Citations

Abstract The burden of HCC is substantial. To address gaps in HCC care, the American Association for the Study of Liver Diseases (AASLD) Practice Metrics Committee (PMC) aimed to develop a standard set of process‐based measures and patient‐reported outcomes (PROs) along the HCC care continuum. We identified candidate process and outcomes measures for HCC care based on structured literature review. A 13‐member panel with content expertise across the HCC care continuum evaluated candidate measures on importance and performance gap using a modified Delphi approach (two rounds of rating) to define the final set of measures. Candidate PROs based on a structured scoping review were ranked by 74 patients with HCC across 7 diverse institutions. Out of 135 measures, 29 measures made the final set. These covered surveillance (6 measures), diagnosis (6 measures), staging (2 measures), treatment (10 measures), and outcomes (5 measures). Examples included the use of ultrasound (± alpha‐fetoprotein [AFP]) every 6 months, need for surveillance in high‐risk populations, diagnostic testing for patients with a new AFP elevation, multidisciplinary liver tumor board (MLTB) review of Liver Imaging‐Reporting and Data System 4 lesions, standard evaluation at diagnosis, treatment recommendations based on Barcelona Clinic Liver Cancer staging, MLTB discussion of treatment options, appropriate referral for evaluation of liver transplantation candidacy, and role of palliative therapy. PROs include those related to pain, anxiety, fear of treatment, and uncertainty about the best individual treatment and the future. The AASLD PMC has developed a set of explicit quality measures in HCC care to help bridge the gap between guideline recommendations and measurable processes and outcomes. Measurement and subsequent implementation of these metrics could be a central step in the improvement of patient care and outcomes in this high‐risk population.

Abstract Background Increased epicardial adipose tissue (EAT) thickness correlates with metabolic syndrome, insulin resistance, microvascular dysfunction and enhanced pericardial restraint. Purpose We measured echocardiographic EAT thickness in heart failure (HF) patients with reduced (HFrEF) or preserved (HFpEF) ejection fraction to determine whether EAT could bear prognostic significance at clinical follow-up. Methods We prospectively enrolled 393 consecutive HF outpatients (205 HFrEF, 188 HFpEF) referred to our hospital due to dyspnoea and/or effort intolerance. We performed a resting clinical and biohumoral evaluation, followed by combined cardiopulmonary-echocardiography exercise stress. A composite of cardiovascular death and HF-related hospitalization was chosen as endpoint during follow-up. Results Patients with HFpEF displayed higher EAT thickness (median 8 mm, interquartile range [IQR] 4–12 mm) than HFrEF (median 3 mm, IQR 2–6 mm; p<0.0001). During a median follow-up of 20.9 months (IQR 15–25 months), we reported 34 cardiovascular deaths and 146 HF hospitalizations, with no significant differences between the two HF subsets. EAT predicted adverse events independently from body mass index and well-established markers of poor prognosis (e.g., NT-proBNP, peak oxygen consumption). The risk of adverse events increased with increasing EAT thickness in HFpEF and with EAT thinning in HFrEF. Kaplan-Meier analyses for the composite endpoint showed that in HFpEF, the survival probability was significantly lower in patients with thicker EAT than those with thinner EAT. In HFrEF, conversely, patients with thicker EAT had a higher survival probability than those with reduced EAT thickness (Figure 1). Conclusion EAT accumulation is more marked in HFpEF than HFrEF and carries different prognostic meanings in the two subsets. In HFpEF, EAT thickening portends adverse outcome, which may be due to increased mechanical restraint and secretion of pro-inflammatory and pro-atherogenic adipokines. In HFrEF, greater EAT thickness seems to have a protective role, while EAT thinning is associated with a worse prognosis, likely reflecting a catabolic state (i.e. cardiac cachexia). Larger studies will clarify whether EAT is a bystander or an active player in the different HF phenotypes. Funding Acknowledgement Type of funding sources: None. Figure 1

Abstract Background Epicardial adipose tissue (EAT) acts as a paracrine organ and could exert direct mechanical compression on the myocardium. Purpose We investigated the impact of echocardiographic EAT thickness on metabolic profile and cardiovascular hemodynamics at rest and during exercise in heart failure (HF) patients with reduced (HFrEF) and preserved (HFpEF) ejection fraction Methods We prospectively enrolled 393 consecutive HF outpatients (205 HFrEF, 188 HFpEF) referred to the our hospital due to dyspnoea and/or effort intolerance. We performed a resting clinical and biohumoral evaluation, followed by combined cardiopulmonary-echocardiography exercise stress. The protocol also included 44 healthy controls. Results Patients with HFpEF displayed the greatest EAT thickness (median 8 mm, interquartile range [IQR] 4–12 mm), while those with HFrEF had thinner EAT (median 3 mm, IQR 2–6 mm) than controls (median 5 mm, IQR 3–7 mm; p<0.0001). In HFrEF, EAT thickness was inversely associated with natriuretic peptides, Troponin T, and C-reactive protein levels. In HFpEF, it was directly correlated with Troponin-T and C-reactive protein levels. EAT thickness was inversely correlated with peak oxygen consumption (VO2) and peripheral oxygen extraction (AVO2diff) in HFpEF, while a direct association was observed in HFrEF (Fig 1). EAT resulted in an independent predictor of peak VO2 and AVO2diff in HFrEF and HFpEF regardless of body mass index, but the relationships were direct in HFrEF (standard regression coefficient [SRC] for peak VO2: 0.18, p=0.02; SRC for peak AVO2diff: 0.17, p=0.03) and indirect in HFpEF (SRC for peak VO2: −0.33, p<0.0001; SRC for peak AVO2diff: −0.25, p<0.0001). The analysis of cardiac mechanics revealed that thinner EAT was associated with worse left ventricle systolic dysfunction (average S') and remodeling (3D left ventricle mass) in HFrEF. In HFpEF, increased EAT was related to more severe left atrio-ventricular (left atrium reservoir strain/left ventricle global longitudinal strain) and right ventriculo-arterial (tricuspid annular plane systolic excursion/systolic pulmonary artery pressure) coupling (Figure 2). Conclusion In HFpEF, EAT accumulation is associated with worse hemodynamic and metabolic profile. In HFrEF, conversely, EAT thinning portends more severe LV dysfunction and impaired global functional capacity. Funding Acknowledgement Type of funding sources: None. Figure 1Figure 2

Abstract Aims We evaluated the impact of echocardiographic epicardial adipose tissue (EAT) on cardiovascular haemodynamics, metabolic profile and prognosis in heart failure (HF) using combined cardiopulmonary‐echocardiography exercise stress. Methods and results We analysed EAT thickness of HF patients with reduced (HFrEF, n = 205) and preserved (HFpEF, n = 188) ejection fraction, including 44 controls. HFpEF patients displayed the highest EAT, while HFrEF patients had lower values than controls. EAT showed an inverse correlation with natriuretic peptides, troponin T and C‐reactive protein in HFrEF, while having a direct association with troponin T and C‐reactive protein in HFpEF. EAT was independently associated with peak oxygen consumption (VO 2 ) and peripheral extraction (AVO 2 diff), regardless of body mass index. EAT was inversely correlated with peak VO 2 and AVO 2 diff in HFpEF, while a direct association was observed in HFrEF, where lower EAT values were associated with worse left ventricular systolic dysfunction. In HFpEF, increased EAT was related to right ventriculo–arterial (tricuspid annular plane systolic excursion/systolic pulmonary artery pressure) uncoupling. After 21 months of follow‐up, 146 HF hospitalizations and 34 cardiovascular deaths were recorded in the HF population. Cox multivariable analysis supported an independent differential role of EAT in HF cohorts (interaction P = 0.01): higher risk of adverse events for increasing EAT in HFpEF [hazard ratio (HR) 1.12, 95% confidence interval (CI) 1.04–1.37] and for decreasing EAT in HFrEF (HR 0.75, 95% CI 0.54–0.91). Conclusion In HFpEF, EAT accumulation is associated with worse haemodynamic and metabolic profile, also affecting survival. Conversely, lower EAT values imply higher left ventricular dysfunction, global functional impairment and adverse prognosis in HFrEF.

Abstract Microdosimetry is an important tool for assessing energy deposition distributions from ionizing radiation at cellular and cellular nucleus scales. It has served as an input parameter for multiple common mathematical models, including evaluation of relative biological effectiveness (RBE) of carbon ion therapy. The most common detector used for microdosimetry is the tissue-equivalent proportional counter (TEPC). Although it is widely applied, TEPC has various inherent uncertainties. Therefore, this work quantified the magnitude of TEPC measurement uncertainties and their impact on RBE estimates for therapeutic carbon beams. Microdosimetric spectra and frequency-, dose-, and saturation-corrected dose-mean lineal energy (****) were calculated using the Monte Carlo toolkit Geant4 for five monoenergetic and three spread-out Bragg peak carbon beams in water at every millimeter along the central beam axis. We simulated the following influences on these spectra from eight sources of uncertainty: wall effects, pulse pile-up, electronics, gas pressure, W-value, gain instability, low energy cut-off, and counting statistics. Statistic uncertainty was quantified as the standard deviation of perturbed values for each source. Bias was quantified as the difference between default lineal energy values and the mean of perturbed values for each systematic source. Uncertainties were propagated to RBE using the modified microdosimetric kinetic model (MKM). Variance introduced by statistic sources in y ¯ F and y ¯ D averaged 3.8% and 3.4%, respectively, and 1.5% in y * across beam depths and energies. Bias averaged 6.2% and 7.3% in y ¯ F and y ¯ D , and 4.8% in y * . These uncertainties corresponded to 1.2 ± 0.9% on average in RBE MKM . The largest contributors to variance and bias were pulse pile-up and wall effects. This study established an error budget for microdosimetric carbon measurements by quantifying uncertainty inherent to TEPC measurements. It is necessary to understand how robust the measurement of RBE model input parameters are against this uncertainty in order to verify clinical model implementation.

Abstract Introduction Asymptomatic and mildly symptomatic dominantly inherited Alzheimer's disease mutation carriers (DIAD‐MC) are ideal candidates for preventative treatment trials aimed at delaying or preventing dementia onset. Brain atrophy is an early feature of DIAD‐MC and could help predict risk for dementia during trial enrollment. Methods We created a dementia risk score by entering standardized gray‐matter volumes from 231 DIAD‐MC into a logistic regression to classify participants with and without dementia. The score's predictive utility was assessed using Cox models and receiver operating curves on a separate group of 65 DIAD‐MC followed longitudinally. Results Our risk score separated asymptomatic versus demented DIAD‐MC with 96.4% (standard error = 0.02) and predicted conversion to dementia at next visit (hazard ratio = 1.32, 95% confidence interval [CI: 1.15, 1.49]) and within 2 years (area under the curve = 90.3%, 95% CI [82.3%–98.2%]) and improved prediction beyond established methods based on familial age of onset. Discussion Individualized risk scores based on brain atrophy could be useful for establishing enrollment criteria and stratifying DIAD‐MC participants for prevention trials.

Abstract Funding Acknowledgements Type of funding sources: None. Aims. Combined cardiopulmonary exercise test (CPET) and exercise stress echocardiography (ESE) provides a non-invasive tool to study cardiopulmonary pathophysiology. We analyzed how cardiac functional reserve during exercise relates to peak oxygen consumption (VO2). Methods and Results. We performed a symptom-limited graded ramp bicycle CPET-ESE in 30 healthy controls and 357 patients: 113 at risk of developing heart failure (American College of Cardiology/American Heart Association HF Stages A-B) and 244 in HF Stage C with preserved (HFpEF, n = 101) or reduced ejection fraction (HFrEF, n = 143). Peak VO2 significantly decreased from controls to Stage A-B and Stage C (Table 1). A multivariable regression model to predict peak VO2 revealed peak left ventricular systolic annulus tissue velocity (S"), peak TAPSE/PAPs (tricuspid annular plane systolic excursion/systolic pulmonary artery pressure) and low-load left atrial reservoir strain/E/e’ were independent predictors, in addition to peak heart rate, stroke volume and workload (adjusted R²=0.76, p < 0.0001). The model was successfully tested in subjects with atrial fibrillation (n = 49), and with (n = 224) and without (n = 163) beta-blockers (all p < 0.01). Peak S’ showed the highest accuracy in predicting peak VO2 < 10 mL/kg/min (cut-point ≤ 7.5 cm/s; AUC = 0.92, p < 0.0001) and peak VO2 > 20 mL/kg/min (cut-point > 12.5 cm/s; AUC = 0.84, p < 0.0001) in comparison to the other cardiac variables of the model (p < 0.05). Conclusions. A model incorporating different measures of cardiac mechanics is strongly related to peak aerobic capacity and may help in identifying different causes of effort intolerance from HF Stage A to C. Table 1 Variable Overall population (n = 387) Controls (n = 30) Stage A-B (n = 113) Stage C-HFpEF (n = 101) Stage c-HFrEF (n = 143) p-value Age, years 68.9 ± 11.1 67.1 ± 10.6 67.7 ± 10.4 70.5 ± 10.1 68.5 ± 11 0.1 Male, n (%) 247 (64) 18 (60) 70 (62) 57 (56) 102 (71) 0.1 VO2 @peak, mL/min/kg 15.7 (12.1-19.6) 23 (21.7- 29.7) 18 (15.4- 20.7)* 13.6 (11.8- 16.8)*† 14.2 (10.7- 17.5)*† <0.0001 Workload @peak, W 90 (65-120) 130 (115-195) 110 (84-130)* 70 (55-100)*† 80 (60-110)*† <0.0001 Heart rate @peak, bpm 123 ± 22 142 ± 12 130 ± 20* 115 ± 17*† 119 ± 23*† <0.0001 Stroke volume @peak, mL 83 (71-99) 98 (85-114) 86 (76-107) 83 (74-97)* 75 (63-95)*† <0.0001 Average S" @peak, cm/s 11.2 ± 3.8 17.1 ± 3.9 13.3 ± 2.9* 10.6 ± 2.5*† 8.7 ± 2.7*†‡ <0.0001 TAPSE/PAPs @peak, mm/mmHg 0.75 (0.46-0.97) 1.05 (0.93- 1.16) 0.81 (0.52- 0.91)* 0.52 (0.38- 0.83)*† 0.58 (0.41- 0.89)*† <0.0001 Left atrial reservoir strain/E/e" @low-load, % 2.25 (1.17-5.04) 6.23 (4.45-6.77) 4.34 (3.89- 5.58)* 2.23 (1.31- 2.86)*† 1.91 (1.07-2.44)*†‡ <0.0001 * p < 0.01 vs Controls; † p < 0.01 vs Stage A-B; ‡ p < 0.01 vs Stage C-HFpEF. PAPs systolic pulmonary artery pressure; TAPSE: tricuspid annular plane systolic excursion; VO2: oxygen consumption. Abstract Figure 1

Abstract Funding Acknowledgements Type of funding sources: None. Aims. We tested the prognostic role of a risk score including bio-humoral evaluation, cardiopulmonary-echocardiographic stress (CPET-ESE) and lung ultrasound, in patients with heart failure (HF) with reduced and preserved ejection fraction (HFrEF and HFpEF), and subjects at risk of developing HF (American College of Cardiology/American Heart Association Stages A and B). Methods and results. We evaluated 318 subjects: 94 in Stages A-B, 194 in Stage C (85 HFpEF and 109 HFrEF), and 30 age and sex-matched controls (Stage 0). During a median follow-up of 18.5 months, we reported 40 urgent HF visits, 31 HF hospitalisations and 10 cardiovascular deaths. Cox proportional-hazards regression for predicting adverse events identified five independent predictors and each was assigned a number of points proportional to its regression coefficient: Δstress-rest B-lines >10 (3 points), peak oxygen consumption <16 mL/kg/min (2 points), minute ventilation/carbon dioxide production slope ≥36 (2 points), peak systolic pulmonary artery pressure ≥50 mmHg (1 point) and resting N-terminal pro-brain natriuretic peptide (NT-proBNP) >900 pg/mL (1 point). We defined three risk categories: low-risk (<3 points), intermediate-risk (3-6 points), and high-risk (>6 points). The event-free survival probability for these three groups were 93%, 52% and 20%, respectively. Hazard Ratio was 4.55 for each risk category upgrade (95% confidence interval [CI], 3.44-5.93). The area-under-curve for the scoring system to predict events was 0.92 (95% CI 0.88-0.96). Conclusion. A multiparametric risk score including indices of exercise-induced pulmonary congestion, markers of cardiopulmonary dysfunction and NT-proBNP identifies patients at increased risk for HF events across the HF spectrum. Table 1 Variable EPYC score EPYC score <3 (low risk) n = 217 EPYC score 3-6 (intermediate risk) n = 70 EPYC score >6 (high risk) n = 31 p-value (between risk categories) Event-free (n = 244) 0 (0 - 2) 210 (97) 32 (46) 2 (6) <0.0001 With events (n = 74) 6 (4 - 9) 7 (3) 38 (54) 29 (94) <0.0001 p-value (event-free vs with events) <0.0001 <0.0001 <0.0001 <0.0001 Stage 0-Controls (n = 30) 0 (0 - 1) 30 0 0 <0.0001 Stages A-B (n = 94) 1 (0 - 2) 85 (45) 6 (9) 3 (10) <0.0001 Stage C-HFpEF (n = 85) 3 (1 - 6)*† 46 (25) 29 (41) 10 (32) <0.0001 Stage C-HFrEF (n = 109) 4 (2 - 7)*† 56 (30) 35 (50) 18 (58) <0.0001 p-value (between HF Stages) <0.0001 <0.0001 <0.0001 <0.0001 Values are mean ± standard deviation, n (%), or median [25th quartile, 75th quartile]. * p < 0.01 vs Stage 0-Controls; † p < 0.01 vs Stages A-B. Abstract Figure 1

Abstract Aims Risk stratification of heart failure (HF) patients with preserved ejection fraction (HFpEF) can promote a more personalized treatment. We tested the prognostic value of a multi-parametric evaluation, including biomarkers, cardiopulmonary exercise testing-exercise stress echocardiography (CPET-ESE), and lung ultrasound, in HFpEF patients and subjects at risk of developing HF (HF Stages A and B). Background Risk stratification of heart failure (HF) patients with preserved ejection fraction (HFpEF) can promote a more personalized treatment. Design We tested the prognostic value of a multi-parametric evaluation, including biomarkers, cardiopulmonary exercise testing-exercise stress echocardiography (CPET-ESE), and lung ultrasound, in HFpEF patients and subjects at risk of developing HF (HF Stages A and B). Methods and results We performed a resting clinical/bio-humoural evaluation and a symptom-limited CPET-ESE in 274 patients (45 Stage A, 68 Stage B, and 161 Stage C-HFpEF) and 30 age- and sex-matched healthy controls. During a median follow-up of 18.5 months, we reported 71 HF hospitalizations and 10 cardiovascular deaths. Cox proportional-hazards regression identified five independent predictors and each was assigned a number of points proportional to its regression coefficient: stress-rest ΔB-lines >10 (3 points), peak oxygen consumption <16 mL/kg/min (2 points), minute ventilation/carbon dioxide production slope ≥36 (2 points), peak systolic pulmonary artery pressure ≥50 mmHg (1 point) and resting N-terminal pro-brain natriuretic peptide (NT-proBNP) >900 pg/mL (1 point). The event-free survival probability for low risk (<3 points), intermediate risk (3–6 points), and high risk (>6 points) were 93%, 52%, and 20%, respectively. The area under the curve (AUC) for the scoring system to predict events was 0.92 (95% CI 0.88–0.96), with an accuracy significantly higher than the individual components of the score (all P < 0.01 vs. individual AUCs). Conclusion A weighted risk score including NT-proBNP, markers of cardiopulmonary dysfunction and indices of exercise-induced pulmonary congestion identifies HFpEF patients at increased risk for adverse events and Stage A and B subjects more likely to progress towards more advanced HF stages.