Effectiveness of Combination of Ultrasonography-Guided Pulsed Radiofrequency Neuromodulation With Steroid at the Suprascapular Nerve in Chronic Shoulder Pain.

BACKGROUND: Suprascapular nerve (SSN) block is a useful tool for pain control of different chronic shoulder pain syndromes. If the short-term effect of nerve block using local anesthetics is not sufficient, pulsed radiofrequency (PRF) neuromodulation of the SSN may provide long-term pain relief. AIM OF INVESTIGATION: The aim of this study was to determine the effectiveness of ultrasonography-guided PRF of the SSN for management of chronic shoulder pain. SUBJECTS AND METHOD: Thirty patients with chronic shoulder pain underwent diagnostic ultrasonography-guided SSN block. After confirmation of positive results (>50% pain relief) via diagnostic testing, PRF of the SSN was performed in 27 patients, followed by application of dexamethasone; however, 2 patients had negative results with diagnostic block and 1 patient dropped out after positive results with diagnostic block. Pain was recorded using the VAS, and active range of motion of the affected shoulder was measured by goniometry (flexion, extension, adduction, abduction, external rotation, internal rotation). Assessments were done on day 0 and after 4, 8, 12, and 24 weeks. STATISTICAL TEST: One-way analysis of variance testing. RESULTS: The VAS score decreased significantly (P < 0.05) immediately after injection, and pain reduction was sustained for up to 24 weeks. Active range of motion in all directions also increased significantly (P < 0.5) after the procedure. CONCLUSION: PRF of the SSN under ultrasonography guidance is a safe and effective treatment modality for management of chronic shoulder pain. The effect of a combination of PRF and a short-acting corticosteroid lasts up to 24 weeks, thereby assisting patients in undergoing relatively painless physiotherapy.

Prevalence of chronic pain, impact on daily life, and treatment practices in India.

OBJECTIVE: Chronic pain is of concern to health professionals, patients, society, and negatively impacts quality of life (QoL). The present epidemiologic study identified point prevalence of chronic pain in India, impact on individual's QoL, unveiling current pain treatment practices, and levels of satisfaction with treatment. METHODS: This epidemiological telephonic survey consisted of two questionnaires: screening questionnaire that assessed prevalence of pain, its frequency during the past week, intensity during last episode, sites of pain, and main causes, and in-depth questionnaire that evaluated demography, frequency, duration, and intensity of pain; impact of pain on QoL; respondent's perception regarding the attitude of their family, friends, and doctors toward their pain. RESULTS: A total of 5004 respondents were included from eight cities across India. The overall point prevalence of chronic pain was 13%, and the mean intensity of pain on NRS scale was 6.93. Respondents with chronic moderate and chronic severe pain were 37% and 63%, respectively. Pain in knees (32%), legs (28%), and joints (22%) was most prevalent. Respondents with chronic pain were no longer able to exercise, sleep, maintain relationships with friends and family, and maintain an independent lifestyle. About 32% of patients lost ≥4 hours of work in the past 3 months. Majority (68%) of respondents were treated for pain with over the counter (OTC) drugs, and most were taking NSAIDs (95%). CONCLUSION: A significant population of India suffers from chronic pain, and their QoL is affected leading to disability. A proportion of respondents receiving pain treatment were taking nonprescription medications with a majority of respondents on NSAIDs. A very few were consulting pain management specialists.

Investigation of Paracetamol Entrapped Nanoporous Silica Nanoparticles in Transdermal Drug Delivery System.

An effort was made to administer paracetamol drug through transdermal patch, as no such formulation of this drug has been developed yet. The primary cause for the lack of such formulations is paracetamol's poor aqueous solubility. As a result, the current research concentrated on preparing nanomedicines, or drug-loaded nanoparticles, for delivery via transdermal formulations. Nanoparticles can improve the solubility of weakly aqueous soluble or even aqueous insoluble drugs by changing the crystalline structure of loaded medicines to an amorphous state and serving as drug permeation boosters. Silica nanoparticles (SNPs) were synthesized through sol-gel technique to achieve the aforementioned goal. DLS data revealed that the average particle size was around 100-200 nm, which was sufficient to penetrate the skin barrier. XRD analysis showed that the SNPs were amorphous, and the drug molecules lost their crystallinity after encapsulation into the nanoparticles, causing the enhancement of dissolution of drug molecules in physiological pH (pH-7.4). Different kinetic models were employed for the ex vivo dissolution data to evaluate the suitable kinetic model followed by the drug release in both burst and sustained phase. In vivo analgesic study was executed on mice applying each of the transdermal formulations to examine the performances of the patches.

Reliability and clinical utility of the chronic pain classification in the 11th Revision of the International Classification of Diseases from a global perspective: results from India, Cuba, and New Zealand.

ABSTRACT: Chronic pain affects 1 in 5 persons and contributes substantially to the global burden of disease. The 11th Revision of the International Classification of Diseases (ICD-11) includes a comprehensive classification of chronic pain. The aim of this ecological implementation field study was to evaluate the classification's interrater reliability and clinical utility in countries with different income levels. The study was conducted in 4 pain clinics in Cuba, India, and New Zealand. Twenty-one clinicians used the ICD-11 to diagnose and code n = 353 patients with chronic pain. Of these, 111 were assessed by 2 clinicians, and Fleiss' kappa was calculated to establish interrater reliability for any diagnosis assigned to ≥15 patients. The clinicians rated the clinical utility of all diagnoses. The interrater reliability could be calculated for 11 diagnoses. It was substantial for 10 diagnoses and moderate for 1 (kappa: 0.596-0.783). The mean clinical utility of the ICD-11 chronic pain diagnoses was rated as 8.45 ± 1.69/10. Clinical utility was rated higher for ICD-11 than for the commonly used classification systems (P < 0.001, η2 = 0.25) and differed between all centers (P < 0.001, η2 = 0.60). The utility of the ICD-11 diagnoses was rated higher than the commonly used diagnoses in Dunedin and Havana, and no difference was found in Kolkata and Hyderabad. The study showed the high interrater reliability of the new chronic pain diagnoses. The perceived clinical utility of the diagnoses indicates their superiority or equality compared with the classification systems currently used in pain clinics. These results suggest the global applicability of the classification in specialized pain treatment settings.

Low back pain expert systems: Clinical resolution through probabilistic considerations and poset.

OBJECTIVE: Proper diagnosis of Low Back Pain (LBP) is quite challenging in especially the developing countries like India. Though some developed countries prepared guidelines for evaluation of LBP with tests to detect psychological overlay, implementation of the recommendations becomes quite difficult in regular clinical practice, and different specialties of medicine offer different modes of management. Aiming at offering an expert-level diagnosis for the patients having LBP, this paper uses Artificial Intelligence (AI) to derive a clinically justified and highly sensitive LBP resolution technique. MATERIALS AND METHODS: The paper considers exhaustive knowledge for different LBP disorders (classified based on different pain generators), which have been represented using lattice structures to ensure completeness, non-redundancy, and optimality in the design of knowledge base. Further the representational enhancement of the knowledge has been done through construction of a hierarchical network, called RuleNet, using the concept of partially-ordered set (poset) with respect to the subset equality (⊆) relation. With implicit incorporation of probability within the knowledge, the RuleNet is used to derive reliable resolution logic along with effective resolution of uncertainties during clinical decision making. RESULTS: The proposed methodology has been validated with clinical records of seventy seven LBP patients accessed from the database of ESI Hospital Sealdah, India over a period of one year from 2018 to 2019. Achieving 83% sensitivity of the proposed technique, the pain experts at the hospital find the design clinically satisfactory. The inferred outcomes have also been found to be homogeneous with the actual or original diagnosis. DISCUSSIONS: The proposed approach achieves the clinical and computational efficiency by limiting the shortcomings of the existing methodologies for AI-based LBP diagnosis. While computational efficiency (with respect to both time and space complexity) is ensured by inferring clinical decisions through optimal processing of the knowledge items using poset, the clinical acceptability has been ascertained reaching to the most-likely diagnostic outcomes through probabilistic resolution of clinical uncertainties. CONCLUSION: The derived resolution technique, when embedded in LBP medical expert systems, would provide a fast, reliable, and affordable healthcare solution for this ailment to a wider range of general population suffering from LBP. The proposed scheme would significantly reduce the controversies and confusion in LBP treatment, and cut down the cost of unnecessary or inappropriate treatment and referral.

Medical expert system for low back pain management: design issues and conflict resolution with Bayesian network.

The paper focuses on the development of a reliable medical expert system for diagnosis of low back pain (LBP) by proposing an efficient frame-based knowledge representation scheme and a suitable resolution logic with conflicts in outcomes being resolved using Bayesian network. Considering that LBP is classified into many diseases based on different pain generators, the proposed methodology infers non-conflicting LBP diseases sorted according to their chances of occurrence. A satisfactory clinical efficacy (average relative error - 0.09, recall 74.44%, precision 76.67%, accuracy 71.11%, and F1-score 73.88%) of the proposed methodology has been found after validating the design with empirically selected thirty LBP patient cases. Constraining that an inferred disease having chance of occurrence, prior to pathological investigations, below 0.75 (as set by four pain specialists) is not accepted clinically; the design can correctly identify, on average, 74.44% of actual diagnosis; and 76.67% of inferred diagnosis is included in actual diagnosis. With the predicted chance of occurrence being lower than 0.75 by a fraction of 0.09 on average, the proposed design performs well for 73.88% cases detecting 71.11% inferred outcomes as accurate. The design offers homogeneity to the actual outcomes, with the chi-squared static being calculated as 11.08 having 12 as degree of freedom. Graphical abstract.