Dynamic prediction model for early postoperative pain based on generalized estimating equations: a multi-timepoint...

Area of Science:

• Anesthesiology and Critical Care Medicine
• Pain Management
• Health Informatics

Background:

• Early postoperative pain is a significant concern for surgical patients in the intensive care unit (ICU).
• Accurate identification of patients at risk for moderate to severe pain (Visual Analogue Scale [VAS] ≥3) post-extubation is crucial for effective analgesic management.
• Existing methods may not adequately capture the dynamic nature of pain risk in the ICU setting.

Purpose of the Study:

• To identify key risk factors associated with early postoperative pain (VAS ≥3) one hour after extubation in ICU surgical patients.
• To develop and validate a dynamic prediction model using generalized estimating equations (GEE) for precise analgesic management.
• To compare the model's performance against the Pain Catastrophizing Scale (PCS).

Main Methods:

• A retrospective longitudinal study involving 373 ICU patients from West China Hospital, with 70% for training and 30% for testing.
• External validation was performed on 124 patients from The People's Hospital of Rugao.
• Multivariable GEE modeling was employed, analyzing clinical, perioperative, and extubation variables collected at 30 minutes, one hour, and two hours post-extubation.

Main Results:

• Independent risk factors included higher Critical-Care Pain Observation Tool scores, BMI, smoking history, older age, higher APACHE-II scores, and intraoperative sedative use.
• Post-extubation analgesic pump use and time were protective factors.
• The GEE model demonstrated good discrimination (AUC: 0.820 training, 0.785 testing, 0.772 external validation) and outperformed the PCS.

Conclusions:

• Early post-extubation pain is influenced by patient, disease, sedation, and behavioral factors.
• The GEE-based dynamic model offers robust discriminative ability and clinical utility for early identification of high-risk patients.
• This model supports individualized analgesic interventions in the ICU setting.