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Record W3097099568 · doi:10.1182/blood-2020-141635

Fatigue, However Measured, Continues to Refine Prognosis in Higher Risk MDS: An MDS-CAN Study

2020· article· en· W3097099568 on OpenAlexaffabout
Irina Amitai, Michelle Geddes, Nancy Zhu, Mary‐Margaret Keating, Mitchell Sabloff, Grace Christou, Brian Leber, Dina Khalaf, Heather A. Leitch, Ève St‐Hilaire, Nicholas Finn, April Shamy, Karen Yee, John M. Storring, Thomas J. Nevill, Robert Delage, Mohamed Elemary, Versha Banerji, Lisa Chodirker, Lee Mozessohn, Anne Parmentier, Mohammed Siddiqui, Alexandre Mamedov, Liying Zhang, Rena Buckstein

Bibliographic record

VenueBlood · 2020
Typearticle
Languageen
FieldMedicine
TopicOccupational and environmental lung diseases
Canadian institutionsUniversity of British ColumbiaDr. Georges-L.-Dumont University Hospital CentreSt. Paul's HospitalMcMaster UniversityCancerCare ManitobaUniversity Health NetworkHealth Sciences CentreOttawa HospitalMcGill University Health CentreMcGill UniversityUniversity of OttawaQueen Elizabeth II Health Sciences CentreUniversity of AlbertaPrincess Margaret Cancer CentreUniversity of CalgaryHôpital de l'Enfant-JésusJuravinski Cancer CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineInternational Prognostic Scoring SystemQuality of life (healthcare)Internal medicineMyelodysplastic syndromesRating scaleDiseasePopulationCancerPhysical therapyGerontologyOncologyPsychologyEnvironmental health

Abstract

fetched live from OpenAlex

Background: The incorporation of patient-reported outcomes with traditional disease risk classification, was found to strengthen survival prediction in patients with myelodysplastic syndromes (MDS). A recently reported model, FA-IPSS(h), found that patients' reported fatigue, assessed by the European Organization for Research and Treatment of Cancer (EORTC) Quality of Life-Core 30 (QLQ-C30), among higher-risk IPSS population, further stratifies them into distinct sub-groups with different survival outcomes (Efficace et al, 2018). Compared to the IPSS, the revised IPSS (IPSS-R) is more refined in prognostic assessment and an IPSS-R score of > 3.5 may identify higher risk disease (Pfeilstocker et al, 2016). The Edmonton Symptom Self Assessment Scale (ESAS) Global Fatigue Scale (GFS), is a single-item fatigue rating scale (0-10, with 10 being the highest degree), which has been previously recommended by the National Comprehensive Cancer Network to screen for fatigue in all cancer populations. Aims: (1) to validate the FA-IPSS(h), among the Canadian MDS registry (2) investigate whether a modified index, integrating higher risk by IPSS-R with patient reported fatigue according to the GFS, is able to identify individual subgroups with divergent overall survival (OS). Methods: All adult patients diagnosed with MDS with an IPSS-R score >3.5 within 6 months before the date of registration were eligible for this analysis. Fatigue was assessed both by the QLQ-C30 questionnaire and the GFS. Frailty was assessed by the Canadian Study of Health and Aging (CSHA) 9 point Rockwood clinical frailty scale. Survival was calculated using standard Kaplan-Meier analysis. Results: This analysis included 331 patients. Median age was 73 years (range, 30-98 years), 65.7% were male, median blast % was 6% (range, 0-30), median IPSS-R score was 5.2 (range, 3.5-10) and 55% had high and intermediate-2 (Int-2) IPSS risk, 68% had high and very high IPSS-R risk disease, 66% were exposed to a hypomethylating agent. Median fatigue scores increased with Rockwood frailty scores. The median QLQ-C30 fatigue score was 33 (interquartile range (IQR), 22-55.6) and 4 (IQR, 2-6) by the GFS with 59% recording high fatigue (>4). At a median follow-up of 17 months (IQR, 9-30 months), 233 deaths were observed. The actuarial median OS was 19.3 months (95% CI, 16.5-21.7). We applied the FA-IPSS(h) using QLQ-C30 fatigue cutoffs of 45 (figure 1a) and found a significant difference in OS (p<0.0001) (table 1). We then divided the cohort into 2 groups: A) IPSS-R score >3.5 + Low Fatigue (<45) (n=226) and B) IPSS-R score >3.5 + High Fatigue (≥45) (n=96). We found a significant difference in OS between these 2 groups, median OS 19.5 months (95% CI, 17.2-24.3) in group A versus 15.2 months (95% CI, 11.9-22.0) in group B (p=0.02) (figure 1b). We found similar results with these refinements, using the QLQ-C30 cutoff of 33 (the median in our patient population) (p<0.0001). Similarly, high fatigue defined by ESAS GFS (>4), was able to distinguish OS using the IPSS (p<0.0001) (figure 1c) and IPSS-R >3.5 (p=0.005) (figure 1d). Conclusions: We were able to externally validate the FA-IPSS (h) using a threshold QLQ-C30 fatigue score of 45, as originally described and 33 (Canadian median), using both the IPSS and IPSS-R (score >3.5) classifications to define higher risk MDS. The easier to deploy ESAS GFS score of >4 further discriminates survival using the IPSS and IPSS-R. This emphasizes the power of self-reported fatigue at refining OS predictions in higher risk MDS and further bolsters the importance of considering patient related outcomes in global assessments. Disclosures Geddes: Taiho: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Jazz: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees, Research Funding. Keating:Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; Hoffman La Roche: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Merck: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Consultancy; Servier: Membership on an entity's Board of Directors or advisory committees; Shire: Membership on an entity's Board of Directors or advisory committees; Taiho: Membership on an entity's Board of Directors or advisory committees. Leber:Lundbeck: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Alexion: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Otsuka Pharmaceutical: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; BMS/Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Abbvie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Takeda/Palladin: Honoraria, Membership on an entity's Board of Directors or advisory committees; Treadwell: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Leitch:AbbVie: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Alexion: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Taiho: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Exjade: Speakers Bureau. Shamy:Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Storring:Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Nevill:Jazz Pharmaceuticals: Honoraria; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Alexion: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Delage:Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Elemary:Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Chodirker:Hoffman Laroche: Honoraria. Buckstein:Novartis: Honoraria; Celgene: Research Funding; Takeda: Research Funding; Celgene: Honoraria; Astex: Honoraria.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score0.218

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.067
GPT teacher head0.286
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations2
Published2020
Admission routes2
Has abstractyes

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