Distinct cardiovascular phenotypes are associated with prognosis in systemic sclerosis: a cardiovascular magnetic
Daniel S Knight1,2,3,4, Nina Karia1,2,4, Alice R Cole5
1National Pulmonary Hypertension Service, Royal Free London NHS Foundation Trust, Pond Street, London, NW3 2QG, UK.
Insights
Cardiovascular magnetic resonance (CMR) identified five systemic sclerosis (SSc) cardiac phenotypes with distinct mortality risks. These phenotypes, including right ventricular failure (RVF) and biventricular failure (BVF), offer new avenues for precision medicine in SSc management.
Area of Science:
- Cardiology
- Radiology
- Rheumatology
Background:
- Cardiovascular involvement in systemic sclerosis (SSc) is complex and poorly understood.
- Current classification methods do not fully capture cardiac manifestations in SSc.
Purpose of the Study:
- To discover distinct cardiac phenotypes in SSc using cardiovascular magnetic resonance (CMR).
- To develop a CMR-based algorithm for SSc cardiac phenotype classification.
- To investigate the association between identified phenotypes and patient mortality.
Main Methods:
- A retrospective observational study of 260 SSc patients undergoing CMR with native T1 and T2 mapping.
- Agglomerative hierarchical clustering of CMR variables to identify patient clusters.
- Survival analysis to examine associations between phenotypes and all-cause mortality.
Main Results:
- Five SSc cardiac phenotypes were identified: RVF, BVF, NF-AC, NF-SC, and NF-LC.
- Phenotypes did not correlate with clinical or antibody classifications.
- Native T1 and RVEF were independent predictors of mortality. RVF, BVF, and NF-LC groups showed significantly higher mortality risks.
Conclusions:
- CMR-defined cardiac phenotypes in SSc have distinct prognostic implications.
- These phenotypes offer a basis for precision-medicine approaches in SSc patient management.
- A CMR-based classification algorithm can aid in risk stratification.
Aims:
Cardiovascular involvement in systemic sclerosis (SSc) is heterogeneous and ill-defined. This study aimed to: (i) discover cardiac phenotypes in SSc by cardiovascular magnetic resonance (CMR); (ii) provide a CMR-based algorithm for phenotypic classification; and (iii) examine for associations between phenotypes and mortality.
Methods And Results:
A retrospective, single-centre, observational study of 260 SSc patients who underwent clinically indicated CMR including native myocardial T1 and T2 mapping from 2016 to 2019 was performed. Agglomerative hierarchical clustering using only CMR variables revealed five clusters of SSc patients with shared CMR characteristics: dilated right hearts with right ventricular failure (RVF); biventricular failure dilatation and dysfunction (BVF); and normal function with average cavity (NF-AC), normal function with small cavity (NF-SC), and normal function with large cavity (NF-LC) sizes. Phenotypes did not co-segregate with clinical or antibody classifications. A CMR-based decision tree for phenotype classification was created. Sixty-three (24%) patients died during a median follow-up period of 3.4 years. After adjustment for age and presence of pulmonary hypertension (PH), independent CMR predictors of all-cause mortality were native T1 (P < 0.001) and right ventricular ejection fraction (RVEF) (P = 0.0032). NF-SC and NF-AC groups had more favourable prognoses (P≤0.036) than the other three groups which had no differences in prognoses between them (P > 0.14). Hazard ratios (HR) were statistically significant for RVF (HR = 8.9, P < 0.001), BVF (HR = 5.2, P = 0.006), and NF-LC (HR = 4.9, P = 0.002) groups. The NF-LC group remained significantly predictive of mortality after adjusting for RVEF, native T1, and PH diagnosis (P = 0.0046).
Conclusion:
We identified five CMR-defined cardiac SSc phenotypes that did not co-segregate with clinical data and had distinct outcomes, offering opportunities for a more precision-medicine based management approach.
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