In order to bring you the best possible user experience, this site uses Javascript. If you are seeing this message, it is likely that the Javascript option in your browser is disabled. For optimal viewing of this site, please ensure that Javascript is enabled for your browser.


The free consultation period for this content is over.

It is now only available year-round to ESC Professional Members, Fellows of the ESC, and Young combined Members

Machine-learning analysis of myocardial deformation patterns to predict incident heart failure or death in the general population

Session Digital health in clinical practice

Speaker Sergio Sanchez

Congress : ESC Congress 2018

  • Topic : e-cardiology / digital health, public health, health economics, research methodology
  • Sub-topic : Digital Health: Big Data Analysis
  • Session type : Rapid Fire Abstracts
  • FP Number : 1105

Authors : S Sanchez-Martinez (Barcelona,ES), M Cikes (Zagreb,HR), B Claggett (Boston,US), N Duchateau (Lyon,FR), G Piella (Barcelona,ES), S Cheng (Boston,US), A Shah (Boston,US), B Bijnens (Barcelona,ES), S Solomon (Boston,US)

Authors:
S. Sanchez-Martinez1 , M. Cikes2 , B. Claggett3 , N. Duchateau4 , G. Piella1 , S. Cheng3 , A. Shah3 , B. Bijnens1 , S. Solomon3 , 1University Pompeu Fabra, Department of Information and Communication Technologies - Barcelona - Spain , 2University of Zagreb School of Medicine, Department of Cardiovascular Diseases - Zagreb - Croatia , 3Brigham and Women's Hospital - Boston - United States of America , 4University Claude Bernard of Lyon, CREATIS - Lyon - France ,

Citation:
European Heart Journal ( 2018 ) 39 ( Supplement ), 225

Introduction: Different measurements derived from myocardial strain data have been identified as predictors of outcome in a broad spectrum of cardiac diseases, including heart failure (HF). We hypothesize that the comprehensive analysis of entire deformation patterns, rather than scalar indices (peak, time-to-peak values) extracted from them, can be more informative in identifying subjects at a higher risk of future events.

Methods: In 1997 subjects enrolled in the Atherosclerosis Risk in Communities study (ARIC) we assessed strain patterns at 12 left ventricular locations (2 basal, 2 mid and 2 apical segments; from the 2ch and 4ch apical views) over a cardiac cycle using an unsupervised machine learning algorithm (multiple kernel learning) that positions subjects based on similarities in deformation. A K-means algorithm identified 4 clusters, for which we compared baseline characteristics and the primary outcome of death or HF event.

Results: The unsupervised analysis of deformation patterns identified 4 clinically-distinct clusters (Figure) with distinct clinical characteristics. One such cluster (Cluster 3) comprised the highest proportion of hypertensive patients (85.2%, p<0.0001) with prior myocardial infarction (3.5%, p=0.04) and atrial fibrillation (37.2%, p<0.0001); the lowest ejection fraction (61.1 (56.0–65.7) %, p<0.0001) and longitudinal strain (-12.9 (-14.4 to -11.7)%, p<0.0001); and the highest values of NT-proBNP (439 (145–1065) pg/mL, p<0.0001), left ventricular mass index (89 (73–111) g/m2, p<0.0001) and left atrial volume index (30.3 (24.3–38.4) ml/m2, p<0.0001). Cluster 3 was associated with a 4.1-fold increase in the risk of primary outcome (HR 4.11 (2.60–6.50), p<0.0001), which persisted after adjusting for age, sex, systolic blood pressure, prevalent coronary heart disease and prevalent atrial fibrillation (HR 2.62 (1.54–4.46), p<0.001).

Conclusion: Our results serve as a proof-of-concept that unsupervised machine learning-based analysis of deformation patterns can agnostically identify subjects at a substantially higher risk of incident HF or death and confirm prior clinical knowledge.

Clusters and survival analysis

The free consultation period for this content is over.

It is now only available year-round to ESC Professional Members, Fellows of the ESC, and Young combined Members



Based on your interests

Members get more

Join now
  • 1ESC Professional Members – access all resources from ESC Congress and ESC Asia with APSC & AFC
  • 2ESC Association Members (Ivory, Silver, Gold) – access your Association’s congress resources
  • 3Under 40 or in training - with a Combined Membership, access resources from all congresses
Join now

Our sponsors

ESC 365 is supported by Bayer, Boehringer Ingelheim and Lilly Alliance, Bristol-Myers Squibb and Pfizer Alliance, Novartis Pharma AG and Vifor Pharma in the form of educational grants. The sponsors were not involved in the development of this platform and had no influence on its content.

logo esc

Our mission: To reduce the burden of cardiovascular disease

Who we are