Evidence map›Paper›PMID 37627047›Full record

ArticleCancers2023

A Personalized Risk Model for Azacitidine Outcome in Myelodysplastic Syndrome and Other Myeloid Neoplasms Identified by Machine Learning Model Utilizing Real-World Data.

Kirsty Sharplin, William Proudman, Rakchha Chhetri, Elizabeth Ngoc Hoa Tran, Jamie Choong, Monika Kutyna, Philip Selby, Aidan Sapio, Oisin Friel, Shreyas Khanna and 7 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
2.3field-weighted citation impact, top 11% 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

4 citing papers in PubMed, 8 citations in OpenAlex.

  1. Review
  2. Review
  3. Review
  4. Review
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

17 authors at 4 institutions in 2 countries.

Kirsty SharplinRoyal Adelaide Hospital, Central Adelaide Local Health Network, Adelaide, SA 5000, Australia.
William ProudmanRoyal Adelaide Hospital, Central Adelaide Local Health Network, Adelaide, SA 5000, Australia.ORCID 0000-0003-4280-7577
Rakchha ChhetriRoyal Adelaide Hospital, Central Adelaide Local Health Network, Adelaide, SA 5000, Australia.
Elizabeth Ngoc Hoa TranPrecision Medicine Theme, South Australian Health and Medical Research Institute (SAHMRI), Adelaide, SA 5000, Australia.
Jamie ChoongRoyal Adelaide Hospital, Central Adelaide Local Health Network, Adelaide, SA 5000, Australia.
Monika KutynaRoyal Adelaide Hospital, Central Adelaide Local Health Network, Adelaide, SA 5000, Australia.
Philip SelbyRoyal Adelaide Hospital, Central Adelaide Local Health Network, Adelaide, SA 5000, Australia.
Aidan SapioRoyal Adelaide Hospital, Central Adelaide Local Health Network, Adelaide, SA 5000, Australia.
Oisin FrielRoyal Adelaide Hospital, Central Adelaide Local Health Network, Adelaide, SA 5000, Australia.
Shreyas KhannaAdelaide Medical School, The University of Adelaide, Adelaide, SA 5000, Australia.
Deepak SinghalRoyal Adelaide Hospital, Central Adelaide Local Health Network, Adelaide, SA 5000, Australia.
Michelle DaminRoyal Adelaide Hospital, Central Adelaide Local Health Network, Adelaide, SA 5000, Australia.
David RossRoyal Adelaide Hospital, Central Adelaide Local Health Network, Adelaide, SA 5000, Australia.ORCID 0000-0001-7171-2935
David YeungRoyal Adelaide Hospital, Central Adelaide Local Health Network, Adelaide, SA 5000, Australia.
Daniel ThomasRoyal Adelaide Hospital, Central Adelaide Local Health Network, Adelaide, SA 5000, Australia.
Chung H KokPrecision Medicine Theme, South Australian Health and Medical Research Institute (SAHMRI), Adelaide, SA 5000, Australia.ORCID 0000-0002-3181-7852
Devendra HiwaseRoyal Adelaide Hospital, Central Adelaide Local Health Network, Adelaide, SA 5000, Australia.
Royal Adelaide Hospital · AUSouth Australia Pathology · AUSouth Australian Health and Medical Research Institute · AUThe University of Adelaide · AU

Funding

National Health and Medical Research Council/Medical Research Future Fund Investigator Grant MRF1195517NHMRC Ideas Grants 1182564 & 1184485
6 · The paper itself

Abstract

Azacitidine is an approved therapy for higher-risk myelodysplastic syndrome (MDS). However, only 30-40% patients respond to azacitidine, and the responses may take up to six cycles to become evident. Delayed responses and the myelosuppressive effects of azacitidine make it challenging to predict which patients will benefit. This is further compounded by a lack of uniform prognostic tools to identify patients at risk of early treatment failure. Hence, we performed a retrospective analysis of 273 consecutive azacytidine-treated patients. The median overall survival was 16.25 months with only 9% alive at 5 years. By using pre-treatment variables incorporated into a random forest machine learning model, we successfully identified those patients unlikely to benefit from azacytidine upfront (7.99 vs. 22.8 months,

Indexed as

azacitidinemachine learningMDSprognosticationsurvival

Identifiers

PMID37627047
PMCPMC10452100
OpenAlexW4385654846

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.