The prevalence of cardiac amyloidosis (CA) in the general population and associated prognostic implications remain poorly understood. We aimed to identify CA prevalence and outcomes in bone scintigraphy referrals. Methods: Consecutive all-comers undergoing 99m Tc-3,3diphosphono-1,2-propanodicarboxylic-acid ( 99m Tc-DPD) bone scintigraphy between 2010 and 2020 were included. Perugini grade 1 was defined as low-grade uptake and grade 2 or 3 as confirmed CA. Allcause mortality, cardiovascular death, and heart failure hospitalization (HHF) served as endpoints. Results: In total, 17,387 scans from 11,527 subjects (age, 61 6 16 y; 63.0% women, 73.6% cancer) were analyzed. Prevalence of 99m Tc-DPD positivity was 3.3% (n 5 376/11,527; grade 1: 1.8%, grade 2 or 3: 1.5%), and was higher among cardiac than noncardiac referrals (18.2% vs. 1.7%). In individuals with more than 1 scan, progression from grade 1 to grade 2 or 3 was observed. Among patients with biopsy-proven CA, the portion of light-chain (AL)-CA was significantly higher in grade 1 than grade 2 or 3 (73.3% vs. 15.4%). After a median of 6 y, clinical event rates were: 29.4% mortality, 2.6% cardiovascular death, and 1.5% HHF, all independently predicted by positive 99m Tc-DPD. Overall, adverse outcomes were driven by confirmed CA (vs. grade 0, mortality: adjusted hazard ratio [AHR] 1.46 [95% CI 1.12-1.90]; cardiovascular death: AHR 2.34 [95% CI 1.49-3.68]; HHF: AHR 2.25 [95% CI 1.51-3.37]). One-year mortality was substantially higher in cancer than noncancer patients. Among noncancer patients, also grade 1 had worse outcomes than grade 0 (HHF/death: AHR 1.45 [95% CI 1.01-2.09]), presumably because of longer observation and higher prognostic impact of early infiltration. Conclusion: Positive 99m Tc-DPD was identified in a substantial number of consecutive 99m Tc-DPD referrals and associated with adverse outcomes.
To investigate the epidemiological and prognostic relationship between heart failure with preserved ejection fraction (HFpEF) and left-sided valve surgery using all-cause mortality as a primary endpoint.
Tricuspid regurgitation secondary to heart failure (HF) is common with considerable impact on survival and hospitalization rates. Currently, insights into epidemiology, impact, and treatment of secondary tricuspid regurgitation (sTR) across the entire HF spectrum are lacking, yet are necessary for healthcare decision-making.
Background
Secondary tricuspid regurgitation (sTR) is the most frequent valvular heart disease and has significant impact on mortality. A high burden of comorbidities often worsens the already dismal prognosis of sTR, while tricuspid interventions remain underused and initiated too late.
Objectives
To examine the most powerful predictors of all-cause mortality in moderate and severe sTR using machine learning techniques and to provide a streamlined approach to risk-stratification using readily available clinical, echocardiographic and laboratory parameters.
Methods
This large-scale, long-term observational study included 3359 moderate and 1509 severe sTR patients encompassing the entire heart failure spectrum (preserved, mid-range and reduced ejection fraction). A random survival forest was applied to investigate the most important predictors and group patients according to their number of adverse features (Figure 1).
Results
The identified predictors and thresholds, that were associated with significantly worse mortality were higher age (≥75 in moderate and ≥70 years in moderate and severe sTR respectively), higher NT-proBNP (≥4000 pg/ml), increased high sensitivity C-reactive protein (≥1.0 mg/dl), serum albumin <40 g/L and hemoglobin <13 g/dL. Additionally, grouping patients according to the number of adverse features yielded important prognostic information, as patients with 4 or 5 adverse features had a sevenfold risk increase in moderate sTR (7.11 [2.27–4.30] HR 95% CI, P<0.001) and fivefold risk increase in severe sTR (5.08 [3.13–8.24] HR 95% CI, P<0.001) (Figure 2: A moderate sTR derivation, B moderate sTR validation, C severe sTR derivation, D severe sTR validation).
Conclusion
This study presents a streamlined, machine learning-derived and internally validated approach to risk-stratification in patients with moderate and severe sTR, that adds important prognostic information to aid clinical decision-making.
Funding Acknowledgement
Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Austrian Science Fund
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