Clinical Risk Score Research Articles

Clinical outcomes according to clinical and surgical risk scores for tricuspid regurgitation

Abstract Background TRIO score, the clinical risk score in significant tricuspid regurgitation (TR), and TRI-SCORE, the surgical mortality risk for tricuspid valve (TV) surgery, have been proposed. We aimed to investigate the correlation and distribution of two risk scores, and to define the clinical benefit of TV surgery in patients with low or intermediate TRIO score and TRI-SCORE. Methods In a total of 10,054 patients diagnosed with moderate to severe TR between January 2008 to December 2020, TRIO score and TRI-SCORE were stratified into low, intermediate, and high-risk groups. Patients of all etiologies of TR were included and patients who underwent prior tricuspid valve surgery were excluded. The clinical outcome was all-cause death. Results TRI-SCORE and TRIO scores were moderately correlated (r =0.39, p<0.001). 9,104 (90.6%) patients (age 67 ± 14 years, 42% male) had low or intermediate TRI and TRIO score. During 12.6 ± 10.1 years, 885 (9.7%) underwent TV surgery and 1,268 (13.9%) experienced clinical outcomes. Kaplan-Meier analysis revealed significant differences in clinical outcomes according to TV surgery in both low and intermediate TRIO score patients (log-rank, p<0.001) and sub-analyzed them according to the TRI-SCORE group (Figure). In multivariable Cox regression analysis, TV surgery (HR 0.37, 95% CI 0.28-0.49, p<0.001) were significantly associated with clinical outcomes after adjusting confounding factors. Conclusions TV surgery is clinically beneficial in moderate to severe TR patients with a low or intermediate TRIO score and TRI-SCORE.

Telehealth-aided outpatient management of acute heart failure in a specialist virtual ward compared to standard care

Abstract Background/Introduction Acute decompensated heart failure (ADHF) leads to hospitalisations, frequent re-hospitalisations and mortality. The safety and efficacy of telehealth-guided outpatient ADHF management (virtual ward-VW) as an alternative to hospitalisation has not been assessed previously. Aim The aim of this study was to assess the safety and outcomes of our acute heart failure virtual ward (HFVW) pathway (Figure 1) when compared to hospitalised ADHF patients. Methods This cohort study (May 2022-October 2023) assessed the outcomes of telehealth-guided outpatient ADHF management using bolus intravenous furosemide in a HF-specialist VW. We compared baseline patient characteristics, NTproBNP, ejection fraction, NYHA Class, clinical risk score (Get With the Guidelines-Heart Failure-GWTG-HF), comorbidities (Charlson Co-morbidity Index-CCI), frailty (Rockwood Clinical Frailty Score-CFS), HF therapies and measured clinical outcomes at 1, 3, 6 and 12 months (re-hospitalisations, mortality) in the HFVW cohort versus standard care (ADHF patients managed without telehealth in 2021). Results 554 HFVW ADHF patients (age 73.1±10.9 years; 46% female) were compared with 402 ADHF patients (74.2±11.8; p=0.15 and 49% female) in the standard care cohort (SC). Despite similar baseline patient characteristics, GWTG-HF score, CCI and CFS, re-hospitalisations were significantly lower in the HFVW compared to standard care (1 month - 11.6% vs. 21%, p=0.002; 3 months - 20.4% vs. 30%, p=0.001; 6 months -29.3% vs 41%, p=0.02 and 12 months-48% vs. 57%,p=0.03) whereas mortality was lower at 1 month (6% vs. 14%; p<0.001), 3 months (10.5% vs. 15%; p=0.02) and 6 months (15.5% vs. 21%; p=0.04) (Figure 2). Multivariate logistic regression analysis showed that an increased daily step count whilst on HFVW independently predicted reduced odds of re-hospitalisations at 1 month (OR 0.85; 95% CI 0.7-0.9; p=0.005), 3 months [OR 0.95 (0.93-0.98); p=0.003] and 1 month mortality [OR 0.85 (0.7-0.95), p=0.01]. Whereas CCI predicted adverse 12-month outcomes [OR 1.2 (1.1-1.4), p=0.03]. Higher GWTG-HF score independently predicted increased odds of re-hospitalisation [1-month OR 1.2 (1.1-1.3), p=0.01; 12-month OR 1.1, 1.05-1.2, p=0.03) as well as mortality [1-month OR 1.2 (1.1-1.4), p=0.01; 12-month OR 1.3 (1.1-1.7), p=0.02]. Similarly higher CFS also independently predicted increased odds of re-hospitalisations [1-month OR 1.5 (1.1-2.2), p=0.03; 12-month OR 1.9 (1.2-3, p=0.01] and mortality [1-month OR 2 (1.1-3.5), p=0.02; 12-month OR 2.6 (1.6-10); p=0.02] throughout the follow-up period. Conclusions A telehealth-guided specialist HFVW management strategy for ADHF may offer a safe and efficacious alternative to hospitalisation in suitable patients. Daily step count, GWTG, CCI and CFS can play a vital role in assessing suitability for VW and in predicting risk of adverse clinical outcomes.Heart Failure Virtual Ward PathwayMortality & Re-hospitalisations outcomes

Serum metabolomics improve risk stratification for incident heart failure

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

The CTOX-Score, a clinical risk prediction tool for cardiovascular toxicity secondary to cancer therapies

Abstract Introduction Cardiovascular toxicity (cardiotoxicity) is recognized as a major side-effect of cancer therapies. As early diagnosis and management is crucial in clinical practice1,2, knowledge about risk factors and risk scores is scarce3,4. Purpose The aim of this study is to develop a clinical prediction score to identify patients at high/very high risk of cardiotoxicity. Methods The CARDIOTOX registry5,6 included patients receiving cancer therapies related to moderate/high risk for cardiotoxicity (2012-2017). Clinical, laboratory and echo parameters were collected at baseline and at 3 weeks, 3 and 6 months, 1, 1.5 and 2 years after initiation of cancer therapy. Patients were classified according to current cardioncology guidelines2 and divided in two groups: no or mild cardiotoxicity vs moderate or high cardiotoxicity. Difference between groups were tested with t-student or U Mann-Withney for continuous variables and Fisher’s exact test for categorical data. Variables with p<0.05 were included in a multivariate binary logistic regression model to find independent predictors of cardiotoxicity and construct a clinical risk score, CTOX-Score. The calibration was evaluated by Hosmer-Lemeshow goodness-of-fit test and discrimination using ROC curves. Results From the 1324 individuals included in the registry we discarded all those with pre-existing symptomatic heart failure or LVEF<40% at baseline and those with incomplete baseline data. We considered 998 patients, aged 54.62±14.15 years, 176 men (17.6%), of which 53 (5.3%) developed moderate or severe cardiotoxicity. These patients had lower baseline LVEF (59.91±7.50% vs 64.20±5.88%), higher creatinine levels (0.90 (0.76, 1.10) mg/dl vs 0.74 (0.62, 0.90) mg/dl) and higher NT-ProBNP (177 (58, 476) pg/ml vs 66 (38, 133) pg/ml) compared with patients with no or mild cardiotoxicity (p<0.001). We found previous radiotherapy (OR=4.66, CI:1.24-17.52), acute myeloid leukemia (OR=6.85; CI:1.16-40.50), monoclonal antibodies (OR=3.27; CI:1.35-7.89), LVEF (OR=0.96; CI:0.84-0.98) and serum creatinine (OR=5.43; CI:1.63-18.14) as independent predictors of cardiotoxicity (p<0.05). A clinical risk score was developed based in beta coefficient of variables (Table 1). The model has good calibration according to Hosmer-Lemeshow goodness-of-fit (χ2=2.800, p=0.424) and good discrimination power (AUC:0.780, CI:0.69-0.87; p<0.001). The best cut-off value of CTOX-Score is ≥3 points (Sensitivity:70.00%, Specificity:72.25%, NPV:98.11%). Patients were classified in four risk categories: low (0-1 points); moderate (2); high (3-4); very high (≥5) (Figure 1). The model has good accuracy to identify very high-risk patients (Specificity 98.80%). Conclusion The CTOX-Score is a clinical risk prediction model to identify patients at high/very high-risk of cardiotoxicity and could allow for more personalized monitoring of patients. Additional studies are needed to externally validate the model prior to implementation in clinical practice.Multivariate binary logistic regressionCTOX-Score values among population

Improving prehospital risk stratification using deep learning ECG interpretation in suspected acute coronary syndrome

Abstract Introduction Most patients presenting with chest pain in the Emergency Medical Services (EMS) setting are suspected of non-ST-elevation acute coronary syndrome (NSTE-ACS). Distinguishing true NSTE-ACS from non-cardiac chest pain based solely on the electrocardiogram (ECG) is challenging. Recent advances in artificial intelligence (AI) models for ECG interpretation show promising results. Purpose The aim of this study is to develop and validate a Convolutional Neural Network (CNN)-based model to diagnose NSTE-ACS within the prehospital ECG and to compare it to current prehospital available diagnostic tools. Methods For this study, a training/internal validation cohort (n= 4891) and external validation cohort (n= 754) were used, both consisting of suspected NSTE-ACS patients, and the first prehospital standard 12-lead ECG at presentation was extracted. A CNN (ECG-AI) was trained and validated to detect NSTE-ACS. The training/internal validation cohort was extracted from suspected NSTE-ACS patients presenting to the emergency department. The external validation cohort was extracted from a prehospital, prospective cohort study in which subjects were assessed by EMS paramedics. A validated prehospital clinical risk score (preHEART) was calculated, which includes manual on-site ECG analyses by EMS paramedic (ECG-EMS) and a point-of-care (POC) troponin assessment, and compared to ECG-AI in diagnosing NSTE-ACS. Results In the external validation cohort, 202 (27%) subjects were diagnosed with NSTE-ACS. ECG-AI had a better diagnostic performance than ECG-EMS (AUC 0.70 (0.66–0.74) vs. AUC 0.65 (0.61–0.70), p=0.045) for diagnosing NSTE-ACS. The overall diagnostic accuracy of preHEART was AUC 0.78 (0.74–0.82) and superior compared to ECG-AI (p= 0.001). Incorporating ECG-AI instead of ECG-EMS into preHEART led to a significant improvement in diagnostic performance (AUC 0.83 (0.79–0.86,p < 0.001)). Conclusion Integrating AI in prehospital ECG interpretation improves NSTE-ACS diagnosis. However, clinical risk scores still yield the best diagnostic performance in the prehospital setting and their accuracy could be further enhanced through AI.ROC prehospital diagnostic toolsROC preheart and preheart-AI

Adverse cardiovascular events in patients with cancer: a biomarker-enhanced clinical risk score

Abstract Background/Introduction Continuously improving cancer-specific survival puts a growing proportion of cancer patients at risk of major adverse cardiovascular events (MACE), but reliable tools for cardiovascular risk prediction remain unavailable (1). Purpose To assess a broad panel of cardiovascular biomarkers and clinical risk factors and to provide a novel risk score for the prediction of MACE in cancer patients. Methods Among 2’192 patients with newly diagnosed or recurrent cancer, a broad panel of cardiovascular biomarkers, including NT-proBNP, P-/E-/L-selectins, ICAM-1, VCAM-1, sLOX-1, CRP, and Lp(a) were measured by blinded study personnel. Patients were followed prospectively for the occurrence of 2-year MACE and 5-year cardiovascular death (2,3). Uni- and multivariable competing risk models were fit to assess independent associations of cardiovascular risk factors and biomarkers with MACE, and a risk score was developed. Results Beyond clinical risk factors, ICAM-1, P-/E-/L-selectins and NT-proBNP levels were independently linked to 2-year MACE risk, though shape of associations differed markedly. A clinical risk score was derived, assigning +1 point for male sex, smoking, and age ≥60 years, and +2 points for ASCVD history, yielding a bootstrapped C-statistic of 0.76 (95%CI:0.71-0.81). Implementation of cardiovascular biomarkers conferred improved performance (0.83, 95%CI:0.78-0.88). A simplified model, solely informed by clinical variables and readily available NT-proBNP (+1 point for NT-proBNP≥230pg/mL), achieved similar performance (0.80, 95%CI:0.74-0.86), with a cumulative 2-year MACE incidence of 0% in low- and 11.0% in high-risk patients. Conclusions Cancer patients are at substantial MACE risk. The herein presented first-of-its-kind risk score, integrating traditional cardiovascular risk factors and biomarkers, including NT-proBNP, effectively predicts 2-year MACE and 5-year cardiovascular death.

Prediction of atrial fibrillation recurrence after ablation: the impact of machine learning and recurrence during the blanking period

Abstract Introduction Pulmonary vein isolation is superior to antiarrhythmic drugs for rhythm control in patients with atrial fibrillation (AF). Nevertheless, recurrences within the first year are still significant. Several clinical risk scores were developed to predict AF recurrence; however, despite good results in development cohorts, the results in external validation are usually poor, limiting the general applicability of these scores. Purpose We aimed to develop machine learning (ML) models to predict AF recurrences within the first year after catheter ablation and compare their performance with conventional risk scores. Methods We used a retrospective dataset of a tertiary hospital center, including all consecutive patients submitted to the first AF ablation between 2017 and 2021. The dataset included 76 features: patient characteristics, medical history, baseline echocardiography, calcium score, procedure variables, and early recurrence (ER) during the 90-day blanking period. The outcome was defined as the occurrence of an electrocardiographically documented late recurrence (LR) of atrial tachycardia/AF > 30s between 3-12 months. Three different supervised ML models (penalized logistic regression, random forests, and XGBoost) were developed on the training cohort, and hyperparameters were tuned using 10-fold cross-validation. A testing dataset was held out to estimate the final performance (25%). The following risk scores were calculated: APPLE, CAAP-AF, and BASE-AF2 (the last includes ER). Areas under the curve (AUC) of receiver operating characteristic curves were compared using DeLong’s test. Results 679 patients were included: 62.7% males, median age of 59±16 years, and 78.2% with paroxysmal AF. The median time from diagnosis to ablation was 2±4 years. 74.2% underwent radiofrequency ablation and the remaining cryoablation. ER occurred in 12.5%, and most of these patients also experienced LR (68.7 vs 19.6% in those without ER, p<0.001). LR was observed in 25.6%. The XGBoost model showed the best performance with an AUC 0.774, 95% confidence interval (CI) 0.688–0.844) outperforming existing scores (picture 1): APPLE score AUC 0.607, 95% CI 0.562–0.653, p-value<0.001; CAAP-AF score AUC 0.622, 95% CI 0.576–0.668, p-value=0.002; BASE-AF2 AUC 0.628, 95% CI 0.581–0.675, p-value= 0.003. Variable importance analysis showed a significant drop in the performance of the models when ER was not considered, indicating its high importance in predicting LR (picture 2). Conclusions Recurrence during the blanking period was the most important predictor of LR in our population. The ML model was superior to conventional risk scores. ML models might be an essential tool to improve the prediction of outcomes and clinical decision-making for optimal follow-up.ROC curvesFeature Importance

Left atrial reservoir strain measured by three-dimensional echocardiography is associated with major adverse cardiovascular events in the general population

Abstract Background Left atrial (LA) strain measured by three-dimensional (3D) echocardiography (3DE) is a novel way of evaluating LA function. The prognostic value of LA strain by 3DE in incident heart failure (HF) and cardiovascular (CV) death has not previously been documented. Purpose We south to investigate whether 3D LA strain measures were associated with incident heart failure (HF) and cardiovascular (CV) death in a population at low risk of CV events. Methods A total of 1,934 participants from a prospective longitudinal cohort study of the general population underwent a comprehensive transthoracic echocardiographic examination including 3DE where LA reservoir strain (LASr), conduit strain (LAScd) and contraction strain (LASct) were quantified. All participants were at sinus-rhythm during 3DE. The endpoint was a composite of HF and CV death. Cox proportional hazards regression was applied. Multivariable adjustments were made for the ARIC-HF risk score (age, gender, heart rate, systolic blood pressure, antihypertensive medication, diabetes, ischemic heart disease, smoking, and body mass index) and left atrial volume index. Results Mean age was 54±17 years, 43% were male. During a median follow-up time of 4.8 years (interquartile range 4.3-5.4 years), 89 participants (4.6%) either developed HF or died of a CV cause. In univariable analysis, LASr and LAScd, but not LASct were significantly associated with the outcome, but after multivariable adjustment only LASr remained significantly associated with outcome (HR 1.04 (95% CI: 1.01-1.08), p=0.016, per 1% decrease) while LAScd and LASct did not. LASr provided incremental prognostic information beyond the ARIC-HF risk score (continuous NRI 0.21±0.11, p=0.027). Compared to participants with a LASr in the 1st tertile (LASr>32%) Participants with an LASr in the in the 3rd tertile (LASr<26.5%) had an almost 7-fold increased risk of HF or CV-death (HR 6.93 (95% CI 3.29-14.58; p=0.001)(Figure). Conclusion LA reservoir strain assessed by 3DE was independently associated with incident HF and CV death, which was not the case for LA conduit and contraction strain. In addition, LA reservoir strain added incremental value beyond clinical risk scores.

Cuproptosis-related lncRNAs emerge as a novel signature for predicting prognosis in prostate carcinoma and functional experimental validation

BackgroundProstate cancer (PCa) is one of the most common malignancies of the urinary system. Cuproptosis, a newly discovered form of cell death. The relationship between cuproptosis-related long non-coding RNAs (ClncRNAs) related to PCa and prognosis remains unclear. This study aimed to explore the clinical significance of novel ClncRNAs in the prognostic assessment of PCa.MethodsClncRNAs and differentially expressed mRNAs linked to these ClncRNAs were identified using Pearson’s correlation and differential expression analyses. A prognostic signature (risk score) comprising three ClncRNAs was established based on multivariable Cox regression analysis. The predictive performance of this ClncRNAs signature was validated using receiver operating characteristic curves and nomograms. Finally, further in vitro cell experiments were conducted for validation, including quantitative polymerase chain reaction (qPCR), western blot (WB), cell proliferation assays, cell migration assays, cell invasion assays, apoptosis, and cell cycle analysis.ResultsWe constructed a prognostic signature of ClncRNAs for PCa comprising three key differentially expressed ClncRNAs(AC010896-1, AC016394-2, and SNHG9). Multivariable Cox regression analysis indicated that clinical staging and risk scores of the ClncRNAs signature were independent prognostic factors for PCa. Compared to other clinical features, the ClncRNAs signature exhibited higher diagnostic efficiency and performed well in predicting the 1-, 3-, and 5-year progression-free intervals (PFIs) for PCa. Notably, in terms of immune activity, PCa patients with high-risk scores exhibited higher tumor mutational burden (TMB) levels, while their Tumor Immune Dysfunction and Exclusion (TIDE) scores were lower than those of PCa patients with low-risk scores. Additionally, in vitro cellular functional experiments, we knocked down SNHG9 that is the most significantly differentially expressed ClncRNA among the three key ClncRNAs. SNHG9 knockdown resulted in a significant increase in G1 phase cells and a decrease in S and G2 phases, indicating inhibition of DNA synthesis and cell cycle progression. Colony formation assays showed reduced clonogenic ability, with fewer and smaller colonies. Western blot analysis revealed the upregulation of the key cuproptosis-related mRNAs FDX1 and DLST. These findings suggested that SNHG9 promotes PCa cell proliferation, migration, and invasion.ConclusionBuilding on the three ClncRNAs, we identified a novel prognostic signature of PCa. The ClncRNA SNHG9 can promote PCa cell proliferation, migration, and invasion.

Machine-learning models to predict the risk of recurrent VT following catheter ablation in patients with structural heart disease

Abstract Background Ventricular tachycardia (VT) is a life-threatening arrhythmia that can complicate structural heart disease. While catheter ablation is effective in treating this condition, the recurrence of VT after the procedure continues to be a concern. Consequently, there is a need for a reliable system to evaluate the likelihood of arrhythmia recurrence following an ablation. Objective To predict recurrence or repeat ablation after a VT catheter ablation procedure using machine learning (ML) models. Methods We conducted a single-center, retrospective study of all patients undergoing catheter ablation for scar-related VT between 2012 and 2022. Data collected included demographics, comorbidities, medications, relevant laboratory abnormalities, electrocardiograms, echocardiograms, detailed procedural characteristics, and outcomes. Python version 3.8.5 for exploratory data analysis (EDA) and visualization. Using six different ML models from the training set (90:10 split), we used 34 variables to predict the primary outcome, including VT recurrence or repeat catheter ablation. The accuracy of these models was compared using ROC curves based on their performance on the test set. Results Out of 508 VT ablation procedures, 261 experienced a recurrence. Support Vector Classifier (SVM) performed the best, having an AUC of 0.73, followed by logistic regression at 0.69 (Figure A). SVM yielded sensitivity, specificity, positive and negative predictive values of 0.73, 0.73, 0.79, and 0.67, respectively. The F1 score was 0.70. Among the factors with the highest feature importance in the model, the most influential variable was the proceduralist’s experience level, followed by QRS width at baseline, LVEF, and patient’s age (Figure B). Conclusion Our model can predict recurrent VT after ablation with reasonable accuracy, outperforming the existing clinical risk scores significantly. Larger training sample sizes will help achieve more robust ML models and improve predictive accuracy further.Figure A: ROC curve depicting the AUROCB. Feature importance graph

The added value of device measured physical activity to the prediction of incident cardiovascular disease.

Abstract Background Conventional risk prediction models for Major Adverse Cardio/Cerebrovascular Events (MACCE) primarily rely on non-modifiable factors such as age, sex, and physiological measurements, including LDL/HDL cholesterol and blood pressure readings, that are prone to variations across medical practices and are often unavailable in many parts of the world. In this study, we investigate the potential of inexpensive wearable measurements of physical activity to enhance MACCE prediction when physiological measurements are unavailable. Purpose To assess the added value of physical activity measurements, collected via wrist-worn accelerometers, to established cardiovascular risk prediction models. Additionally, we seek to investigate whether physical activity measurements can substitute for common physiological measurements in established cardiovascular risk models. Methods We obtained accelerometer-measured physical activity over 7 days between 2013 and 2015 from 69,898 UK Biobank (UKB) participants without a prior history of MACCE, defined as death or hospitalisation due to (1) myocardial infarction, (2) heart failure, or (3) Stroke/transient ischaemic attack. We assessed the added value of daily step counts, sleep duration, and a deep learning-derived activity health score (Fig 1), calculated from complete 7-day accelerometer recordings, to established clinical risk models (SCORE2, QRISK3 and AHA) before and after excluding measurements of cholesterol and systolic blood pressure (SBP). The primary outcome was the first recorded MACCE within 6 years. Risk scores were developed in men and women using Cox proportional hazards models. We assessed predictive performance using Harrel’s C-index, net reclassification index (NRI), and net benefit at the recommended treatment threshold for each model. Results Of the 69,898 UKB participants in the study, 3,386 (4.8%) experienced a MACCE over a 6-year follow-up period. In place of HDL ratio and SBP, the addition of deep-learned activity health scores modestly improved performance in the best clinical baseline, QRISK3 for Female participants ΔC-index women:0.003 (95% CI 0.002-0.004); Δnet benefit 0.02(0.01-0.03); NRI 0.04, (0.02-0.07), and outperformed manually extracted steps and sleep measurements. We report no significant improvements for men. We find a greater increase in model performance when both HDL ratio and SBP, plus activity health scores, are included in the model. ΔC-index women:0.008, (0.007-0.009); men: 0.003 (0.002-0.004) (Fig 2). Conclusion Our findings indicate that in the absence of cholesterol and systolic blood pressure, the addition of device-measured physical activity modestly improves the performance of clinical risk scores among individuals without prior cardiovascular disease (CVD). These findings could further refine intervention strategies for targeted prevention of CVD, particularly in settings where physiological measurements may be unavailable.

Long-term outcomes after transcatheter aortic valve implantation: derivation and validation of the long-TAVI prediction model

Abstract Background Identification of high-risk individuals after transcatheter aortic valve implantation (TAVI) is essential to personalized care. Established risk models require a plethora of variables, fail to account for biomarker dynamics and imaging parameters, mostly focus on the short term, and offer limited predictive performance. Purpose We aimed to develop an easily applicable prediction model for long-term mortality or heart failure (HF) admission following TAVI. Methods Patients undergoing TAVI at a high-volume university centre in the United Kingdom (n = 990) were recruited into a prospective cohort study. Enrolled patients were divided into a development cohort (07/2007 – 12/2016) and a validation cohort (01/2017 – 11/2018) and followed for up to 15.1 (median 5.3, interquartile range [IQR] 2.8 – 7.4) years. Patient characteristics along with pre- and post-procedural imaging results and brain natriuretic peptide (BNP) levels before the procedure and at 3 months were recorded. A clinical risk score for long-term mortality and HF admission (i.e., Long-TAVI risk score) was derived using Cox regression and evaluated using time-dependent area under the receiver operating characteristics curve (AUC). Results Age at presentation was 79.8 (standard deviation [SD] 8.9) years, 961 (99.0%) patients had signs of heart failure, left ventricular ejection fraction was 52 (SD 11.2), and pre-TAVI BNP levels were 298.0 (interquartile range [IQR] 159.0 – 571.5) pg/mL. High BNP levels at presentation (higher vs. lower than 400 pg/mL: HR 1.50, 95% CI 1.17 – 1.92, P < 0.001) and dynamic changes in BNP with the first 3 months (low/high vs. low/low: 2.61, 95% CI 1.17 – 5.86, P = 0.020; high/high vs. low/low: 2.41, 95% CI 1.26 – 4.64, P = 0.008) were independent predictors mortality or HF admission. The simple 8-item Long-TAVI score accounts for periprocedural BNP dynamics and shows good discrimination (AUC 0.76, 95% CI 0.72 – 0.79) and calibration in the development cohort performing similarly well in the external validation cohort (AUC 0.70, 95% CI 0.63 – 0.77). Long-TAVI significantly outperformed the logistic EURO score (AUC 0.56, 95% CI 0.52 – 0.59, P < 0.001). Conclusion Both pre-TAVI BNP levels and periprocedural BNP trajectories are independently associated with long-term outcomes after TAVI. Accounting for clinical characteristics, imaging parameters, and circulating BNP levels, the newly developed Long-TAVI risk score achieves high performance and provides the first prediction model for long-term mortality or HF admission. Long-TAVI represents a novel tool for personalized risk assessment in patients undergoing TAVI.Correlation at baseline A) and FUP B)Score performance A) and risk groups B)

Coronary inflammation stratifies risk in patients undergoing coronary CT angiography: the Oxford Risk Factors and Non-Invasive Imaging (ORFAN) study

Abstract Background Patients with chest pain symptoms often undergo coronary CT angiography (CCTA) for the diagnosis of obstructive coronary artery disease (CAD) to guide revascularisation. However, acute cardiac events could occur in the absence of obstructive CAD. Further risk stratification of patients could potentially be useful by quantifying coronary inflammation using the Fat Attenuation Index (FAI) Score, and an artificial intelligence (AI)-assisted prognostic model that captures the inflammatory risk by integrating CCTA-derived metrics (including FAI Score and plaque burden) and the patient’s clinical risk factors. Purpose To evaluate the risk of cardiac events among patients without obstructive CAD, and the prognostic value of integrating coronary inflammation in risk stratifying this patient group. Methods From the Oxford Risk Factors and Non-invasive imaging (ORFAN) study, patients undergoing CCTA in 7 UK Hospitals (n=40,091) were followed up for the incidence of cardiac mortality over a median(IQR) of 2.7(1.4-5.3) years. In a nested cohort of 3,393 patients with long-term follow up, we evaluated the long-term prognostic value of FAI Score and the performance of AI-Risk algorithm over a median(IQR) follow up of 7.7(6.4-9.1) years. Reclassification of risk category from AI-Risk was compared with contemporary clinical risk score (QRISK3). Results Patients without obstructive CAD (n=32,533, 81.1%) accounted for 63.7% of all cardiac deaths (n=1,754) during follow up, highlighting the substantial proportion of cardiac events in absolute terms given a large proportion of patients without obstructive CAD. In the nested cohort with long-term follow up, FAI Score in any of the 3 epicardial coronary arteries was significantly associated with cardiac mortality with or without obstructive CAD. In the whole cohort, the highest quartile of FAI Score in LAD showed 20-times higher risk of cardiac death compared with the lowest quartile (Panel A). Integrating coronary inflammation with clinical risk factors and plaque burden, patients with very-high AI-Risk classification showed 6-fold higher risk of cardiac mortality compared to low/medium risk, with good alignment of the incident predicted events from AI-Risk and observed events. (Panel B) The predicted cardiac mortality from AI-Risk also demonstrated good calibration with the observed events in the whole population. (Panel C) Finally, AI-Risk significantly reclassify the risk above clinical risk factor-based prediction (QRISK3) (Panel D). Conclusion Patients with high coronary inflammation measured by FAI Score showed significantly elevated risk for cardiac mortality. The AI-enabled absolute risk prediction algorithm leads to significant reclassification of patients undergoing routine CCTA, and can be used as a precision medicine tool.

Factors associated with mortality and evaluation of clinical risk scores among cancer patients admitted with post-chemotherapy infection in Uganda: a prospective cohort study

Abstract Background Little is known about outcomes from cancer chemotherapy-associated infections in sub-Saharan Africa. Accordingly, among cancer patients admitted with post-chemotherapy infection in Mbarara, Uganda, we aimed to determine 1) the 30-day case fatality rate, 2) factors associated with mortality, and 3) clinical risk score performance. Methods We enrolled participants aged 18 years or older if they 1) received cancer chemotherapy within the past 30 days, 2) were admitted to the oncology ward, and 3) were prescribed intravenous antibiotics. We used Cox proportional hazards regression to determine predictors of 30-day mortality and calculated the area under the receiver operating characteristic curve (AUC) for each clinical risk score. Results Among 150 participants, 67 (45%) were female and the median (IQR) age was 56 (43-66) years. Esophageal cancer (18%) and pneumonia (42%) were the most common malignancy and infection, respectively. Death by 30 days occurred in 63 (42%) participants. qSOFA ≥2 (adjusted hazard ratio [aHR] 2.51, 95% confidence interval [CI] 1.42-4.44, p=0.001), and UVA score >4 (aHR 2.13, 95% CI 1.08-4.18, p=0.03) were independently associated with 30-day mortality. An ECOG score ≥3 was similarly independently associated with 30-day mortality in the qSOFA and UVA models. The AUCs for qSOFA and UVA were 0.70 (95% CI 0.63-0.79) and 0.72 (95% CI 0.64-0.80), respectively. Conclusions In participants with post-chemotherapy infection in Mbarara, Uganda, the case fatality rate was high. ECOG, qSOFA, and UVA scores were associated with 30-day mortality.

Precision phenotyping from routine laboratory parameters for out of hospital survival prediction in an all comers prospective PCI registry

Out-of-hospital mortality in coronary artery disease (CAD) is particularly high and established adverse event prediction tools are yet to be available. Our study aimed to investigate whether precision phenotyping can be performed using routine laboratory parameters for the prediction of out-of-hospital survival in a CAD population treated by percutaneous coronary intervention (PCI). All patients treated by PCI and discharged alive in a tertiary center between January 2016 – December 2022 that have been included prospectively in the local registry were analyzed. 115 parameters from the PCI registry and 266 parameters derived from routine laboratory testing were used. An extreme gradient-boosted decision tree machine learning (ML) algorithm was trained and used to predict all-cause and cardiovascular-cause survival. A total of 4027 patients with 4981 PCI hospitalizations were randomly included in the 70% training dataset and 1729 patients with 2160 PCI hospitalizations were randomly included in the 30% validation dataset. All-cause and cardiovascular cause mortality was 17.5% and 12.2%. The integrated area under the receiver operator characteristic curve for prediction of all-cause and cardiovascular cause mortality by the ML on the validation dataset was 0.844 and 0.837, respectively (all p < 0.001). Parameters reflecting renal function (first and maximum serum creatinine), hematologic function (mean corpuscular hemoglobin concentration, platelet distribution width), and inflammatory status (lymphocyte per monocyte ratio) were among the most important predictors. Accurate out-of-hospital survival prediction in CAD can be achieved using routine laboratory parameters. ML outperformed clinical risk scores in predicting out-of-hospital mortality in a prospective all-comers PCI population and has the potential to precisely phenotype patients.

Incremental Prognostic Value of a Coronary Heart Disease Polygenic Risk Score in Type 2 Diabetes.

The recent availability of cardiovascular risk-reducing type 2 diabetes (T2D) therapies makes it imperative to optimally identify individuals who could derive benefit. Current clinical risk prediction may misclassify individuals as low risk and could be improved. Our aim was to determine the incremental prognostic value of a coronary heart disease (CHD) genome-wide polygenic risk score (PRS) to a clinical risk score in prediction of major adverse cardiovascular events (MACE) in patients with T2D. We evaluated 10,556 individuals with T2D aged 40-79 without a prior cardiovascular hospitalization. We calculated 10-year clinical cardiovascular risk at the date of recruitment using the Pooled Cohort Equation (PCE Risk) and constructed a CHD PRS. The primary outcome was time to first MACE incidence, and we assessed the additional incremental predictive value of the CHD PRS to the PCE risk. At 10 years, there were 1,477 MACE. After adjustment for clinical risk, the CHD PRS was significantly associated with MACE (hazard ratio [HR] 1.69 per SD increase, 95% CI 1.60-1.79). Individuals with PCE Risk <7.5% but in the top quintile of CHD PRS had a significantly increased likelihood of MACE (HR 10.69, 95% CI 5.07-22.55) compared with those in the lowest. The addition of the PRS to the clinical risk score led to significant improvements in risk prediction, particularly in those at low clinical risk. The addition of a CHD PRS to clinical assessment improved MACE prediction in T2D individuals without prior cardiovascular disease, particularly in those at low clinical risk.

Identification of PANoptosis Subtypes to Assess the Prognosis and Immune Microenvironment of Lung Adenocarcinoma Patients: A Bioinformatics Combined Machine Learning Study.

PANoptosis, a novelty mechanism of cell death involving crosstalk between apoptosis, pyroptosis, and necroptosis, is strongly associated with tumor cell death and immunotherapy efficacy. However, its relevance in lung adenocarcinoma (LUAD) remains to be elucidated. In this study, we acquired 18 PANoptosis-related differentially expressed gene (PRDEG) of LUAD. Based on these genes, LUAD samples were identified with different sub-types by unsupervised clustering. Next, we compared the differences between the subtypes, including clinical features, immune microenvironment, and potentially sensitive drugs. Further-more, we used machine learning to identify hub prognostic PRDEGs, construct a risk score, and validate it on other external datasets. We incorporated the patient's clinical information and risk score into the proportional hazards model and lasso-cox models to find key prognostic features and constructed five prognostic models. The best model was identified via the area under the curve and validated on an external dataset. LUAD patients were divided into two clusters named C1 and C2, respectively. The C2 cluster exhibited shorter survival time, more advanced tumor stage, higher suppressive immune cell scores, such as dendritic cells, and higher expression of inhibitory immune checkpoints, such as LAG3 and CD86. TIMP1, CAV1, and CD69 were recognized as key prognostic factors, and risk scores predicted survival with significant differences in the external validation set. Risk score and N-stage were identified as critical prognostic features. The Coxph model outper-formed other machine learning clinical models. The 1-, 3-, and 5-year time-ROCs in the exter-nal validation set were 0.55, 0.59, and 0.60, respectively. We demonstrated the potential of PANoptosis-based molecular clustering and prognostic features in predicting the survival of patients with LUAD as well as the tumor mi-croenvironment.

High-Sensitivity Troponin: Finding a Meaningful Delta.

High-sensitivity cardiac troponin (hs-cTn) assays have significantly refined the resolution of biomarker-level detection and have emerged as the gold standard cardiac biomarker in evaluating myocardial injury. Since its introduction, hs-cTn has been integrated into the Fourth Universal Definition of Myocardial Infarction and various European Society of Cardiology (ESC) and American College of Cardiology/American Heart Association (ACC/AHA) guidelines for the evaluation and diagnosis of chest pain syndromes. However, despite its integral role in caring for patients with chest pain, there are still substantive gaps in our knowledge of the clinical interpretation of dynamic changes in hs-cTn values. Whether a relative or absolute hs-cTn delta should be used to detect acute myocardial injury remains debatable. There are also emerging considerations of possible sex and racial/ethnic differences in clinically significant troponin deltas. In the emergency department, there is debate about the optimal time frame to recheck hs-cTn after symptom onset for myocardial infarction rule-out and whether hs-cTn deltas should be integrated into clinical risk scores. In this review, we will provide an overview of the history of clinical utilization of cardiac biomarkers, the development of hs-cTn assays, and the ongoing search for a meaningful delta that can be clinically applicable.

Prognostic modeling of hepatocellular carcinoma based on T-cell proliferation regulators: a bioinformatics approach.

The prognostic value and immune significance of T-cell proliferation regulators (TCRs) in hepatocellular carcinoma (HCC) have not been previously reported. This study aimed to develop a new prognostic model based on TCRs in patients with HCC. This study used The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) and International Cancer Genome Consortium-Liver Cancer-Riken, Japan (ICGC-LIRI-JP) datasets along with TCRs. Differentially expressed TCRs (DE-TCRs) were identified by intersecting TCRs and differentially expressed genes between HCC and non-cancerous samples. Prognostic genes were determined using Cox regression analysis and were used to construct a risk model for HCC. Kaplan-Meier survival analysis was performed to assess the difference in survival between high-risk and low-risk groups. Receiver operating characteristic curve was used to assess the validity of risk model, as well as for testing in the ICGC-LIRI-JP dataset. Additionally, independent prognostic factors were identified using multivariate Cox regression analysis and proportional hazards assumption, and they were used to construct a nomogram model. TCGA-LIHC dataset was subjected to tumor microenvironment analysis, drug sensitivity analysis, gene set variation analysis, and immune correlation analysis. The prognostic genes were analyzed using consensus clustering analysis, mutation analysis, copy number variation analysis, gene set enrichment analysis, and molecular prediction analysis. Among the 18 DE-TCRs, six genes (DCLRE1B, RAN, HOMER1, ADA, CDK1, and IL1RN) could predict the prognosis of HCC. A risk model that can accurately predict HCC prognosis was established based on these genes. An efficient nomogram model was also developed using clinical traits and risk scores. Immune-related analyses revealed that 39 immune checkpoints exhibited differential expression between the high-risk and low-risk groups. The rate of immunotherapy response was low in patients belonging to the high-risk group. Patients with HCC were further divided into cluster 1 and cluster 2 based on prognostic genes. Mutation analysis revealed that HOMER1 and CDK1 harbored missense mutations. DCLRE1B exhibited an increased copy number, whereas RAN exhibited a decreased copy number. The prognostic genes were significantly enriched in tryptophan metabolism pathways. This bioinformatics analysis identified six TCR genes associated with HCC prognosis that can serve as diagnostic markers and therapeutic targets for HCC.

The cumulative impact of clinical risk on brain networks and associations with executive function impairments in adolescents with congenital heart disease.

Patients with congenital heart disease (CHD) demonstrate altered structural brain network connectivity. However, there is large variability between reported results and little information is available to identify those patients at highest risk for brain alterations. Thus, we aimed to investigate if network connectivity measures were associated with the individual patient's cumulative load of clinical risk factors and with family-environmental factors in a cohort of adolescents with CHD. Further, we investigated associations with executive function impairments. In 53 adolescents with CHD who underwent open-heart surgery during infancy, and 75 healthy controls, diffusion magnetic resonance imaging and neuropsychological assessment was conducted at a mean age of 13.2 ± 1.3 years. Structural connectomes were constructed using constrained spherical deconvolution tractography. Graph theory and network-based statistics were applied to investigate network connectivity measures. A cumulative clinical risk (CCR) score was built by summing up binary risk factors (neonatal, cardiac, neurologic) based on clinically relevant thresholds. The role of family-environmental factors (parental education, parental mental health, and family function) was investigated. An age-adjusted executive function summary score was built from nine neuropsychological tests. While network integration and segregation were preserved in adolescents with CHD, they showed lower edge strength in a dense subnetwork. A higher CCR score was associated with lower network segregation, edge strength, and executive function performance. Edge strength was particularly reduced in a subnetwork including inter-frontal and fronto-parietal-thalamic connections. There was no association with family-environmental factors. Poorer executive functioning was associated with lower network integration and segregation. We demonstrated evidence for alterations of network connectivity strength in adolescents with CHD - particularly in those patients who face a cumulative exposure to multiple clinical risk factors over time. Quantifying the cumulative load of risk early in life may help to better predict trajectories of brain development in order to identify and support the most vulnerable patients as early as possible.