Evidence map›Paper›PMID 39439193›Full record

ArticleBritish journal of haematology2025

A machine learning-based workflow for predicting transplant outcomes in patients with sickle cell disease.

Haiou Li, Vandana Sachdev, Xin Tian, My-Le Nguyen, Matthew Hsieh, Courtney Fitzhugh, Emily Limerick, Wynona Coles, Nancy Asomaning, Anna Conrey and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in British journal of haematology, 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. Article
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

12 authors.

Haiou LiSickle Cell Branch, National Heart, Lung, and Blood Institute, NIH, Bethesda, Maryland, USA.ORCID https://orcid.org/0000-0002-9057-1346
Vandana SachdevEchocardiography Laboratory, National Heart, Lung, and Blood Institute, NIH, Bethesda, Maryland, USA.ORCID https://orcid.org/0000-0002-1168-5055
Xin TianOffice of Biostatistics Research, National Heart, Lung, and Blood Institute, NIH, Bethesda, Maryland, USA.ORCID https://orcid.org/0000-0003-1896-2462
My-Le NguyenEchocardiography Laboratory, National Heart, Lung, and Blood Institute, NIH, Bethesda, Maryland, USA.
Matthew HsiehCellular and Molecular Therapeutics Branch, National Heart, Lung, and Blood Institute, NIH, Bethesda, Maryland, USA.ORCID https://orcid.org/0000-0002-3706-6615
Courtney FitzhughCellular and Molecular Therapeutics Branch, National Heart, Lung, and Blood Institute, NIH, Bethesda, Maryland, USA.ORCID https://orcid.org/0000-0002-5306-7167
Emily LimerickCellular and Molecular Therapeutics Branch, National Heart, Lung, and Blood Institute, NIH, Bethesda, Maryland, USA.
Wynona ColesCellular and Molecular Therapeutics Branch, National Heart, Lung, and Blood Institute, NIH, Bethesda, Maryland, USA.
Nancy AsomaningSickle Cell Branch, National Heart, Lung, and Blood Institute, NIH, Bethesda, Maryland, USA.
Anna ConreySickle Cell Branch, National Heart, Lung, and Blood Institute, NIH, Bethesda, Maryland, USA.
Colin O WuOffice of Biostatistics Research, National Heart, Lung, and Blood Institute, NIH, Bethesda, Maryland, USA.ORCID https://orcid.org/0000-0002-4514-0926
Swee Lay TheinSickle Cell Branch, National Heart, Lung, and Blood Institute, NIH, Bethesda, Maryland, USA.ORCID https://orcid.org/0000-0002-9835-6501

Funding

Studies of the Natural History of Sickle Cell DiseaseZIAHL006233 · NHLBI · NATIONAL HEART, LUNG, AND BLOOD INSTITUTE · PI THEIN, SWEE LAY · 2017 to 2025
$10.9M
Intramural NIH HHS Z99 HL999999Intramural NIH HHS ZIA HL006233Intramural Research Program
6 · The paper itself

Abstract

Allogeneic haematopoietic cell transplantation (HCT) with HLA-matched sibling donor remains the most established curative therapeutic option for patients with sickle cell disease (SCD). However, it is not without risks, highlighting the need for a risk stratification system. Utilizing a machine learning (ML) approach that combines clinical and imaging variables, we identified red cell distribution width and renal organ damage as important risk factors for patients undergoing HCT. This ML-based algorithm, similar to an approach previously reported for predicting mortality in patients with SCD, should be applicable to risk factor discovery in similar studies.

Indexed as

Anemia, Sickle CellHematopoietic Stem Cell TransplantationMachine LearningAdolescentAdultFemaleHumansMaleRisk FactorsTreatment OutcomeWorkflowYoung Adulthaemopoietic cell transplantmachine learningmortalityrisk assessmentsickle cell disease

Identifiers

PMID39439193
PMCPMC11886942

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Registered trials

None linked

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.