Evidence map›Paper›PMID 40307417›Full record

ArticleNPJ digital medicine2025

Predicting progression events in multiple myeloma from routine blood work.

Maximilian Ferle, Nora Grieb, Markus Kreuz, Jonas Ader, Hartmut Goldschmidt, Elias K Mai, Uta Bertsch, Uwe Platzbecker, Thomas Neumuth, Kristin Reiche and 2 more

Erratum issuedAbstract read
In one paragraph

Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
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  5. AI-powered in silico twins: redefining precision medicine through simulation, personalization, and predictive healthcare.Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society · 2025
    Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors.

Maximilian FerleCenter for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig, Universität Leipzig, Leipzig, Germany. maximilian.ferle@uni-leipzig.de.ORCID http://orcid.org/0000-0002-2730-4093
Nora GriebInnovation Center Computer Assisted Surgery (ICCAS), University of Leipzig, Leipzig, Germany.
Markus KreuzDepartment of Medical Bioinformatics, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany.
Jonas AderCenter for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig, Universität Leipzig, Leipzig, Germany.
Hartmut GoldschmidtDepartment of Internal Medicine V, University Hospital Heidelberg, Heidelberg, Germany.
Elias K MaiDepartment of Internal Medicine V, University Hospital Heidelberg, Heidelberg, Germany.
Uta BertschDepartment of Internal Medicine V, University Hospital Heidelberg, Heidelberg, Germany.
Uwe PlatzbeckerDepartment of Hematology, Hemostaseology, Cellular Therapy and Infectiology, University Hospital of Leipzig, Leipzig, Germany.
Thomas NeumuthCenter for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig, Universität Leipzig, Leipzig, Germany.
Kristin Reiche *Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig, Universität Leipzig, Leipzig, Germany.
Alexander Oeser *Innovation Center Computer Assisted Surgery (ICCAS), University of Leipzig, Leipzig, Germany.
Maximilian Merz *Department of Hematology, Hemostaseology, Cellular Therapy and Infectiology, University Hospital of Leipzig, Leipzig, Germany.ORCID http://orcid.org/0000-0002-2805-5973

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI SELWYN M VICKERS · 1985 to 2026
$347.4M
NCI NIH HHS P30 CA008748
6 · The paper itself

Abstract

This study introduces a system for predicting disease progression events in multiple myeloma patients from the CoMMpass study (N = 1186). Utilizing a hybrid neural network architecture, our model predicts future blood work from historical lab results with high accuracy, significantly outperforming baseline estimators for key disease parameters. Disease progression events are annotated in the forecasted data, predicting these events with significant reliability. We externally validated our model using the GMMG-MM5 study dataset (N = 504), and could reproduce the main results of our study. Our approach enables early detection and personalized monitoring of patients at risk of impeding progression. Designed modularly, our system enhances interpretability, facilitates integration of additional modules, and uses routine blood work measurements to ensure accessibility in clinical settings. With this, we contribute to the development of a scalable, cost-effective virtual human twin system for optimized healthcare resource utilization and improved outcomes in multiple myeloma patient care.

Identifiers

PMID40307417
PMCPMC12043975

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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.