A predictive model for risk of early grade ≥ 3 infection in patients with multiple myeloma not eligible for transplant: ... View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-06

AUTHORS

Charles Dumontet, Cyrille Hulin, Meletios A. Dimopoulos, Andrew Belch, Angela Dispenzieri, Heinz Ludwig, Philippe Rodon, Jan Van Droogenbroeck, Lugui Qiu, Michele Cavo, Ann Van de Velde, Juan José Lahuerta, Olivier Allangba, Jae Hoon Lee, Eileen Boyle, Aurore Perrot, Philippe Moreau, Salomon Manier, Michel Attal, Murielle Roussel, Mohamad Mohty, Jean Yves Mary, Alexandre Civet, Bruno Costa, Antoine Tinel, Yann Gaston-Mathé, Thierry Facon

ABSTRACT

Infections are a major cause of death in patients with multiple myeloma. A post hoc analysis of the phase 3 FIRST trial was conducted to characterize treatment-emergent (TE) infections and study risk factors for TE grade ≥ 3 infection. The number of TE infections/month was highest during the first 4 months of treatment (defined as early infection). Of 1613 treated patients, 340 (21.1%) experienced TE grade ≥ 3 infections in the first 18 months and 56.2% of these patients experienced their first grade ≥ 3 infection in the first 4 months. Risk of early infection was similar regardless of treatment. Based on the analyses of data in 1378 patients through multivariate logistic regression, a predictive model of first TE grade ≥ 3 infection in the first 4 months retained Eastern Cooperative Oncology Group performance status and serum β2-microglobulin, lactate dehydrogenase, and hemoglobin levels to define high- and low-risk groups showing significantly different rates of infection (24.0% vs. 7.0%, respectively; P < 0.0001). The predictive model was validated with data from three clinical trials. This predictive model of early TE grade ≥ 3 infection may be applied in the clinical setting to guide infection monitoring and strategies for infection prevention. More... »

PAGES

1404-1413

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41375-018-0133-x

DOI

http://dx.doi.org/10.1038/s41375-018-0133-x

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1103695321

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29784907


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