Evidence map›Paper›PMID 32817791›Full record

ArticleBiomarker research2020

AML risk stratification models utilizing ELN-2017 guidelines and additional prognostic factors: a SWOG report.

Era L Pogosova-Agadjanyan, Anna Moseley, Megan Othus, Frederick R Appelbaum, Thomas R Chauncey, I-Ming L Chen, Harry P Erba, John E Godwin, Isaac C Jenkins, Min Fang and 12 more

Abstract read
In one paragraph

Article in Biomarker research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed
–field-weighted citation impact
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

29 citing papers in PubMed.

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  17. Defining Current Patterns of Blood Product Use during Intensive Induction Chemotherapy in Newly Diagnosed Acute Myeloid Leukemia Patients.Transfusion medicine and hemotherapy : offizielles Organ der Deutschen Gesellschaft fur Transfusionsmedizin und Immunhamatologie · 2023
    Article
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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

22 authors.

Era L Pogosova-AgadjanyanClinical Research Division, Fred Hutch, 1100 Fairview Ave N, D5-112, Seattle, WA 98109 USA.
Anna MoseleySWOG Statistical Center, Fred Hutch, Seattle, WA USA.
Megan OthusSWOG Statistical Center, Fred Hutch, Seattle, WA USA.
Frederick R AppelbaumClinical Research Division, Fred Hutch, 1100 Fairview Ave N, D5-112, Seattle, WA 98109 USA.
Thomas R ChaunceyClinical Research Division, Fred Hutch, 1100 Fairview Ave N, D5-112, Seattle, WA 98109 USA.
I-Ming L ChenDepartment of Pathology, University of New Mexico, UNM Comprehensive Cancer Center, Albuquerque, NM USA.
Harry P ErbaDuke Cancer Institute, Durham, NC USA.
John E GodwinProvidence Cancer Center, Earle A. Chiles Research Institute, Portland, OR USA.
Isaac C JenkinsClinical Research Division, Fred Hutch, 1100 Fairview Ave N, D5-112, Seattle, WA 98109 USA.
Min FangDepartments of Laboratory Medicine and Pathology, University of Washington, Seattle, WA USA.
Mike HuynhClinical Research Division, Fred Hutch, 1100 Fairview Ave N, D5-112, Seattle, WA 98109 USA.
Kenneth J KopeckySWOG Statistical Center, Fred Hutch, Seattle, WA USA.
Alan F ListMalignant Hematology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL USA.
Jasmine NaruClinical Research Division, Fred Hutch, 1100 Fairview Ave N, D5-112, Seattle, WA 98109 USA.
Jerald P RadichClinical Research Division, Fred Hutch, 1100 Fairview Ave N, D5-112, Seattle, WA 98109 USA.
Emily StevensClinical Research Division, Fred Hutch, 1100 Fairview Ave N, D5-112, Seattle, WA 98109 USA.
Brooke E WillborgClinical Research Division, Fred Hutch, 1100 Fairview Ave N, D5-112, Seattle, WA 98109 USA.
Cheryl L WillmanDepartment of Pathology, University of New Mexico, UNM Comprehensive Cancer Center, Albuquerque, NM USA.
Brent L WoodDepartments of Laboratory Medicine and Pathology, University of Washington, Seattle, WA USA.
Qing ZhangBioinformatics Shared Resource, Fred Hutch, Seattle, WA USA.
Soheil MeshinchiClinical Research Division, Fred Hutch, 1100 Fairview Ave N, D5-112, Seattle, WA 98109 USA.
Derek L StirewaltClinical Research Division, Fred Hutch, 1100 Fairview Ave N, D5-112, Seattle, WA 98109 USA.

Funding

SWOG Network Group Operations Center of the NCTNU10CA180888 · NCI · UNIVERSITY OF CALIFORNIA AT DAVIS · PI PRIMO N. LARA · 2014 to 2026
$152.1M
SWOG Statistics & Data Management Center complex - extension supplement for GY06U10CA180819 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI Michael L. LeBlanc · 2014 to 2026
$115.2M
SWOG U24 Funding for 2026-2027U24CA196175 · NCI · RESEARCH INST NATIONWIDE CHILDREN'S HOSP · PI James Michael Rae, Nilsa Del Carmen Ramirez Milan · 2015 to 2026
$37.6M
Biology/prognostic implications of Flt3 mutations in AMLR01CA114563 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI MESHINCHI, SOHEIL · 2005 to 2016
$4.4M
ITSC for Leukemia: Novel Molecular Strategies for NCTN: "Individualized" TherapieU10CA180861 · NCI · OHIO STATE UNIVERSITY · PI BLOOMFIELD, CLARA D, MARCUCCI, GUIDO · 2014 to 2018
$3.4M
Hutchinson Center as Lead Academic Participating Site (U10)U10CA180828 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI APPELBAUM, FREDERICK · 2014 to 2018
$3.0M
Novel Biomarkers to Risk-Stratify AML Patients with NPM1^Pos/FLT3-IT^Neg GenotypeR01CA190661 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI STIREWALT, DEREK L · 2016 to 2020
$2.7M
Discovery and Optimization of AML Prognostic BiomarkersR01CA160872 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI STIREWALT, DEREK L · 2012 to 2016
$2.5M
NCI NIH HHS R01 CA114563NCI NIH HHS R01 CA160872NCI NIH HHS R01 CA190661NCI NIH HHS U10 CA180819NCI NIH HHS U10 CA180828NCI NIH HHS U10 CA180861NCI NIH HHS U10 CA180888NCI NIH HHS U24 CA196175
6 · The paper itself

Abstract

backgroundThe recently updated European LeukemiaNet risk stratification guidelines combine cytogenetic abnormalities and genetic mutations to provide the means to triage patients with acute myeloid leukemia for optimal therapies. Despite the identification of many prognostic factors, relatively few have made their way into clinical practice.

methodsIn order to assess and improve the performance of the European LeukemiaNet guidelines, we developed novel prognostic models using the biomarkers from the guidelines, age, performance status and select transcript biomarkers. The models were developed separately for mononuclear cells and viable leukemic blasts from previously untreated acute myeloid leukemia patients (discovery cohort,

resultsModels using European LeukemiaNet guidelines were significantly associated with clinical outcomes and, therefore, utilized as a baseline for comparisons. Models incorporating age and expression of select transcripts with biomarkers from European LeukemiaNet guidelines demonstrated higher area under the curve and C-statistics but did not show a substantial improvement in performance in the validation cohort. Subset analyses demonstrated that models using only the European LeukemiaNet guidelines were a better fit for younger patients (age < 55) than for older patients. Models integrating age and European LeukemiaNet guidelines visually showed more separation between risk groups in older patients. Models excluding results for

conclusionsWhile European LeukemiaNet guidelines remain a critical tool for triaging patients with acute myeloid leukemia, the findings illustrate the need for additional prognostic factors, including age, to improve risk stratification.

Indexed as

Acute myeloid leukemiaAMLBiomarkersElderlyELNEuropean LeukemiaNet guidelinesMathematical modelingModel development and validationPrognostic factors

Identifiers

PMID32817791
PMCPMC7425159

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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.