AIMS: This retrospective analysis sought to develop and validate a model using the measured diagnostic variables in cardiac resynchronization therapy (CRT) devices to predict mortality. METHODS AND RESULTS: Data used in this analysis came from two CRT studies: Cardiac Resynchronization Therapy Registry Evaluating Patient Response with RENEWAL Family Devices (CRT RENEWAL) (n = 436) and Heart Failure-Heart Rate Variability (HF-HRV) (n = 838). Patients from CRT RENEWAL were used to create a model for risk of death using logistic regression and to create a scoring system that could be used to predict mortality. Results of both the logistic regression and the clinical risk score were validated in a cohort of patients from the HF-HRV study. Diagnostics significantly improved over time post-CRT implant (all P < 0.001) and were correlated with a trend of decreased risk of death. The regression model classified CRT RENEWAL patients into low (2.8%), moderate (6.9%), and high (13.8%) risk of death based on tertiles of their model predicted risk. The clinical risk score classified CRT RENEWAL patients into low (2.8%), moderate (10.1%), and high (13.4%) risk of death based on tertiles of their score. When both the regression model and the clinical risk score were applied to the HF-HRV study, each was able to classify patients into appropriate levels of risk. CONCLUSION: Device diagnostics may be used to create models that predict the risk of death.
AIMS: This retrospective analysis sought to develop and validate a model using the measured diagnostic variables in cardiac resynchronization therapy (CRT) devices to predict mortality. METHODS AND RESULTS: Data used in this analysis came from two CRT studies: Cardiac Resynchronization Therapy Registry Evaluating Patient Response with RENEWAL Family Devices (CRT RENEWAL) (n = 436) and Heart Failure-Heart Rate Variability (HF-HRV) (n = 838). Patients from CRT RENEWAL were used to create a model for risk of death using logistic regression and to create a scoring system that could be used to predict mortality. Results of both the logistic regression and the clinical risk score were validated in a cohort of patients from the HF-HRV study. Diagnostics significantly improved over time post-CRT implant (all P < 0.001) and were correlated with a trend of decreased risk of death. The regression model classified CRT RENEWAL patients into low (2.8%), moderate (6.9%), and high (13.8%) risk of death based on tertiles of their model predicted risk. The clinical risk score classified CRT RENEWAL patients into low (2.8%), moderate (10.1%), and high (13.4%) risk of death based on tertiles of their score. When both the regression model and the clinical risk score were applied to the HF-HRV study, each was able to classify patients into appropriate levels of risk. CONCLUSION: Device diagnostics may be used to create models that predict the risk of death.
Authors: Robert K Altman; Kimberly A Parks; Christopher L Schlett; Mary Orencole; Mi-Young Park; Quynh A Truong; Peerawut Deeprasertkul; Stephanie A Moore; Conor D Barrett; Gregory D Lewis; Saumya Das; Gaurav A Upadhyay; E Kevin Heist; Michael H Picard; Jagmeet P Singh Journal: Eur Heart J Date: 2012-05-21 Impact factor: 29.983
Authors: Maarten Z H Kolk; Diana M Frodi; Tariq O Andersen; Joss Langford; Soeren Z Diederichsen; Jesper H Svendsen; Hanno L Tan; Reinoud E Knops; Fleur V Y Tjong Journal: Cardiovasc Digit Health J Date: 2021-11-24
Authors: Martin R Cowie; Shantanu Sarkar; Jodi Koehler; David J Whellan; George H Crossley; Wai Hong Wilson Tang; William T Abraham; Vinod Sharma; Massimo Santini Journal: Eur Heart J Date: 2013-03-19 Impact factor: 29.983