Evidence map›Paper›PMID 39994333›Full record

ArticleBone marrow transplantation2025

A model for predicting day-100 stem cell transplant-related mortality in AL amyloidosis.

Eli Muchtar, Angela Dispenzieri, Vaishali Sanchorawala, Hamza Hassan, Raphael Mwangi, Matthew Maurer, Francis Buadi, Hans C Lee, Muzaffar Qazilbash, Andrew Kin and 12 more

Abstract readMulticenter Study
In one paragraph

Article in Bone marrow transplantation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
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

22 authors.

Eli MuchtarDivision of Hematology, Mayo Clinic, Rochester, MN, USA. muchtar.eli@mayo.edu.ORCID http://orcid.org/0000-0003-2210-2174
Angela DispenzieriDivision of Hematology, Mayo Clinic, Rochester, MN, USA.ORCID http://orcid.org/0000-0001-8780-9512
Vaishali SanchorawalaSection of Hematology and Oncology, Amyloidosis Center, Boston University Chobanian & Avedisian School of Medicine, Boston Medical Center, Boston, MA, USA.ORCID http://orcid.org/0000-0002-6307-2445
Hamza HassanSection of Hematology and Oncology, Amyloidosis Center, Boston University Chobanian & Avedisian School of Medicine, Boston Medical Center, Boston, MA, USA.ORCID http://orcid.org/0000-0003-1775-2703
Raphael MwangiDepartment of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA.
Matthew MaurerDepartment of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA.ORCID http://orcid.org/0000-0002-1867-0526
Francis BuadiDivision of Hematology, Mayo Clinic, Rochester, MN, USA.ORCID http://orcid.org/0000-0003-3214-0203
Hans C LeeDepartment of Lymphoma and Myeloma, Division of Cancer Medicine, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA.ORCID http://orcid.org/0000-0001-8545-3292
Muzaffar QazilbashDepartment of Stem Cell Transplantation and Cellular Therapy, Division of Cancer Medicine, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA.ORCID http://orcid.org/0000-0003-2876-2886
Andrew KinBarbara Ann Karmanos Cancer Institute, Detroit, MI, USA.ORCID http://orcid.org/0000-0002-3263-2374
Jeffrey ZonderBarbara Ann Karmanos Cancer Institute, Detroit, MI, USA.
Sally AraiStanford University School of Medicine, Stanford, CA, USA.ORCID http://orcid.org/0000-0003-1993-4172
Michelle M ChinStanford University School of Medicine, Stanford, CA, USA.
Rajshekhar ChakrabortyDivision of Hematology/Oncology, Columbia University Medical Center, New York, NY, USA.ORCID http://orcid.org/0000-0001-7336-3003
Suzanne LentzschDivision of Hematology/Oncology, Columbia University Medical Center, New York, NY, USA.
Hila MagenHematology Institute, Chaim Sheba Medical Center, Tel Hashomer, and Faculty of Medicine, Tel Aviv University, Tel-Aviv, Israel.ORCID http://orcid.org/0000-0002-3990-394X
Eden ShkuryHematology Institute, Chaim Sheba Medical Center, Tel Hashomer, and Faculty of Medicine, Tel Aviv University, Tel-Aviv, Israel.
Caitlin SarubbiDivision of Hematologic Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Heather LandauDivision of Hematologic Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.ORCID http://orcid.org/0000-0002-3152-1189
Stefan SchönlandMedical Department V, Amyloidosis Center, University of Heidelberg, Heidelberg, Germany.ORCID http://orcid.org/0000-0002-4853-5579
Ute HegenbartMedical Department V, Amyloidosis Center, University of Heidelberg, Heidelberg, Germany.ORCID http://orcid.org/0000-0003-1917-6746
Morie GertzDivision of Hematology, Mayo Clinic, Rochester, MN, USA.ORCID http://orcid.org/0000-0002-3853-5196

Funding

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

Abstract

Autologous stem cell Transplant (ASCT)-related mortality (TRM) in AL amyloidosis remains elevated. AL amyloidosis patients (n = 1718) from 9 centers, transplanted 2003-2020 were included. Pre-ASCT variables of interest were assessed for association with day-100 all-cause mortality. A random forest (RF) classifier with 10-fold cross-validation assisted in variable selection. The final model was fitted using logistic regression. The median age at ASCT was 58 years. Day-100 TRM occurred in 75 patients (4.4%) with the predominant causes being shock, high-grade arrhythmia, and organ failure. Ten factors were associated with day-100 TRM on univariate analysis. RF classifier using these variables generated a model with an area under the curve (AUC) of 0.72 ± 0.12. To refine the model selection using importance hierarchy function, a 4-variable model [NT-proBNP/BNP, serum albumin, ECOG performance status (PS), and systolic blood pressure] was built with an AUC of 0.70 ± 0.12. Based on logistic regression coefficients, ECOG PS 2/3 was assigned two points while other adverse predictors 1-point each. The model score range was 0-5, with a day-100 TRM of 0.46%, 3.2%, 5.8%, and 14.5% for 0, 1, 2, and ≥3 points, respectively. This model to predict day-100 TRM in AL amyloidosis allows better-informed decision-making in this heterogeneous disease.

Indexed as

Hematopoietic Stem Cell TransplantationImmunoglobulin Light-chain AmyloidosisAdultAgedFemaleHumansMaleMiddle Aged

Identifiers

PMID39994333
PMCPMC12854259

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.