Evaluation of the Skin to Epidural and Subarachnoid Space Distance in Young Children Using Magnetic Resonance Imaging. Issue 3 (1st May 2015)

• Record Type: Journal Article
• Authors: Franklin, Andrew D.; Lorinc, Amanda N.; Shotwell, Matthew S.; Greene, Elton B.; Wushensky, Curtis A.
• Is Part Of: Regional anesthesia and pain medicine. Volume 40:Issue 3(2015)
• Page Start: 245
• Page End: 248
• Publication Date: 2015-05-01
• Subjects: Conduction anesthesia -- Periodicals; Pain medicine -- Periodicals; 617.964
• DOI: 10.1097/AAP.0000000000000234
• Languages: English
• ISSNs: 1098-7339

Abstract

Abstract : Background and Objectives: Epidural catheters placed for perioperative analgesia in young children confer clinical benefits but are technically challenging to insert. Approximations of the skin to epidural space depth in this population are limited to direct needle measurement and ultrasonography. Magnetic resonance imaging (MRI) is the most comprehensive imaging modality of the spine. This study aims to produce a more clinically useful formula from MRI data to estimate pediatric epidural depth. Methods: Seventy children with normal lumbar spine MR images were enrolled. After determination of epidural depth, linear regression was used to estimate a weight-based formula. Analysis of variance and bootstrap methods were used to evaluate this formula against 4 commonly cited formulae. The quality of predictions was evaluated using the mean absolute prediction error. Results: The estimated weight-based formula as derived by MRI data is given by: skin to epidural depth (mm) = 9.00 + 0.62 * weight in kilograms. The mean absolute prediction error was 2.56 mm (95% confidence interval [95% CI], 2.12–3.04) for the new formula. Additional derived formulae are skin to dorsal dura depth (mm) = 13.52 + 0.71 * weight in kilograms (mean absolute prediction error, 2.48 mm; 95% CI, 2.00–3.03) and skin to ventral dural depth (mm) = 23.08 + 0.86 * weight in kilograms (mean absolute prediction error, 2.50 mm; 95% CI, 2.04–3.06). Conclusions: We provide the first predictive formulae, based on MRI data, for pediatric epidural depth estimation.