Predicting Severity of Acute Pain After Cesarean Delivery: A Narrative Review

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Abstract

Cesarean delivery is one of the most common surgical procedures in the United States, with over 1.3 million performed annually. One-fifth of women who undergo cesarean delivery will experience severe pain in the acute postoperative period, increasing their risk of developing chronic pain and postpartum depression, and negatively impacting breastfeeding and newborn care. A growing body of research has investigated tools to predict which patients will experience more severe pain and have increased analgesic consumption after cesarean delivery. These include quantitative sensory testing, assessment of wound hyperalgesia, response to local anesthetic infiltration, and preoperative psychometric evaluations such as validated psychological questionnaires and simple screening tools. For this review, we searched MEDLINE, the Cochrane database, and Google Scholar to identify articles that evaluated the utility of various tools to predict severe pain and/or opioid consumption in the first 48 hours after cesarean delivery. Thirteen articles were included in the final review: 5 utilizing quantitative sensory testing, including patient responses to pressure, electrical, and thermal stimuli; 1 utilizing hyperalgesia testing; 1 using response to local anesthetic wound infiltration; 4 utilizing preoperative psychometric evaluations including the State-Trait Anxiety Inventory, the Pain Catastrophizing Scale, the Pittsburgh Sleep Quality Index, the Hospital Anxiety and Depression Scale, and simple questionnaires; and 2 utilizing a combination of quantitative sensory tests and psychometric evaluations. A number of modalities demonstrated statistically significant correlations with pain outcomes after cesarean delivery, but most correlations were weak to modest, and many modalities might not be clinically feasible. Response to local anesthetic infiltration and a tool using 3 simple questions enquiring about anxiety and anticipated pain and analgesic needs show potential for clinical use, but further studies are needed to evaluate the utility of these predictive tests in clinical practice.

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