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March 10, 2022Frontiers in Cardiovascular Medicine5 citationsOpen Access

Identification of Specific Coronary Artery Disease Phenotypes Implicating Differential Pathophysiologies

JKJona B. KrohnYNY Nhi NguyenMAMohammadreza Akhavanpoor

Key Result

Cluster analysis of coronary angiograms identified four distinct CAD phenotypes with different spatial distributions of stenoses, demographic characteristics, and mortality risks.

Study Design

Type

Cohort (n=7,473)

Multicenter

Yes

Structured PICO

Does the spatial distribution of coronary artery stenoses (CAD phenotype) predict cardiovascular mortality and correlate with specific cardiovascular risk profiles in patients undergoing coronary angiography?

P
Population
7,473 patients undergoing coronary angiography across two cohorts (Heidelberg cohort n=4,344; LURIC cohort n=3,129). Heidelberg overall cohort: mean age 65.8, 58.4% male. LURIC cohort: mean age 62.4, 73.8% male. Exclusions included chronic non-cardiac diseases, history of malignancy within 5 years, previous coronary artery bypass graft, or acute illnesses other than acute coronary syndromes.
I
Intervention
Cluster analysis of coronary angiography reports based on the spatial distribution of high-grade stenoses (>50% vessel diameter) across 15 Gensini segments to identify distinct coronary artery disease phenotypes.
C
Comparator
Comparison among four identified phenotypic clusters (Cluster 1: mild vessel wall irregularities; Cluster 2: proximal RCA stenosis; Cluster 3: proximal LAD stenosis; Cluster 4: diffuse high-grade stenoses).
O
Outcome
Cardiovascular mortality over a 12-month observation period (with median survival 8.9-9.9 years).hard clinical

Limitations

  • Degree of stenosis is based on the interventionalist's estimate and is therefore prone to some degree of variation due to inter-observer bias

Abstract

Background and Aims: The roles of multiple risk factors of coronary artery disease (CAD) are well established. Commonly, CAD is considered as a single disease entity. We wish to examine whether coronary angiography allows to identify distinct CAD phenotypes associated with major risk factors and differences in prognosis. Methods: In a cohort of 4,344 patients undergoing coronary angiography at Heidelberg University Hospital between 2014 and 2016, cluster analysis of angiographic reports identified subgroups with similar patterns of spatial distribution of high-grade stenoses. Clusters were independently confirmed in 3,129 patients from the LURIC study. Results: Four clusters were identified: cluster one lacking critical stenoses comprised the highest percentage of women with the lowest cardiovascular risk. Patients in cluster two exhibiting high-grade stenosis of the proximal RCA had a high prevalence of the metabolic syndrome, and showed the highest levels of inflammatory biomarkers. Cluster three with predominant proximal LAD stenosis frequently presented with acute coronary syndrome and elevated troponin levels. Cluster four with high-grade stenoses throughout had the oldest patients with the highest overall cardiovascular risk. All-cause and cardiovascular mortality differed significantly between the clusters. Conclusions: We identified four phenotypic subgroups of CAD bearing distinct demographic and biochemical characteristics with differences in prognosis, which may indicate multiple disease entities currently summarized as CAD.

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Cite This Study

Krohn et al. (2022) conducted a cohort in Coronary artery disease (n=7,473). Cluster analysis of angiographic reports was evaluated on Distinct CAD phenotypes and differences in prognosis (all-cause and cardiovascular mortality). Cluster analysis of coronary angiograms identified four distinct CAD phenotypes with different spatial distributions of stenoses, demographic characteristics, and mortality risks.

synapsesocial.com/papers/6a0e1a867a57fdc4e227a78bhttps://doi.org/10.3389/fcvm.2022.778206
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