Analgesic effects of ketamine infusion therapy in korean patients with neuropathic pain: A 2-week, open-label, uncontrolled study

Abstract Background Acupuncture is used to treat patients with diabetic peripheral neuropathy; however, the evidence is unclear. We present the design and methodology of the ACUDPN (ACUpuncture in Diabetic Peripheral Neuropathy) trial, which investigates the effectiveness of acupuncture for the treatment of diabetic peripheral neuropathy (DPN) symptoms. The aim of this study is to investigate whether acupuncture is effective for the treatment of DPN symptoms. Methods This study is a two-armed, randomized, controlled, parallel group, open-label, confirmatory, multicenter trial (8-week intervention period plus 16 weeks of follow-up). Physicians in outpatient units in Germany who specialize in acupuncture treatment will treat 110 diabetes type II patients with clinical symptoms of peripheral neuropathy in the feet and legs with signs of neuropathy according to nerve conduction testing. The patients will be randomized in a 1:1 ratio to one of the following two groups: (a) semi-standardized acupuncture plus routine care or (b) routine care alone. Acupuncture will consist of 12 treatments per patient over 8 weeks. The primary outcome will be the overall DPN-related complaints in the extremities after 8 weeks as measured by the Visual Analog Scale (VAS). Further outcome measures will include DPN-related pain, the Neuropathic Pain Symptom Inventory (NPSI), Diabetic Peripheral Neuropathic Pain Impact (DPNPI) scores, and nerve conduction parameters of the sural nerve at weeks 8, 16, and 24. Discussion The results of this trial will be available in 2021 and will help clarify whether acupuncture can be considered effective for the treatment of DPN with regard to the subdimensions of the neuropathic clinical picture. Trial registration ClinicalTrials.gov NCT03755960. Registered on 11 August 2018.

Download Full-text

Differential response to scrambler therapy by neuropathic pain phenotypes

Scientific Reports ◽

10.1038/s41598-021-89667-6 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Young Gi Min ◽

Hyun Seok Baek ◽

Kyoung-Min Lee ◽

Yoon-Ho Hong

Keyword(s):

Neuropathic Pain ◽

Treatment Outcome ◽

Rating Scale ◽

Numerical Rating Scale ◽

Brief Pain Inventory ◽

Differential Response ◽

Open Label ◽

Numerical Rating ◽

Scrambler Therapy ◽

Post Hoc

AbstractScrambler therapy is a noninvasive electroanalgesia technique designed to remodulate the pain system. Despite growing evidence of its efficacy in patients with neuropathic pain, little is known about the clinical factors associated with treatment outcome. We conducted a prospective, open-label, single-arm trial to assess the efficacy and safety of scrambler therapy in patients with chronic neuropathic pain of various etiologies. A post-hoc analysis was performed to investigate whether cluster analysis of the Neuropathic Pain Symptom Inventory (NPSI) profiles could identify a subgroup of patients regarding neuropathic pain phenotype and treatment outcome. Scrambler therapy resulted in a significant decrease in the pain numerical rating scale (NRS) score over 2 weeks of treatment (least squares mean of percentage change from baseline, − 15%; 95% CI − 28% to − 2.4%; p < 0.001). The mean score of Brief Pain Inventory (BPI) interference subdimension was also significantly improved (p = 0.022), while the BPI pain composite score was not. Hierarchical clustering based on the NPSI profiles partitioned the patients into 3 clusters with distinct neuropathic pain phenotypes. Linear mixed-effects model analyses revealed differential response to scrambler therapy across clusters (p = 0.003, pain NRS; p = 0.072, BPI interference subdimension). Treatment response to scrambler therapy appears different depending on the neuropathic pain phenotypes, with more favorable outcomes in patients with preferentially paroxysmal pain rather than persistent pain. Further studies are warranted to confirm that capturing neuropathic pain phenotypes can optimize the use of scrambler therapy.

Download Full-text