The factors influencing compliance in post-EVAR surveillance regarding the perspectives of practitioners and patients

Abstract

Aim: Lifelong monitoring is essential to detect aorta-related complications. Yet, patient non-compliance with surveillance programs poses a significant obstacle. We explored reasons for adherence by engaging in clear, patient-centred communication and analyzed factors contributing to non-compliance. Material and Methods: In 2019, our clinic conducted elective EVAR procedures on 50 patients. They were informed of annual surveillance, including hospital visits, starting with a first-month appointment post-discharge. Patients with no or only one follow-up were deemed non-compliant, while two to three follow-ups indicated moderate compliance, and four to seven follow-ups qualified as high compliance. We recorded each patient's age, education level and demographics. We retrospectively assessed the factors on their compliance level as our first outcome. The second was to explore whether the survey could enlighten any reasons from the patient's perspective. Results: The average age of our cohort was 68.8±8.45 years, with only three females. Non-attendance at follow-ups was observed in 8 patients (16%). Seventy percent (35 patients) complied with at least two appointments. Residing in the same city markedly increased compliance by 20 times (p<0.001). Older age appeared to influence compliance positively, on the borderline significantly (p=0.057). The primary motivation for attendance was adherence to doctor recommendations (n=14), while the predominant reason for non-attendance was patients’ feeling well (n=29). Notably, patients with lower compliance than predicted more frequently cited COVID-19 as a deterrent (p=0.02). Conclusion: Residence within the same city positively influences follow-up compliance. The national databases might be helpful tools for enhancing the surveillance anywhere the patients live. The existing hypotheses by healthcare professionals about patient follow-up compliance are only partially corroborated. Incorporating the patient's perspective into these models may be helpful in comprehensively understanding the underlying factors.