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

Evaluation of Chronic Postsurgical Pain Risk in Patients Undergoing Cardiac Implantable Electronic Device (CIED) Implantation Using an Artificial Intelligence-Based Clinical Decision (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 prevention of CPSP, optimize postoperative pain management strategies, and enhance patient quality of life.

Study Overview

• Status: Not yet recruiting
• Conditions: Chronic Pain; Neuropathic Pain; Pain Measurement; Chronic Post Surgical Pain; Cardiac Implantable Electrical Devices

Detailed Description

This prospective, single-center observational study will investigate chronic postsurgical pain after cardiac implantable electronic device (CIED) implantation and develop an artificial intelligence (AI)-based risk prediction model. The study will be conducted at the Istanbul University-Cerrahpaşa Cardiology Institute, Electrophysiology Outpatient Clinic, and will include 180 consecutive patients who received their first CIED within the past 12 months.

Study Design and Population Eligible participants are adults (≥ 18 years) undergoing first-time CIED implantation (pacemaker, ICD, or CRT device). Exclusion criteria include preexisting neuropathic disorders, neurodegenerative or autoimmune diseases, malignancy, trauma, infection, or previous generator replacement. All participants will provide written informed consent.

Data Collection and Measurements Patients will be evaluated at 3, 6, and 12 months post-implantation. Subjective pain assessment: Visual Analog Scale (VAS) and DN-4 questionnaire (neuropathic pain screening).

Objective pain threshold: Pressure Pain Threshold (PPT) measured with a digital pressure algometer on both the implantation and contralateral sides.

Quality of life: SF-12 (Physical and Mental Component Scores). Risk factors: Demographic, preoperative, intraoperative, and postoperative variables (age, sex, BMI, comorbidities, anesthesia type, surgical time, device type, lead number, battery position, acute postoperative pain, rehabilitation, analgesic use).

Data Management and Quality Control All patient data will be anonymized and stored on password-protected institutional servers. Data accuracy and completeness will be checked regularly. Source data verification will be performed by comparing electronic records and case report forms. Range and consistency checks will be applied to identify out-of-range or inconsistent entries.

Sample Size and Statistical Analysis Based on previous literature reporting an estimated CPSP prevalence of 24% after CIED implantation, a sample size of 163 was calculated (α = 0.05, power = 0.95, OR = 2.0). Allowing for a 10% dropout rate, the target enrollment is 180 participants.

Descriptive statistics will summarize demographic and clinical variables. Between-group comparisons will use independent-sample t-tests or Mann-Whitney U tests for continuous variables and χ² or Fisher's exact tests for categorical data. Logistic regression will identify independent predictors of CPSP (VAS > 3 or DN-4 ≥ 4). Correlations between continuous variables will be analyzed with Pearson or Spearman coefficients. A two-tailed p < 0.05 will denote statistical significance.

Secondary Analysis Description

Multivariable logistic regression analysis will be performed to evaluate the association between preoperative, intraoperative, and postoperative risk factors and the presence of chronic postsurgical pain (CPSP) at 12 months after cardiac implantable electronic device implantation.

Candidate variables will include age, sex, body mass index (BMI), preexisting chronic pain, psychological status, comorbidities (ischemic heart disease, heart failure, diabetes mellitus, hypertension, chronic kidney disease, thyroid disease), type of anesthesia, surgical duration, device type (pacemaker, ICD, CRT), pocket side and location, number of leads, postoperative pain management and analgesic use, and rehabilitation.

Results will be reported as adjusted odds ratios (ORs) with 95% confidence intervals (CIs).

Artificial Intelligence Model Development Collected data will be split into training and validation sets. Supervised machine-learning algorithms (logistic regression, random forest, XGBoost, artificial neural networks) will be trained to predict CPSP occurrence at 12 months. Model performance will be evaluated using ROC-AUC, accuracy, sensitivity, and specificity metrics. Feature importance analysis will identify the most influential predictors.

The best-performing model will be incorporated into a prototype Clinical Decision Support System (CDSS), accessible via a secure web interface and mobile application. This tool will allow clinicians to input patient parameters and obtain individualized CPSP risk estimates to guide preventive analgesic strategies.

• Study Type: Observational
• Enrollment (Estimated): 180

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

Patients who meet the inclusion criteria and have undergone CIED implantation will constitute the study population.

Description

Inclusion Criteria:

• Adults aged ≥18 years.
• Patients who have undergone first-time implantation of a cardiac implantable electronic device (CIED), including pacemaker (PM), implantable cardioverter-defibrillator (ICD), or cardiac resynchronization therapy device (CRT).
• Device implantation performed within the past 12 months at Istanbul University-Cerrahpaşa Cardiology Institute.
• Ability to attend scheduled follow-up visits at 3, 6, and 12 months post-implantation.
• Ability to understand study procedures and provide written informed consent.

Exclusion Criteria:

• History of neuropathic or chronic pain syndromes prior to implantation (e.g., fibromyalgia, diabetic neuropathy).
• Presence of neurodegenerative, autoimmune, or vascular disorders affecting peripheral or central pain pathways.
• Known malignancy, active infection, or recent trauma in the chest or upper extremities.
• Prior CIED replacement or battery exchange procedures.
• Use of chronic analgesic or neuropathic pain medications (e.g., opioids, gabapentinoids, antidepressants) for conditions unrelated to the procedure.
• Cognitive impairment or psychiatric disorders that may interfere with study participation or reliable pain assessment.
• Inability to comply with follow-up or withdrawal of consent at any time.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

CIED-Implanted Patients; This cohort includes adult patients who have undergone first-time implantation of a cardiac implantable electronic device (pacemaker, implantable cardioverter-defibrillator, or cardiac resynchronization therapy device) within the past year. Participants are prospectively followed at 3, 6, and 12 months after device implantation. Pain intensity is assessed using the Visual Analog Scale (VAS) and the DN-4 questionnaire, while objective pain sensitivity is measured using a digital pressure algometer. Quality of life is evaluated with the SF-12 form. Based on the one-year follow-up findings, patients will be categorized according to the presence or absence of chronic postsurgical pain (CPSP) to identify predictive risk factors and to develop an artificial intelligence-based clinical decision support model for early pain risk prediction.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Incidence of Chronic Postsurgical Pain (CPSP) Based on Subjective VAS Assessments	CPSP is defined as persistent or newly occurring pain at or near the device pocket (or corresponding dermatomes) lasting ≥3 months after surgery and not explained by other causes. Pain intensity and neuropathic features will be measured using the Visual Analog Scale (VAS) and DN-4; Pressure Pain Threshold (PPT) by digital algometry provides objective corroboration. The primary outcome is the proportion of patients meeting CPSP criteria at 12 months, with intermediate assessments at 3 and 6 months used for longitudinal description and sensitivity analyses. Type of Measure: Clinical assessment (subjective scales + objective PPT). VAS> 4 means positive result.	Assessed at 3, 6, and 12 months after CIED implantation; primary analysis at 12 months
Incidence of Chronic Postsurgical Pain (CPSP) Based on DN-4 Assessments	CPSP is defined as persistent or newly occurring pain at or near the device pocket (or corresponding dermatomes) lasting ≥3 months after surgery and not explained by other causes. Pain intensity and neuropathic features will be measured using the Visual Analog Scale (VAS) and DN-4; Pressure Pain Threshold (PPT) by digital algometry provides objective corroboration. The primary outcome is the proportion of patients meeting CPSP criteria at 12 months, with intermediate assessments at 3 and 6 months used for longitudinal description and sensitivity analyses. Type of Measure: Clinical assessment (subjective scales + objective PPT). DN4 ≥ 4 means positive result.	Assessed at 3, 6, and 12 months after CIED implantation; primary analysis at 12 months
Incidence of Chronic Postsurgical Pain (CPSP) Based on Pressure Pain Threshold Assessments	CPSP is defined as persistent or newly occurring pain at or near the device pocket (or corresponding dermatomes) lasting ≥3 months after surgery and not explained by other causes. Pain intensity and neuropathic features will be measured using the Visual Analog Scale (VAS) and DN-4; Pressure Pain Threshold (PPT) by digital algometry provides objective corroboration. The primary outcome is the proportion of patients meeting CPSP criteria at 12 months, with intermediate assessments at 3 and 6 months used for longitudinal description and sensitivity analyses. Type of Measure: Clinical assessment (subjective scales + objective PPT).There is no known cut-off value for the pain threshold in this patient group. One of the aims of this study is to determine a threshold value for chronic pain in this patient population using algometry, in correlation with clinical examination findings. We will evaluate the association between lower values and increased pain sensitivity in patients.	Assessed at 3, 6, and 12 months after CIED implantation; primary analysis at 12 months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Change in Health-Related Quality of Life (SF-12 PCS/MCS)	The SF-12 is a validated measure of health-related quality of life and provides two summary scores: the Physical Component Summary (PCS-12) and the Mental Component Summary (MCS-12). Higher PCS-12 and MCS-12 scores indicate better physical and mental health status, respectively, whereas lower scores reflect greater physical limitation, higher pain burden, and poorer psychological well-being. In patients with chronic pain, lower PCS-12 scores are associated with increased pain-related functional impairment, while lower MCS-12 scores suggest a greater psychological impact of pain. SF-12 scores are normalized to a population mean of 50 with a standard deviation of 10; scores below 50 indicate below-average health status, and scores above 50 indicate better-than-average health-related quality of life.	3, 6, and 12 months post-implantation
Determination of Pressure Pain Threshold (PPT) Cut-off for CPSP	The primary outcome is the identification of a pressure pain threshold (PPT) cut-off that best discriminates chronic postsurgical pain (CPSP) in patients with cardiac implantable electronic devices (CIEDs). PPT will be measured using a handheld algometer on the device pocket and contralateral sides, with three measurements obtained in four predefined quadrants per side and averaged to derive side-specific mean values. Side-to-side PPT differences (ΔPPT) will be calculated for each patient. Discriminatory performance of ΔPPT for CPSP will be assessed using receiver operating characteristic (ROC) analysis, with the optimal cut-off determined by the Youden index and reported as area under the curve (AUC), sensitivity, and specificity.	Up to 12 months (end of follow-up)
Performance of the AI-Based CPSP Risk Prediction Model	Diagnostic/predictive performance of the AI-based clinical decision support model integrating clinical, subjective, and PPT features. Metrics: ROC-AUC, sensitivity, specificity, accuracy, F1-score (with internal validation via cross-validation/bootstrapping). The diagnostic and predictive performance of an AI-based clinical decision support model for identifying chronic postsurgical pain (CPSP) in patients with cardiac implantable electronic devices (CIEDs). The model integrates clinical variables, patient-reported subjective measures, and pressure pain threshold (PPT) features derived from side-to-side (pocket vs. contralateral) algometric assessments. Model performance will be evaluated against the clinical diagnosis of CPSP using receiver operating characteristic area under the curve (ROC-AUC), sensitivity, specificity, accuracy, and F1-score. Internal validation will be performed using cross-validation and/or bootstrapping techniques.	After 12-month data lock (model training/validation phase)
Prevalence of Neuropathic Pain Features (DN-4)	Prevalence and trajectory of neuropathic pain features defined as DN-4 ≥ 4, reported at each visit and cumulatively at 12 months; evaluated in relation to CPSP status. Type of Measure: Clinical classification (screening instrument threshold)	3, 6, and 12 months

Collaborators and Investigators

• Sponsor: Istanbul University - Cerrahpasa

Study record dates

Study Major Dates

• Study Start (Estimated): September 30, 2026
• Primary Completion (Estimated): December 1, 2026
• Study Completion (Estimated): March 1, 2027

Study Registration Dates

• First Submitted: November 18, 2025
• First Submitted That Met QC Criteria: December 22, 2025
• First Posted (Actual): January 7, 2026

Study Record Updates

• Last Update Posted (Actual): May 5, 2026
• Last Update Submitted That Met QC Criteria: May 4, 2026
• Last Verified: May 1, 2026

More Information

Terms related to this study

• Keywords: Quality of Life; Defibrillators, Implantable; Chronic Pain; Artificial Intelligence; postoperative pain; Decision Support Systems, Clinical; Pacemaker, Artificial; Cardiac Resynchronization Therapy Devices
• Additional Relevant MeSH Terms: Pain; Neurologic Manifestations; Nervous System Diseases; Postoperative Complications; Pathologic Processes; Neuromuscular Diseases; Peripheral Nervous System Diseases; Pathological Conditions, Signs and Symptoms; Signs and Symptoms; Pain, Postoperative; Chronic Pain; Neuralgia
• Other Study ID Numbers: IUC-CIEDPAIN-AI-2025

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No