Development and validation of a prediction tool for pain reduction in adult patients undergoing elective lumbar spinal fusion: a multicentre cohort study

Abstract

Purpose On average, 56% of patients report a clinically relevant reduction in pain after lumbar spinal fusion (LSF). Preoperatively identifying which patient will benefit from LSF is paramount to improve clinical decision making, expectation management and treatment selection. Therefore, this multicentre study aimed to develop and validate a clinical prediction tool for a clinically relevant reduction in pain 1 to 2 years after elective LSF. Methods The outcomes were defined as a clinically relevant reduction in predominant (worst reported pain in back or legs) pain 1 to 2 years after LSF. Patient-reported outcome measures and patient characteristics from 202 patients were used to develop a prediction model by logistic regression. Data from 251 patients were used to validate the model. Results Nonsmokers (odds ratio = 0.41 [95% confidence interval = 0.19–0.87]), with lower Body Mass Index (0.93 [0.85–1.01]), shorter pain duration (0.49 [0.20–1.19]), lower American Society of Anaesthesiologists score (4.82 [1.35–17.25]), higher Visual Analogue Scale score for predominant pain (1.05 [1.02–1.08]), lower Oswestry Disability Index (0.96 [0.93–1.00]) and higher RAND-36 mental component score (1.03 [0.10–1.06]) preoperatively had a higher chance of a clinically relevant reduction in predominant pain. The area under the curve of the externally validated model yielded 0.68. A nomogram was developed to aid clinical decision making. Conclusions Using the developed nomogram surgeons can estimate the probability of achieving a clinically relevant pain reduction 1 to 2 years after LSF and consequently inform patients on expected outcomes when considering treatment.

Prediction of outcome after spinal surgery—using The Dialogue Support based on the Swedish national quality register

“…For satisfaction, it ranged from 0.60 (DDD) to 0.66 (LDH). Other reports with PROMs as outcome measure and logistic regression as analytic method describe AUC values and c-indexes ranging from 0.64 to 0.79, mostly tested on singlecentre cohorts [5,6,9,11,14].…”

“…org Patient-centred outcome prediction is a growing focus in spine research, producing several reports annually. The majority discuss prediction in terms of Patient-Reported Outcome Measures (PROMs) [5][6][7][8][9][10][11][12][13][14][15], a few deals with adverse events [16,17], length of stay [18], revision surgery [19] or return to work [20]. Among the PROM analyses, the outcome measure is usually dichotomized.…”

“…Among the PROM analyses, the outcome measure is usually dichotomized. The predictive modelling differs, but the most frequently used is based on multivariate logistic regression algorithms [5,6,9,11,13,14]. In recent years, machine learning has gained interest as an alternative [9,12,21,22].…”

FUSE-ML: development and external validation of a clinical prediction model for mid-term outcomes after lumbar spinal fusion for degenerative disease

“…The hopes of being able to predict the effects of fusion surgery more robustly by generating "objective" risk-benefit profiles for each individual patient have not been fulfilled to date [26]. Janssen et al [27] achieved an externally validated AUC of 0.68 for prediction of MCID in the predominant pain complaint using a nomogram. Apart from this nomogram, to our best knowledge, the only other externally validated prediction tools that predict pain and functional outcomes for this population are the prediction models of Khor et al [14].…”

Exploring clinically relevant risk profiles in patients undergoing lumbar spinal fusion: a cohort study

“…In clinical practice, patients eligible for LSF are often divided into subgroups by their medical diagnosis, in order to estimate the success probability after surgery [9]. Previous research showed various risk factors are also important to consider when estimating a person's postoperative outcomes after LSF: male gender, young age, working people, non-smokers and high income are predictors of a good outcome [10,11]. However, relying on these variables alone to categorize patients into risk profiles foregoes other important information related to postoperative success chance, as the population undergoing LSF is rather heterogeneous.…”