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Personalized CRT Study

Evaluating real-world outcomes for heart failure patients receiving Cardiac Resynchronization Therapy (CRT).

The Personalized CRT study is part of the Medtronic Product Surveillance Registry. It is a prospective, real-world, observational study with the objective to understand CRT response and evaluate new CRT technologies to optimize heart failure treatment. These include product performance, patient safety, and clinical outcomes.

The study's design provides timely access to a robust data set. Currently, there are over 12,000 patients enrolled at 300 centers in 16 countries. We have been enrolling patients since 2001 and it is ongoing. 

STUDY DESIGN 

Flowchart of study design showing criteria of patients who must have a CRT and LV Lead System. Criteria includes NYHA Class, QRS, EF, Medication, and Medical History. Also explains what is covered at a 6 month follow-up like device data, CRT Response, hospitalizations and mortality.

KEY RESULTS 

Improved survival with dynamic optimization of CRT pacing using AdaptivCRT algorithm: Analysis of real-world patient data

ANALYSIS DESIGN

1,835 patients with an eligible CRT system were enrolled in the Personalized CRT Study, part of the Product Surveillance Registry. We compared patients with the AdaptivCRT™ Algorithm "on" versus "off" to evaluate mortality status between the two groups. Frailty survival models were used to evaluate the potential survival benefit of the algorithm, adjusting for patient heterogeneity and center variability.

Chart showing that AdpativCRT Is associated with a 31% relative reduction in mortality

CONCLUSION

Patients who received AdaptivCRT were associated with a 31% relative reduction in all-cause mortality versus conventional CRT (after adjusting for other potential risk factors*).1

Learn More About AdaptivCRT
*

Age, gender, LVEF, NYHA Class, QRS duration, AF, CAD, Hypertension, AV Block, and LBBB.

1

Singh JP, et al. Improved Survival With Dynamic Optimization Of CRT Pacing Using Adaptivcrt Algorithm: Analysis Of Real-world Patient Data. Presented at HRS 2018 (Abstract B-AB37 -06).