A Predictive Model for Postoperative Analgesic Requirements After Abdominal Surgery

Opioid Analgesic Requirement, Pain Management After Abdominal Surgery, Postoperative Analgesia, Postoperative Pain

Postoperative Pain, Postoperative Analgesia, Patient-Controlled Analgesia, Repeated Time-to-Event Modelling, Pharmacokinetic-Pharmacodynamic Modelling, Model-Informed Precision Dosing

Postoperative pain remains a common challenge after abdominal surgery despite the use of standard multimodal pain management strategies. Poorly controlled pain can delay recovery, limit early mobilisation, reduce patient comfort, and increase the risk of longer-term pain problems. This study aims to develop a predictive model to better understand and anticipate patients' postoperative pain relief needs after abdominal surgery. The study will observe adults undergoing elective abdominal surgery and collect information on their pain scores, pain medication use, surgical characteristics, and recovery during the first 48 hours after surgery. Participants will receive standard postoperative pain management according to usual clinical practice. The study will analyse patterns of rescue pain medication use, including when and how often additional pain relief is required after surgery. The collected information will be used to develop a model that can predict factors associated with increased pain medication requirements. The goal of this research is to improve understanding of individual differences in postoperative pain and provide a foundation for more personalised pain management strategies in the future. The findings may help healthcare providers identify patients who are more likely to require additional pain relief and support the development of safer and more effective approaches to postoperative pain control.

Postoperative pain remains a significant clinical challenge following abdominal surgery. Although multimodal analgesic strategies are widely implemented, many patients continue to experience moderate-to-severe pain during the early postoperative period and require repeated rescue analgesic administration. Current approaches to evaluate postoperative analgesic requirements often rely on total opioid consumption or time to first rescue analgesia. However, these methods may not fully capture the dynamic and repeated nature of postoperative pain and analgesic demand. This study aims to develop a predictive model to characterise postoperative analgesic requirements in adults undergoing elective abdominal surgery. The study will use repeated time-to-event pharmacokinetic-pharmacodynamic modelling to analyse the timing and pattern of rescue morphine administration during the first 48 hours after surgery. This is a prospective observational cohort study involving adult patients undergoing elective open or laparoscopic abdominal surgery under general anaesthesia. Participants will receive standard postoperative analgesic management according to routine clinical practice. No changes to clinical care will be introduced as part of the study. Data collected will include patient demographics, surgical characteristics, anaesthetic details, postoperative pain scores, and analgesic administration records. Rescue analgesic events will be captured from patient-controlled analgesia (PCA) device records, including the timing and dose of successful morphine administrations. Individual morphine exposure will be estimated using documented dosing histories and published population pharmacokinetic parameters. The collected data will be analysed using repeated time-to-event pharmacokinetic-pharmacodynamic modelling. The model will evaluate factors influencing the likelihood of requiring rescue analgesia over time, including temporal patterns, circadian effects, morphine exposure, and patient or surgical characteristics. Model performance will be assessed using simulation-based evaluation methods, including predictive checks and bootstrap analysis. The expected outcome of this study is the development of a validated predictive framework for postoperative analgesic requirements after abdominal surgery. This model may provide a foundation for future personalised analgesic strategies, allowing earlier identification of patients at higher risk of inadequate pain control and supporting safer, more effective postoperative pain management.

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