values exhibited the highest risk of in-hospital mortality. This analysis advocates for the inclusion of commonly obtained LFTs in future risk-prediction tools for enhanced prognostication.

Prognostic utility of heart-type fatty acid binding protein in patients with cardiac amyloidosis

Abstract Background Heart-type fatty acid binding protein (H-FABP) is a biomarker that has been shown to provide long-term prognostic information in patients with heart failure (HF), yet its role in patients with cardiac amyloidosis (CA) is unknown. Purpose We aim to investigate the association of H-FABP levels with prognosis in patients with CA. Methods Consecutive patients diagnosed with cardiac amyloidosis from October 2015 to May 2022 at Heart Failure Center, of our hospital were enrolled. Patients were categorized into 2 categories based on H-FABP levels measured in the baseline blood sample: < or = 7 ng/ml (low H-FABP), >7 ng/ml (high H-FABP). Univariate and multivariate Cox models were used to identify predictors of survival. Kaplan-Meier analysis was performed to compare survival between patients with low or high H-FABP. Results Overall, ninety-five patients were included. H-FABP was elevated (>7 ng/ml) in 37 patients (39%). Patients with high H-FABP were more likely to present with higher levels of high-sensitivity troponin-T (hs-TNT)(0.17ng/mL vs. 0.11ng/mL, p =0.01) and higher levels of N-terminal pro-B-type natriuretic peptide (NT-proBNP) (18098.76pg/mL vs. 6211.69pg/mL, p<0.001). Patients were followed for an average follow-up period of 440 days, in which 53 (56%) patients died. Univariate and multivariate analysis demonstrated that H-FABP levels (HR 1.48, 95% CI 1.03-2.13, per 1-SD greater) was independently associated with prognosis, even after adjusting for NT-proBNP, hs-TNT and renal function. Conclusions Elevation of H-FABP is associated with an increased risk of death in patients with cardiac amyloidosis. H-FABP can serve as a novel biomarker independent of other established predictors for prognosis in cardiac amyloidosis.Kaplan-Meier curves for overall survival

What is the true normal: comparison of different equations for estimating peak oxygen uptake for outcome prediction in heart failure

Abstract Background and Objectives One of the strongest prognostic parameters obtained during Cardiopulmonary exercise testing (CPET) in patients with heart failure (HF) is the peak oxygen uptake (pVO2), as a standalone or as a percentage of the predicted peak oxygen uptake (ppVO2) reached. However, traditional standards by which ppVO2 is calculated have limitations, having been derived from small cohorts, lacking portability and poorly represented by women. Thus, we aim to compare the FRIEND Registry equation to previously used methods of calculating ppVO2 as it relates to prognostic value imparted by the percentage of ppVO2 (%ppVO2) reached during CPET and portability to the Portuguese HF population. Methods Single centre retrospective study of patients with HF with LVEF < 50% who underwent CPET between 2015-2021. Patients who did not reach RER≥ 1.05 were excluded from our analysis. All tests were evaluated and ventilatory thresholds determined by 3 independent, experienced operators. ppVO2 was calculated according to 1) the Wasserman equation; 2) the Wasserman equation using ideal body weight; 3) the Neder equation, 4) the SHIP equation and 5) the FRIEND equation. Our primary endpoint was a composite of CV death, urgent transplant or left ventricular assist device implantation and HF hospitalization. Results We included 238 patients (mean age 59 ± 11 years, 83% males). The HF aetiology was mostly ischemic in nature (68%), with LVEF of 34 ± 9% and median NTproBNP of 776 (2566 – 2342) pg/mL. Most patients (67%) were in class NYHA I-II and with high prevalence of GDMT (93% ACEi/ARB; 97% beta-blockers and 64% on MRA). Mean BMI in this group was 27.3 ± 4.7 kg/min. Patients performed CPET on a treadmill with a protocol adjusted to their predicted exercise capacity. Mean pVO2 was 18.1 ± 6.2mL/kg/min, corresponding to a median %ppVO2 that ranges from 51 ± 16% to 71 ± 22%, depending on the equation that is used. Mean VE/VCO2 Slope was 42 ± 13 and 48 % (n = 98) of patients showed exercise oscillatory ventilation (EOV). Mean ppVO2 as per the FRIEND and Wasserman equations was 18.9 ± 6.6 and 21.2 ± 4.5 mL/kg/min, with no statistically significant difference between both. %ppVO2 as calculated through all equations was an independent predictor of prognosis on multivariate analysis adjusted for LVEF, NTproBNP and VE/VCO2 Slope (p < 0.001 for values derived from all equations). However, the FRIEND and Wasserman equations showed a slight advantage over the remaining 2 equations, with higher AUC on ROC curve analysis (see figure). Conclusions In our population of Portuguese patients with HF, both the FRIEND registry and Wasserman equations for predicting pVO2 yielded strong prognostic power. However, these equations provide different absolute predicted VO2 values for the same patients and thus should not be used interchangeably. Standardisation of the type ppVO2 equation used should be recommended by local scientific societies.

Combining fractional flow reserve and the index of microcirculatory resistance for prognosis prediction in renal artery stenosis interventions: a feasibility sub-study of FAIR-pilot trial

Abstract Background Interventional therapies for renal artery stenosis (RAS) was debatable in previous randomized trials. Fractional flow reserve (FFR), while useful in coronary interventions, does not assess microcirculatory status, which may be critical in RAS. The index of microcirculatory resistance (IMR) provides additional microvascular information, which has not been used in RAS patients. Purpose To investigate the feasibility of IMR measurement in RAS patients and the association with the outcome. Methods FAIR-pilot study is a multicenter pilot randomized controlled trial exploring the application of FFR to guide the renal artery stenting in patients with renovascular hypertension (NCT05732077). A pressure-temperature sensor guidewire was used to measure the IMR. To derive mean transit time at rest, thermodilution curves were obtained by 3 injections of 4 mL of room temperature saline down the renal artery. Hyperemia was induced using dopamine 50μg/kg bolus to the renal artery. The IMR was calculated as distal arterial pressure (Pd) x mean transit time during hyperemia (TmnH) (Figure 1). Prognostic evaluation was based on improvements in postoperative blood pressure, which was assessed using both ambulatory blood pressure monitor (ABPM) and anti-hypertensive medication number, along with renal function, as determined by the estimated glomerular filtration rate (eGFR). Results The IMR was measured among five patients enrolled in FAIR-pilot study. As shown in Table 1 and 2, stent implantation was conducted on five lesions across patients 1 to 4 based on renal FFR < 0.8, with each experiencing varying degrees of improved blood pressure control subsequently. Patient 5 did not receive a stent and was lost to follow-up. At three-month, the recorded decrease in systolic blood pressure ranged from a maximum of 34 mmHg to a minimum decrease of -6 mmHg among the four patients. The antihypertensive medication number showed a maximum reduction of 2 and no change as the minimum. We observed a correlation between baseline IMR and baseline renal function. No significant change in IMR was noted after interventional procedures. Preliminary findings suggest that IMR measurement is feasible in patients with RAS, while the ∆T (temperature change time) was very small, which potentially increasing the measurement bias. Conclusion The study suggests measuring IMR in patients with RAS is feasible and can offer information into the renal microvasculature. However, the small ∆T encountered during measurement may introduce increased bias, which should be considered when interpreting results.

SRSF12 is a prognostic biomarker involved in immune infiltration in acute myeloid leukemia

ABSTRACT Background Acute myeloid leukemia (AML) is a disease characterized by the proliferation of abnormal cells originating from blood stem cells. Understanding the pathogenesis and progression of AML, as well as identifying molecular markers for early diagnosis and prognosis assessment, is of paramount importance. Methods The AML dataset was obtained from the Cancer Genome Atlas (TCGA), while the normal sample dataset was derived from the Genotype-Tissue Expression(GTEx) dataset. Furthermore, the association between SRSF12 expression and drug response was assessed using the Cancer Therapeutic Response Portal (CTRP) database to evaluate its potential therapeutic value. Finally, SRSF12 expression in AML cells was measured using quantitative real-time PCR (qRT-PCR). Result The difference in SRSF12 expression between 13 types of tumors and normal tissue samples was found to be statistically significant (P < 0.05). SRSF12 exhibited predominantly high expression in AML, indicating its potential as a diagnostic and prognostic marker for AML patients. High expression of SRSF12, along with age and cytogenetic risk, emerged as independent predictors of favorable prognosis in AML patients (P < 0.05). PCR demonstrated a significant increase in SRSF12 expression in AML patients. Conclusion Overall, our findings suggest that the high expression of SRSF12 in AML patients is a poor prognostic factor, which may provide a new option for the diagnosis, and targeted therapy of AML.

Contributing to the prediction of prognosis for treated hepatocellular carcinoma: Imaging aspects that sculpt the future

Contributing to the prediction of prognosis for treated hepatocellular carcinoma: Imaging aspects that sculpt the future

18F-FDG PET/CT impact in grade 3B follicular lymphoma.

[18F]-fluoro-D-glucose positron emission tomography/computed tomography (18F-FDG PET/CT) is a non-invasive imaging tool that has a fundamental role in the management of FDG-avid lymphoma (also FL) in different settings, especially in the evaluation of treatment response and prognostication. The report by Barraclough etal. demonstrated that the treatment response evaluation by 18F-FDG PET/CT was a strong predictor of prognosis in grade 3B follicular lymphoma (G3BFL). Moreover, among semiquantitative baseline PET features, standardized uptake value (SUV) and TGL showed to be useful in predicting progression-free survival (PFS). Commentary on: Barraclough etal. The value of semiquantitative PET features and end-of-therapy PET in grade 3B follicular lymphoma. Br J Haematol 2024 (Online ahead of print). doi: 10.1111/bjh.19823.

HOXA9 and CD163 potentiate pancreatic ductal adenocarcinoma progression

BackgroundThe role of HOXA9 requires investigations in pancreatic ductal adenocarcinoma (PDAC) as HOXA9 inhibitors are being developed. HOXA9 might attract CD163 expressed tumor associated macrophages (TAM) and could affect PDAC prognosis. This work aims to study the expression and relevance of HOXA9 and CD163 in PDAC progression.Materials and methodsSelected 98 PDAC and 98 adjacent non tumor tissues as a control group were immunostained with HOXA9 and CD163 antibodies.ResultsPDAC displayed highly significant higher HOXA9 staining intensity, percent and H score values than control group. HOXA9 staining of PDAC cases showed significant associations with poor prognostic indicators including larger tumor size, higher grade and advanced stage. PDAC showed highly significant differences regarding CD163 macrophage-specific staining intensity, percent and H score values than control group. CD163 showed significant higher expressions with larger tumor size, higher histological grade and advanced stage group. HOXA9 staining in PDAC showed highly significant direct correlations with CD163 positive macrophages. Follow up of PDAC cases revealed that high median H score of HOXA9 and CD163 were significantly associated with worse overall survival. CD163 was an independent prognostic marker of worse survival.ConclusionsIn conclusion, HOXA9 could potentiate PDAC progression by stimulating CD163 expressed TAM attraction in tumors. HOXA9 and CD163 could participate in PDAC therapy. HOXA9 and CD163 could be predictors of worse prognosis and shorter survival in PDAC.

Evaluating Machine Learning Models for Stroke Prognosis and Prediction in Atrial Fibrillation Patients: A Comprehensive Meta-Analysis

Background/Objective: Atrial fibrillation (AF) complicates the management of acute ischemic stroke (AIS), necessitating precise predictive models to enhance clinical outcomes. This meta-analysis evaluates the efficacy of machine learning (ML) models in three key areas: stroke prognosis in AF patients, stroke prediction in AF patients, and AF prediction in stroke patients. The study aims to assess the accuracy and variability of ML models in forecasting AIS outcomes and detecting AF in stroke patients, while exploring the clinical benefits and limitations of integrating these models into practice. Methods: We conducted a systematic search of PubMed, Embase, and Cochrane databases up to June 2024, selecting studies that evaluated ML accuracy in stroke prognosis and prediction in AF patients and AF prediction in stroke patients. Data extraction and quality assessment were performed independently by two reviewers, with random-effects modeling applied to estimate pooled accuracy metrics. Results: The meta-analysis included twenty-four studies comprising 7,391,645 patients, categorized into groups for stroke prognosis in AF patients (eight studies), stroke prediction in AF patients (thirteen studies), and AF prediction in stroke patients (three studies). The pooled AUROC was 0.79 for stroke prognosis and 0.68 for stroke prediction in AF, with higher accuracy noted in short-term predictions. The mean AUROC across studies was 0.75, with models such as Extreme Gradient Boosting (XGB) and Random Forest (RF) showing superior performance. For stroke prognosis in AF, the mean AUROC was 0.78, whereas stroke prediction yielded a mean AUROC of 0.73. AF prediction post-stroke had an average AUROC of 0.75. These findings indicate moderate predictive capability of ML models, underscoring the need for further refinement and standardization. The absence of comprehensive sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) metrics limited the ability to conduct full meta-analytic modeling. Conclusions: While ML models demonstrate potential for enhancing stroke prognosis and AF prediction, they have yet to meet the clinical standards required for widespread adoption. Future efforts should focus on refining these models and validating them across diverse populations to improve their clinical utility.

Development and validation of a novel endoplasmic reticulum stress-related lncRNAs signature in osteosarcoma

Osteosarcoma (OS) is a cancerous tumor, and its development is greatly influenced by long non-coding RNA (lncRNA). Endoplasmic reticulum stress (ERS) is an essential biological defense process in cells and contributes to the progression of tumors. However, the exact mechanisms remain elusive. This study aims to develop a signature of lncRNAs associated with ERS in OS. This signature will guide the prognosis prediction and the determination of appropriate treatment strategies. The UCSC Xena database collected transcriptional and clinical data of OS and muscle, after identifying ERS differentially expressed genes, we utilized correlation analysis to determine the endoplasmic reticulum stress lncRNAs (ERLs). The Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression analysis were utilized to develop an ERLs signature. To clarify the fundamental mechanisms controlling gene expression in low and high-risk groups, Gene Set Variation Analysis (GSVA) were conducted. In addition, the distinction between the two groups regarding drug sensitivity and immune-related activity was investigated to determine the immunotherapy effects. Utilizing RT-qPCR, the expression of model lncRNAs in OS cell lines was ascertained. The functional analysis of LINC02298 was carried out through in vitro experiments and pan-cancer analysis. This study successfully constructed an ERLs prognostic signature for OS, which comprised 5 lncRNAs (AC023157.3, AL031673.1, LINC02298, LINC02328, SNHG26). The risk signature predicted overall survival in patients with OS and was confirmed by assessing the validation and whole cohorts. Further, it was discovered that individuals classified as high-risk displayed suppressed immune activation, decreased infiltration of immune cells, and decreased responsiveness to immunotherapy. The RT-qPCR showed that the constructed risk prognosis model is reliable. Experimental validation has demonstrated that LINC02298 can promote OS cells’ invasion, migration, and proliferation. In addition, LINC02298 exhibited significant differential expression in many types of cancer. Moreover, LINC02298 is an important biomarker in a variety of tumors. This study established a novel ERLs signature, which successfully predicted the prognosis of OS. The function of LINC02298 in OS was elucidated via in vitro experiments. Therefore, it offers new opportunities for predicting the clinical prognosis of OS and establishes the basis for targeted therapy in OS.