Risk Reclassification Research Articles

Improved Risk Prediction in DLBCL By Combining Clinical and PET Features with Interim PET Assessment

Background: Accurate detection of patients at high risk of treatment failure following frontline immunochemotherapy in diffuse large B-cell lymphoma (DLBCL) is of paramount importance as these patients might benefit from early treatment escalation. Recently, we introduced the IMPI prognostic model based on metabolic tumor volume (MTV), age and stage that outperformed the international prognostic index (IPI). However, radiomic features such as Dmax bulk and SUV peak as well as an early treatment response at interim PET (i-PET) as a measure of chemosensitivity using ΔSUVmax may have additional predictive value. We tested different models for risk prediction aiming at a dynamic risk tool in the era of evolving radiomic features in functional imaging. Methods: All patients within the PETRA database with newly diagnosed DLBCL, who were treated with R-CHOP and had available clinical data, baseline PET and i-PET scans were included. The optimal transformation of Dmax bulk, SUV peak and ΔSUV max was determined by choosing the best fitting Cox regression model with 3-year PFS as outcome, with highest R2 and lowest Akaike Information Criterion (AIC), while the cross-validated c-index was obtained as a measure for discrimination. Subsequently, risk models were developed using clinical, baseline PET and i-PET data. The best risk model was compared to the IMPI model and our subsequent ClinicalPET model, also incorporating radiomic features (MTV, IPI, age, SUV peak and D maxbulk) by determination of risk re-classification rates and by generating kaplan-meier (KM)-curves based on 60-30-10 PFS risk groups. Results: 1014 patients were included in the analyses. Adding i-PET reponse (ΔSUV max) to the IMPI model markedly improved outcome prediction (AIC 3177.44, c-index 0.72) and was superior to IMPI model alone (AIC 3247.09, c-index 0.68). By adding D maxbulk outcome prediction was further improved (AIC 3143.23, c-index 0.74), while SUV peak did not show significant impact on outcome (p=0.07). Compared to the IMPI and the ClinicalPET model, the new model combining baseline features (MTV, age and D maxbulk) with i-PET reponse (ΔSUV max) led to a sharper segregation of KM-curves with an improved rate of correct progression risk classification (22%; 95% confidence interval 12.1-31.1%). Conclusions: Adding i-PET reponse to baseline clinical and PET parameters optimizes risk classification in DLBCL enabling individualized risk assessment in early phase of frontline treatment and outperforms our previous IMPI model and ClinicalPET models.

Plasma indole-3-aldehyde as a novel biomarker of acute kidney injury after cardiac surgery: a reanalysis using prospective metabolomic data

BackgroundAcute kidney injury (AKI) is a frequent complication of cardiac surgery that poses significant risks for both the development of chronic kidney diseases and mortality. Our previous study illustrated that heightened expression levels of faecal and plasma indole metabolites before the operation were associated with ischemic AKI. In this study, we aimed to validate the supposition that plasma indole-3-aldehyde (I3A) could serve as a predictive biomarker for AKI in patients undergoing cardiac surgery.MethodsThis statistical reanalysis utilized AKI metabolomic data from patients scheduled for cardiac surgery between April 2022 and July 2022 in two tertiary hospitals. Faecal and blood samples were prospectively collected before surgery within 24 h, and variables related to the preoperative, intraoperative, and postoperative periods were recorded. AKI diagnosis was based on the Kidney Disease Improving Global Outcomes criteria.ResultsIn this study, 55 patients who underwent cardiac surgery were analyzed, and 27 of them (49.1%) developed postoperative AKI. Before surgery, these patients had significantly higher levels of faecal indole metabolites, including skatole, trans-3-indoleacrylic acid, and 5-methoxyindoleacetic acid. The plasma I3A, clinical model that considered perioperative and intraoperative variables, and their combination had area under the receiver operating characteristic curve (ROC) values of 0.79 (95% CI 0.67–0.91), 0.78 (95% CI 0.66–0.90), and 0.84 (95% CI 0.74–0.94) for predicting AKI, respectively. Furthermore, by utilizing net reclassification improvement and integrated discrimination improvement, plasma I3A showed significant improvements in risk reclassification compared to the clinical model alone.ConclusionsThe dysregulation of gut microbiota metabolism in patients scheduled for cardiac surgery can result in an increase in indoles from tryptophan metabolism, which may be associated with postoperative acute kidney injury (AKI). This suggests that indoles may serve as a predictive biomarker for AKI in patients undergoing cardiac surgery.

Cardiovascular risk assessment in patients with antiphospholipid syndrome: a cross-sectional performance analysis of nine clinical risk prediction tools

ObjectivesThis study aimed to assess the performance of cardiovascular risk (CVR) prediction models reported by European Alliance of Associations for Rheumatology and European Society of Cardiology recommendations to identify high-atherosclerotic...

Oxidized phospholipids in cardiovascular disease.

Prolonged or excessive exposure to oxidized phospholipids (OxPLs) generates chronic inflammation. OxPLs are present in atherosclerotic lesions and can be detected in plasma on apolipoprotein B (apoB)-containing lipoproteins. When initially conceptualized, OxPL-apoB measurement in plasma was expected to reflect the concentration of minimally oxidized LDL, but, surprisingly, it correlated more strongly with plasma lipoprotein(a) (Lp(a)) levels. Indeed, experimental and clinical studies show that Lp(a) particles carry the largest fraction of OxPLs among apoB-containing lipoproteins. Plasma OxPL-apoB levels provide diagnostic information on the presence and extent of atherosclerosis and improve the prognostication of peripheral artery disease and first and recurrent myocardial infarction and stroke. The addition of OxPL-apoB measurements to traditional cardiovascular risk factors improves risk reclassification, particularly in patients in intermediate risk categories, for whom improving decision-making is most impactful. Moreover, plasma OxPL-apoB levels predict cardiovascular events with similar or greater accuracy than plasma Lp(a) levels, probably because this measurement reflects both the genetics of elevated Lp(a) levels and the generalized or localized oxidation that modifies apoB-containing lipoproteins and leads to inflammation. Plasma OxPL-apoB levels are reduced by Lp(a)-lowering therapy with antisense oligonucleotides and by lipoprotein apheresis, niacin therapy and bariatric surgery. In this Review, we discuss the role of role OxPLs in the pathophysiology of atherosclerosis and Lp(a) atherogenicity, and the use of OxPL-apoB measurement for improving prognosis, risk reclassification and therapeutic interventions.

Evaluation of Clinical Applicability of Coronary Artery Calcium Assessment on Non–Gated Chest Computed Tomography, Compared With the Classic Agatston Score on Cardiac Computed Tomography

Given current pretest probability (PTP) estimations tend to overestimate patients' risk for obstructive coronary artery disease, evaluation of patients' coronary artery calcium (CAC) is more precise. The value of CAC assessment with the Agatston score on cardiac computed tomography (CT) for risk estimation has been well indicated in patients with stable chest pain. CAC can be equally well assessed on routine non-gated chest CT, which is often available. This study aims to determine the clinical applicability of CAC assessment on non-gated CT in patients with stable chest pain compared with the classic Agatston score on gated CT. Consecutive patients referred for evaluation of the Agatston score, who had a previously performed non-gated chest CT for evaluation of noncardiac diseases, were included. CAC on non-gated CT was ordinally scored. Subsequently, patients were stratified according to CAC severity and PTP. The agreement and correlation between the classic Agatston score and CAC on non-gated CT were evaluated. The discriminative power for risk reclassification of both CAC assessment methods was assessed. Invasive coronary angiography was used as the gold standard, when available. A total of 140 patients aged between 30 and 88years were included. The agreement between ordinally scored CAC and the Agatston score was excellent (κ=0.82) and the correlation strong (r=0.94). Most patients (80%) with an intermediate PTP had no or mild CAC on non-gated CT. They were reclassified at low risk with 100% accuracy compared with invasive coronary angiography. Similarly, 86% of patients had an Agatston score <300. These patients were reclassified with 98% accuracy. In patients with high PTP, the accuracy remained substantial and comparable, 94% and 89%, respectively. In conclusion, we believe this is the first study to assess the clinical applicability of CAC on non-gated CT in patients with stable chest pain, compared with the classic Agatston score. The agreement between methods was excellent and the correlation strong. Furthermore, CAC assessment on non-gated CT could reclassify patients' risk for obstructive coronary artery disease as accurately as could the classic Agatston score.

Dynamic Changes of Molecular Subtype Classification and Genomic Classifier Scores in High-Risk Prostate Cancer Patients Undergoing Pre-Operative Stereotactic Body Radiation Therapy

The radiobiology of irradiated human prostate cancer (PCa) remains poorly understood as irradiated tissues often remain in situ. PREPARE-SBRT (NCT03663218) is a clinical trial testing the safety of neoadjuvant MRI-guided stereotactic body radiation therapy (SBRT) for men with high-risk PCa. We leveraged paired samples from pre-treatment biopsy and irradiated prostatectomy (RP) specimens to evaluate transcriptomic changes in irradiated tumors at acute timepoints following SBRT. Tumor RNA expression profiles were generated using Decipher GRID by Veracyte on 12 subjects enrolled on NCT03663218 with paired pre/post-SBRT tissues (n = 24). Descriptive statistics using Decipher Genomic Classifier (GC) Score [0-1] and GC risk group (low/int/high) were generated from a validated 22-gene GC. Tumor biology signatures reported as dichotomous variables evaluated changes in androgen receptor (AR) activity [higher v lower] and cell cycle progression (CCP) [lower v higher]. Decipher prostate subtype classifier [PSC, luminal differentiated (LD), luminal proliferating (LP), basal immune (BI), basal neuroendocrine like (BN)] classification and PAM50 molecular subtype [luminal A (lumA), luminal B (lumB), basal] at pre/post-SBRT timepoints were reported. A control cohort of transcriptomic profiles of 803 matched untreated biopsy and RP samples from the same patients were used to control for signature differences attributable to sample type. The median pre- and post-SBRT GC scores were 0.55 and 0.72 (Δ+0.17), respectively. By comparison, median GC scores in a control cohort (n = 803) of biopsy and RP specimens were 0.50 and 0.56 (Δ+0.06), respectively. SBRT increased GC score in 9/12 subjects (75%) with a median increase of 0.3. Changes in GC score resulted in reclassification of baseline GC risk in 7 of 12 subjects with 71% of reclassified subjects (5/7) transitioning to a higher genomic risk. 92% of subjects (11/12) had higher AR activity scores at baseline. Of this subgroup, 5/11 (45%) converted to lower AR activity score after SBRT. CCP signatures remained stable in 9 of 12 subjects (75%) with 7/12 subjects exhibiting lower CCP score at baseline and only 1 subject transitioning from lower to higher CCP score. Distribution of PAM50 molecular subtype at baseline and after SBRT was lumA (33>53%), lumB (25>17%), basal (42>25%) resulting in 83% of subjects (10/12) annotated to a different PAM50 molecular subtype at pre/post-SBRT assessment. PSC subtype distribution at baseline was LD (33%), LP (25%), BI (33%) and BN (8%). Strikingly, after SBRT, 92% (11/12) of subjects were classified as BI molecular subtype with several immune activation signatures also increased after SBRT. A majority of subjects demonstrate a post-SBRT increase in GC score with reclassification of genomic-prognostic risk group in 58%. An enrichment of the basal-immune molecular subtype was observed following SBRT suggesting a convergence towards this biology in irradiated tumors.

Use of Coronary Artery Calcium Quantification and Distribution for Coronary Vascular Disease Risk Reclassification in a Primary Prevention Setting

In a large screening program of asymptomatic middle-aged individuals, we sought to assess the degree of risk reclassification provided by comparing multiethnic study on subclinical atherosclerosis coronary artery calcium scoring (CACS) versus atherosclerotic cardiovascular disease (ASCVD) and Reynolds risk score (RRS) score. All 5,324 consecutive patients (aged 57 ± 8years, 76% male) who underwent CACS screening at the Cleveland Clinic as part of a primary prevention executive health between March 16 and October 21 were included. The 10-year ASCVD, RRS, and multiethnic study on subclinical atherosclerosis CACS (MESA-CACS) risk scores were calculated and categorized as <1, 1 to 4.99, 5 to 9.99, and ≥10%. Compared with ASCVD, using MESA-CACS resulted in a downgraded risk in 1,667 subjects (31%), whereas 738 (14%) had an upgrade in risk (total of 45% reclassification). Similarly, compared with RRS, using MESA-CACS resulted in an upgraded risk in 797 (15%) and a downgrade in 1,380 (26%) subjects (total of 41% reclassification). However, by further dividing by the distribution of the coronary calcification, ASCVD overestimates the risk only for patients with coronary artery calcium (CAC) in 0 or 1 coronary artery only, whereas MESA-CACS overestimates if the CAC was noted in ≥2 arteries. Similarly, RRS only overestimates the risk for patients with 0 CAC, whereas it underestimates the risk for patients with any CAC. In conclusion, the use of MESA-CACS, along with CAC distribution in primary prevention clinics, results in differential and significant reclassification of traditional scores when calculating the 10-years coronary vascular disease risk. Overall, RRS underestimates and ASCVD overestimates the cardiovascular disease risk compared with MESA-CACS.

Modern algorithms for statin therapy

HMG-CoA reductase inhibitors (statins) are a key class of drugs for the treatment and prevention of atherosclerosis and its complications. This class of drugs has become firmly established in Russian and international guidelines with a high level of evidence. However, the use of these drugs in practice is not yet optimal. A significant proportion of patients at very high cardiovascular risk, taking initial doses of statins, do not achieve target levels of low-density lipoprotein cholesterol. The article is devoted to the analysis of international and Russian guidelines for statin monotherapy optimization, including their prescription in primary prevention using cardiovascular risk reclassification, coronary calcium score, carotid Doppler ultrasound and lipoprotein(a) levels.

Late gadolinium enhancement entropy as a new measure of myocardial tissue heterogeneity for prediction of adverse cardiac events in patients with hypertrophic cardiomyopathy

ObjectivesEntropy is a new late gadolinium enhanced (LGE) cardiac magnetic resonance (CMR)–derived parameter that is independent of signal intensity thresholds. Entropy can be used to measure myocardial tissue heterogeneity by comparing full pixel points of tissue images. This study investigated the incremental prognostic value of left ventricular (LV) entropy in patients with hypertrophic cardiomyopathy (HCM).MethodsThis study enrolled 337 participants with HCM who underwent 3.0-T CMR. The LV entropy was obtained by calculating the probability distribution of the LV myocardial pixel signal intensities of the LGE sequence. Patients who underwent CMR imaging were followed up for endpoints. The primary endpoint was defined as readmission to the hospital owing to heart failure. The secondary endpoint was the composite of the primary endpoint, sudden cardiac death and non-cardiovascular death.ResultsDuring the median follow-up of 24 months ± 13 (standard deviation), 43 patients who reached the primary and secondary endpoints had a higher entropy (6.20 ± 0.45, p < 0.001). The patients with increased entropy (≥ 5.587) had a higher risk of the primary and secondary endpoints, compared with HCM patients with low entropy (p < 0.001 for both). In addition, Cox analysis showed that LV entropy provided significant prognostic value for predicting both primary and secondary endpoints (HR: 1.291 and 1.273, all p < 0.001). Addition of LV entropy to the multivariable model improved model performance and risk reclassification (p < 0.05).ConclusionLV entropy assessed by CMR was an independent predictor of primary and secondary endpoints. LV entropy assessment contributes to improved risk stratification in patients with HCM.Critical relevance statementMyocardial heterogeneity reflected by entropy the derived parameter of LGE has prognostic value for adverse events in HCM. The measurement of LV entropy helped to identify patients with HCM who were at risk for heart failure and sudden cardiac death.Key points• Left ventricular entropy can reflect myocardial heterogeneity in HCM patients.• Left ventricular entropy was significantly higher in HCM patients who reached endpoint events.• Left ventricular entropy helps to predict the occurrence of heart failure and death in HCM patients.Graphical

Predicted Heart Mass: A Tale of 2 Ventricles.

Total predicted heart mass (PHM) is the recommended metric to assess donor-recipient size matching in patients undergoing heart transplantation. Separately measuring right ventricular (RV) and left ventricular (LV) PHM may improve risk prediction of 1-year graft failure. Adult heart transplant recipients from the UNOS database from 2000 to 2018 were included in the study. LV and RV PHM were modeled as restricted cubic splines. The association with 1-year graft failure was determined using adjusted Cox regression. The risk reclassification of using both LV and RV PHM versus total PHM was assessed using the net reclassification index. A total of 34 976 recipients were included. We observed a U-shaped association between total PHM and 1-year graft failure, such that risk increased for hearts undersized by >15% and those oversized by more than 27%. Graft failure incrementally increased when LV PHM was undersized by more than 5% and when RV was oversized by >20%. There was 1.5-fold greater risk of graft failure for an LV undersized by >26% or an RV oversized by more than 40%. Using LV and RV PHM risk-assessment separately led to a net reclassification index=8.5% ([95% CI, 5.3%-11.7%], nonevent net reclassification index=9.1%, event net reclassification index=-0.6%). The association between donor-recipient PHM match and the risk of graft failure after heart transplantation can be further understood as risk attributable to LV undersizing and RV oversizing. Assessing LV and RV PHM separately instead of total PHM could further refine the methods used to match donors and recipients for heart transplantation, minimize the risk of 1-year graft failure, and increase the use of donor organs.

Plasma Amino Acid Neurotransmitters and Ischemic Stroke Prognosis: A Multicenter Prospective Study

BackgroundPlasma amino acid neurotransmitter dysregulation is suggested to be implicated in the development of ischemic stroke, but its prognostic value for ischemic stroke remains controversial. ObjectiveWe aimed to prospectively investigate the associations between plasma amino acid neurotransmitters levels and adverse outcomes after ischemic stroke in a large-scale multicenter cohort study. MethodsWe measured 4 plasma amino acid neurotransmitters (glutamic acid, aspartic acid, gamma-aminobutyric acid, and glycine) among 3486 patients with ischemic stroke from 26 hospitals across China. The primary outcome is the composite outcome of death or major disability (modified Rankin Scale score ≥3) at 3 mo after ischemic stroke. ResultsAfter multivariate adjustment, the odds ratios of death or major disability for the highest versus the lowest quartile were 2.04 (95% confidence interval [CI]: 1.60,2.59; P-trend < 0.001) for glutamic acid, 2.03 (95% CI: 1.59, 2.59; P-trend < 0.001) for aspartic acid, 1.35 (95% CI: 1.06, 1.71; P-trend = 0.016) for gamma-aminobutyric acid, and 0.54 (95% CI: 0.42, 0.69; P-trend < 0.001) for glycine. Each standard deviation increment of log-transformed glutamic acid, aspartic acid, gamma-aminobutyric acid, and glycine was associated with a 34%, 34%, and 9% increased risk, and a 23% decreased risk of death or major disability, respectively (all P < 0.05), in a linear fashion as indicated by spline regression analyses (all P for linearity < 0.05). Addition of the 4 plasma amino acid neurotransmitters to conventional risk factors significantly improved the risk reclassification, as evidenced by integrated discrimination improvement and net reclassification improvement (all P < 0.05). ConclusionsIncreased glutamic acid, aspartic acid, and gamma-aminobutyric acid and decreased glycine in plasma are associated with adverse outcomes after ischemic stroke, suggesting that plasma amino acid neurotransmitters may be potential intervention targets for improving prognosis of ischemic stroke.The CATIS trial was registered at clinicaltrials.gov (registration number: NCT01840072; URL: ===https://clinicaltrials.gov/ct2/show/NCT01840072?cond=NCT01840072&draw=2&rank=1)

ApoE genotyping and cardiovascular risk re-classification of subjects, a single center experience 2008-2021

ApoE genotyping and cardiovascular risk re-classification of subjects, a single center experience 2008-2021

A machine learning based approach to identify carotid subclinical atherosclerosis endotypes.

To define endotypes of carotid subclinical atherosclerosis. We integrated demographic, clinical, and molecular data (n = 124) with ultrasonographic carotid measurements from study participants in the IMPROVE cohort (n = 3340). We applied a neural network algorithm and hierarchical clustering to identify carotid atherosclerosis endotypes. A measure of carotid subclinical atherosclerosis, the c-IMTmean-max, was used to extract atherosclerosis-related features and SHapley Additive exPlanations (SHAP) to reveal endotypes. The association of endotypes with carotid ultrasonographic measurements at baseline, after 30 months, and with the 3-year atherosclerotic cardiovascular disease (ASCVD) risk was estimated by linear (β, SE) and Cox [hazard ratio (HR), 95% confidence interval (CI)] regression models. Crude estimates were adjusted by common cardiovascular risk factors, and baseline ultrasonographic measures. Improvement in ASCVD risk prediction was evaluated by C-statistic and by net reclassification improvement with reference to SCORE2, c-IMTmean-max, and presence of carotid plaques. An ensemble stacking model was used to predict endotypes in an independent validation cohort, the PIVUS (n = 1061). We identified four endotypes able to differentiate carotid atherosclerosis risk profiles from mild (endotype 1) to severe (endotype 4). SHAP identified endotype-shared variables (age, biological sex, and systolic blood pressure) and endotype-specific biomarkers. In the IMPROVE, as compared to endotype 1, endotype 4 associated with the thickest c-IMT at baseline (β, SE) 0.36 (0.014), the highest number of plaques 1.65 (0.075), the fastest c-IMT progression 0.06 (0.013), and the highest ASCVD risk (HR, 95% CI) (1.95, 1.18-3.23). Baseline and progression measures of carotid subclinical atherosclerosis and ASCVD risk were associated with the predicted endotypes in the PIVUS. Endotypes consistently improved measures of ASCVD risk discrimination and reclassification in both study populations. We report four replicable subclinical carotid atherosclerosis-endotypes associated with progression of atherosclerosis and ASCVD risk in two independent populations. Our approach based on endotypes can be applied for precision medicine in ASCVD prevention.

Plasma homeostatic chemokines CCL19 and CCL21 and the prognosis of ischemic stroke in two Chinese prospective cohorts.

The homeostatic chemokines CCL19 and CCL21 are involved in carotid plaque vulnerability and post-ischemic neuroinflammatory responses. This study aimed to examine the prognostic values of CCL19 and CCL21 in ischemic stroke. Plasma CCL19 and CCL21 were measured in 4483 ischemic stroke patients from two independent cohorts of CATIS (China Antihypertensive Trial in Acute Ischemic Stroke) and IIPAIS (Infectious Factors, Inflammatory Markers, and Prognosis of Acute Ischemic Stroke), and participants were followed up at 3 months after stroke. The primary outcome was the composite outcome of death or major disability. The associations of CCL19 and CCL21 levels with the primary outcome were examined. In CATIS, multivariable-adjusted odds ratios of the primary outcome in the highest quartiles of CCL19 and CCL21 compared with the lowest quartiles were 2.06 and 2.62, respectively. In IIPAIS, odds ratios of the primary outcome in the highest quartiles of CCL19 and CCL21 were 2.81 and 2.78 compared with the lowest quartiles, respectively. In the pooled analysis of the two cohorts, odds ratios of the primary outcome associated with the highest quartiles of CCL19 and CCL21 were 2.24 and 2.66, respectively. Similar findings were observed in the analysis with major disability, death, and the composite outcome of death or cardiovascular events as the secondary study outcomes. Adding CCL19 and CCL21 to conventional risk factors significantly improved risk reclassification and discrimination for adverse outcomes. Both CCL19 and CCL21 levels were independently associated with adverse outcomes within 3 months after ischemic stroke and should be further investigated for risk stratification and potential therapeutic targets of ischemic stroke.

Plasma urea cycle metabolite levels and the risk of moyamoya disease.

Urea cycle metabolites are expected to be the biomarkers for cerebrovascular diseases. However, the effects of circulating urea cycle metabolites on the risk of MMD and its subcategories remain unclear. The aim of this study was to prospectively investigate the association between plasma urea cycle metabolites and the risk of MMD and its subcategories. We measured plasma urea cycle metabolite levels for 360 adult MMD patients and 89 matched healthy controls. Clinical and laboratory characteristics were obtained from the medical record. The study was conducted from July 2020 to December 2021. After multivariate adjustment, the risk of MMD increased with each increment in ornithine level (per natural log [ornithine] increment: OR, 3.893; 95% CI, 1.366-11.090). The risk of MMD decreased with each increment in arginine level (per natural log [arginine] increment: OR, 0.109; 95% CI, 0.028-0.427), urea level (per natural log [urea] increment: OR, 0.261; 95% CI, 0.072-0.940), and global arginine bioavailability ratio (GABR) level (per natural log [GABR] increment: OR, 0.189; 95% CI, 0.074-0.484). The addition of plasma arginine (integrated discrimination improvement: 1.76%, p = 0.021) or GABR (integrated discrimination improvement: 1.76%, p = 0.004) to conventional risk factors significantly improved the risk reclassification for MMD. Plasma ornithine levels are positively associated with the risk of MMD. By contrast, the levels of arginine, urea, and GABR are inversely related to the risk of MMD. Plasma urea cycle metabolites might be potential biomarkers for the risk of MMD.

GRACE scores or high-sensitivity troponin for timing of coronary angiography in non-ST-elevation acute coronary syndromes

BackgroundThe GRACE risk score is generically recommended by guidelines for timing of invasive coronary angiography without stating which score should be used. The aim was to determine the diagnostic performance of different GRACE risk scores in comparison to the ESC 0/1 h-algorithm using high-sensitivity cardiac troponin (hs-cTn).MethodsProspectively enrolled patients presenting with symptoms suggestive of myocardial infarction (MI) in two large studies testing biomarker diagnostic strategies were included. Five GRACE risk scores were calculated. The amount of risk reclassification and the theoretical impact on guideline-recommended timing of invasive coronary angiography was studied.ResultsOverall, 8,618 patients were eligible for analyses. Comparing different GRACE risk scores, up to 63.8% of participants were reclassified into a different risk category. The proportion of MIs identified (i.e., sensitivity) dramatically differed between GRACE risk scores (range 23.8–66.5%) and was lower for any score than for the ESC 0/1 h-algorithm (78.1%). Supplementing the ESC 0/1 h-algorithm with a GRACE risk score slightly increased sensitivity (P < 0.001 for all scores). However, this increased the number of false positive results.ConclusionThe substantial amount of risk reclassification causes clinically meaningful differences in the proportion of patients meeting the recommended threshold for pursuing early invasive strategy according to the different GRACE scores. The single best test to detect MIs is the ESC 0/1 h-algorithm. Combining GRACE risk scoring with hs-cTn testing slightly increases the detection of MIs but also increases the number of patients with false positive results who would undergo potential unnecessarily early invasive coronary angiography.Graphical abstract

Implications of Social Disadvantage Score in Cardiovascular Outcomes and Risk Assessment: Findings From the Multi-Ethnic Study of Atherosclerosis.

Social determinants of health contribute to disparate cardiovascular outcomes, yet they have not been operationalized into the current paradigm of cardiovascular risk assessment. Data from the Multi-Ethnic Study of Atherosclerosis, which includes participants from 6 US field centers, were used to create an index of baseline Social Disadvantage Score (SDS) to explore its association with incident atherosclerotic cardiovascular disease (ASCVD) and all-cause mortality and impact on ASCVD risk prediction. SDS, which ranges from 0 to 4, was calculated by tallying the following social factors: (1) household income less than the federal poverty level; (2) educational attainment less than a high school diploma; (3) single-living status; and (4) experience of lifetime discrimination. Cox models were used to examine the association between SDS and each outcome with adjustment for traditional cardiovascular risk factors. Changes in the discrimination and reclassification of ASCVD risk by incorporating SDS into the pooled cohort equations were examined. A total of 6434 participants (mean age, 61.9±10.2 years; female 52.8%; non-white 60.9%) had available SDS 1733 (26.9%) with SDS 0; 2614 (40.6%) with SDS 1; 1515 (23.5%) with SDS 2; and 572 (8.9%) with SDS ≥3. In total, 775 incident ASCVD events and 1573 deaths were observed over a median follow-up of 17.0 years. Increasing SDS was significantly associated with incident ASCVD and all-cause mortality after adjusting for traditional risk factors (ASCVD: per unit increase in SDS hazard ratio, 1.15 [95% CI, 1.07-1.24]; mortality: per unit increase in SDS hazard ratio, 1.13 [95% CI, 1.08-1.19]). Adding SDS to pooled cohort equations components in a Cox model for 10-year ASCVD risk prediction did not significantly improve discrimination (P=0.208) or reclassification (P=0.112). Although SDS is independently associated with incident ASCVD and all-cause mortality, it does not improve 10-year ASCVD risk prediction beyond pooled cohort equations.

A multi-ancestry polygenic risk score improves risk prediction for coronary artery disease

Identification of individuals at highest risk of coronary artery disease (CAD)—ideally before onset—remains an important public health need. Prior studies have developed genome-wide polygenic scores to enable risk stratification, reflecting the substantial inherited component to CAD risk. Here we develop a new and significantly improved polygenic score for CAD, termed GPSMult, that incorporates genome-wide association data across five ancestries for CAD (>269,000 cases and >1,178,000 controls) and ten CAD risk factors. GPSMult strongly associated with prevalent CAD (odds ratio per standard deviation 2.14, 95% confidence interval 2.10–2.19, P < 0.001) in UK Biobank participants of European ancestry, identifying 20.0% of the population with 3-fold increased risk and conversely 13.9% with 3-fold decreased risk as compared with those in the middle quintile. GPSMult was also associated with incident CAD events (hazard ratio per standard deviation 1.73, 95% confidence interval 1.70–1.76, P < 0.001), identifying 3% of healthy individuals with risk of future CAD events equivalent to those with existing disease and significantly improving risk discrimination and reclassification. Across multiethnic, external validation datasets inclusive of 33,096, 124,467, 16,433 and 16,874 participants of African, European, Hispanic and South Asian ancestry, respectively, GPSMult demonstrated increased strength of associations across all ancestries and outperformed all available previously published CAD polygenic scores. These data contribute a new GPSMult for CAD to the field and provide a generalizable framework for how large-scale integration of genetic association data for CAD and related traits from diverse populations can meaningfully improve polygenic risk prediction.

Screening Test for Evaluation of Cardiovascular Disease in Patients with Diabetes

Diabetes mellitus (DM) per se confers a two-fold excess risk of vascular outcomes, but the risk is subject to wide variation depending on the population and contemporary prophylactic medical therapy. Although patients with DM have a high rate of asymptomatic coronary heart disease (CHD), routine screening for CHD has not been shown to improve clinical outcomes in asymptomatic DM patients being treated with intensive medical therapy for cardiovascular risk factors. Therefore, professional societies do not recommend routine screening for CHD in asymptomatic patients. However, they recommend additional imaging such as coronary calcium score or carotid ultrasonography to provide risk reclassification and reason for treatment intensification. In this review, we provide recent guidelines regarding screening for cardiovascular disease in asymptomatic DM patients and updated the discussion of the role of electrocardiography, coronary calcium score, and carotid ultrasonography.

Predicting 1-Year Mortality in Outpatients With Heart Failure With Reduced Left Ventricular Ejection Fraction: Do Empiric Models Outperform Physician Intuitive Estimates? A Multicenter Cohort Study.

Many studies have demonstrated that physicians often err in estimating patient prognosis. No studies have directly compared physician to model predictive performance in heart failure (HF). We aimed to compare the accuracy of physician versus model predictions of 1-year mortality. This multicenter prospective cohort study on 11 HF clinics in 5 provinces in Canada included consecutive consented outpatients with HF with reduced left ventricular ejection fraction (<40%). By collecting clinical data, we calculated predicted 1-year mortality using the Seattle HF Model (SHFM), the Meta-Analysis Global Group in Chronic HF score, and the HF Meta-Score. HF cardiologists and family doctors, blinded to model predictions, estimated patient 1-year mortality. During 1-year follow-up, we recorded the composite end point of mortality, urgent ventricular assist device implant, or heart transplant. We compared physicians and model discrimination (C statistic), calibration (observed versus predicted event rate), and risk reclassification. The study included 1643 patients with ambulatory HF with a mean age of 65 years, 24% female, and mean left ventricular ejection fraction of 28%. Over 1-year follow-up, 9% had an event. The SHFM had the best discrimination (SHFM C statistic 0.76; HF Meta-Score 0.73; Meta-Analysis Global Group in Chronic Heart Failure 0.70) and calibration. Physicians' discrimination differed little (0.75 for HF cardiologists and 0.73 for family doctors) but both physician groups substantially overestimated risk by >10% in both low- and high-risk patients (poor calibration). In risk reclassification analysis, among patients without events, the SHFM better classified 51% in comparison to HF cardiologists and 43% in comparison to family doctors. In patients with events, the SHFM erroneously assigned lower risk to 44% in comparison to HF cardiologists and 34% in comparison to family doctors. Family doctors and HF cardiologists showed adequate risk discrimination, with however substantial overestimation of absolute risk. Predictive models showed higher accuracy. Incorporating models in family and HF cardiology practices may improve patient care and resource use in HF with reduced left ventricular ejection fraction. URL: https://www. gov; Unique identifier: NCT04009798.