# Factors Associated with Quality of Life Among Patients with Cardiac Pacemakers Assessed by Two Scales

## TL;DR

This study identifies factors affecting the quality of life in patients with cardiac pacemakers using two assessment scales.

## Contribution

The study uniquely evaluates QoL in pacemaker patients using both SF-36 and EQ-5D-5L scales.

## Key findings

- NYHA functional class, comorbidities, and patient information level significantly affect QoL.
- Programming rate response and place of residence are also linked to QoL outcomes.
- Personalized care plans should consider disease burden, comorbidities, and patient knowledge.

## Abstract

Background/Objectives: Permanent cardiac pacemakers (PPMs) are small electronic implanted devices that regulate cardiac rhythm. Measurement of quality of life (QoL) serves as a powerful tool for gaining in-depth insights into pacing therapy and ultimately guiding patient-centered management strategies. The aim of the present study was to evaluate factors affecting QoL among PPM patients by applying the two generic questionnaires: SF-36 and EQ-5D-5L. Materials and Methods: A total of 120 patients with PPM were enrolled. QoL data were collected through interviews using the 36-Item Short Form Health Survey (SF-36) and the Euro QoL 5-Dimensions 5-Levels Health Questionnaire (EQ-5D-5L). Patients’ characteristics were also recorded. Results: The majority of participants were male (54.2%), retired (83.3%) residents in urban areas (75.5%), had a DDD pacemaker (82.5%), had rate response programmed on (77.5%), and had comorbidities (83.3%). Regarding QoL measured by SF-36, the Physical Component Summary Score (PCS) was significantly associated with programming rate response in their pacemaker (p = 0.046), comorbidities (p = 0.047), and the NYHA functional class (p = 0.047). The Mental Component Summary Score (MCS) was significantly associated with sex (p = 0.034), place of residence (p = 0.003), NYHA functional class (p = 0.001), and patients’ level of information about the device (p = 0.039). Patients’ QoL, as measured by the EQ-5D-5L, was significantly associated with sex (p = 0.001), age (p = 0.019), occupation (p = 0.040), pacing mode (p = 0.034), comorbidities (p = 0.019), NYHA functional class (p = 0.047), and level of information about the device (p = 0.005). Conclusions: NYHA functional class, comorbidities, and level of information as reported by patients were the factors associated with QoL, as shown by the two scales. All three factors guide a personalized care plan since NYHA class shows the burden of disease, comorbidities add to the complexity, and patient information determines the effectiveness of management.