Stratifying patients with peripheral neuropathic pain based on sensory profiles : algorithm and sample size recommendations

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http://hdl.handle.net/10138/222893

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Vollert , J , Maier , C , Attal , N , Bennett , D L H , Bouhassira , D , Enax-Krumova , E K , Finnerup , N B , Freynhagen , R , Gierthmuehlen , J , Haanpaa , M , Hansson , P , Hullemann , P , Jensen , T S , Magerl , W , Ramirez , J D , Rice , A S C , Schuh-Hofer , S , Segerdahl , M , Serra , J , Shillo , P R , Sindrup , S , Tesfaye , S , Themistocleous , A C , Toelle , T R , Treede , R-D & Baron , R J 2017 , ' Stratifying patients with peripheral neuropathic pain based on sensory profiles : algorithm and sample size recommendations ' , Pain , vol. 158 , no. 8 , pp. 1446-1455 . https://doi.org/10.1097/j.pain.0000000000000935

Title: Stratifying patients with peripheral neuropathic pain based on sensory profiles : algorithm and sample size recommendations
Author: Vollert, Jan; Maier, Christoph; Attal, Nadine; Bennett, David L. H.; Bouhassira, Didier; Enax-Krumova, Elena K.; Finnerup, Nanna B.; Freynhagen, Rainer; Gierthmuehlen, Janne; Haanpaa, Maija; Hansson, Per; Hullemann, Philipp; Jensen, Troels S.; Magerl, Walter; Ramirez, Juan D.; Rice, Andrew S. C.; Schuh-Hofer, Sigrid; Segerdahl, Marta; Serra, Jordi; Shillo, Pallai R.; Sindrup, Soeren; Tesfaye, Solomon; Themistocleous, Andreas C.; Toelle, Thomas R.; Treede, Rolf-Detlef; Baron, Ralf j
Contributor: University of Helsinki, Clinicum
Date: 2017-08
Language: eng
Number of pages: 10
Belongs to series: Pain
ISSN: 0304-3959
URI: http://hdl.handle.net/10138/222893
Abstract: In a recent cluster analysis, it has been shown that patients with peripheral neuropathic pain can be grouped into 3 sensory phenotypes based on quantitative sensory testing profiles, which are mainly characterized by either sensory loss, intact sensory function and mild thermal hyperalgesia and/or allodynia, or loss of thermal detection and mild mechanical hyperalgesia and/or allodynia. Here, we present an algorithm for allocation of individual patients to these subgroups. The algorithm is nondeterministic-ie, a patient can be sorted to more than one phenotype-and can separate patients with neuropathic pain from healthy subjects (sensitivity: 78%, specificity: 94%). We evaluated the frequency of each phenotype in a population of patients with painful diabetic polyneuropathy (n = 151), painful peripheral nerve injury (n = 335), and postherpetic neuralgia (n = 97) and propose sample sizes of study populations that need to be screened to reach a subpopulation large enough to conduct a phenotype-stratified study. The most common phenotype in diabetic polyneuropathy was sensory loss (83%), followed by mechanical hyperalgesia (75%) and thermal hyperalgesia (34%, note that percentages are overlapping and not additive). In peripheral nerve injury, frequencies were 37%, 59%, and 50%, and in postherpetic neuralgia, frequencies were 31%, 63%, and 46%. For parallel study design, either the estimated effect size of the treatment needs to be high (> 0.7) or only phenotypes that are frequent in the clinical entity under study can realistically be performed. For crossover design, populations under 200 patients screened are sufficient for all phenotypes and clinical entities with a minimum estimated treatment effect size of 0.5.
Subject: Quantitative sensory testing
German Research Network on Neuropathic Pain
Diabetic polyneuropathy
Peripheral nerve injury
Postherpetic neuralgia
PLACEBO-CONTROLLED TRIAL
GERMAN RESEARCH NETWORK
POSTHERPETIC NEURALGIA
DOUBLE-BLIND
CLINICAL-TRIALS
EVALUATION TOOL
GRADING SYSTEM
PHENOTYPE
MECHANISMS
LIDOCAINE
3112 Neurosciences
3124 Neurology and psychiatry
3126 Surgery, anesthesiology, intensive care, radiology
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