Comparison of prognostication by IPSS-M, IPSS-R and AIPSS-MDS in the context of limited availability of molecular data in daily clinical practice
Year: 2025
Type: article
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.
Source: Annals of Hematology
Authors: Felicitas Schulz, Carolin Kellersmann, Beate Betz, Barbara Hildebrandt, Annika Kasprzak, Corinna Strupp, Felicitas Thol, Michael Heuser, Christina Ganster, Fabian Beier, Katja Sockel, Wolf-Karsten Hofmann, Andrea Kuendgen, Paul Jaeger, Michael Pfeilstoecker, Michael Lauseker, Sascha Dietrich, Nobert Gattermann, Kathrin Nachtkamp, Detlef Haase, Ulrich Germing
Institutions: Heinrich Heine University Düsseldorf, Medizinische Hochschule Hannover, Universitätsmedizin Göttingen, University of Göttingen, RWTH Aachen University, University Hospital Carl Gustav Carus, Heidelberg University, Medizinische Fakultät Mannheim, Hanusch Hospital, Institut für Medizinische Informationsverarbeitung, Biometrie und Epidemiologie, Ludwig-Maximilians-Universität München
Language: English
Subfield: Hematology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $4,590
Funder: Deutsche Krebshilfe
OpenAlex ID: W4414040489
