Sex differences in variables affecting short-term success in substance use disorder treatment.

Because of the stigma surrounding patients with substance use disorder (SUD) and difficulties with follow-up, data on outcomes is limited. We explore real-world data from a prospectively collected database to determine characteristics that contribute to the completion of acute treatment. Our cohort consisted of data from 1039 patients treated at a single facility. Success was defined as successful discharge from the program. Failure was defined as relapse or signing out against medical advice during treatment. We examined 43 distinct features collected at time of treatment using multivariate analysis. In the total cohort and both sexes, longer length of stay (p ≤ 0.01) was linked to treatment failure. When we examined the cohort by sex, variables associated with success and failure differed between groups. Among females, goal-directed thinking (p ≤ 0.05) correlated with treatment success. Taking unnecessary risks (p < 0.05), having a detailed suicide plan (p ≤ 0.001), and constricted thinking (p ≤ 0.01) predicted treatment failure. In males, prior arrest for driving under the influence (p ≤ 0.05), and presence of phobias, paranoias, and delusions (p ≤ 0.05) were associated with treatment failure. Identifying patients prone to acute therapy failure may guide more personalized treatment, thereby increasing success rates. When considering SUD treatments for patients, we must stratify based on patient characteristics.

Machine Learning in Spinal Cord Stimulation for Chronic Pain.

Spinal cord stimulation (SCS) is an effective treatment for chronic neuropathic pain. The success of SCS is dependent on candidate selection, response to trialing, and programming optimization. Owing to the subjective nature of these variables, machine learning (ML) offers a powerful tool to augment these processes. Here we explore what work has been done using data analytics and applications of ML in SCS. In addition, we discuss aspects of SCS which have narrowly been influenced by ML and propose the need for further exploration. ML has demonstrated a potential to complement SCS to an extent ranging from assistance with candidate selection to replacing invasive and costly aspects of the surgery. The clinical application of ML in SCS shows promise for improving patient outcomes, reducing costs of treatment, limiting invasiveness, and resulting in a better quality of life for the patient.