Clinical effectiveness of nirmatrelvir plus ritonavir in patients with COVID-19 and substance use disorders based on real-world data.

This study assessed the clinical efficacy of nirmatrelvir plus ritonavir (NMV-r) in treating patients with coronavirus disease-2019 (COVID-19) and substance use disorders (SUDs). This study included two cohorts: the first examined patients with SUDs, with and without a prescription for NMV-r, while the second compared patients prescribed with NMV-r, with and without a diagnosis of SUDs. SUDs were defined using ICD-10 codes, related to SUDs, including alcohol, cannabis, cocaine, opioid, and tobacco use disorders (TUD). Patients with underlying SUDs and COVID-19 were identified using the TriNetX network. We used 1:1 propensity score matching to create balanced groups. The primary outcome of interest was the composite outcome of all-cause hospitalization or death within 30 days. Propensity score matching yielded two matched groups of 10 601 patients each. The results showed that the use of NMV-r was associated with a lower risk of hospitalization or death, 30 days after COVID-19 diagnosis (hazard ratio (HR), 0.640; 95% confidence interval (CI): 0.543-0.754), as well as a lower risk of all-cause hospitalization (HR, 0.699; 95% CI: 0.592-0.826) and all-cause death (HR, 0.084; 95% CI: 0.026-0.273). However, patients with SUDs had a higher risk of hospitalized or death within 30 days of COVID-19 diagnosis than those without SUDs, even with the use of NMV-r (HR, 1.783; 95% CI: 1.399-2.271). The study also found that patients with SUDs had a higher prevalence of comorbidities and adverse socioeconomic determinants of health than those without SUDs. Subgroup analysis showed that the benefits of NMV-r were consistent across most subgroups with different characteristics, including age (patients aged ≥60 years [HR, 0.507; 95% CI: 0.402-0.640]), sex (women [HR, 0.636; 95% CI: 0.517-0.783] and men [HR, 0.480; 95% CI: 0.373-0.618]), vaccine status (vaccinated <2 doses [HR, 0.514; 95% CI: 0.435-0.608]), SUD subtypes (alcohol use disorder [HR, 0.711; 95% CI: 0.511- 0.988], TUD [HR, 0.666; 95% CI: 0.555-0.800]) and Omicron wave (HR, 0.624; 95% CI: 0.536-0.726). Our findings indicate that NMV-r could reduce all-cause hospitalization and death in the treatment of COVID-19 among patients with SUDs and support the use of NMV-r for treating patients with SUDs and COVID-19.

Determinants of coronavirus disease 2019 infection by artificial intelligence technology: A study of 28 countries.

OBJECTIVES: The coronavirus disease 2019 pandemic has affected countries around the world since 2020, and an increasing number of people are being infected. The purpose of this research was to use big data and artificial intelligence technology to find key factors associated with the coronavirus disease 2019 infection. The results can be used as a reference for disease prevention in practice. METHODS: This study obtained data from the "Imperial College London YouGov Covid-19 Behaviour Tracker Open Data Hub", covering a total of 291,780 questionnaire results from 28 countries (April 1~August 31, 2020). Data included basic characteristics, lifestyle habits, disease history, and symptoms of each subject. Four types of machine learning classification models were used, including logistic regression, random forest, support vector machine, and artificial neural network, to build prediction modules. The performance of each module is presented as the area under the receiver operating characteristics curve. Then, this study further processed important factors selected by each module to obtain an overall ranking of determinants. RESULTS: This study found that the area under the receiver operating characteristics curve of the prediction modules established by the four machine learning methods were all >0.95, and the RF had the highest performance (area under the receiver operating characteristics curve is 0.988). Top ten factors associated with the coronavirus disease 2019 infection were identified in order of importance: whether the family had been tested, having no symptoms, loss of smell, loss of taste, a history of epilepsy, acquired immune deficiency syndrome, cystic fibrosis, sleeping alone, country, and the number of times leaving home in a day. CONCLUSIONS: This study used big data from 28 countries and artificial intelligence methods to determine the predictors of the coronavirus disease 2019 infection. The findings provide important insights for the coronavirus disease 2019 infection prevention strategies.