Abstract
Autologous stem-cell transplantation is a fundamental therapy for multiple myeloma. Although inpatient chemo-based stem-cell mobilization (SCM) is standard care in Germany, outpatient approaches could ease healthcare constraints. We analyzed 109 myeloma patients undergoing SCM and collection at the University Medical Center Göttingen for safety. We then trained machine learning models to predict adverse events (AEs) requiring hospitalization and to forecast AE onset timing for optimized ward management. In our cohort, 97% achieved successful collection, but 69% experienced severe AEs necessitating hospitalization. Simulations suggest a risk-stratified outpatient protocol could cut bed usage by at least one third without compromising safety. Classification models accurately predicted some AE types (e.g., elevated creatinine, ROC-AUC 1.0), though neutropenic fever remained challenging (ROC-AUC 0.67). Regression models forecast AE onset with a mean error of just over one day. These results outline a data-driven roadmap for safely adopting outpatient SCM and optimizing resource allocation in clinical practice.
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Data availability
The datasets generated and/or analyzed during the current study are not publicly available due to privacy and ethical restrictions, but may be available from the corresponding author upon reasonable request.
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All scripts used for the analysis will be available upon reasonable request.
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Acknowledgements
FS was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – SFB 1690/1 – B03, by the Else Kröner Fresenius Foundation via the Else Kröner Fresenius Center for Optogenetic Therapies, and by the Ministry for Science and Culture of Lower Saxony (MWK) and the Volkswagen Foundation through the program “Niedersächsisches Vorab”. FS acknowledges further support through the Center for Biostructural Imaging of Neurodegeneration, Göttingen, Germany. We acknowledge support by the Open Access Publication Funds/transformative agreements of the Göttingen University.
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Conceptualization, E.A., F.S., N.B., and L.L.; data curation, all authors.; investigation, F.S. and E.A.; methodology, F.S., E.A. and N.B.; supervision, E.A., N.B. and G.W.; validation, all authors.; visualization, F.S. and E.A.; writing—original draft, F.S. and E.A.; writing—review and editing, F.S., E.A., L.L., M.M., N.B., and G.W.; All authors have read and agreed to the published version of the manuscript.
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Schwarz, F., Levien, L., Maulhardt, M. et al. Predicting adverse events for risk stratification of chemotherapy based stem cell mobilization in multiple myeloma. npj Digit. Med. (2026). https://doi.org/10.1038/s41746-026-02394-y
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DOI: https://doi.org/10.1038/s41746-026-02394-y
