Abstract

A model that predicts a patient's risk of developing chronic, burn-related nerve pain may guide medical and/or surgical management. This study determined anatomy-specific variables and constructed a mathematical model to predict a patient's risk of developing burn-related nerve pain. A retrospective analysis was conducted from 1862 adults admitted to a burn center from 2014 to 2019. One hundred thirteen patients developed burn-related nerve pain. Comparisons were made using 11 anatomy-specific locations between patients with and without burn-related nerve pain. The modified Delphi technique was used to select 14 potential risk variables. Multivariate regression techniques, Brier scores, area under the curve, Hosmer-Lemeshow goodness-of-fit, and stratified K-fold cross-validation was used for model development. Chronic pain was defined as pain lasting 6 or more months after release from the Burn Center. Prevalence rates of burn-related nerve pain were similar in the development (6.1 percent) and validation (5.4 percent) cohorts [Brier score = 0.15; stratified K-fold cross-validation (K = 10): area under the curve, 0.75; 95 percent CI, 0.68 to 0.81; Hosmer-Lemeshow goodness-of-fit, p = 0.73; n = 10 groups]. Eight variables were included in the final equation. Burn-related nerve pain risk score = -6.3 + 0.02 (age) + 1.77 (tobacco use) + 1.04 (substance abuse) + 0.67 (alcohol abuse) + 0.84 (upper arm burn) + 1.28 (thigh burn) + 0.21 (number of burn operations) + 0.01 (hospital length-of-stay). Burn-related nerve pain predicted probability = 1 - 1/[1 + exp(burn-related nerve pain risk score)] for 6-month burn-related nerve pain risk score. As the number of risk factors increased, the probability of pain increased. Risk factors were identified for developing burn-related nerve pain at 11 anatomical locations. This model accurately predicts a patient's risk of developing burn-related nerve pain at 6 months. Age, tobacco use, substance abuse, alcohol abuse, upper arm burns, thigh burns, the number of burn operations, and hospital length of stay represented the strongest predictors. Risk, III.

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