| Literature DB >> 32565639 |
Ankush Singla1, Pushpinder Singh2, Mridul Panditrao1, Minnu M Panditrao1.
Abstract
BACKGROUND: Recreational drug abuse is a serious health problem that poses detrimental effects on central nervous system. Neuroimaging plays a pivotal role in the detection of these abnormal changes in the brain associated with the drug abuse. This study focuses on the grading of cerebral atrophy in the opioid-addicted patients and their association with the age and duration of opioid abuse.Entities:
Keywords: Chronic opioid abuse; General cerebral atrophy; Neuroimaging techniques; Probable association
Year: 2020 PMID: 32565639 PMCID: PMC7297238 DOI: 10.5005/jp-journals-10071-23410
Source DB: PubMed Journal: Indian J Crit Care Med ISSN: 0972-5229
Commonly abused opioids with their routes and cost
| Natural opioids (nonprescription opioids) | Oral | Rs. 100/g | |
| Natural opioids (nonprescription opioids) | Oral | Rs. 3000–5000/kg | |
| Smack/ | Synthetic opioids (nonprescription opioids) | Inhalational/intravenous | Rs. 5000/g |
| Buprenorphine | Synthetic opioids (prescription opioids) | Oral | Rs. 7.5–30/tablet |
| Tramadol | Synthetic opioids (prescription opioids) | Oral/intramuscular | Rs. 20/tablet, Rs. 25/injection |
Grading of Pasquier scale
| 0 | Normal volume/no ventricular enlargement |
| I | Opening of sulci/mild ventricular enlargement |
| II | Volume loss of gyri/moderate ventricular enlargement |
| III | Knife blade atrophy/severe ventricular enlargement |
Sociodemographic profile and duration of addiction of study participants
| Age (years) | 16–20 | 4 | 10.0 |
| 21–25 | 5 | 12.5 | |
| 26–30 | 7 | 17.5 | |
| 31–35 | 10 | 25.0 | |
| 36–40 | 14 | 35.0 | |
| Education | Illiterate | 5 | 12.5 |
| Primary | 28 | 70.0 | |
| Secondary and above | 7 | 17.5 | |
| Place of residence | Urban | 7 | 17.5 |
| Rural | 33 | 82.5 | |
| Duration of abuse | 0–4 years | 11 | 27.5 |
| 4–8 years | 13 | 32.5 | |
| 8–12 years | 10 | 25.0 | |
| >12 years | 6 | 15.0 |
Fig. 1Mild cerebral atrophy (MRI FLAIR sequence)
Fig. 3Severe cerebral atrophy (MRI FLAIR sequence)
Fig. 4Scoring of cerebral atrophy
Association of sociodemographic and clinical factors with cerebral atrophy
| Age | 16–20 | 1 | <0.05 |
| 21–25 | 1 | ||
| 26–30 | 4 | ||
| 31–35 | 7 | ||
| 36–40 | 12 | ||
| Education | Illiterate | 3 | >0.05 |
| Primary | 18 | ||
| Secondary and above | 4 | ||
| Place of residence | Urban | 4 | >0.05 |
| Rural | 21 | ||
| Duration of substance abuse | 0–4 years | 4 | <0.05 |
| 4–8 years | 7 | ||
| 8–12 years | 9 | ||
| >12 years | 5 |
Statistically significant
Fig. 5Age group wise cerebral atrophy findings
Fig. 6Duration of abuse wise cerebral atrophy findings
Cross-tabular data depicting the incidence of cerebral atrophy and its correlation with age and duration of abuse
| 1 | 0 | 18 | 2 |
| 2 | 0 | 18 | 2 |
| 3 | 0 | 18 | 2 |
| 4 | 1 | 18 | 6 |
| 5 | 0 | 23 | 2 |
| 6 | 0 | 23 | 2 |
| 7 | 0 | 23 | 2 |
| 8 | 1 | 23 | 6 |
| 9 | 0 | 23 | 10 |
| 10 | 1 | 28 | 2 |
| 11 | 1 | 28 | 6 |
| 12 | 1 | 28 | 6 |
| 13 | 0 | 28 | 6 |
| 14 | 1 | 28 | 10 |
| 15 | 0 | 28 | 10 |
| 16 | 0 | 28 | 14 |
| 17 | 1 | 33 | 2 |
| 18 | 1 | 33 | 2 |
| 19 | 0 | 33 | 2 |
| 20 | 1 | 33 | 6 |
| 21 | 1 | 33 | 6 |
| 22 | 0 | 33 | 6 |
| 23 | 0 | 33 | 6 |
| 24 | 1 | 33 | 10 |
| 25 | 1 | 33 | 10 |
| 26 | 1 | 33 | 14 |
| 27 | 1 | 38 | 2 |
| 28 | 1 | 38 | 6 |
| 29 | 1 | 38 | 6 |
| 30 | 0 | 38 | 6 |
| 31 | 0 | 38 | 6 |
| 32 | 1 | 38 | 10 |
| 33 | 1 | 38 | 10 |
| 34 | 1 | 38 | 10 |
| 35 | 1 | 38 | 10 |
| 36 | 1 | 38 | 10 |
| 37 | 1 | 38 | 14 |
| 38 | 1 | 38 | 14 |
| 39 | 1 | 38 | 14 |
| 40 | 1 | 38 | 14 |
| Sum | 25 | 1245 | 284 |
Multiple regression and ANOVA tests were used to calculate the R and F values which were 0.50 and 0.004, respectively