Skip to main content

Machine Learning Algorithm Predicts Cardiac Resynchronization Therapy Outcomes: Lessons From the COMPANION Trial.

Publication ,  Journal Article
Kalscheur, MM; Kipp, RT; Tattersall, MC; Mei, C; Buhr, KA; DeMets, DL; Field, ME; Eckhardt, LL; Page, CD
Published in: Circ Arrhythm Electrophysiol
January 2018

BACKGROUND: Cardiac resynchronization therapy (CRT) reduces morbidity and mortality in heart failure patients with reduced left ventricular function and intraventricular conduction delay. However, individual outcomes vary significantly. This study sought to use a machine learning algorithm to develop a model to predict outcomes after CRT. METHODS AND RESULTS: Models were developed with machine learning algorithms to predict all-cause mortality or heart failure hospitalization at 12 months post-CRT in the COMPANION trial (Comparison of Medical Therapy, Pacing, and Defibrillation in Heart Failure). The best performing model was developed with the random forest algorithm. The ability of this model to predict all-cause mortality or heart failure hospitalization and all-cause mortality alone was compared with discrimination obtained using a combination of bundle branch block morphology and QRS duration. In the 595 patients with CRT-defibrillator in the COMPANION trial, 105 deaths occurred (median follow-up, 15.7 months). The survival difference across subgroups differentiated by bundle branch block morphology and QRS duration did not reach significance (P=0.08). The random forest model produced quartiles of patients with an 8-fold difference in survival between those with the highest and lowest predicted probability for events (hazard ratio, 7.96; P<0.0001). The model also discriminated the risk of the composite end point of all-cause mortality or heart failure hospitalization better than subgroups based on bundle branch block morphology and QRS duration. CONCLUSIONS: In the COMPANION trial, a machine learning algorithm produced a model that predicted clinical outcomes after CRT. Applied before device implant, this model may better differentiate outcomes over current clinical discriminators and improve shared decision-making with patients.

Duke Scholars

Altmetric Attention Stats
Dimensions Citation Stats

Published In

Circ Arrhythm Electrophysiol

DOI

EISSN

1941-3084

Publication Date

January 2018

Volume

11

Issue

1

Start / End Page

e005499

Location

United States

Related Subject Headings

  • Ventricular Function, Left
  • Stroke Volume
  • Predictive Value of Tests
  • Middle Aged
  • Male
  • Machine Learning
  • Humans
  • Heart Failure
  • Heart Conduction System
  • Female
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Kalscheur, M. M., Kipp, R. T., Tattersall, M. C., Mei, C., Buhr, K. A., DeMets, D. L., … Page, C. D. (2018). Machine Learning Algorithm Predicts Cardiac Resynchronization Therapy Outcomes: Lessons From the COMPANION Trial. Circ Arrhythm Electrophysiol, 11(1), e005499. https://doi.org/10.1161/CIRCEP.117.005499
Kalscheur, Matthew M., Ryan T. Kipp, Matthew C. Tattersall, Chaoqun Mei, Kevin A. Buhr, David L. DeMets, Michael E. Field, Lee L. Eckhardt, and C David Page. “Machine Learning Algorithm Predicts Cardiac Resynchronization Therapy Outcomes: Lessons From the COMPANION Trial.Circ Arrhythm Electrophysiol 11, no. 1 (January 2018): e005499. https://doi.org/10.1161/CIRCEP.117.005499.
Kalscheur MM, Kipp RT, Tattersall MC, Mei C, Buhr KA, DeMets DL, et al. Machine Learning Algorithm Predicts Cardiac Resynchronization Therapy Outcomes: Lessons From the COMPANION Trial. Circ Arrhythm Electrophysiol. 2018 Jan;11(1):e005499.
Kalscheur, Matthew M., et al. “Machine Learning Algorithm Predicts Cardiac Resynchronization Therapy Outcomes: Lessons From the COMPANION Trial.Circ Arrhythm Electrophysiol, vol. 11, no. 1, Jan. 2018, p. e005499. Pubmed, doi:10.1161/CIRCEP.117.005499.
Kalscheur MM, Kipp RT, Tattersall MC, Mei C, Buhr KA, DeMets DL, Field ME, Eckhardt LL, Page CD. Machine Learning Algorithm Predicts Cardiac Resynchronization Therapy Outcomes: Lessons From the COMPANION Trial. Circ Arrhythm Electrophysiol. 2018 Jan;11(1):e005499.

Published In

Circ Arrhythm Electrophysiol

DOI

EISSN

1941-3084

Publication Date

January 2018

Volume

11

Issue

1

Start / End Page

e005499

Location

United States

Related Subject Headings

  • Ventricular Function, Left
  • Stroke Volume
  • Predictive Value of Tests
  • Middle Aged
  • Male
  • Machine Learning
  • Humans
  • Heart Failure
  • Heart Conduction System
  • Female