Key result
A high-risk phenogroup of older females with comorbidities had a significantly higher risk of post-operative cardiovascular events compared to the reference group (HR 3.57; 1.72-7.44; P<0.001).
Why the study?
Does phenomapping using clinical and echocardiographic data identify distinct risk categories for post-operative cardiovascular events in patients undergoing valve surgery for primary mitral regurgitation?
Cohort (n=122)
Does phenomapping using clinical and echocardiographic data identify distinct risk categories for post-operative cardiovascular events in patients undergoing valve surgery for primary mitral regurgitation?
Effect estimate: HR 3.57 (95% CI 1.72-7.44)
p-value: p=<0.001
Unbiased clustering analysis of pre-operative phenotypic data can identify distinct subclasses of primary mitral regurgitation patients with varying risks of post-operative cardiovascular events, allowing for targeted monitoring and potentially earlier intervention.
May support targeted post-operative monitoring in high-risk phenogroups; leaves open whether phenomapping improves mitral valve surgery risk stratification.
Aims: Primary mitral regurgitation (PMR) can be considered as a heterogeneous clinical disease. The optimal timing of valve surgery for severe PMR remains unknown. To determine whether unbiased clustering analysis using dense phenotypic data (phenomapping) could identify phenotypically distinct PMR categories of patients. Methods and results: One hundred and twenty-two patients who underwent surgery were analysed, excluding patients with pre-operative permanent atrial fibrillation (AF), were prospectively included before surgery. They were given an extensive echocardiographic evaluation before surgery, and clinical data were collected. These phenotypic variables were grouped in clusters using hierarchical clustering analysis. Then, different groups were created using a dedicated phenomapping algorithm. Post-operative outcomes were compared between the groups. The primary endpoint was post-operative cardiovascular events (PCE), defined as a composite of: deaths, AF, stroke, and rehospitalization. The secondary endpoint was post-operative AF. Data from three phenogroups with different characteristics and prognoses were identified. Phenogroup-1 (67 patients) was the reference group. Phenogroup-2 (33 patients) included intermediate-risk male and smoker patients with heart remodelling. Phenogroup-3 (22 patients) included older female patients with comorbidities (chronic renal failure, paroxysmal AF) and diastolic dysfunction. They had a higher risk of developing both PCE [(hazard ratio) HR = 3.57(1.72-7.44), P < 0.001] and post-operative AF [HR = 4.75(2.03-11.10), P < 0.001]. Pre-operative paroxysmal AF was identified as an independent risk factor for PCE. Conclusion: Classification of PMR can be improved using statistical learning algorithms to define therapeutically homogeneous patient subclasses. High-risk patients can be identified, and these patients should be carefully monitored and may even be treated earlier.
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Pimor et al. (2018) conducted a cohort in Primary mitral regurgitation (n=122). Phenogroup-3 (older females with comorbidities and diastolic dysfunction) vs. Phenogroup-1 (reference group) was evaluated on Post-operative cardiovascular events (composite of deaths, AF, stroke, and rehospitalization) (HR 3.57, 95% CI 1.72-7.44, p=<0.001). A high-risk phenogroup of older females with comorbidities had a significantly higher risk of post-operative cardiovascular events compared to the reference group (HR 3.57; 1.72-7.44; P<0.001).