With high NNTs for indiscriminative use in chronic pain, treatment unavoidably entails frustrating long trial and errors. It is timely to identify biomarkers that can predict analgesic efficacy for the individual patient.
The investigators propose a framework of interrelations between patient's pain modulation profile (PMP) and the drug's mode of action (MOA) based on two principles: (1) 'fix the dysfunction', relevant for drugs whose main mode of action is to modulate central pain processing; the more the dysfunctional the better the modulating drug efficacy. For example, patients with pro-nociceptive PMP due to reduced endogenous pain inhibition, as expressed by less efficient CPM will benefit from drugs that fix this dysfunction such as SNRIs, relative to patients whose pain inhibitory capacity is well functioning. Thus, for the modulating drugs, pro-nociceptivity predicts better efficacy. (2) 'bear with the dysfunction', relevant for drugs which are mostly non-modulating, acting mainly in the periphery; the more dysfunctionalת the less the non-modulating drug efficacy. This is since efficacy is limited by the dysfunctional modulation system, despite the drug's MOA-like reduction of peripheral pain mediators. Thus, for the non-modulating drugs, for example NSAIDs, pro-nociceptivity predicts less good efficacy. The likely protocol suggests that patients with anti-nociceptive PMP should be treated primarily by non-modulating drugs, while pro-nociceptive ones should be given modulating drugs.
EEG is an additional source of relevant data on brain pain processing. Being objective and stable along time, EEG based parameters are, thus, very attractive candidates to be useful biomarkers for prediction of analgesia efficacy.
This study will focus on the patients with painful knee osteoarthritis.
The aims of this study are:
Study design:
The study design includes two experimental meeting sessions (before and at the end / after the treatment) which include clinical and experimental assessments. After the first experimental session, the patients will be asked to rate twice a week their daily pain along two weeks, in order to confirm their OA pain level; the patients with the mean pain score of ≥4 will be supplied with the study medications. Along the 8 weeks-long treatment period, they will provide the rating of OA pain, subjective estimation of pain alleviation and reports of side effects
Clinical assessment: Will be performed by the study physician. The data on OA severity by Kellgren and Lawrence system classification, range of motion and current OA pain (last 48 h) will be collected. In addition, all patients will fill the brief pain inventory questioner (BPI) to assess their pain characteristics. In addition, all patients will be tested for the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) for assessment of OA pain, stiffness and physical function.
Experimental session:
Treatment follow-up:
Phone follow-up will be performed: weekly reports at weeks 1-2 and 5-6; twice a week for weeks 3-4 and 7-8. The patients will provide their OA pain score, rating of the pain-relieving drug effect (0-100 scale) and describe the treatment-related side effects for the period of last 48 hours).
Statistical analysis
The classical statistical analysis will be based on correlations between PMP and degree of drug efficacy, represented by percentage pain reduction. We then construct 3 independent model systems, one for each of the 3 PMP parameters (CPM, TS, and EEG based connectivity). Within each model we first test the two correlations, under the presumed pain modulating and non-modulating drugs, between PMP and drug efficacy. A machine learning-based cross-validation and permutation tests will be used in order to access generalizability and statistical significance of the of the findings.
Condition | Knee, Osteoarthritis (OA), Biomarkers for Prediction of OA Treatment Efficacy |
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Treatment | Etoricoxib 60 mg, Omeprazol, Duloxetine 60mg |
Clinical Study Identifier | NCT05256342 |
Sponsor | Rambam Health Care Campus |
Last Modified on | 11 March 2022 |
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