Automatic sensor algorithms expedite pacemaker follow-ups.
OBJECTIVE
Automatic algorithms can be used to optimize settings and reduce the duration of pacemaker (PM) clinical follow-up.


METHODS
This study prospectively evaluated 87 patients (74.2 +/- 10.7 years old, 52% men) who received PM with the Autoslope algorithm. Patients randomized to the manual group (group M, n = 43) performed a walk test and used sensor-indicated rate histograms to adjust the sensor, while in the automatic group (group A, n = 44) the sensor was automatically adjusted by the Autoslope. The patients were followed for 6 months. Follow-up time required for device interrogation and optimal sensor set-up, and the number of sensor parameters reprogramming were recorded. Changes in the patients' activity level were also evaluated.


RESULTS
Group A required significantly less follow-up time than group M (9.4 +/- 5.7 min vs 13.5 +/- 8.5 min, P = 0.0002). The average number of sensor parameters reprogrammed during visits was significantly lower in group A than M (0.6 +/- 0.9 vs 0.9 +/- 1.3, P = 0.048). Threshold was adjusted 34.4% of the time in the sensor evaluations in group M versus 12.9% in group A (P = 0.0004). Although more patients in group A reported being more active, the changes in patients' activity level did not lead to increasing sensor setup time or number of parameter reprogramming in either group.


CONCLUSIONS
Auto sensor adjustment required less time during routine PM clinical follow-up by reducing steps needed for manual sensor threshold adjustment.
Klonis
Demo
D
LaBette County Medical Center, Parsons, 1902 S. US Hwy 59, Parsons, KS 67357, USA. dklonis@joplin.com
Zhang
Xiaozheng
X
Patel
Umesh
U
Gulamhusein
Sajad
S
Patel
Jagdish
J
Hurwit
Handre
H
Banish
Dorothy
D
Marco
Dave
D
D000368:Aged; D000465:Algorithms; D002304:Cardiac Pacing, Artificial / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D011446:Prospective Studies