The Reasons for Higher Mortality Rate in Opium Addicted Patients with COVID-19: A Narrative Review

Association between opioid abuse and COVID-19 susceptibility: a propensity score matched study

BACKGROUND

Opioid use disorder (OUD) has been associated with adverse health outcomes, and its potential impact on COVID-19 outcomes is of significant concern. This study aimed to assess the susceptibility and clinical outcomes of hospitalized COVID-19 patients with OUD using a propensity score-matched design.

METHODS

A historical cohort study was conducted in Alborz province, Iran, during the early months of the COVID-19 pandemic. Patients aged 18 years and above with confirmed COVID-19 were included in the study. OUD was defined as a compulsive urge to use opioids or opioid-derivative drugs. Non-opioid abusers with COVID-19 were selected as the control group. Data on demographics, clinical characteristics, laboratory factors, comorbidities, and vital signs were collected. Propensity score matching (PSM) was used to balance the groups and assess the impact of OUD on ICU admission, mortality, the need for intubation, and the severity of pulmonary involvement on CT scans.

RESULTS

A total of 442 patients were included in the study, with 351 discharged and 34 deceased. The PSM analysis showed that OUD was not significantly associated with ICU admission (OR: 1.87, 95% CI: 0.22-2.91, p = 0.631). However, opium users had an increased risk of mortality (OR: 2.38, 95% CI: 1.30-4.35, p = 0.005) and a higher likelihood of requiring intubation (OR: 3.57, 95% CI: 1.38-9.39, p = 0.009) compared to non-opioid abusers. The severity of pulmonary involvement on CT scans did not show a significant association with OUD.

CONCLUSION

OUD among hospitalized COVID-19 patients was associated with an increased risk of mortality and the need for intubation. These findings highlight the importance of addressing OUD as a potential risk factor in the management and treatment of COVID-19 patients. Further research is warranted to explore the underlying mechanisms and develop appropriate interventions to mitigate the impact of OUD on COVID-19 outcomes.

Predicting the necessity of oxygen therapy in the early stage of COVID-19 using machine learning

Medical oxygen is a critical element in the treatment process of COVID-19 patients which its shortage impacts the treatment process adversely. This study aims to apply machine learning (ML) to predict the requirement for oxygen-based treatment for hospitalized COVID-19 patients. In the first phase, demographic information, symptoms, and patient’s background were extracted from the databases of two local hospitals in Iran, and preprocessing actions were applied. In the second step, the related features were selected. Lastly, five ML models including logistic regression (LR), random forest (RF), XGBoost, C5.0, and neural networks (NNs) were implemented and compared based on their accuracy and capability. Among the variables related to the patient’s background, consuming opium due to the high rate of opium users in Iran was considered in the models. Of the 398 patients included in the study, 112 (28.14%) received oxygen-based treatment. Shortness of breath (71.42%), fever (62.5%), and cough (59.82%) had the highest frequency in patients with oxygen requirements. The most important variables for prediction were shortness of breath, cough, age, and fever. For opioid-addicted patients, in addition to the high mortality rate (23.07%), the rate of oxygen-based treatment was twice as high as non-addicted patients. XGBoost and LR obtained the highest area under the curve with values of 88.7% and 88.3%, respectively. For accuracy, LR and NNs achieved the best and same accuracy (86.42%). This approach provides a tool that accurately predicts the need for oxygen in the treatment process of COVID-19 patients and helps hospital resource management.

Drugs of Abuse and Their Impact on Viral Pathogenesis

Commonly misused substances such as alcohol, cocaine, heroin, methamphetamine, and opioids suppress immune responses and may impact viral pathogenesis. In recent years, illicit use of opioids has fueled outbreaks of several viral pathogens, including the human immunodeficiency virus (HIV), hepatitis B virus (HBV), and hepatitis C virus (HCV). This review focuses on the myriad of mechanisms by which drugs of abuse impact viral replication and disease progression. Virus–drug interactions can accelerate viral disease progression and lead to increased risk of virus transmission.