Evidence map›Paper›PMID 35015781›Full record

ArticlePloS one2022

Evaluation of the frailty characteristics and clinical outcomes according to the new frailty-based outcome prediction model (Myeloma Risk Profile-MRP) in a UK real-world cohort of elderly newly diagnosed Myeloma patients.

Faouzi Djebbari, Alexandros Rampotas, Fotios Panitsas, Wen Yuen Lim, Charlotte Lees, Ismini Tsagkaraki, Ana Rita Gomes, Steve Prideaux, Lucia Chen, Catherine Prodger and 8 more

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.3field-weighted citation impact, top 20% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 9 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors at 8 institutions in 2 countries.

Faouzi DjebbariDepartment of Clinical Haematology, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom.ORCID 0000-0001-9578-7632
Alexandros RampotasDepartment of Clinical Haematology, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom.ORCID 0000-0002-2681-5860
Fotios PanitsasDepartment of Haematology, Laiko General Hospital, Athens, Greece.
Wen Yuen LimDepartment of Clinical Haematology, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom.
Charlotte LeesRoyal Berkshire NHS Foundation Trust, Reading, United Kingdom.
Ismini TsagkarakiBuckinghamshire Healthcare NHS Trust, Aylesbury, United Kingdom.
Ana Rita GomesDepartment of Clinical Haematology, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom.
Steve PrideauxGreat Western Hospitals NHS Foundation Trust, Swindon, United Kingdom.
Lucia ChenMilton Keynes University Hospital NHS Foundation Trust, Milton Keynes, United Kingdom.
Catherine ProdgerDepartment of Clinical Haematology, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom.
Akhil KheraDepartment of Clinical Haematology, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom.
Nicola GrayWexham Park Hospital, Slough, United Kingdom.
Lauren EllisFrimley Health NHS Foundation Trust, Frimley, United Kingdom.
Gina SanghaDepartment of Clinical Haematology, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom.
Toby A EyreDepartment of Clinical Haematology, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom.
Sally MooreDepartment of Clinical Haematology, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom.
Jaimal KothariDepartment of Clinical Haematology, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom.
Karthik RamasamyDepartment of Clinical Haematology, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom.
Oxford University Hospitals NHS Trust · GBBuckinghamshire Healthcare NHS Trust · GBFrimley Health NHS Foundation Trust · GBGreat Western Hospitals NHS Foundation Trust · GBLaiko General Hospital of Athens · GRMilton Keynes Hospital NHS Foundation Trust · GBRoyal Berkshire NHS Foundation Trust · GBWexham Park Hospital · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The management of myeloma in the elderly is shifting its focus towards reducing the risk of under-treating fit patients and the risk of over-treating frail patients. Frailty assessment is required in this patient group in order to individualise treatment decisions. In addition to the proven prognostic values of the International Myeloma Working Group (IMWG) frailty score and the revised Myeloma Co-morbidity Index (R-MCI), a new easy-to-use frailty-based risk profile score (high-risk (i.e. frail), medium risk (i.e. intermediate-fitness) and low-risk (i.e. fit)) named Myeloma Risk Profile (MRP) was shown to be predictive of survival in the clinical trial setting. In this retrospective real-world study, we set out to evaluate the frailty characteristics and clinical outcomes according to the different MRP scoring algorithm categories (frail vs. intermediate vs fit), in a high risk cohort of elderly newly diagnosed myeloma patients treated with the fixed-duration triplet therapy VCD (bortezomib with cyclophosphamide and dexamethasone). Clinical outcomes included: reason for treatment discontinuation, overall response rate (ORR), overall survival (OS), progression-free survival (PFS), and adverse events (AEs). Out of 100 patients, 62 were frail, 27 were intermediate and 11 were fit, according to MRP scores. To enable meaningful comparisons between comparable numbers, subgroups analyses for ORR, OS, PFS, and AEs focused on frail (n = 62) versus intermediate or fit (n = 38) patients. The proportion of patients in each subgroup who were able to complete the planned course of treatment was (frail: 43.5% vs. intermediate or fit: 55.3%). A higher proportion in the frail subgroup discontinued therapy due to progressive disease (19.4% vs. 2.6%). Discontinuation due to toxicity was comparable across subgroups (14.5% vs. 15.8%), ORR in the total cohort was 75%, and this was comparable between subgroups (frail: 74.2% vs. intermediate or fit: 76.3%). There was a trend for a shorter median OS in the frail subgroup but without a statistical significance: (frail vs. intermediate or fit): (46 months vs. not reached, HR: 1.94, 95% CI 0.89-4.2, p = 0.094). There was no difference in median PFS between subgroups: (frail vs. intermediate or fit): (11.8 vs. 9.9 months, HR: 0.99, 95% CI: 0.61-1.61, P = 0.982). This cohort demonstrated a higher incidence rate of AEs in frail patients compared to those in the intermediate or fit group: patients with at least one any grade toxicity (85.5% vs. 71.1%), patients with at least one ≥G3 AE (37.1% vs. 21.1%). In conclusion, our study is to the first to evaluate clinical outcomes according to MRP in a high risk real-world cohort of patients treated exclusively with the proteasome inhibitor-based VCD therapy. Our study demonstrated a trend for worse OS in addition to worse AE outcomes in the frail group, but no difference in PFS with this fixed-duration therapy. MRP is an easy-to-use tool in clinical practice; its prognostic value was validated in the real-world in a large cohort of patients from the Danish Registry. Further evaluation of MRP in the real-world when continuous therapies are used, can further support the generalisability of its prognostic value in elderly myeloma patients.

Indexed as

Models, StatisticalAgedAged, 80 and overAntineoplastic Combined Chemotherapy ProtocolsBortezomibCyclophosphamideDexamethasoneFemaleFollow-Up StudiesFrailtyHumansLenalidomideMaleMultiple MyelomaPrognosisRetrospective StudiesBortezomibCyclophosphamideDexamethasoneLenalidomide

Identifiers

PMID35015781
PMCPMC8752006
OpenAlexW4206649514

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.