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- Heyckendorf, J, et al.
(författare)
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Prediction of anti-tuberculosis treatment duration based on a 22-gene transcriptomic model
- 2021
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Ingår i: The European respiratory journal. - : European Respiratory Society (ERS). - 1399-3003 .- 0903-1936. ; 58:3
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Tidskriftsartikel (refereegranskat)abstract
- The World Health Organization recommends standardised treatment durations for patients with tuberculosis (TB). We identified and validated a host-RNA signature as a biomarker for individualised therapy durations for patients with drug-susceptible (DS)- and multidrug-resistant (MDR)-TB.MethodsAdult patients with pulmonary TB were prospectively enrolled into five independent cohorts in Germany and Romania. Clinical and microbiological data and whole blood for RNA transcriptomic analysis were collected at pre-defined time points throughout therapy. Treatment outcomes were ascertained by TBnet criteria (6-month culture status/1-year follow-up). A whole-blood RNA therapy-end model was developed in a multistep process involving a machine-learning algorithm to identify hypothetical individual end-of-treatment time points.Results50 patients with DS-TB and 30 patients with MDR-TB were recruited in the German identification cohorts (DS-GIC and MDR-GIC, respectively); 28 patients with DS-TB and 32 patients with MDR-TB in the German validation cohorts (DS-GVC and MDR-GVC, respectively); and 52 patients with MDR-TB in the Romanian validation cohort (MDR-RVC). A 22-gene RNA model (TB22) that defined cure-associated end-of-therapy time points was derived from the DS- and MDR-GIC data. The TB22 model was superior to other published signatures to accurately predict clinical outcomes for patients in the DS-GVC (area under the curve 0.94, 95% CI 0.9–0.98) and suggests that cure may be achieved with shorter treatment durations for TB patients in the MDR-GIC (mean reduction 218.0 days, 34.2%; p<0.001), the MDR-GVC (mean reduction 211.0 days, 32.9%; p<0.001) and the MDR-RVC (mean reduction of 161.0 days, 23.4%; p=0.001).ConclusionBiomarker-guided management may substantially shorten the duration of therapy for many patients with MDR-TB.
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| 2. |
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| 3. |
- Heyckendorf, J, et al.
(författare)
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Treatment responses in multidrug-resistant tuberculosis in Germany
- 2018
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Ingår i: The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease. - : International Union Against Tuberculosis and Lung Disease. - 1815-7920. ; 22:4, s. 399-
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Tidskriftsartikel (refereegranskat)
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| 6. |
- Salzer, HJF, et al.
(författare)
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Personalized Medicine for Chronic Respiratory Infectious Diseases: Tuberculosis, Nontuberculous Mycobacterial Pulmonary Diseases, and Chronic Pulmonary Aspergillosis
- 2016
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Ingår i: Respiration. - : S. Karger AG. - 1423-0356 .- 0025-7931. ; 92:4, s. 199-214
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Tidskriftsartikel (refereegranskat)abstract
- Chronic respiratory infectious diseases are causing high rates of morbidity and mortality worldwide. Tuberculosis, a major cause of chronic pulmonary infection, is currently responsible for approximately 1.5 million deaths per year. Although important advances in the fight against tuberculosis have been made, the progress towards eradication of this disease is being challenged by the dramatic increase in multidrug-resistant bacilli. Nontuberculous mycobacteria causing pulmonary disease and chronic pulmonary aspergillosis are emerging infectious diseases. In contrast to other infectious diseases, chronic respiratory infections share the trait of having highly variable treatment outcomes despite longstanding antimicrobial therapy. Recent scientific progress indicates that medicine is presently at a transition stage from programmatic to personalized management. We explain current state-of-the-art management concepts of chronic pulmonary infectious diseases as well as the underlying methods for therapeutic decisions and their implications for personalized medicine. Furthermore, we describe promising biomarkers and techniques with the potential to serve future individual treatment concepts in this field of difficult-to-treat patients. These include candidate markers to improve individual risk assessment for disease development, the design of tailor-made drug therapy regimens, and individualized biomarker-guided therapy duration to achieve relapse-free cure. In addition, the use of therapeutic drug monitoring to reach optimal drug dosing with the smallest rate of adverse events as well as candidate agents for future host-directed therapies are described. Taken together, personalized medicine will provide opportunities to substantially improve the management and treatment outcome of difficult-to-treat patients with chronic respiratory infections.
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