Evidence map›Paper›PMID 24798484›Full record

Trial reportLeukemia2014

Velocity of early BCR-ABL transcript elimination as an optimized predictor of outcome in chronic myeloid leukemia (CML) patients in chronic phase on treatment with imatinib.

B Hanfstein, V Shlyakhto, M Lauseker, R Hehlmann, S Saussele, C Dietz, P Erben, A Fabarius, U Proetel, S Schnittger and 20 more

Abstract readRandomized Controlled Trial
PubMed Publisher
In one paragraph

Trial report in Leukemia, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers.

0numbers the graph read from it
0cells of the map it votes in
57citing papers in PubMed
11.6field-weighted citation impact, top 1% 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

57 citing papers in PubMed, 136 citations in OpenAlex.

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  18. Predictive Factors for Molecular Response in Chronic Myeloid Leukemia: Reduction Ratio and Halving Time ofTurkish journal of haematology : official journal of Turkish Society of Haematology · 2022
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  19. Initial Rate ofTurkish journal of haematology : official journal of Turkish Society of Haematology · 2022
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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

30 authors at 17 institutions in 2 countries.

B HanfsteinIII. Medizinische Universitätsklinik, Medizinische Fakultät Mannheim der Universität Heidelberg, Mannheim, Germany.
V ShlyakhtoIII. Medizinische Universitätsklinik, Medizinische Fakultät Mannheim der Universität Heidelberg, Mannheim, Germany.
M LausekerInstitut für Medizinische Informationsverarbeitung, Biometrie und Epidemiologie der Ludwig-Maximilians-Universität München, München, Germany.
R HehlmannIII. Medizinische Universitätsklinik, Medizinische Fakultät Mannheim der Universität Heidelberg, Mannheim, Germany.
S SausseleIII. Medizinische Universitätsklinik, Medizinische Fakultät Mannheim der Universität Heidelberg, Mannheim, Germany.
C DietzIII. Medizinische Universitätsklinik, Medizinische Fakultät Mannheim der Universität Heidelberg, Mannheim, Germany.
P ErbenIII. Medizinische Universitätsklinik, Medizinische Fakultät Mannheim der Universität Heidelberg, Mannheim, Germany.
A FabariusIII. Medizinische Universitätsklinik, Medizinische Fakultät Mannheim der Universität Heidelberg, Mannheim, Germany.
U ProetelIII. Medizinische Universitätsklinik, Medizinische Fakultät Mannheim der Universität Heidelberg, Mannheim, Germany.
S SchnittgerMLL Münchner Leukämielabor, München, Germany.
S W KrauseMedizinische Klinik 5, Universitätsklinikum Erlangen, Erlangen, Germany.
J SchubertKlinik für Hämatologie, Onkologie und Palliativmedizin, Evangelisches Krankenhaus, Hamm, Germany.
H EinseleMedizinischen Klinik und Poliklinik II, Universitätsklinikum Würzburg, Würzburg, Germany.
M HänelKlinik für Innere Medizin III, Klinikum Chemnitz, Chemnitz, Germany.
J DenglerMedizinische Universitätsklinik, Abteilung Innere Medizin V, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany.
C FalgeMedizinische Klinik 5, Klinikum Nürnberg Nord, Nürnberg, Germany.
L KanzMedizinische Klinik II, Universitätsklinikum Tübingen, Tübingen, Germany.
A NeubauerKlinik für Innere Medizin, Schwerpunkt Hämatologie, Onkologie und Immunologie, Universitätsklinikum Gießen und Marburg, Marburg, Germany.
M KnebaII. Medizinische Klinik und Poliklinik im Städtischen Krankenhaus, Universitätsklinikum Schleswig-Holstein, Kiel, Germany.
F StegelmannKlinik für Innere Medizin III, Universitätsklinikum Ulm, Ulm, Germany.
M PfreundschuhInnere Medizin I; Universitätsklinikum des Saarlandes, Homburg, Germany.
C F WallerKlinik für Innere Medizin I, Universitätsklinikum Freiburg, Freiburg, Germany.
K SpiekermannMedizinische Klinik und Poliklinik III, Ludwig-Maximilians-Universität München, München, Germany.
G M BaerlocherUniversitätsklinik für Hämatologie, Inselspital Bern, Bern, Switzerland.
M PfirrmannInstitut für Medizinische Informationsverarbeitung, Biometrie und Epidemiologie der Ludwig-Maximilians-Universität München, München, Germany.
J HasfordInstitut für Medizinische Informationsverarbeitung, Biometrie und Epidemiologie der Ludwig-Maximilians-Universität München, München, Germany.
W-K HofmannIII. Medizinische Universitätsklinik, Medizinische Fakultät Mannheim der Universität Heidelberg, Mannheim, Germany.
A HochhausAbteilung für Hämatologie und Internistische Onkologie, Klinik für Innere Medizin II, Universitätsklinikum Jena, Jena, Germany.
M C MüllerIII. Medizinische Universitätsklinik, Medizinische Fakultät Mannheim der Universität Heidelberg, Mannheim, Germany.
SAKK and the German CML Study Group
Heidelberg University · DELudwig-Maximilians-Universität München · DEUniversity Hospital Heidelberg · DEMedizinische Fakultät MannheimEvangelisches Krankenhaus Hamm · DEJena University Hospital · DEKlinikum Chemnitz · DEMunich Leukemia Laboratory (Germany) · DENuremberg Hospital · DEUniversitätsklinikum Erlangen · DEUniversitätsklinikum Gießen und Marburg · DEUniversitätsklinikum Tübingen · DEUniversitätsklinikum Würzburg · DEUniversity Hospital of Bern · CHUniversity Hospital Ulm · DEUniversity Medical Center Freiburg · DEUniversity of Lübeck · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

unlabelledEarly assessment of response at 3 months of tyrosine kinase inhibitor treatment has become an important tool to predict favorable outcome. We sought to investigate the impact of relative changes of BCR-ABL transcript levels within the initial 3 months of therapy. In order to achieve accurate data for high BCR-ABL levels at diagnosis, beta glucuronidase (GUS) was used as a reference gene. Within the German CML-Study IV, samples of 408 imatinib-treated patients were available in a single laboratory for both times, diagnosis and 3 months on treatment. In total, 301 of these were treatment-naïve at sample collection.

results(i) with regard to absolute transcript levels at diagnosis, no predictive cutoff could be identified; (ii) at 3 months, an individual reduction of BCR-ABL transcripts to the 0.35-fold of baseline level (0.46-log reduction, that is, roughly half-log) separated best (high risk: 16% of patients, 5-year overall survival (OS) 83% vs 98%, hazard ratio (HR) 6.3, P=0.001); (iii) at 3 months, a 6% BCR-ABL(IS) cutoff derived from BCR-ABL/GUS yielded a good and sensitive discrimination (high risk: 22% of patients, 5-year OS 85% vs 98%, HR 6.1, P=0.002). Patients at risk of disease progression can be identified precisely by the lack of a half-log reduction of BCR-ABL transcripts at 3 months.

Indexed as

AdolescentAdultAgedAged, 80 and overAntineoplastic AgentsBenzamidesDisease-Free SurvivalFemaleFusion Proteins, bcr-ablGlucuronidaseHumansImatinib MesylateLeukemia, Myelogenous, Chronic, BCR-ABL PositiveMaleMiddle AgedPiperazinesAntineoplastic AgentsBenzamidesFusion Proteins, bcr-ablGlucuronidaseImatinib MesylatePiperazinesPyrimidines

Identifiers

PMID24798484
OpenAlexW2072703295

What OpenQuestion holds

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Registered trials

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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.