Evidence map›Paper›PMID 33506990›Full record

Observational studyBritish journal of haematology2021

A phenotypic risk score for predicting mortality in sickle cell disease.

Vandana Sachdev, Xin Tian, Yuan Gu, James Nichols, Stanislav Sidenko, Wen Li, Andrea Beri, W Austin Layne, Darlene Allen, Colin O Wu and 1 more

Open access · greenAbstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in British journal of haematology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
2.5field-weighted citation impact, top 10% of its field
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

14 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Observational
  4. Observational
  5. Frailty phenotype in adults with sickle cell disease.The journals of gerontology. Series A, Biological sciences and medical sciences · 2026
    Article
  6. Review
  7. Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. Article
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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

11 authors at 2 institutions in 1 country.

Vandana SachdevNational Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA.ORCID 0000-0002-1168-5055
Xin TianNational Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA.ORCID 0000-0003-1896-2462
Yuan GuNational Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
James NicholsNational Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
Stanislav SidenkoNational Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
Wen LiNational Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
Andrea BeriClinical Center, National Institutes of Health, Bethesda, MD, USA.
W Austin LayneClinical Center, National Institutes of Health, Bethesda, MD, USA.
Darlene AllenNational Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
Colin O WuNational Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
Swee Lay TheinNational Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA.ORCID 0000-0002-9835-6501
National Institutes of Health · USNational Institutes of Health Clinical Center · US

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
)DHHSIntramural NIH HHS ZIA HL006233Intramural Research ProgramNHLBI NIH HHSNIH
6 · The paper itself

Abstract

Risk assessment for patients with sickle cell disease (SCD) remains challenging as it depends on an individual physician's experience and ability to integrate a variety of test results. We aimed to provide a new risk score that combines clinical, laboratory, and imaging data. In a prospective cohort of 600 adult patients with SCD, we assessed the relationship of 70 baseline covariates to all-cause mortality. Random survival forest and regularised Cox regression machine learning (ML) methods were used to select top predictors. Multivariable models and a risk score were developed and internally validated. Over a median follow-up of 4·3 years, 131 deaths were recorded. Multivariable models were developed using nine independent predictors of mortality: tricuspid regurgitant velocity, estimated right atrial pressure, mitral E velocity, left ventricular septal thickness, body mass index, blood urea nitrogen, alkaline phosphatase, heart rate and age. Our prognostic risk score had superior performance with a bias-corrected C-statistic of 0·763. Our model stratified patients into four groups with significantly different 4-year mortality rates (3%, 11%, 35% and 75% respectively). Using readily available variables from patients with SCD, we applied ML techniques to develop and validate a mortality risk scoring method that reflects the summation of cardiopulmonary, renal and liver end-organ damage. Trial Registration: ClinicalTrials.gov Identifier: NCT#00011648.

Indexed as

PhenotypeRisk AssessmentAdolescentAdultAgedAged, 80 and overAge FactorsAlkaline PhosphataseAnemia, Sickle CellBlood Urea NitrogenBody Mass IndexCase-Control StudiesCluster AnalysisFemaleFollow-Up StudiesHeart RateAlkaline Phosphatasemachine learningrisk assessmentsickle cell anaemia

Identifiers

PMID33506990
PMCPMC9123430
OpenAlexW3123862843

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.