Dataset Information

Safety of mechanical lung vibrator and high-frequency chest wall oscillation in patients with cardiac implantable electronic device.

ABSTRACT:

Background

Chest physiotherapy (CPT) is a non-pharmacological therapy to facilitate airway secretion removal. There have been concerns about potential electromagnetic interference (EMI) and lead integrity problems during the use of vibrating CPT devices in patients with cardiac implantable electronic devices (CIEDs).

Hypothesis

Two CPT devices can be used safely in patients with CIED.

Methods

Volunteer patients with CIED underwent device interrogation to check lead integrity and device function before and after application of CPT devices. Mechanical lung vibrator and high-frequency chest wall oscillation (HFCWO) vests were used while monitoring surface electrocardiograms and intra-cardiac electrograms.

Results

We prospectively enrolled 46 patients with CIEDs (25 pacemakers, 15 implantable cardioverter-defibrillators, and six cardiac resynchronization therapy-defibrillators). There was no noise detection or EMI during CPT in any patient. None of the patients showed clinically significant changes in lead integrity parameters. HFCWO inappropriately accelerated the pacing rate up to the maximal programmed value in five patients with pacemakers and two with cardiac resynchronization therapy-defibrillators.

Conclusion

CPT may be safely applied to patients with CIED without compromising lead integrity or device function, except for unwanted increase in pacing rate caused by misdetection of chest wall vibration as patients' activity while using HFCWO. Deactivation of the accelerometer-based activity sensor may be needed when HFCWO is planned for CPT.

SUBMITTER: Gwag HB

PROVIDER: S-EPMC8027569 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:ObjectiveWith the recent development of various MRI-conditional cardiac implantable electronic devices (CIEDs), the accurate identification and characterization of CIEDs have become critical when performing MRI in patients with CIEDs. We aimed to develop and evaluate a deep learning-based algorithm (DLA) that performs the detection and characterization of parameters, including MRI safety, of CIEDs on chest radiograph (CR) in a single step and compare its performance with other related algorithms that were recently developed.Materials and methodsWe developed a DLA (X-ray CIED identification [XCID]) using 9912 CRs of 958 patients with 968 CIEDs comprising 26 model groups from 4 manufacturers obtained between 2014 and 2019 from one hospital. The performance of XCID was tested with an external dataset consisting of 2122 CRs obtained from a different hospital and compared with the performance of two other related algorithms recently reported, including PacemakerID (PID) and Pacemaker identification with neural networks (PPMnn).ResultsThe overall accuracies of XCID for the manufacturer classification, model group identification, and MRI safety characterization using the internal test dataset were 99.7% (992/995), 97.2% (967/995), and 98.9% (984/995), respectively. These were 95.8% (2033/2122), 85.4% (1813/2122), and 92.2% (1956/2122), respectively, with the external test dataset. In the comparative study, the accuracy for the manufacturer classification was 95.0% (152/160) for XCID and 91.3% for PPMnn (146/160), which was significantly higher than that for PID (80.0%,128/160; p < 0.001 for both). XCID demonstrated a higher accuracy (88.1%; 141/160) than PPMnn (80.0%; 128/160) in identifying model groups (p < 0.001).ConclusionThe remarkable and consistent performance of XCID suggests its applicability for detection, manufacturer and model identification, as well as MRI safety characterization of CIED on CRs. Further studies are warranted to guarantee the safe use of XCID in clinical practice.