Supervised learning-derived tailored risk-stratification in patients with severe secondary mitral regurgitation Article Swipe
Gregor Heitzinger
,
Georg Spinka
,
S Prausmueller
,
N Pavo
,
Varius Dannenberg
,
Carolina Donà
,
Andreas Kammerlander
,
Christian Nitsche
,
Stefan Kastl
,
Guido Strunk
,
M Huelsmann
,
Raphaël Rosenhek
,
Christian Hengstenberg
,
P Bartko
,
Georg Goliasch
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1093/eurheartj/ehac544.1643
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1093/eurheartj/ehac544.1643
Background Mitral regurgitation secondary to heart failure (sMR) has considerable impact on quality of life, heart failure (HF) rehospitalizations and mortality. A diverse burden of comorbidities suggests multifaceted aspects of individual risks. This risk-spectrum has never been studied but is essential to understand disease trajectories. Objectives To provide a comprehensive and structured decision-tree-like approach to risk-stratification in patients with severe sMR. Methods This large-scale, long-term observational study included 1317 patients with severe sMR from the entire HF spectrum (preserved, mid-range and reduced ejection fraction). Primary endpoint was all-cause mortality and survival tree analysis, a supervised learning technique, was applied to identify patient subgroups with excessive risk of mortality (Figure 1). Results Eight distinct subgroups that differed significantly in long-term survival were identified (Figure 2). Subgroup 7, characterized by younger age (≤66), higher hemoglobin (>12.7 g/dl) and higher albumin levels (>40.6 g/l) had the best survival. In contrast, subgroup 5 displayed a 20-fold risk of mortality (HR 95% CI: 20.38 ([0.78–38.52]), P<0.001) and presented with older age (>68 years) and low serum albumin (≤40.6 g/l) and higher NT-proBNP levels (≥9750 pg/ml). Results were consistent in internal and temporal validation. Conclusion Supervised machine learning reveals an unexpected heterogeneity in the sMR risk-spectrum, indicating the clinical challenges tied to severe sMR. A decision-tree-like model can guide through the risk spectrum and provide tailored risk-stratification. This structured approach provides the foundation to generate hypotheses towards improved therapeutic strategies and optimized patient care. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Austrian Science Fund
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/eurheartj/ehac544.1643
- https://academic.oup.com/eurheartj/article-pdf/43/Supplement_2/ehac544.1643/46370516/ehac544.1643.pdf
- OA Status
- bronze
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4306320402
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4306320402Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/eurheartj/ehac544.1643Digital Object Identifier
- Title
-
Supervised learning-derived tailored risk-stratification in patients with severe secondary mitral regurgitationWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-10-01Full publication date if available
- Authors
-
Gregor Heitzinger, Georg Spinka, S Prausmueller, N Pavo, Varius Dannenberg, Carolina Donà, Andreas Kammerlander, Christian Nitsche, Stefan Kastl, Guido Strunk, M Huelsmann, Raphaël Rosenhek, Christian Hengstenberg, P Bartko, Georg GoliaschList of authors in order
- Landing page
-
https://doi.org/10.1093/eurheartj/ehac544.1643Publisher landing page
- PDF URL
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https://academic.oup.com/eurheartj/article-pdf/43/Supplement_2/ehac544.1643/46370516/ehac544.1643.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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bronzeOpen access status per OpenAlex
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https://academic.oup.com/eurheartj/article-pdf/43/Supplement_2/ehac544.1643/46370516/ehac544.1643.pdfDirect OA link when available
- Concepts
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Medicine, Ejection fraction, Risk stratification, Internal medicine, Heart failure, Mitral regurgitation, Observational study, Cardiology, Clinical endpoint, Clinical trialTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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