001     285996
005     20240306155143.0
024 7 _ |a 10.1038/s41375-023-02105-6
|2 doi
024 7 _ |a pmid:38062124
|2 pmid
024 7 _ |a 0887-6924
|2 ISSN
024 7 _ |a 1476-5551
|2 ISSN
024 7 _ |a altmetric:157604510
|2 altmetric
037 _ _ |a DKFZ-2023-02577
041 _ _ |a English
082 _ _ |a 610
100 1 _ |a Mai, Elias K
|0 0000-0002-6226-1252
|b 0
245 _ _ |a Predictors of early morbidity and mortality in newly diagnosed multiple myeloma: data from five randomized, controlled, phase III trials in 3700 patients.
260 _ _ |a London
|c 2024
|b Springer Nature
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1709736662_31139
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
500 _ _ |a 2024 Mar;38(3):640-647
520 _ _ |a Early morbidity and mortality affect patient outcomes in multiple myeloma. Thus, we dissected the incidence and causes of morbidity/mortality during induction therapy (IT) for newly diagnosed multiple myeloma (NDMM), and developed/validated a predictive risk score. We evaluated 3700 transplant-eligible NDMM patients treated in 2005-2020 with novel agent-based triplet/quadruplet IT. Primary endpoints were severe infections, death, or a combination of both. Patients were divided in a training (n = 1333) and three validation cohorts (n = 2367). During IT, 11.8%, 1.8%, and 12.5% of patients in the training cohort experienced severe infections, death, or both, respectively. Four major, baseline risk factors for severe infection/death were identified: low platelet count (<150/nL), ISS III, higher WHO performance status (>1), and age (>60 years). A risk score (1 risk factor=1 point) stratified patients in low (39.5%; 0 points), intermediate (41.9%; 1 point), and high (18.6%; ≥2 points) risk. The risk for severe infection/death increased from 7.7% vs. 11.5% vs. 23.3% in the low- vs. intermediate- vs. high-risk groups (p < 0.001). The risk score was independently validated in three trials incorporating quadruplet IT with an anti-CD38 antibody. Our analyses established a robust and easy-to-use score to identify NDMM patients at risk of severe infection/death, covering the latest quadruplet induction therapies. Trial registrations: HOVON-65/GMMG-HD4: EudraCT No. 2004-000944-26. GMMG-MM5: EudraCT No. 2010-019173-16. GMMG-HD6: NCT02495922. EMN02/HOVON-95: NCT01208766. GMMG-HD7: NCT03617731.
536 _ _ |a 313 - Krebsrisikofaktoren und Prävention (POF4-313)
|0 G:(DE-HGF)POF4-313
|c POF4-313
|f POF IV
|x 0
588 _ _ |a Dataset connected to CrossRef, PubMed, , Journals: inrepo02.dkfz.de
700 1 _ |a Hielscher, Thomas
|0 P:(DE-He78)743a4a82daab55306a2c88b9f6bf8c2f
|b 1
|u dkfz
700 1 _ |a Bertsch, Uta
|b 2
700 1 _ |a Salwender, Hans J
|0 0000-0001-7803-0814
|b 3
700 1 _ |a Zweegman, Sonja
|b 4
700 1 _ |a Raab, Marc S
|b 5
700 1 _ |a Munder, Markus
|b 6
700 1 _ |a Pantani, Lucia
|b 7
700 1 _ |a Mancuso, Katia
|0 0000-0002-1169-0129
|b 8
700 1 _ |a Brossart, Peter
|b 9
700 1 _ |a Beksac, Meral
|0 0000-0003-1797-8657
|b 10
700 1 _ |a Blau, Igor W
|b 11
700 1 _ |a Dürig, Jan
|b 12
700 1 _ |a Besemer, Britta
|b 13
700 1 _ |a Fenk, Roland
|b 14
700 1 _ |a Reimer, Peter
|b 15
700 1 _ |a van der Holt, Bronno
|0 0000-0001-6414-2671
|b 16
700 1 _ |a Hänel, Mathias
|b 17
700 1 _ |a von Metzler, Ivana
|b 18
700 1 _ |a Graeven, Ullrich
|0 0000-0001-6082-7710
|b 19
700 1 _ |a Müller-Tidow, Carsten
|b 20
700 1 _ |a Boccadoro, Mario
|0 0000-0001-8130-5209
|b 21
700 1 _ |a Scheid, Christof
|b 22
700 1 _ |a Dimopoulos, Meletios A
|0 0000-0001-8990-3254
|b 23
700 1 _ |a Hillengass, Jens
|b 24
700 1 _ |a Weisel, Katja C
|0 0000-0001-9422-6614
|b 25
700 1 _ |a Cavo, Michele
|0 0000-0003-4514-3227
|b 26
700 1 _ |a Sonneveld, Pieter
|0 0000-0002-0808-2237
|b 27
700 1 _ |a Goldschmidt, Hartmut
|b 28
773 _ _ |a 10.1038/s41375-023-02105-6
|0 PERI:(DE-600)2008023-2
|n 3
|p 640-647
|t Leukemia
|v 38
|y 2024
|x 0887-6924
909 C O |p VDB
|o oai:inrepo02.dkfz.de:285996
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 1
|6 P:(DE-He78)743a4a82daab55306a2c88b9f6bf8c2f
913 1 _ |a DE-HGF
|b Gesundheit
|l Krebsforschung
|1 G:(DE-HGF)POF4-310
|0 G:(DE-HGF)POF4-313
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-300
|4 G:(DE-HGF)POF
|v Krebsrisikofaktoren und Prävention
|x 0
914 1 _ |y 2023
915 _ _ |a DEAL Springer
|0 StatID:(DE-HGF)3002
|2 StatID
|d 2023-08-23
|w ger
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2023-08-23
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2023-08-23
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1050
|2 StatID
|b BIOSIS Previews
|d 2023-08-23
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2023-08-23
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2023-08-23
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1030
|2 StatID
|b Current Contents - Life Sciences
|d 2023-08-23
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1190
|2 StatID
|b Biological Abstracts
|d 2023-08-23
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2023-08-23
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b LEUKEMIA : 2022
|d 2023-08-23
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2023-08-23
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0600
|2 StatID
|b Ebsco Academic Search
|d 2023-08-23
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b ASC
|d 2023-08-23
915 _ _ |a IF >= 10
|0 StatID:(DE-HGF)9910
|2 StatID
|b LEUKEMIA : 2022
|d 2023-08-23
920 1 _ |0 I:(DE-He78)C060-20160331
|k C060
|l C060 Biostatistik
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-He78)C060-20160331
980 _ _ |a UNRESTRICTED


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21