Correlations Between Family History of Psychiatric Illnesses and Outcomes of Spinal Cord Stimulation

Sheldon, Breanna L; Khazen, Olga; Feustel, Paul J.; Gechtman, Guy; Rosoklija, Gavril; Patel, Shrey; DiMarzio, Marisa; Bridger, Cheyanne; Dentinger, Rachel Mason; Slyer, Julia; Pilitsis, Julie G.

doi:10.1111/ner.13162

Cited by 7 publications

(2 citation statements)

References 31 publications

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“…Currently, patient selection for SCS is based on the subjective experience of the implanting physician. 4 Here, we address previous limitations by using the largest single-center database of prospectively collected longitudinal SCS outcomes [5][6][7][8] and applying a combination of unsupervised clustering and supervised classification to obtain individualized models for each subgroup/cluster of patients. Specifically, we use machine learning (ML) techniques for developing and internally validating predictive models for long-term SCS response.…”

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confidence: 99%

Development of Machine Learning–Based Models to Predict Treatment Response to Spinal Cord Stimulation

et al. 2022

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BACKGROUND:Despite spinal cord stimulation's (SCS) proven efficacy, failure rates are high with no clear understanding of which patients benefit long term. Currently, patient selection for SCS is based on the subjective experience of the implanting physician.OBJECTIVE:To develop machine learning (ML)–based predictive models of long-term SCS response.METHODS:A combined unsupervised (clustering) and supervised (classification) ML technique was applied on a prospectively collected cohort of 151 patients, which included 31 features. Clusters identified using unsupervised K-means clustering were fitted with individualized predictive models of logistic regression, random forest, and XGBoost.RESULTS:Two distinct clusters were found, and patients in the cohorts significantly differed in age, duration of chronic pain, preoperative numeric rating scale, and preoperative pain catastrophizing scale scores. Using the 10 most influential features, logistic regression predictive models with a nested cross-validation demonstrated the highest overall performance with the area under the curve of 0.757 and 0.708 for each respective cluster.CONCLUSION:This combined unsupervised–supervised learning approach yielded high predictive performance, suggesting that advanced ML-derived approaches have potential to be used as a functional clinical tool to improve long-term SCS outcomes. Further studies are needed for optimization and external validation of these models.

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Development of Machine Learning–Based Models to Predict Treatment Response to Spinal Cord Stimulation

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“…In essence, pain is a subjective, conscious perception separable from pure nociception. Depression and even suicide is not uncommon in patients suffering from CNP, and factors of depression, loneliness, and anxiety have been associated with poorer outcomes following SCS [ 75 , 76 , 77 , 78 ]. However, these cognitive components also constitute unique avenues for pain management that can be applied alone or in addition to standard treatments to enhance recovery.…”

Section: Cognitive Affective and Emotional Approachesmentioning

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Review of the Treatments for Central Neuropathic Pain

et al. 2022

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Central neuropathic pain (CNP) affects millions worldwide, with an estimated prevalence of around 10% globally. Although there are a wide variety of treatment options available, due to the complex and multidimensional nature in which CNP arises and presents symptomatically, many patients still experience painful symptoms. Pharmaceutical, surgical, non-invasive, cognitive and combination treatment options offer a generalized starting point for alleviating symptoms; however, a more customized approach may provide greater benefit. Here, we comment on the current treatment options that exist for CNP and further suggest the need for additional research regarding the use of biomarkers to help individualize treatment options for patients.

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