Качественная клиническая практика (Sep 2020)
Analysis of the long-term effectiveness of a drug in the treatment of a chronic disease is the basis of rational pharmacotherapy (on the example of the use of roflumilast in the treatment of chronic obstructive pulmonary disease)
Abstract
The aim of the study consisted in assessing the long-term effectiveness of the drug in the treatment of chronic disease based on Markov modeling (on the example of the use of roflumilast in the treatment of chronic obstructive pulmonary disease).Materials and methods. The data on the dependence of exacerbations on the stage of COPD from the ECLIPSE study (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints) were used as materials for the study. The simulation was carried out using MO Excel software based on the Markov model. The input data were 1 000 patients with COPD II stage. The time horizon was 10 years. The Markov cycle was 1 year. Additional patients were not included in the model and did not drop out during the simulation.Results. The number of patients with stage IV for the last modeling cycle (10 years) will be 265 patients with the addition of roflumilast to COPD, then without roflumilast, this figure will be 296 patients. The addition of roflumilast to COPD therapy slows the transition to stage IV COPD by 10.5 % for 10 years.The conclusion. The Markov model of the transition of patients suffering from COPD through stages of the disease characterizes the course of the disease on the time horizon at 10 years. Of the model group of 1000 patients (suffering from stage II COPD), only 48.4 % of patients remain in this stage at the end of the simulation. On the last cycle of the modeling time horizon, in stage III there are 22.1% of the initial cohort of patients, in stage IV — 29.6 %. ttese data reflect actual clinical practice. When modeling using the frequencies of transitions between states obtained during studies in which the efficacy of adding roflumilast, inhibitor of PDE-4 to treatment regimens was studied, modeling slowed down the progression of the disease.
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