Evaluation of Chronic Postsurgical Pain Risk in Patients Undergoing CIED Implantation Using an AID Support System

Chronic postsurgical pain (CPSP) remains a significant clinical and public health challenge despite major advances in surgical and anesthetic techniques. Patients receiving cardiac implantable electronic devices (CIEDs)-including pacemakers, implantable cardioverter-defibrillators (ICDs), and cardiac resynchronization therapy devices (CRTs)-constitute a unique population with high rates of multimorbidity, psychological vulnerability, and limited analgesic options due to cardiovascular comorbidities. Insufficient postoperative pain management in this group may lead to persistent pain at the generator site, neuropathic pain features, reduced quality of life, and increased healthcare utilization. The present study aims to evaluate the prevalence, risk factors, and clinical predictors of CPSP following CIED implantation using both subjective and objective pain assessments. Pain intensity will be measured using validated self-report scales (Visual Analog Scale and DN-4 questionnaire), and objective pain thresholds will be determined using a calibrated digital pressure algometer. Quality of life will be assessed with the validated Turkish version of the Short Form-12 (SF-12) instrument. This prospective observational study will include 180 adult patients who underwent first-time CIED implantation at Istanbul University-Cerrahpaşa, Cardiology Institute. Participants will be evaluated at 3, 6, and 12 months post-implantation. Collected data will include preoperative, intraoperative, and postoperative variables such as demographics, comorbidities, anesthesia type, surgical duration, and acute postoperative pain control. Using the obtained data, an artificial intelligence-based clinical decision support system will be developed to predict individual CPSP risk before implantation. The model will integrate subjective scales, objective algometric data, and clinical factors to generate personalized risk estimates. Ultimately, this system aims to improve early detection and pre