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Papers of the Week

Papers: 16 Feb 2019 - 22 Feb 2019

2019 Sep

J Pain



Systematic review and neural network analysis to define predictive variables in implantable motor cortex stimulation to treat chronic intractable pain.


Henssen DJHA, Witkam RL, Dao JCML, Comes DJ, van Walsum V CAM, Kozicz T, van Dongen R, Vissers K, Bartels RHMA, de Jong G, Kurt E
J Pain. 2019 Sep; 20(9):1015-1026.
PMID: 30771593.


Implantable Motor Cortex Stimulation (iMCS) has been performed for over 25 years to treat various intractable pain syndromes. Its effectiveness shows to be highly variable and although various studies revealed predictive variables, none of these were found repeatedly. This study uses neural network analysis (NNA) to identify predictive factors of iMCS treatment of intractable pain. A systematic review provided a database of patient data on an individual level of patients who underwent iMCS to treat refractory pain between 1991 and 2017. Responders were defined as patients with a pain relief >40% as measured by numerical rating scale (NRS) score. NNA was carried out to predict outcome of iMCS and to identify predictive factors that impacted the outcome of iMCS. The outcome prediction value of the NNA was expressed as mean accuracy, sensitivity and specificity. The NNA furthermore provided the mean weight of predictive variables, which shows the impact of the predictive variable on the prediction. The mean weight was converted into the mean relative influence (M), a value that varies between 0-100%. A total of 358 patients were included (202 males (56.4%); mean age: 54.2 ±13.3), 201 of which were responders to iMCS. NNA had a mean accuracy of 66.3% and a sensitivity and specificity of 69.8% and 69.4%, respectively. NNA further identified six predictive variables that had a relatively high M: 1) the sex of the patients (M=19.7%); 2) the origin of the lesion (M=15.1%); 3) the preoperative NRS score (M= 9.2%); 4) preoperative use of rTMS (M=7.3%); 5) preoperative intake of opioids (M=7.1%) and; 6) the follow-up period (M= 13.1%). The results from the present study show that these six predictive variables influence the outcome of iMCS and that based on these variables, a fair prediction model can be built to predict outcome after iMCS surgery. PERSPECTIVE: The presented neural network analysis (NNA) analyzed the functioning of computational models and modeled non-linear statistical data. Based on this NNA, six predictive variables were identified which are suggested to be of importance in the improvement of future implantable motor cortex stimulation (iMCS) to treat chronic pain.