RESUMO
AIMS: Therapeutic drug monitoring of infliximab can guide clinical decisions in patients with loss of response and in those who can benefit from a de-intensification. The aim of this study was to determine the impact of therapeutic drug monitoring combined with Bayesian forecasting methodology on clinical response in a real-world dataset of patients suffering from inflammatory bowel disease. METHODS: We performed a single-centre prospective study with one-group pre-test/post-test design in 108 adult inflammatory bowel disease patients treated with model-based dosing of infliximab maintenance treatment. We recorded clinical activity scores (Harvey-Bradshaw index and partial Mayo) and inflammatory biomarkers per patient. RESULTS: The initial infliximab regimen was maintained in 49 (45.4%) patients and was adjusted in 59 (54.6%) patients (34 treatment intensifications, 9 de-intensifications and 16 treatment discontinuations or therapy replacements). The median time from intervention to index measurement was 126 (103-160) days. The overall proportion of patients in clinical remission increased from 65.7% to 80.4% (P < .0001) and the median infliximab trough concentrations increased from 3.21 (0.99-5.45) to 5.13 mg/L (3.57-6.53) (P < .0001). In the intensified group, the remission rate increased from 35.3% to 61.8% (P = .001) and the percentage of patients in clinical remission or with mild symptoms increased from 76.5% to 94.1%. In the de-intensification cohort, no patients experienced an increase in the Harvey-Bradshaw index or partial Mayo scores, and all patients maintained an infliximab trough concentration of >5 mg/L. CONCLUSION: In our cohort of inflammatory bowel disease patients, Bayes-based optimized dosing improved the short-term efficacy of infliximab treatment.
Assuntos
Fármacos Gastrointestinais , Doenças Inflamatórias Intestinais , Adulto , Teorema de Bayes , Monitoramento de Medicamentos , Humanos , Doenças Inflamatórias Intestinais/tratamento farmacológico , Infliximab , Estudos ProspectivosRESUMO
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