Pain practice : the official journal of World Institute of Pain · 2026
Development and Validation of a Clinical Prediction Rule to Identify Preoperatively Patients With Acute Refractory Pain After Lumbar Spine Surgery.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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van de Wijgert Ilse H, Vissers Kris C P, Heymans Martijn W, Rood Akkie, de Goeij Moniek, van Boekel Regina L M, van Hooff Miranda L
Аннотация
BACKGROUND: Preoperative risk stratification to identify patients who develop acute refractory pain after lumbar spine surgery is a first step to prevent unfavorable outcomes. In this study, we aimed to develop and internally validate a clinically relevant, multidimensional prediction rule for preoperative risk assessment.
METHODS: This is a retrospective analysis of prospectively collected data conducted at an orthopedic hospital specialized in spine care. Baseline and outcome data on patients who underwent lumbar spine surgery between July 2012 and December 2022 were included in this study. 105 factors were considered as potential predictive factors for acute refractory pain after lumbar spine surgery. Multivariable logistic lasso regression analysis and multivariable logistic regression analysis were performed to develop a prediction model. Performance measures (ROC-AUC and Nagelkerke's R2) and a prediction rule for clinical use were presented.
RESULTS: Data on 1156 patients was included in this analysis. The prevalence of patients developing acute refractory pain was 19.6% (n = 228/1156). Multivariable logistic lasso regression identified 13 relevant predictive factors for acute refractory pain after lumbar spine surgery: age, prior hospital treatment for back pain, professional sports participation, duration of back pain, back pain before age 45, numeric pain rating scale for back pain, preoperative opioid use, circumferent surgery, instrumented surgery, anxiety, catastrophizing, return-to-work attitude, high score for psychosocial burden of low back pain. Five of these were selected by logistic regression. The discriminative ability of the multivariable logistic lasso regression model was ROC-AUC of 0.69 (95% CI 0.66-0.73), and the multivariable logistic regression model 0.67 (95% CI 0.63-0.71). Nagelkerke's R2 of the logistic regression model was 8.4%.
CONCLUSION: Multidimensional predictive factors for acute refractory pain were identified using a clinical prediction rule. This rule creates a patient profile which can help anesthesiologists to preoperatively determine the individual risk for developing acute refractory pain despite the use of a standardized multimodal analgesic treatment.
METHODS: This is a retrospective analysis of prospectively collected data conducted at an orthopedic hospital specialized in spine care. Baseline and outcome data on patients who underwent lumbar spine surgery between July 2012 and December 2022 were included in this study. 105 factors were considered as potential predictive factors for acute refractory pain after lumbar spine surgery. Multivariable logistic lasso regression analysis and multivariable logistic regression analysis were performed to develop a prediction model. Performance measures (ROC-AUC and Nagelkerke's R2) and a prediction rule for clinical use were presented.
RESULTS: Data on 1156 patients was included in this analysis. The prevalence of patients developing acute refractory pain was 19.6% (n = 228/1156). Multivariable logistic lasso regression identified 13 relevant predictive factors for acute refractory pain after lumbar spine surgery: age, prior hospital treatment for back pain, professional sports participation, duration of back pain, back pain before age 45, numeric pain rating scale for back pain, preoperative opioid use, circumferent surgery, instrumented surgery, anxiety, catastrophizing, return-to-work attitude, high score for psychosocial burden of low back pain. Five of these were selected by logistic regression. The discriminative ability of the multivariable logistic lasso regression model was ROC-AUC of 0.69 (95% CI 0.66-0.73), and the multivariable logistic regression model 0.67 (95% CI 0.63-0.71). Nagelkerke's R2 of the logistic regression model was 8.4%.
CONCLUSION: Multidimensional predictive factors for acute refractory pain were identified using a clinical prediction rule. This rule creates a patient profile which can help anesthesiologists to preoperatively determine the individual risk for developing acute refractory pain despite the use of a standardized multimodal analgesic treatment.