Comparison of prognostication by IPSS-M, IPSS-R and AIPSS-MDS in the context of limited availability of molecular data in daily clinical practice

The IPSS-M was developed to revolutionize the prediction of MDS patients' survival by incorporating molecular data. To compensate for lack of access to molecular analyses, the AIPSS-MDS, a supervised machine learning algorithm exclusively based on clinical and cytogenetic data, was developed by the Spanish MDS Group. We used data of the Düsseldorf MDS Registry and included 207 of more than 8500 registry patients whose IPSS-M-requested complete molecular data were known to compare and validate prognostication regarding OS and LFS of the IPSS-M, IPSS-R and AIPSS-MDS. All three tools reliably prognosticated median OS of patients even in a comparatively small patient cohort. The IPSS-M provided the most accurate prediction of median OS while the frequent lack of molecular data persists as an obstacle in daily clinical practice. Due to these circumstances, the IPSS-R remains the prognostication tool with the widest applicability. Based on our data, prognostication using the AIPSS-MDS is also feasible but less precise.

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Publication Details

Journal
Annals of Hematology
Published
2025-09-01
DOI
https://doi.org/10.1007/s00277-025-06570-0
Primary Topic
Acute Myeloid Leukemia Research
Type
article
Field-Weighted Citation Impact
0.00

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article

Comparison of prognostication by IPSS-M, IPSS-R and AIPSS-MDS in the context of limited availability of molecular data in daily clinical practice

Paul Jäger, Kathrin Nachtkamp, Beate Betz, Michael Pfeilstöecker et al.
Annals of Hematology
Acute Myeloid Leukemia Research
article

Comparison of prognostication by IPSS-M, IPSS-R and AIPSS-MDS in the context of limited availability of molecular data in daily clinical practice

Paul Jäger, Kathrin Nachtkamp, Beate Betz, Michael Pfeilstöecker, A. Kuendgen, Michael Heuser, Annika Kasprzak, Felicitas Thol, Sascha Dietrich, Carolin Kellersmann, Fabian Beier, Felicitas Schulz, Michael Lauseker, Katja Sockel, Corinna Strupp, Christina Ganster, N. Gattermann, Detlef Haase, Barbara Hildebrandt, Wolf‐Karsten Hofmann, Ulrich Germing
article en

Abstract

The IPSS-M was developed to revolutionize the prediction of MDS patients' survival by incorporating molecular data. To compensate for lack of access to molecular analyses, the AIPSS-MDS, a supervised machine learning algorithm exclusively based on clinical and cytogenetic data, was developed by the Spanish MDS Group. We used data of the Düsseldorf MDS Registry and included 207 of more than 8500 registry patients whose IPSS-M-requested complete molecular data were known to compare and validate prognostication regarding OS and LFS of the IPSS-M, IPSS-R and AIPSS-MDS. All three tools reliably prognosticated median OS of patients even in a comparatively small patient cohort. The IPSS-M provided the most accurate prediction of median OS while the frequent lack of molecular data persists as an obstacle in daily clinical practice. Due to these circumstances, the IPSS-R remains the prognostication tool with the widest applicability. Based on our data, prognostication using the AIPSS-MDS is also feasible but less precise.

Annals of HematologyVol. 104(9)
Heidelberg University (DE), University Hospital Heidelberg (DE), Medizinische Hochschule Hannover (DE), Zimmer Biomet (Germany) (DE), Hanusch Hospital (AT), Universitätsmedizin Göttingen (DE), University Medical Centre Mannheim (DE), University Hospital Carl Gustav Carus (DE), Heinrich Heine University Düsseldorf (DE), Ludwig-Maximilians-Universität München (DE), RWTH Aachen University (DE)
Deutsche Krebshilfe
Openalex Percentile: Top 69%
Acute Myeloid Leukemia Research
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