Study of the trend pattern of COVID-19 using spline-based time series model: a Bayesian paradigm

Real-world effectiveness of dupilumab in patients with asthma: Findings from the US ADVANTAGE study

BACKGROUND

Dupilumab is approved as an add-on maintenance therapy for patients (≥6 years) with moderate-to-severe asthma. Better understanding of real-world effectiveness is needed.

OBJECTIVE

To characterize the real-world effectiveness of dupilumab in asthma management.

METHODS

This retrospective study included patients (≥12 years of age) diagnosed with asthma, initiating dupilumab between November 2018 and September 2020. The study used a US electronic medical record database (TriNetX Dataworks, Cambridge, Massachusetts). Asthma exacerbation rates before and after the initiation of dupilumab were analyzed using generalized estimating equations models with Poisson probabilistic link to estimate incidence rate ratios (IRRs). Sensitivity analyses were conducted based on previous exacerbation data, eosinophil levels, history of atopic dermatitis or chronic rhinosinusitis with nasal polyps, previous use of biologics, and presence of SARS-CoV-2 (COVID-19).

RESULTS

A total of 2400 patients initiating dupilumab met all study criteria. After initiation of dupilumab, risk of asthma exacerbation was reduced by 44% (IRR, 0.56; 95% CI, 0.47-0.57; P = <0.0001) and systemic corticosteroid prescriptions by 48% (IRR, 0.52; 95% CI, 0.48, 0.56; P = <0.0001) compared with those before initiation of dupilumab. Adjustment for COVID-19 showed a greater reduction in asthma exacerbations (IRR, 0.50; 95% CI, 0.45-0.55; P = <0.0001).

CONCLUSION

Current real-world efficacy evidence indicates that dupilumab reduces asthma exacerbations and total systemic corticosteroid prescriptions in clinical practice. The effectiveness of dupilumab was observed independent of exacerbation history, eosinophil levels, or COVID-19 impact.

Monitoring COVID-19 Cases and Vaccination in Indian States and Union Territories Using Unsupervised Machine Learning Algorithm

The worldwide spread of the novel coronavirus originating from Wuhan, China led to an ongoing pandemic as COVID-19. The disease being a contagion transmitted rapidly in India through the people having travel histories to the affected countries, and their contacts that tested positive. Millions of people across all states and union territories (UT) were affected leading to serious respiratory illness and deaths. In the present study, two unsupervised clustering algorithms namely k-means clustering and hierarchical agglomerative clustering are applied on the COVID-19 dataset in order to group the Indian states/UTs based on the pandemic effect and the vaccination program from the period of March, 2020 to early June, 2021. The aim of the study is to observe the plight of each state and UT of India combating the novel coronavirus infection and to monitor their vaccination status. The research study will be helpful to the government and to the frontline workers coping to restrict the transmission of the virus in India. Also, the results of the study will provide a source of information for future research regarding the COVID-19 pandemic in India.