Semi-quantitative assessment of right ventricular function in comparison to a 3D volumetric approach: A cardiovascular magnetic resonance study

Right ventricular (RV) volume measurements with cardiovascular magnetic resonance (CMR) is considered the gold standard, but acquisition and analysis remain timeconsuming. The aim of our study was therefore to investigate the accuracy and performance of a semi-quantitative assessment of RV function in CMR, compared to the standard quantitative approach. Seventy-five subjects with pulmonary hypertension (15), anterior myocardial infarction (15), inferior myocardial infarction (15), Brugada syndrome (15) and normal subjects (15) underwent cine CMR. RV end-systolic and enddiastolic volumes were determined to calculate RV ejection fraction (EF). Four-chamber cine images were used to measure tricuspid annular plane systolic excursion (TAPSE). RV fractional shortening (RVFS) was calculated by dividing TAPSE by the RV end-diastolic length. RV EF correlated significantly with TAPSE (r = 0.62, p < 0.01) and RVFS (r = 0.67, p < 0.01). Sensitivity to predict RV dysfunction was comparable between TAPSE and RVFS, with higher specificity for RVFS, but comparable areas under the ROC curve. Intra- and inter-observer variability of RV EF was better than TAPSE (3%/4% versus 7%/15%, respectively). For routine screening in clinical practice, TAPSE and RVFS seem reliable and easy methods to identify patients with RV dysfunction. The 3D volumetric approach is preferred to assess RV function for research purposes or to evaluate treatment response

CMR findings in patients with hypertrophic cardiomyopathy and atrial fibrillation

<p>Abstract</p> <p>Objectives</p> <p>We sought to evaluate the relation between atrial fibrillation (AF) and the extent of myocardial scarring together with left ventricular (LV) and atrial parameters assessed by late gadolinium-enhancement (LGE) cardiovascular magnetic resonance (CMR) in patients with hypertrophic cardiomyopathy (HCM).</p> <p>Background</p> <p>AF is the most common arrhythmia in HCM. Myocardial scarring is also identified frequently in HCM. However, the impact of myocardial scarring assessed by LGE CMR on the presence of AF has not been evaluated yet.</p> <p>Methods</p> <p>87 HCM patients underwent LGE CMR, echocardiography and regular ECG recordings. LV function, volumes, myocardial thickness, left atrial (LA) volume and the extent of LGE, were assessed using CMR and correlated to AF. Additionally, the presence of diastolic dysfunction and mitral regurgitation were obtained by echocardiography and also correlated to AF.</p> <p>Results</p> <p>Episodes of AF were documented in 37 patients (42%). Indexed LV volumes and mass were comparable between HCM patients with and without AF. However, indexed LA volume was significantly higher in HCM patients with AF than in HCM patients without AF (68 ± 24 ml·m^-2versus 46 ± 18 ml·m^-2, p = 0.0002, respectively). The mean extent of LGE was higher in HCM patients with AF than those without AF (12.4 ± 14.5% versus 6.0 ± 8.6%, p = 0.02). When adjusting for age, gender and LV mass, LGE and indexed LA volume significantly correlated to AF (r = 0.34, p = 0.02 and r = 0.42, p < 0.001 respectively). By echocardiographic examination, LV diastolic dysfunction was evident in 35 (40%) patients. Mitral regurgitation greater than II was observed in 12 patients (14%). Multivariate analysis demonstrated that LA volume and presence of diastolic dysfunction were the only independent determinant of AF in HCM patients (p = 0.006, p = 0.01 respectively). Receiver operating characteristic curve analysis indicated good predictive performance of LA volume and LGE (AUC = 0.74 and 0.64 respectively) with respect to AF.</p> <p>Conclusion</p> <p>HCM patients with AF display significantly more LGE than HCM patients without AF. However, the extent of LGE is inferior to the LA size for predicting AF prevalence. LA dilation is the strongest determinant of AF in HCM patients, and is related to the extent of LGE in the LV, irrespective of LV mass.</p

Temporal patterns of macrophage- and neutrophil-related markers are associated with clinical outcome in heart failure patients

Aims: Evidence on the association of macrophage- and neutrophil-related blood biomarkers with clinical outcome in heart failure patients is limited, and, with the exception of C-reactive protein, no data exist on their temporal evolution. We aimed to investigate whether temporal patterns of these biomarkers are related to clinical outcome in patients with stable chronic heart failure (CHF). Methods and Results: In 263 patients with CHF, we performed serial plasma measurements of scavenger receptor cysteine-rich type 1 protein M130 (CD163), tartrate-resistant acid phosphatase type 5 (TRAP), granulins (GRN), spondin-1 (SPON1), peptidoglycan recognition protein 1 (PGLYRP1), and tissue factor pathway inhibitor (TFPI). The Cardiovascular Panel III (Olink Proteomics AB, Uppsala, Sweden) was used. During 2.2 years of follow-up, we collected 1984 samples before the occurrence of the composite primary endpoint (PE) or censoring. For efficiency, we selected 567 samples for the measurements (all baseline samples, the last two samples preceding the PE, and the last sample before censoring in event-free patients). The relationship between repeatedly measured biomarker levels and the PE was evaluated by joint models. Mean (±standard deviation) age was 67 ± 13 years; 189 (72%) were men; left ventricular ejection fraction (%) was 32 ± 11. During follow-up, 70 (27%) patients experienced the PE. Serially measured biomarkers predicted the PE in a multivariable model adjusted for baseline clinical characteristics [hazard ratio (95% confidence interval) per 1-standard deviation change in biomarker]: CD163 [2.07(1.47–2.98), P < 0.001], TRAP [0.62 (0.43–0.90), P = 0.009], GRN [2.46 (1.64–3.84), P < 0.001], SPON1 [3.94 (2.50–6.50), P < 0.001], and PGLYRP1 [1.62 (1.14–2.31), P = 0.006]. Conclusions: Changes in plasma levels of CD163, TRAP, GRN, SPON1, and PGLYRP1 precede adverse cardiovascular events in patients with CHF

Renal tubular damage and worsening renal function in chronic heart failure:Clinical determinants and relation to prognosis (Bio-SHiFT study)

Background It is uncertain that chronic heart failure (CHF) patients are susceptible to renal tubular damage with that of worsening renal function (WRF) preceding clinical outcomes. Hypothesis Changes in tubular damage biomarkers are stronger predictors of subsequent clinical events than changes in creatinine (Cr), and both have different clinical determinants. Methods During 2.2 years, we repeatedly simultaneously collected a median of 9 blood and 8 urine samples per patient in 263 CHF patients. We determined the slopes (rates of change) of the biomarker trajectories for plasma (Cr) and urinary tubular damage biomarkers N-acetyl-beta-d-glucosaminidase (NAG), and kidney-injury-molecule (KIM)-1. The degree of tubular injury was ranked according to NAG and KIM-1 slopes: increase in neither, increase in either, or increase in both; WRF was defined as increasing Cr slope. The composite endpoint comprised HF-hospitalization, cardiac death, left ventricular assist device placement, and heart transplantation. Results Higher baseline NT-proBNP and lower eGFR predicted more severe tubular damage (adjusted odds ratio, adj. OR [95%CI, 95% confidence interval] per doubling NT-proBNP: 1.26 [1.07-1.49]; per 10 mL/min/1.73 m(2) eGFR decrease 1.16 [1.03-1.31]). Higher loop diuretic doses, lower aldosterone antagonist doses, and higher eGFR predicted WRF (furosemide per 40 mg increase: 1.32 [1.08-1.62]; spironolactone per 25 mg decrease: 1.76 [1.07-2.89]; per 10 mL/min/1.73 m(2) eGFR increase: 1.40 [1.20-1.63]). WRF and higher rank of tubular injury individually entailed higher risk of the composite endpoint (adjusted hazard ratios, adj. HR [95%CI]: WRF 1.9 [1.1-3.4], tubular 8.4 [2.6-27.9]; when combined risk was highest 15.0 [2.0-111.0]). Conclusion Slopes of tubular damage and WRF biomarkers had different clinical determinants. Both predicted clinical outcome, but this association was stronger for tubular injury. Prognostic effects of both appeared independent and additive

Machine learning–based biomarker profile derived from 4210 serially measured proteins predicts clinical outcome of patients with heart failure

Aims Risk assessment tools are needed for timely identification of patients with heart failure (HF) with reduced ejection fraction (HFrEF) who are at high risk of adverse events. In this study, we aim to derive a small set out of 4210 repeatedly measured proteins, which, along with clinical characteristics and established biomarkers, carry optimal prognostic capacity for adverse events, in patients with HFrEF. Methods and results In 382 patients, we performed repeated blood sampling (median follow-up: 2.1 years) and applied an aptamer-based multiplex proteomic approach. We used machine learning to select the optimal set of predictors for the primary endpoint (PEP: composite of cardiovascular death, heart transplantation, left ventricular assist device implantation, and HF hospitalization). The association between repeated measures of selected proteins and PEP was investigated by multivariable joint models. Internal validation (cross-validated c-index) and external validation (Henry Ford HF PharmacoGenomic Registry cohort) were performed. Nine proteins were selected in addition to the MAGGIC risk score, N-terminal pro-hormone B-type natriuretic peptide, and troponin T: suppression of tumourigenicity 2, tryptophanyl-tRNA synthetase cytoplasmic, histone H2A Type 3, angiotensinogen, deltex-1, thrombospondin-4, ADAMTS-like protein 2, anthrax toxin receptor 1, and cathepsin D. N-terminal pro-hormone B-type natriuretic peptide and angiotensinogen showed the strongest associations [hazard ratio (95% confidence interval): 1.96 (1.17–3.40) and 0.66 (0.49–0.88), respectively]. The multivariable model yielded a c-index of 0.85 upon internal validation and c-indices up to 0.80 upon external validation. The c-index was higher than that of a model containing established risk factors (P = 0.021). Conclusion Nine serially measured proteins captured the most essential prognostic information for the occurrence of adverse events in patients with HFrEF, and provided incremental value for HF prognostication beyond established risk factors. These proteins could be used for dynamic, individual risk assessment in a prospective setting. These findings also illustrate the potential value of relatively ‘novel’ biomarkers for prognostication.</p

Machine learning–based biomarker profile derived from 4210 serially measured proteins predicts clinical outcome of patients with heart failure

Aims: Risk assessment tools are needed for timely identification of patients with heart failure (HF) with reduced ejection fraction (HFrEF) who are at high risk of adverse events. In this study, we aim to derive a small set out of 4210 repeatedly measured proteins, which, along with clinical characteristics and established biomarkers, carry optimal prognostic capacity for adverse events, in patients with HFrEF. Methods and results: In 382 patients, we performed repeated blood sampling (median follow-up: 2.1 years) and applied an aptamer-based multiplex proteomic approach. We used machine learning to select the optimal set of predictors for the primary endpoint (PEP: composite of cardiovascular death, heart transplantation, left ventricular assist device implantation, and HF hospitalization). The association between repeated measures of selected proteins and PEP was investigated by multivariable joint models. Internal validation (cross-validated c-index) and external validation (Henry Ford HF PharmacoGenomic Registry cohort) were performed. Nine proteins were selected in addition to the MAGGIC risk score, N-terminal pro-hormone B-type natriuretic peptide, and troponin T: suppression of tumourigenicity 2, tryptophanyl-tRNA synthetase cytoplasmic, histone H2A Type 3, angiotensinogen, deltex-1, thrombospondin-4, ADAMTS-like protein 2, anthrax toxin receptor 1, and cathepsin D. N-terminal pro-hormone B-type natriuretic peptide and angiotensinogen showed the strongest associations [hazard ratio (95% confidence interval): 1.96 (1.17–3.40) and 0.66 (0.49–0.88), respectively]. The multivariable model yielded a c-index of 0.85 upon internal validation and c-indices up to 0.80 upon external validation. The c-index was higher than that of a model containing established risk factors (P = 0.021). Conclusion: Nine serially measured proteins captured the most essential prognostic information for the occurrence of adverse events in patients with HFrEF, and provided incremental value for HF prognostication beyond established risk factors. These proteins could be used for dynamic, individual risk assessment in a prospective setting. These findings also illustrate the potential value of relatively ‘novel’ biomarkers for prognostication

Long-term reliability of the phospholamban (PLN) p.(Arg14del) risk model in predicting major ventricular arrhythmia:a landmark study

Aims:Recently, a genetic variant-specific prediction model for phospholamban (PLN) p.(Arg14del)-positive individuals was developed to predict individual major ventricular arrhythmia (VA) risk to support decision-making for primary prevention implantable cardioverter defibrillator (ICD) implantation. This model predicts major VA risk from baseline data, but iterative evaluation of major VA risk may be warranted considering that the risk factors for major VA are progressive. Our aim is to evaluate the diagnostic performance of the PLN p.(Arg14del) risk model at 3-year follow-up. Methods:We performed a landmark analysis 3 years after presentation and selected only patients with no prior major VA. Data were and results collected of 268 PLN p.(Arg14del)-positive subjects, aged 43.5 ± 16.3 years, 38.9% male. After the 3 years landmark, subjects had a mean follow-up of 4.0 years (± 3.5 years) and 28 (10%) subjects experienced major VA with an annual event rate of 2.6% [95% confidence interval (CI) 1.6–3.6], defined as sustained VA, appropriate ICD intervention, or (aborted) sudden cardiac death. The PLN p.(Arg14del) risk score yielded good discrimination in the 3 years landmark cohort with a C-statistic of 0.83 (95% CI 0.79–0.87) and calibration slope of 0.97. Conclusion:The PLN p.(Arg14del) risk model has sustained good model performance up to 3 years follow-up in PLN p.(Arg14del)positive subjects with no history of major VA. It may therefore be used to support decision-making for primary prevention ICD implantation not merely at presentation but also up to at least 3 years of follow-up.</p