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Identification of novel pheno-groups in heart failure with preserved ejection fraction using machine learning
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- Hedman, A. K. (author)
- Karolinska Institutet
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- Hage, C. (author)
- Karolinska Institutet
- Sharma, A. (author)
- show more...
- Brosnan, M. J. (author)
- Buckbinder, L. (author)
- Shah, S. J. (author)
- Linde, C. M. (author)
- Donal, E. (author)
- Daubert, J. C. (author)
- Ziemek, D. (author)
- show less...
- (creator_code:org_t)
- 2020-01-07
- 2020
- English.
- In: Heart. - : BMJ. - 1355-6037 .- 1468-201X. ; 106:5, s. 342-349
- Journal article (peer-reviewed)
Abstract
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- Objective Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome. We aimed to derive HFpEF phenotype-based groups ('phenogroups') based on clinical and echocardiogram data using machine learning, and to compare clinical characteristics, proteomics and outcomes across the phenogroups. Methods We applied model-based clustering to 32 echocardiogram and 11 clinical and laboratory variables collected in stable condition from 320 HFpEF outpatients in the Karolinska-Rennes cohort study (56% female, median 78 years (IQR: 71-83)). Baseline proteomics and the composite end point of all-cause mortality or heart failure (HF) hospitalisation were used in secondary analyses. Results We identified six phenogroups, for which significant differences in the prevalence of concomitant atrial fibrillation (AF), anaemia and kidney disease were observed (p<0.05). Fifteen out of 86 plasma proteins differed between phenogroups (false discovery rate, FDR<0.05), including biomarkers of HF, AF and kidney function. The composite end point was significantly different between phenogroups (log-rank p<0.001), at short-term (100 days), mid-term (18 months) and longer-term follow-up (1000 days). Phenogroup 2 was older, with poorer diastolic and right ventricular function and higher burden of risk factors as AF (85%), hypertension (83%) and chronic obstructive pulmonary disease (30%). In this group a third experienced the primary outcome to 100 days, and two-thirds to 18 months (HR (95%CI) versus phenogroups 1, 3, 4, 5, 6: 1.5 (0.8-2.9); 5.7 (2.6-12.8); 2.9 (1.5-5.6); 2.7 (1.6-4.6); 2.1 (1.2-3.9)). Conclusions Using machine learning we identified distinct HFpEF phenogroups with differential characteristics and outcomes, as well as differential levels of inflammatory and cardiovascular proteins.
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Kardiologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cardiac and Cardiovascular Systems (hsv//eng)
Keyword
- predicting survival
- risk score
- association
- dysfunction
- subgroups
- therapies
- outcomes
- Cardiovascular System & Cardiology
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- ref (subject category)
- art (subject category)
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- By the author/editor
- Hedman, A. K.
- Hage, C.
- Sharma, A.
- Brosnan, M. J.
- Buckbinder, L.
- Gan, Li-Ming, 19 ...
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- Shah, S. J.
- Linde, C. M.
- Donal, E.
- Daubert, J. C.
- Malarstig, A.
- Ziemek, D.
- Lund, L.
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- About the subject
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- MEDICAL AND HEALTH SCIENCES
- MEDICAL AND HEAL ...
- and Clinical Medicin ...
- and Cardiac and Card ...
- Articles in the publication
- Heart
- By the university
- University of Gothenburg
- Karolinska Institutet
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