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.

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.

Figures