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Anesthesiology. 2006 Mar;104(3):417-25.

Multifactorial preoperative predictors for postcesarean section pain and analgesic requirement.

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Department of Anesthesiology, Wake Forest University School of Medicine, Winston-Salem, North Carolina 27157-1009, USA.



The study aimed to determine predictive factors for postcesarean pain and analgesia using an assessment of pain threshold and suprathreshold thermal stimuli as well as degree of somatization and anxiety.


Thirty-four healthy parturients scheduled for cesarean delivery under subarachnoid anesthesia were enrolled. Preoperative thermal pain threshold, intensity, and unpleasantness to heat stimuli applied to arm and lower back, State Trait Anxiety Inventory, and patient expectation for postoperative pain and need for analgesia were assessed. After surgery, overall, resting, and movement pain and analgesic consumption were recorded. Prediction of pain and analgesic use outcomes was made by principal component factor analysis, followed by stepwise linear regression.


Resting pain was predicted by two factors, thermal pain and unpleasantness and patient expectation (r2 = 0.26, P < 0.01), evoked pain by thermal pain threshold in the back (r2= 0.20, P < 0.009), composite pain by thermal pain and unpleasantness and preoperative blood pressure (r2 = 0.28, P < 0.008), intraoperative analgesic need by preexisting pain (r2 = 0.22, P < 0.006), recovery room analgesia by thermal pain threshold and State Trait Anxiety Inventory (r2 = 0.27, P < 0.01), and total analgesic need by State Trait Anxiety Inventory (r2 = 0.22, P < 0.01). These models predicted the upper twentieth percentile of composite pain scores and analgesic requirement with sensitivity of 0.71 to 0.80 and specificity of 0.76 to 0.80.


The authors' results suggest a meaningful combination of preoperative patient responses from physical and psychological tests yields a valid multifactorial predictive model for postoperative pain and analgesic requirement with significant improvements over individual predictive variables.

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