A new evidence-based echocardiographic approach to predict cardiovascular events and myocardial fibrosis in mitral valve prolapse: The STAMP algorithm

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A new evidence-based echocardiographic approach to predict
cardiovascular events and myocardial fibrosis in mitral valve prolapse:
The STAMP algorithm
Olivier Huttin Thierry Le Tourneau Laure Filippetti Nathalie Pace
Jean Marc Sellal Marine Beaumont Damien Mandry Pierre-Yves
Marie Christine Selton-Suty Nicolas Girerd
PII: S1875-2136(24)00020-2
DOI: https://doi.org/doi:10.1016/j.acvd.2024.01.001
Reference: ACVD 1625
To appear in: Archives of Cardiovascular Diseases
Received Date: 21 December 2023
Revised Date: 9 January 2024
Accepted Date: 10 January 2024
Please cite this article as: Huttin O, Tourneau TL, Filippetti L, Pace N, Sellal JM, Beaumont M,
Mandry D, Marie P-Yves, Selton-Suty C, Girerd N, A new evidence-based echocardiographic
approach to predict cardiovascular events and myocardial fibrosis in mitral valve prolapse:
The STAMP algorithm, Archives of Cardiovascular Diseases (2024),
doi: https://doi.org/10.1016/j.acvd.2024.01.001

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A new evidence-based echocardiographic approach to predict cardiovascular events and
myocardial fibrosis in mitral valve prolapse: The STAMP algorithm
Abbreviated title: The STAMP algorithm for mitral valve prolapse risk stratification
Tweet: New echocardiographic algorithm to predict cardiovascular events and myocardial fibrosis in
mitral valve prolapse: The STAMP algorithm
Olivier Huttina,, Thierry Le Tourneaub,c, Laure Filippetti a , Nathalie Pacea, Jean Marc Sellala, Marine
Beaumontd, Damien Mandrya,d, Pierre-Yves Marie a,d, Christine Selton-Sutya, Nicolas Girerde
a Service de Cardiologie, Institut Lorrain du Cœur et des Vaisseaux, CHRU de Nancy, 54500
Vandœuvre-lès-Nancy, France
b CIC 1413, CHU de Nantes, Université de Nantes, 44000 Nantes, France
c l’Institut du Thorax, CHU de Nantes, Université de Nantes, CNRS, INSERM, 44000 Nantes, France
d U1433, CIC-IT, CHRU de Nancy, 54000 Vandœuvre-lès-Nancy; and INSERM U1254, IADI,
Université de Lorraine, 54500 Vandœuvre-lès-Nancy, France
e CIC 1433, CHRU de Nancy, INSERM, Université de Lorraine, 54000 Vandœuvre-lès-Nancy;
INSERM U1116, Université de Lorraine, 54505 Vandœuvre-lès-Nancy ; and INI-CRCT (Investigation
Network Initiative-Cardiovascular and Renal Clinical Trialists), F-CRIN, CHRU de Nancy, 54000
Nancy, France
* Corresponding author. Service de Cardiologie, Institut Lorrain du Cœur et des Vaisseaux, CHRU de
Nancy, 54500 Vandœuvre-lès-Nancy, France
E-mail address: o.huttin@chru-nancy.fr (O. Huttin).
Keywords:
Mitral valve prolapse
Risk stratification
Fibrosis

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Cardiac magnetic resonance imaging
Echocardiography
Abbreviations
CMR cardiac magnetic resonance
LA left atrial
LGE late gadolinium enhancement
LV left ventricular
MR mitral regurgitation
MVP mitral valve prolapse
Mitral valve prolapse (MVP), often detected through echocardiography, generally follows a benign
course [1, 2], but can evolve into primary mitral regurgitation (MR). Determining the ideal timing for
intervention and assessing risk in patients with severe primary MR remain significant challenges.
Consequently, research is intensifying to discover new and reliable prognostic indicators. Imaging
biomarkers, particularly myocardial deformation, have shown promise in forecasting cardiovascular
outcomes and aiding the decision-making process regarding surgery [3, 4]. Additionally, cardiac
magnetic resonance (CMR) imaging is gaining prominence in the evaluation of MVP. The ability of
CMR to assess structural changes and quantify fibrosis is crucial because of their prognostic
significance and pathophysiological implications [5]. Interestingly, the extent of remodelling in MVP
does not always correspond with the severity of MR; however, it is still capable of predicting adverse
outcomes [6]. These findings indicate that a more precise classification of MVP has yet to be
established.
Given that the process of left ventricular (LV) myocardial remodelling begins before the onset of
symptoms, guidelines place special emphasis on the detection of subclinical LV dysfunction and the
timely identification of patients who are at high risk of developing overt heart failure or irreversible LV
dysfunction after surgery. In the setting of asymptomatic MVP, we need to improve prediction of heart
failure and arrhythmic events rather than diagnosing the latter at late/symptomatic stages, which are
only moderately reversible under treatment.

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Our team has previously advanced our expertise in artificial intelligence and clustering through the
development of an evidence-based cardiovascular imaging algorithm, which successfully identifies
patients at risk of developing heart failure [7, 8]. Drawing on this experience, we strongly believe that
relying solely on a monovariable approach, focusing just on LV/left atrial (LA) remodelling or increased
pulmonary pressure, is insufficient to significantly improve the predictive accuracy of cardiovascular
events in MVP cases. To put it simply, to accurately pinpoint individuals within a broad MVP
population who are at heightened risk of developing myocardial fibrosis, arrhythmic events and
manifest heart failure, we advocate for a non-supervised multivariable approach. This method could
provide vital additional insights beyond what monovariable analysis offers.
Despite the extensive use of echocardiography for guideline-based decisions regarding the timing
of surgery, there is actually a low level of evidence regarding the most commonly used
echocardiographic variables. Clustering techniques have been used effectively to subdivide
heterogeneous into homogeneous phenotypes (e.g. early cardiac alterations). We suggested that the
assessment of patients with MVP would be improved by identifying echocardiographic phenotypes
and their respective association with myocardial fibrosis and prognosis, using a complex combination
of variables by means of machine learning. We recently reported that late gadolinium enhancement
(LGE) was observed in 25–30% of patients with MVP [9], and appeared to be a landmark of MVP
cardiomyopathy. LGE is attributed to chronic maladaptive LV remodelling and abnormal constraints
exerted on the myocardial wall and papillary muscle. Moreover, patients with less than moderate MR
as a result of MVP have already been shown to potentially exhibit early LV and LA remodelling, which
predicts neither MR progression nor mortality using conventional variables [10-12]. A simple algorithm
using a few key variables in a large cohort would be the best way to help the cardiologist to stratify
patients with MVP in routine clinical practice.
In a recent study published in JACC: Cardiovascular Imaging [13], using data from the STAMP
study at Nancy University Hospital and the MVP genetic study from Nantes University Hospital, we
introduced a novel segmentation approach for MVP. This method, based on clustering analysis,
identified four distinct echocardiographic phenotypes: cluster 1 showed minimal remodelling, primarily
with mild MR; cluster 2 served as a transitional stage; cluster 3 was marked by significant LV and LA
remodelling coupled with severe MR; and cluster 4 was characterized by remodelling accompanied by
a decrease in LV systolic strain. Notably, clusters 3 and 4, which had the highest risk of cardiovascular

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events, displayed the most pronounced echocardiographic abnormalities, including significant LV and
LA remodelling, with (in cluster 4) or without (in cluster 3) LV longitudinal dysfunction. Compared with
cluster 1, which we used as a reference, clusters 2, 3 and 4 were significantly associated with higher
rates of myocardial fibrosis. Specifically, nearly half of the patients in cluster 4 exhibited myocardial
fibrosis (LGE ≥ one segment), with a rate of LGE on CMR imaging four times higher than that
observed in cluster 1. Cluster analysis significantly improved diagnostic accuracy over conventional
analysis. We built a simple decision tree (STAMP algorithm; Fig. 1), based on severity of MR, LV
systolic strain (threshold value 21%) and LA volume (threshold value 42 mL/m²), which adequately
classified patients across the four echocardiographic phenotypes. When applying this algorithm in our
validation cohort, the clusters remained significantly associated with cardiovascular outcomes:
considering cluster 1 as reference, clusters 3/4 were significantly associated with cardiovascular
outcomes, with the hazard ratio within the same range as the one observed in the derivation cohort.
In this work, we were able, while removing a priori considerations: (1) to identify a subgroup of
patients who may benefit from an earlier interventional strategy based on echocardiographic variables;
and (2) to show that such echocardiographic phenotypes are associated with different levels of fibrosis
as well as different prognoses in terms of long-term cardiovascular events.
Our STAMP algorithm offers a valuable screening tool for determining which patients should
undergo CMR imaging. This is particularly relevant, given the impracticality of routinely performing
CMR imaging for systematic evaluation in patients with asymptomatic MVP in daily clinical practice.
Previous studies have highlighted the clinical importance of CMR imaging, especially for screening
regional replacement fibrosis in patients with MVP, as it helps to identify those at high risk of cardiac
events [9]. In our study, the incidence of CMR-detected myocardial fibrosis (LGE ≥ one segment)
increased progressively from 13% in cluster 1 to 44% in cluster 4. This underscores the need for
specific recommendations to identify MVP patient subgroups with a higher prevalence of myocardial
fibrosis who could benefit from systematic CMR screening, irrespective of symptoms.
This approach is also crucial for stratifying patients at risk of ventricular arrhythmic events,
detecting disproportionate LV remodelling and predicting MVP cardiomyopathy. Furthermore, the
ability of CMR imaging to measure the interstitial component of cardiac tissue provides a quantitative
assessment of extracellular volume, allowing for the detection and longitudinal monitoring of fibrosis
changes, which are valuable for patient selection and outcome measures [14].

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Although our study does not specifically address the various grades of MR, recent reports using
CMR imaging have shed light on disproportionate remodelling in patients with non-severe MR [6]. In
line with this finding, patients in cluster 2 in our study, who only have less than severe MR, already
showed significant atrial and ventricular remodelling, with myocardial fibrosis three times more
frequent, and an increased cardiovascular risk [9]. Hence, our findings reinforce the notion of an MVP
outcome effect, independent of the impact of the regurgitant volume on the left heart chambers. The
role of MR in this context is complex, with numerous patients exhibiting no to mild MR. Fibrosis in the
context of significant MR might be linked to MR-induced ventricular remodelling, neurohormonal
activation or the stress caused by the prolapsing leaflet on the myocardium. Understanding the
pathophysiology of this process in the absence of severe MR is key to proposing earlier medical and
interventional therapies.
Data-driven echocardiographic phenotypes may assist clinicians in categorizing patients with
MVP into different archetypes, with increasing levels of cardiovascular remodelling and cardiac
fibrosis, and a subsequent higher risk of cardiovascular events. The good prognostic performance of
this approach suggests its potential usefulness in determining the follow-up strategy and possibly the
therapeutic management to be applied in these patients. Which preventive strategy to use in patients
who are asymptomatic or have disproportionate remodelling still remains an open question. On a
case-by-case evaluation, we believe that an optimized follow-up and possibly cardiovascular
preventive treatment could be proposed to patients in the highest risk categories of our algorithm,
especially in case of low strain value, suggesting high prevalence of myocardial fibrosis. In addition, in
order to define the place of preventive medical therapy, randomized controlled trials are needed to
confirm the benefit related to this strategy. To achieve this goal, it is likely that some level of risk
stratification should be undertaken, which could be based on the quantification of fibrosis with CMR
imaging and/or the algorithm proposed herein [15].
Sources of funding
None.
Disclosure of interest
The authors declare that they have no competing interest.

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Figure legend
Fig. 1. Echocardiography-based mitral valve prolapse (MVP) risk stratification using the STAMP
algorithm. CV: cardiovascular; GLS: global longitudinal strain; LAVI: left atrial volume index; LA: left
atrial; LV: left ventricular; MR: mitral regurgitation; MRI: magnetic resonance imaging; SV:
supraventricular; TT: treatment V: ventricular.